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llARZA·EUSCO SUSITNA JOINT VENTURE
ME.MORANDUM
4-o,tz .. z
LOCATION ______ A_n_c_h __ o_r_a�g�e ________________________ __ DATE September 27, 1983
TO Henry Chen NUMBER
FROM W. David Augus tine
SUBJECT ------�C�oQo�k�I�n�l�e�t�N�a�t�u�r�a�l�G�a�s�----------
The attached information attempts to ansner some of the questions you
raised in your September 22, 1983 memo co�'l�erning estimates of gas
reserves and und iscovered gas resour ces in the Cook Inlet Area. The Cook
Inlet Area is shown in Figure 1. This memo is organized in to four
general areas; identified resources on reserves, undiscovered re sources,
price elasticity, and recommendations.
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Identified Reso ur ces
Identi fied gas resources are tho se resources who se location and quanti
ty are known or are estimated from specific geologic evidence. Identi
fied resources can be subdivided into reserves (measured and indicated)
and infer red reserves as shown in Figure 2 w�ich has been extracted from
the Dept. of the Interior Geological Sur vey Circular 860.
Measured reser ves are determined us�ng engineering measur ements, �.e.
changes in field pressure caused by production of gas, together wit h
geologic evidence from the field (drill cores and seismic data).
For th ose fields which have flowing wells and a history (of at least
several years) of gas production� a gas equation of the following form �s
used:
PV = z h R T
Where:
P = Pressure of the field
V = Volume of gas in the field
z = A correction facto r (required because the gas does not act
exactly like a per fect gas)
n = Number of moles of gas
R = The gas constant
T = Absolute temper ature of the gas.
Using this equation, and assuming a field termination pressure� the Alaska OiJ and Gas Conservation Commission (OGCC) can estimate the total vol ume of
gas that can be extracted from the field. For those fields that are not
producing, such as Fal ls Creek where one well was drilled and then the well
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was shut-in (1961), the OGCC uses the original well pressure together with
an estimate of the field size which is obtained from seismic data.
Indicated reserves are th3 additional quantity of gas th at m i gh t be
available using improved reco very techniques (see Exhibit 1 for a full
definition). For gas, these are probably small and the U.S.G.S. data �n
Circular 860 does now show any indicated reserves (or else they are
included in the measured reserve nu mbers). Inferred reserves are
additional gas that might be recoverd from known fields thro ugh extensions,
revisions, and new pay zones. This requires drilling new wells of drilling
existing wells deeper. As can b� seen from Figure 2, infer red reserves
have less a s sura nce of being recovered thafi indicated reserves which have
less assurance of recovery than measured reserves.
Estimates of the level of inferred reserves are based on historical
experience where the actual amo unts of oil and gas extracted from produ cing
fields were usually la rger than original estimates of reserves (see P:�hibit
2 for a more complete discussion). Estimates 0£ inferred reserves by the
United States Geo logic Survey (U.S.G.S.) for Alask a are based on experience
in the Low er Forty Eight States since Alaskan fields have not as of yet
been exhausted.
Estimates of Identified Re�ources in Ala ska ha ve been made by the U.S.G.S.
and the Alaska Oil and Ga� Conservation Commission (OGCC). The U.S.G.S.
made an estimate in 1975 (Circular 725) and 1980 (C i rc u lar 860). The OGCC
makes an annual estimated by field and the resu lts are pub lished in their St atis t ical Report. The U�S.G.S. estimates are for total Alaska with
onshore and offshore subcategories and by type of gas, i.e. associated and
non-associated. An estimate for Cook Inlet by itself is not included in
Circulars 725 and 860 and according to the U.S.G.S. (Bob McMullen) a
separate estimate for Cook Inlet was not made.
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The OGCC's estimate of reserves is, according to Bob McMullen, equivalent
to the Demonstrated Rserves (measured plus indicated) of th� U!�.G.S. as
shown in Figu re 2. The OGCC's estimate of Alaskan gas reserves as of 1/1/82 are shown in Exhibit 3 and they are designated as associated or
non-associated. The locations of the Coo k Inlet fields are shown in Figure 3 and the non-associated (dry) gas fields are circled in red. all other
fields are producing associated gas (with oil ). General information, well
location, and seismic maps are available from the OGCC's Statistical Report
for each gas fiel d. An example of this type of information is at tached as
Exhibit 4.
The OGCC's estimate of gas reserves in the Cook Inlet Area has changed in
the last five years but primarily from producticm. As gas is produced and
used, reserves decrease. When productio? is add ed back, the reserve I
figures for the last three years show little change as ill ustrated in
Exhibi t 5. The rea son that the estimate of reserves has not changed very
much is probably due to the fact that prolucers are not drilling for gas.
Since there is presently no market in the Cook Inlet Area for additional
gas, producers do not wasste money proving up additional gas reserves and
any additions to reserves come from wills that ar e drilled for oil, i.e.
associated gas.
The U.S.G.S. 1975 and 1980 estimates of associated and non-asso ciated gas
reserves for all of Alaska are show n in Exhibit 6� The estimate of total
measured reserves has not changed much, even when production for the years
1975-19 80 is subtracted from the 1975 estimate of 31.85 TCF (In the 1975
estimate, associated and non-associated reserves were not separated as they
were in the 1980 estimate)v In both estimates, the U.S.G.S. has included
inferred reserves al though the estimated quanity has decreased
substantially from the 1975 estimate to the 1980 estimate (14.8 TCF to 5.5
TCF). Most of the non-associ ated gas reserves in Alaska are located in
Cook Inlet, so the U.S.G.S. is es timating that there are inferred reserves
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in the Coo k Inlet Area in addit i o n to me as ured reserves. Unfor t unatel y ,
the y do not provide a separa t e estimate for Cook Inlet. A ro ugh estimate
can be made however by looking at the: U.S .G .S .'s ratio of inferred reserves
to measured reserv e s . These ratios are:
As sociated g as
Non-associated gas
O nsh ore
O f f sh ore
2.1 I 26.1
2.3 I 3.5 1.1 I 1.1
= 0.08
= 0.66
= 0.65
Applying the se ratios to the OGCC's reserves provides the following
e s ti mat e for Cook I nl e t inf erred reserves:
Associated gas
Non-associated gas Tot a l
(0.330 TCF) (0.08) = 0.03
(3.264 TCF) (0.655)= 2.14 2.17
If t h is rough estimate is in the ba llp ark, the U.S.G.S. would say that in
addition to the OGCC proved reserves of 3.6 TCF (as of Jan. 1, 1982) t h ere
are in ferred reserves of about 2 TCF in the Cook Inlet area. This may be
th e basis for Greg Erickson's "gut fe eling" th a t there are more gas
rese rve s in the area than shown by the OCGG re se rves. (The OGCC does not
p r ovid e an estimate of "i nf erred reserves ). The OGCC esti ma t e is probably
a c onser v ative , but remember that the u.s.G.S. inferred reserves are
developed using l ower for ty-ei gh t historical data an d also that there is a
._. prob abi li� associated with the 2 TCF of some t h i ng less than 0.5 (see my
memo to fil e -conve rsation with Bob McMullen dated 9123/83). In addition,
Russell Douglass, Petroleum Reservoir E n gineer for OGCC, says be wo u l dn't
put much weight on the U.S.G.S.'s e�timates of in ferr ed rese rves.
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Undiscovered Resources
Undiscovered gas resources are resources that would be found outside of know n fields. The resources are estimated using broad geologic knowledge
and theory. There are several general methods that are used to esti mat e
undiscovered resourc es and these are brie fly described in Exhibit 7.
Analysis and �riticism of some of the methods was made in a paper by
L. P. (Red) White which is attached as Exhi b i t 8. In the paper, Mr. White
al so provides a c omplete description of the "Play Analysis Method".
Estimates of total Ala ska and Cook Inlet undiscovered resources have been
developed by the U.S.G.S. in 1975 and 1980 and by the Alaska Dept. of
Natural Res our ce s in 1983. In its 1975 and 1980 estimate, the U.S.G.S.
used a direct sub je c ti ve method where the gas resources are estimated by a
team of expertso Geological infoma tion and resul t s from use of other
methods (e.g. volumetric-yield, play analysis, etc.) are reviewed and
weighed by the experts (Delphi techniques). A full expla nation of their
approach is descr i bed in Circular 860.
The U.S.G.S. estimates are presented in the form of a 5 t h fractile (Fs),
a 95th fractile CFg5), and a mean. The 5th fractile is a value which
has only a 5% ch a nce of being exceeded while the 9 5 th fractile has a 95%
-:hance of being exceeded. Another interpre tation is that there J.s a 90%
cnance that the amount of gas found will be between the 5th and 95th
fr actiles (remember that all probab ilities are subje ctive, i.�. estimated by t he experts involved ). The mean, or weighted average value, is the
quantity of gas most likely to be found. The U.S.G.S. 1975 and 1980 -
estimates �or t o t al Alaska are shown in Exhibit 9. Also shown in E xhi bit 9
is the U. s:G. S. 1 s 1980 e s t imate for undiscovered gas in Cook Inlet. As can
be seen, this estimate ranges from 2.12 TCF (Fgs) to 12.44 TCF (Fs)
with a mean or expected value of 5.72 TCF. The U.S.G.S. Cook I n l e t Area is
shown as areas 24 a nd 67 in Exhibit 10.
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The undiscovered quanities of gas estimated by the U.S.G.S. are
economically reco verable resources. Price-cost relationships and
technological trends that �lrevailed in 1980 were assumed, as well as a gas
recovery ratio of 80 -90 percent (see Circular 860, p. 7). Onshore field
size assumed was the �ize and type of fiel ds that historically have been
found, developed, and produced. Offsh ore field size depends on water depth
and the guidelines used are presented in Exhi bit 11.
The Alaska Dept. of Natural Resources (DNR) developed an estimate in ea rly 1983 of undiscovered gas resources in the Cook Inlet Area as defined by the
cross hatched area in Figure 1. The DNR method used was a "Play Approach"
which determines the amount of hydrocarbon in a "play" or prosepect
through use of reservoir engineering equations taking geologic ri sk
factors into account. Inputs for variables are. in the form of
estimated probability distributions and Monte Carlo methods are used to
develop a probability distribution for the amount of hydrocarbons� The
following quot es from Mr. White's paper further explain the method:
A/4/5
Among the activities the integrated model
explicitly simulates are two of the major ec onomic
decisions that, occur repeatedly in the development
of a petroleum province. The fist decision
involves the determination of whether a particul ar
prospect merits testing with an exploratory well;
the second, whether the resources contained in a
discov ered deposit, or combination of deposits,
merit development, production, transportation and
distribution to market ••••
The exploration process is simulated through the
integration of two independent submodels: a
geology model that is based upon a probabilistic
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assessment of the most important geologic
parameters in a pro v1.nce, and an exploration model
that simul ates the search for oil and gas 1.n the
province. The geologic model generates a list of
prospects (potential drilling targets) and a
resource appraisal of the oil and gas in place
using subjective probability distributions
developed by experts familiar with the geology of
the area. The exploration model simulates the
economic evaluati•::>n of prospects by an explorer and
the drilling decision, generating a sequence of
discoveries that form an inventory of pools to be
evaluated for development.
These two sub models are integrated i·n a Monte Carlo
simulation of exploratory activity over an extended
time period. Each Monte Carlo pa ss begins with the
geologic submodel sampling from probability
distributions for the important geologic parameters
. ' to s1.mulate a possible state of geologic nature for
the province. This state of geologic nature is
composed of a particular number of prospects, some
of which are simulated as act ual deposits of oil
and gas with the remaining ones dry. After
simulating their expected size, these prospects are
ranked according to expected volume to form a
simulated target list for the discovery process.
The discovery process is then represented, on a
year by year basis, as the sequential evaluation of
prospects on the target list. The status (i.e., a
deposit or dry) of the prospects is unknown to the
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simulated explorer. If the expected economic value
of a particular prospect justifies drilling an
explorator y well the simulated decision is to test it and determine whether it contains hydrocarbons.
This procedure continues each year in the Monte
Carlo pass or until all prospe cts have been tes ted.
The learning process in exploration is simulated by
using the drilling results each year to upd ate the
simulated explorer1s perGeived state of geologic
naturee The output of the explora t ion mo del each
year is a list of dry wells and discovered dep o sits
of oil and gas. The discovered deposits are added
to an inventory of pools to be considered for
de ve l o pment. A large number of Monte Carlo passes
are ma de to generate �requency distr ibutions for
the important outp ut variables such as total oil
and gas resources . place, discovered �n reserves,
and production .*
The discover y process is then repre s ented, on a year
by year basis, as the sequential evaluation of
prospects on the target list. The status (i.eo, a
deposit or dry ) of the prospects is unknown to the
s i mulated explorer. If the expected economic value of a particular prospect j us tif ies drilli ng an
*White, L.P. 11A Play Approach to Hydrocarbo n Resource Assess ment and
Evaluation", Office of Minerals Policy and Research Analy s i s, U.S.
Dept . of the Interior May 14, 1979, pp. 4-6.
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exploratory well the simulated decision is to test
it and determine whether it contains hydrocarbons.
This procedure continues each year in the Monte
Carlo pass or until all prospects have been tested.
The learning process in exploration is simulated
byusing the drilling results each year to update the
simulated explorer's perceived state of geologic
nature. The output of the exploration model each
year is a list of dry wells and discovered deposits "f oil and gas. The discovered deposits are added
to an inventory of pool s to be co nsidered for
devel opment. A large number of Monte Carlo passes
are ma de to generate frequency distributions for the
important output variables such as total oil and gas
resources in place, discovered reserves, and
production.*
The DNR results are presented f o r: 1) total gas in place, and 2) economically recoverable gas. Both estimates are in the
form of a cumulative probability distribution with a quanity
of gas versus the probability that the amount found will be
at least that quanity. The average or expected value is also
presented.
The DNR es timate ec onomically recoverable gas, the DNR
assumed that the minimum commercial deposit size was 200 BCF
* White, L.P. 11A Pla y Approach to Hydrocarbon Resource
Assessment and Evalu ation", Office of Minerals Policy &
Research An alysis, U.S. Dep t. of the Interior May 14, 1979,
Pgs. 4-6.
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and that the recovery factor was Oo9. As ca n be seen, expecte d gas in
place was es timat ed to be 3.36 TCF and the average or expected economically
recoverable gas was 2.04 TDF.
The exact plays or prospec ts tha t the DNR analyzed are no t yet
available to the public. DNR plans to publish a report on the estimate
at an unknown fu ture date and the plays will be discussed in that
report. Preliminary talks with Red White of the DNR indicate that most
of the plays are at a depth of around 5-10,000 ft. However, one play
in the deepter part of the inlet, is at about 20,000 ft.
Comparison of U.S.G.S and DNR Estimat es:
The economically recoverable expected value of 5.72 TCF fr.:>m the
U.S.G.S. es timate (Exhibit 9) is considerably larger than the
comparable value of 2.04 TCF from the DNR estimate (Exhibit 12). The
exact reason or reasons for this difference are unknow n but development
of the estimates differ in at least three major areas. These are:
1) time of estimate, 2) area analyzed, and 3) estimating method
empl oyed.
The U.S.G.S. stimate was ma de using data available in 1980 while no
explora tion for non-associated gas occurred during the 1980·-82 period,
oil exploration continued so the DNR had some explora tion information
that was not available to the U.S.G.S� in 1980.
The Cook Inlet area analyzed by the U.S.G.S. was larger than the Cook
Inlet basin analyzed in the DNR estimate. The larger amount con sisted
mostly of additional onshore areas on the Seward Penninsula and to the
west and north of Cook Inlet. Compare the U.SaG.S. area consisting of
parce ls 24 and 67 in Exhibit 10 wit h the DNR area which is the
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cross-hatched area in Figure 1. According to Red White, Operation
Research Senior Analyst at DNR, inclusion of the additional onshore
area that U.S.G.S. included would not mat erially change DNR's
es timate.
The estimating methods used by the U.S.G.S. and DNR were different.
The U.S.G.S. used a direct subjective method while the DNR used a play
analysis approach. Both metho ds requ.ire a considerable amount of
subjective probability input as to whether gas will be found, and if
so, the quanity. The methods diffe r in that the play approach begins
with each individual pot ential hydrocarbon prospect and builds up to a
total estimate for the ar ea while in the direct subjective method, the
total amount of hydrocarbon is estimated in aggregate after reviewing
all information on the ar ea. Which method is superior is of course
open to opinion, but R.H. McMullin who worked on the U.S.G.S. estimate
and has also utili zed the play analysis method believes the play
approach to be superior. Another expert, Dave White, Senior Research
Scientist f0r Exxon Corp., al so heavily favors the play approach.
(Also note that Gregg Erickson agreed the DNR's estimate of
undiscovered re sources was the best to date -memo to file, 3/29/8 3).
Price Elast i city
For the FERC application, we as sumed that the uncommitted reserves of
1.6 TCF and the 2.0 TCF expected value (0.40 < p < 0.50) of
economically recoverable, undiscovered resources would be available for
elec tric generati on (and ot her ar eas) at $2.47/MMBtu in 1983 (future
prices esc!alated pending on assumed oil price scenario). It is unknown
whether this assumption is valid because the prices that producers
would be �iilling to accept for existing uncommitted reserves and the
price that would motivate them to search for the potential und iscovered
resources is known only to them. It is more certain, however, that
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they would accpet $2�47/MK�tu for the uncommitted reserves than for
undiscovered resources since the recent Enstar sale for $2.47/MMBtu was
for unc.��mmitted reserves. An upper constraint on the price for all
future sales of Cook Inlet gas would be the trade-off or indifferent
cost of using coal for gener ation and that cost is around $3.20/MMBtu.
Development of an estimated gas supply curve for the Cook Inlet is
possible but according to Red White of the DNR it would be a lengthy
and costly undertaking. DNR apparently has a complex computer program
(10,000 liner of Fortran code) that could be utilized but does not have
available, qualified personnel to undertake such a project at this time
(qualified personnel are Red White and 2 to 3 others� all of whom are
fully commit ted to other projects and tasks). Even if a supply curve
was estimated it would still be highly subjec tive si nce so many of the
inputs such as costs, dis count rates, etc. are judgmentalo
Short of estimat ing a complete supply curve, one might think of a
trigger price that would motivate producers to search for additional
gas. However, there is no single price that would cause exploration to
commence. The exploration process is very complex, commencing with the
acquisition of leases, followed by the decision to drill and then the
decision to produce if gas is fo un d. The decision to produce is a
marginal decision, i.e. independent of the amount of money previously
spent on leases and drilling. In addit ion, ea ch play or prospect would
probably have to be treat ed independently whi(.:h is what would be. done
in the development of a complete supply curve.
A graphical description of the foregoin.� discussion is sh own in exhibit
13. The estimat ed supply curve that could conceivably be developed is
shown for illustrative purposes (only). The shape of the illust rative
cu rve is smooth but in reality it would probably be lumpy, reflecting
the price and quanity of indivi dual prospects. The verticle position
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of the curve is, of course, also unknown and it could be shifted up or
down from the position shown.
Recommendations
The issues considered in this memo were Cook Inlet reserves,
particu larly inferred reserves, estimates of undiscovered gas
resources in Cook Inlet, and the development of a Cook Inlet gas supply
curve. The following are my recommendations concerning these issues.
In our analysis of the availability of gas from the Cook Inlet area, we
have used the OGCC estimate of reserve� which does not inlcude what the
U.S.G.S6 calls inferred reserves. This approach may be conservative
bu t I recommend that we stick wit h it because the OGCC (Russell
Douglass) does not believe there are any inferred reserves and the DNR
estimate (Red White) of the undiscovered gas resources may include the
U.S.G.S.'s inferred reserves such that the sum of the OGCC reserves and
the DNR undiscovered gas resources may be the best estimate of total
probable gas. If one were to take an optimistic view, an additional
l.0-1e5 ( 0.45) TCF could conceivably be added to the OGCC'ds
es timate of reserves. These two analyses are presented in Table 1.
The best estimate of undiscovered gas resources appears to be that
developed by the Alaska DNR. The U.S.G.S. 1980 estimate is out of date
and the method employed is probably not as good as that used by the
DNR. In addition, Gregg Erickson, a potential critic of gas
availability estimates, is of the opinion that the DNR es timate is the
best so far. I therefore recommend that we remain with the DNR
expected value for undiscovered gas of 2.u TCF with an approximate
probability of occurance of 0.45.
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Development of a Cook Inlet gas supply curve appears to be difficult if
not im possible
availability.
be high, with
at the pre sent time due to constraints on manp ower
In addition, the cost to devel op a curve would proba bly
the res ults highly subjective and therefore
controversial. I therefore reconnnend that we not attem pt to develop a
gas supply curve.
cc: Ned Sesmi ch (w/o attachments)
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FIGURE 1
TALKEET
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ECONOMIC
I MARGINALLy
ECONOMIC
SUS·
ECONOMIC
IDENTIFIED RESOURCES UNDISCOVERED RESOURCES
Demonstrated
I Inferred
Measured Indicated
I -:.::..:·��0"''"�--'·· •"-"'•//.•" �:-:-�---------�.-::�.;,.-. _,.�..,,·,.::.,...-:: -� ...-:, .. ·._,·""., .... : .... ·.� . ..,.· .�,_.,.,.�., ..... .,_�/·""/,.
I W2%?t.fN' ofsc6velil�t:Y�:�;;;?i:
Reserves Inferred Reserves 1%>:-' --:-::: •. ·"/;--/:'-':-"-::'�-:/;'���-;/ ·<' � < :� ��ECQVERABLE RESOURCES�--·
// ...... � • -•• ' • <"' ... �/' • . . . • jf----I ------r;:..· ··.//._:· ·• �.:-;;;.>.-..-;/":: �--<�-··2......,;:� ·.::::
I
f..-------------
t( INCPitEASJNC3 <3EOLOC31C ASSURANCE ---------
FIGURE 2 -Petroleum Resource Classification (shaded area
indicates undiscovered recoverable resources
estimated in U.S.G.S. Circular 860).
SOURCE: Geological Survey Circular 860, Pg. 6
u -
J 0 z
0 u I.LJ
(,!) z
(/) �
w
tt:
(.)
z
... --
-....... � .. "
.......... .......... -
i '
): �8111C111ULL
::.:�-�
\,
'T
2
s
T 3 s
T 4 $
!o:
1::!
1::!
\.,)
1
RI2W
ALASKA
OIL AND GAS
CONSERVATION COMMISSION
ANCHORAGE,ALASKA
FIELD AND FACILITY LOCATION MAP
• -
co
01� Flt•D
GAS rltLD
OIL LINES
__ ,,. __ G.A1 t.UID . .
FIGURE 3
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I
)::F:., :::·�;;s
{c'5'()ur-�e defi:1.i:��:�� ::>unt!.::i�l:�d by �·�c: '.:.'. .'\Utt=·!.U Jf '·lin�s �tn'"'! :·1t:" ·.:.s. tri;:oLJ�i�,,:. :1ur·:t;!�; .'!�..,:Z.:::..\;t;:·:, t-;!73; .:.s. :.br.;-1u Jf \li-;�s 1'1-i !_,:;, :...:;�,;�..:.1:. Survt:.-, l9;tJ, t9� ) ·u·:t: be�:::"� -:todt:i .. ..:'� :., :1?;>1:: :i�)ecifica.:.� ... � t-..: ..:r·.ljt: ,Jilj :hlt..iCi!. �,::., .H'.J '1J':Jrii ..;l::i i.i:1ui.is. :n sone :.·�s::i:1.::t::.,., J.;�i.:1i::iv:1::> ;''.lbLi-;ncd by .;?: ::.97<}) . .-er"t: used .. ;i::h :;:!Odi::i.::.H:i•)n. "21e ;)rinctpal t.e r-:s ;.�se.i :..•1 tht:! ;>rcBt:!nr. scud:: ,.He je ti:�ed as
:.�:.:.1ws, anJ, ,.;nt:!re .l;JPr'.l?riJ.::�, -He iJ.::nttEi.ed
"--.' ···"''•:'c'·--C,.H1.;t:!:1tCit:.i-Jn::> ,f ·u::Jr.l::-:
lC�urri:1� : i:�uij .)r" �d:;�-J�s a�·dr-JcJ.r�ons �a th=
:::ar::..1' s �r'.l::i �, sone ;)cHt •lt ..rhi.:h is .::urr en t !.y
,,r :hJt�!l�i_i:!.·.,� �con.JMi�3.ll.:: �:<.tra�tJ.bl�.
� -:} .. ,.;_ .. �.: �� · J·::rr::-:..;'Y)., .. � :;:.· Vl�-:';:;�<.,..,·.'l.;,a.--ihJ'5e
res0urc�s, both id e n t if ied �nd �ndis..:avered, that dre economicalLy ex::.r3ctable. �� tn�s
::; :::.·.Jd:.-, pr lee-c o s t re:.J.t i''nshi?s Jnd tec:t:t·.)-L)�i�.l_;_ trends ?revailb-s .1t t'\e ::ime .)f .1ssessnenc ( i9c3\.1} -..1ere assuned. Speci:ic_?_l_:_y
excluded .1re q�antities that �ay b� :echnicdlly e:<to..:::J ?_�':.__ l>_uc not eco:1onicdlly s.), :berefore,
excbded .1re .1e;:>vsits ::.h1t Jr<: t)') s::�all, C)•)
Jis;;ersed n ---- � econ ")ni c., and ::hos e d�?OSits tct;n.:"l•J�us�.::. s �naC!.
porti.::>ns >i
no:�-extra.:::..ible
:.•.a-: �:'-3'YJ·�= � � Yu� a.;�t..,.,·��a • --Idc!lt i eel!.
;lresencl:'
economi..:: in d
an
Eube�:-rzc""ic -nes:uYJ�Gc.--Resvurces ::nat nJve
even more r emo te liKelihood )r extra�tion
than do ,.,�,.,.::"=�: ::, 8:'�,:��:�� "'<:?S�:�,.,.,e<: • They are
c ons i dered ·to be the l ar ge l :• unex::ractablt:
por t i on of the ori gin a : ·'.li: and gils i ��;:>lace.
Some part e ve n t ual ly may become re covera n �e as l
result of major c h an �es in t�chn0l0gy anc
economic conditions; �owever, si,:(nificant:
por t io -ls may never be recoverable. "'f""..;�VI-.... • :-..,· €.'.; ,.,8 �,...:.,,�s. --Res�Jur�t::S wh�sc !.ocatl�� ·��d-q �a:-ttit:�·
ar�-�nown <H dr�.:: t!s::.i::ld:�.::
from specific geologic e vi den ce and ::: .. 1:: inc l �-'!t.:
economi c , marginally econonic, and sub.::c,::mv:-:�:
c omponents. :dentified resources a:�� .�an �.,.
su b d iv id ed (fig. =:_, into »:e:zs�n..:;:) :-.::·
�
:
z
:
-
.
·, and -::�-"'ql'1.,€i Y'es=:�YJ�e�, express inb va r":/:. ��
d eg rees .of ge o l og ic certainty. Ye-:zc:lne:i nese.,.,!.'<:?S·--That ?art of tt;t:
economi<:: identified resource that is estba:•.'!:!
from geologic evidence su pported direc tly !:>" .. '1ea s u r ed rese rv .;:s engineering me a su r emen .. s. . • here are e qu iv a len t tJ �n�re: nesen�qs as
defined by API (197b, p. 1).
�A1a,.,gi -.,.,�: �� e�onc"'!i� <t?qsouP':£"S. --Resources
not presen tly recoverable because of te chno lag i..:
and (or) economic fa ctor:>, but that :":lay bec,mE:
recove ra b le in the fu::ure. :hey .He thar. 1>art
of the resources in:er:nediate bet-.:een thE:
economic an d subeconomi c ca tegori es (fi;. ��.
::l'Zi-:.-::�ted. nese.,.�\","' .--i{eserves equivalent t·::.
:\PI .,.ese"'!:es, that are def i ned <'.S econ om ic reserves in "�nown
pro ductive r E:ser voirs in exist in g fiel d s
expected to respond to improved recove ry
techniques such as fluid injection where (a) an
improved recovery te chn i que has been installed
but i ts 8ffect cannot ye t be fully e va l u a ted i or
{b) an imoroved techni q ue has not been i ns tal led
but knowledge of reservoir characteristics and
the re su l ts of a know11 t e chnique installed in a
s�iliar s i t uation are available f or use in the
esti rnati!'l� pt·ocedure.11 (API, 1976, p.. l, 2. � :Y;-... -:.?n��i .,..,asel'1t'rsc.--That part u: t;:e
iden ti f .ied econonic resourc.:: that will be added
to i<.no�,o,-n f ie:ds thr ough ex tensi ons , revisions, and new pay zones. (See p. 22 and Ap !)e nd i x F for d e ri va t ion of inferred reserves used in r.his
study.}
· -.:.i:s�:':�e"Yte:i l'1e:.-a:1-t-n?e.� .--·�esour�s, :>u ts idt
of Known fields , estina.ted fr:>::'. broad .seol-..�gi.::
knm.·ledgeai1{ -� theor y .-Als.:> ii1cluced 3 r;;; resources -f r.o"m undiSCOVere:i pools t h at '?CCUr :;IS
unre la te d accumulations controlled hy distinctl:
se?arate struc tura l features .:1nd ; J.r 1
s:::.ratigrapnic conditions within a.rt::as 1f "-flO'-":\
fie :ds . · ·.. -�...., ,-:�� ... : ..-:-� � 1(':<. --The t.-,-, td l '.) i: ) r
nat�ral �as that is in under�r0und reservoir r.J�i\. -..:ith ou t qua!ificati;:�� as tc1 ..:r.a:: por tion may be c �ns id8re d eich�r cu r ren t ly )r
oo:entia;.::: extractablt�. )i: ,Jr gus i:1-place is essentially equivalent t0 tata! resources. �:r-:-: .---·\ di s c r t:!t E: �a:.· .. ral ,:tccu��l.Jti�n ,__,r Jr ;as i:1 an undt'r�r ·unJ re s �rv 0 ir , �Jnfined �. "�arr::.ers .1f ...ra�er >r i:::Der'"'lo;:..-:b:"" r.1c�, ,'i:1t::
.;r·Ju��j d�, _)r rr2 :d t.-=:d t.:>, J single
i ., .... strati�raohi� f�aturt::.
�in�:a fi��j �av be
scruc�ura: lnd i. \' ictua: :; e :1a rate ;i
vert:icJ.:!..:..:.· �:: i:-:ner\·ivu� �:r3ta �;Jr l�tr:rally oy l1c1l �e0lo�i.: �arrit:!rs.
EXHIBIT 1 -Definitions of Oil & Gas Reserves
SOURCE: Geological Survey Circular 860, pgs. 6 & 7
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EXHIBIT 2
Estimation of Inferred Plus Indicated Reserves for the United States
By D. H. Root
Estimates of the amounts of crude oil and
natural gas discovered in the United States and
Canada are published by the American Petroleum
Institute, the American Gas Association and the
Canadian Petroleum Association (AP I, AGA, and
CPA). These estimates have been updated
annually since 19b7 (API and others, 1967-
1979). Ultimate recovery (defined as past
production plus proved reserves) of oil or gas
in fields discovered in a given year usually
increases from one estimate to the next.
Increases are proportionately larger for younger
discoveries than for older discoveries;
The changes in estimates of the amounts of
oil and gas discovered in a given year could be
due to several reasons: (1) drilling could
prove that some fields were larger or smaller
than had been thought, (2) production experience
could indicate that the assumed recovery factors
'.:ere too high or too low, (3) application of
improved recovery techniques could change the
anticipated crude-oil recovery, (4) a field
could be reported to the reserves co mmittee for
the first time several years after its
discovery, (5) the discovery year assigned to a
field could be changed which would shift the
estimate of the field's oil or gas to another
discovery year, and (6) new producing zones
could be found in an old field.
The phenomenon of growth in estimates of
the amount of oil and gas has been studied by
many authors (Arrington, 1960; Hubbert, 1974;
�1arsh, 1971; Mast and Dingler, 1975; Pelto,
1973; and White and others, 1975) in an effort
to estimate what future increases could be
expected. Methods and data used by those
authors to estimate future additions to proved
reserves from growth of past discoveries are
similar to those used here.
The. future growth of estimates of ultimate
recoverv from fields di scovered before 19/9 ·-is
esumat�d fie're-un'derc he--as sumpti Ons that ( 1)
w"fle"i1a-n-e1<1 has beetl ktrown-ror· 59· ve1!rs-, Tis iSt'i-;;��d ultimat:e----r'ecove'r'�· will-· �0 lOng'e r
cnange, and (2) estimates of recoverab1e 6ii and ga5"'""i n recently discover·ea· fie ids wiTr-·s-how ch e -· ·-.. same percentage growth-w i th similar age as do
es timat_!=s for f:leTc!S_ that _were discovered years 'tii2• Annual data (API and others, 19b7-1979) go
back only to 19�0; hence, the choice of 59 years
83
in assumption "1." Although this petroleum data
series began in 1967 (API and others, 1967),
only those books for the years 1971 through 1978
were used to estimate growth factors for
recoverable oil. The length of the data series
used has an effect on the estimated future
growth. Figure Fl shows how varying the nu mber
of years of data changes the estimated future
ad ditions to reserves from pre-1979 fields. Use
of 1971 through 1978 data gives an average
amount of growth.
The growth remaining in the fields
discovered in a given year is calculated by
estimating the expected percentage growth for
each year of aging until the fields become 59
years old and then accumulating the 1-year
growth factors. The calculation of the amount
of growth from the first to the second estimate
serves as an example. Several estimates of
ultimate recovery are available for oil fields
discovered in the years 1971 through 1977; they
II) 40 ...1 w a: 3!5 a:: <( Q z 0 2!5 ::::i ...J cc "' 0 1!5 z -z" iO ...... � !5 0 a: e,:, 0 13 12 11 10 g B 7 6 !5 4 3 2
NUMBER OF YEARS OF OAT A
Figure Fl.--Projected total growth of estimates
of the amount of ultimately
recoverable crude ·oil in fields
disco vered be.fore 1979 in the
cc..nterminous United States versus
the. number of years of data used.
Thirteen years of data are from
1966 to 1978; two years of data are
from 1977 to 1978 (American
Petroleum Institute and others
1967-1979).
I
include estimates made at the end of the year of
their discovery and estimates made 1 year after
that. From these estimates, the expected
percen�age increase between the first and second
estimate can be calculated. Let w(i9j) be the
estimate as of the end of year j of recoverable
oil in all fields discovered during year i. The
estimated 1-ye&r growth factor from the first to
the second estimate is then given by the ratio
1977 :E w(i ,i+1)
i .. 1971 = r(l) (1)
1977 � '.Y(i, i)
i = 1971
In general, the estimated 1-year growth
factor from the n-1 year estimate to the nth
year estimate is given by
1978-n :E w(i, i+n)
i = max of 1972-n, 1919 = r(n) (2)
1978-n !J w(i, i+n -1)
i = max of 1972-n, 1919
For the purposes of these calculations, all
fields discovered before 1920 were credited to
1919. The amount by which the estimate of the
recoverable oil discovered in the conterminous
United States in a given year is expected to
increase is obtained by multiplying the 1978
estimated ultimate recovery estimate by all the
r( n) from equation (2) where n is greater than
the difference between 1978 and the discovery
year and is less than 60. The growth factor for
an estimate as of Dec. 31, 1978 of recoverable
� 75� -------------------------------------� 7 � w 6 ..J <( z a a: 4 0 1.1. 3 0
5 10 15 20 25 30 35 40 45 50 55 60 YEARS AFTER DISCOVERY
Figure F2.--The growth of estimates of the
amount of recoverable oil
discovered in a given year in the
conterminous United States versus
the number of years after the year
of discovery. Data from the
American Petroleum Institute and
others (1972-1979, v. 26 through 33).
84
oil in fields discovered n-1 years
Dec. 31, 1978 is given by R(n), the
all the 1-year growth factors, ·c( i)
greater than n-1.
59
R(n) •1T r(i)
i=n
(3)
Figure F2 shows how the estimate of recoverable oil discovered in a given year in the
conterminous United States is projected to
increase from its first estimate to its fj.fty
ninth estimate. Similar growth curves cim be
calculated for individual reporting areas.· The
estimated known ultimate production from '1<nown
oil fields (inc luding growth) was calcula ��d by
applying the growth factors for each rep dr ting
area to the corresponding oil-discovery ·�Q.ata.
The es timated inferred and indicated reserves
are derived from these figures by subtract f9n of
API cumu lative production and proved reserves.
The results of the individual State calcula.tions
are presented in table Fl. Note that the sum of
the State estimates differs somewhat from. the
estimate calculated for the contermi nous United
States as a whole.
The American Gas Association estimates of
the amount of natural gas discovered in the
Unit�d States are divided by geographic
location, year of discovery, and whether the gas
is associated with crude oil or not (API and
others, 19 67-1979). Growth factors for natural
gas were calculated in the same manner as for
oil. In addition to the growth factors
calculated for total natural gas (fig.f: F3),
individual growth factors for associated and
non-as sociated natural gas also were
calculated. Calculations indicated that most of t.h.e-g4.0lotl:.LiD:iiaiY.ii_Cg�s. -:rs· _ e�E�� ted--to be-in
non-assaci(ted gas; not m�ch growth in
associated gas. Because trends in natural gas !"' �4�-------------------==-
� ... --'-�-/�· � 3-<( � � a:. 0 1.1. 0 w .... Q.
1 2 -1
1 I.
� 0 ------------_________ ......_. _ __. ::l 0 � 5 10 15 2(1 25 30 35 40 45 50 55 6 0
YEARS AFTER DISCOVERY
Figu re F3.--T he growth of estimatEs of the amount of recoverable n.1tural gas discoverd in a given year in the conterminous United States versus the number of years after the year of discovery. Data from the American Petroleum Institute and others (1971-1979, v. 25 through 33).
I
Table F-1.--�3tiMated infePPed plus indiaatec PesePVes of cPude oil in fields discovePed in the United States excl�.<sive of Alasl(a as of Dec. Jl� 1978
(Tabulated values are compiled from American Petroleum Institute data (American Petroleum
Institute an.1 others, 1967-1979). Values shown are in thousand barrels. Asterisk, *, indicates that the offshore is included]
Estimated ultimate production
from known fields
Reporting area (including growth) 1
Alabama.................... 289,977
Arkansas................... 1,701,211
California:
*Co astal ••••••••••••••••
*Los Angeles basin ••••••
*San Joaquin basin •••••.
Colorado •••••••••••••••.•••
Illinois •••••••••••••••••••
Indiana ••••••••••••••••••••
Kansas •••••••••••••••• t?t. •••
Kentucky, Ohio,
Pa., W. Va ............... .
Lou isiana: North•••a•••••••••••••••
*South •••••••••••••••••••
Michigan •••••••••••••••••••
�1ississippi ............... .
:!an ta na ••••.•••••.••.••••..
Nebraska •••••••••••••••••••
�ew �texico:
�orthwest •••••••••••••••
Southeast •••••••••••••••
�or t h Dakota ..•••.•..••.•.•
Oklahoma .•••..••••.•••••• o.
Texas districts:
RRl •.•••••••••••••••••••
*RR2 .•.•••••••••••••••••••
*RR3 • • • • • . • • • • • • • • • • • • •• •
*RR4 •••••••••••••••••••••
R"RS ••••••••••••••• �•····
RR6 •••••••••••••••••••••
RR7B ••••••••••••••••••••
RR7C ••••••••••••••••••••
RRS ••••••••••••••• • • .... •
RR8A ••••••••••• .,. ••••••••
RR9 and 10 ••••••••••••••
Utah and Wyomt ng •••••••••••
Miscellaneous •••••••••••••
Total United States
e xc l usi ve of Alaska ••••• Gulf of �1exico2 ••••••••••••
5,678,865
8,131,261
11,161,506
1,704,301
3,267,886
548,932
5,710,048
3,640,751
2,482,399
20,997,900
1,782,923
2,150,088
1,439,195
423,121
266,112
5,355,099
905,445
13;749,133
1,274,930
2,554,210
7,640,943
3,079,096
977,621
8,247,950
2,229,344
1,655,041
13,251,172
10,093,575
4,914,207
9,105,129
1,377,139
157,786,510
8,168,899
Cumulative production
200,747
1,446,573
3,156,060
7,152,843
7,789,342
1,230,506
3,100,162
464,690
4,789,712
3,305,658
1, 981,695
13,874,593
769,008
1,636,855
987,950
370,871
171,722
3,223,574
524,969
11,598,031
785,812
2,172,047
6,811,104
2,934,220
923,298
6,584,387
1,665,402
1,434,728
9,111,527
6,196,870
4,385,534
5,074,035
551,344
116,405,869
5,299,088
Proved
reserves
33,107
94,038
601,756
879,222
1,990,323
198,012
137�927
26,515
350,367
240,858
208,742
2,684,659
190,164
187,587
140,466
29,291
16,826
468,814
161,213
1,073,469
110,258
382,189
745,558
122,586
54,323
1,512,934
188,922
166,334
2,389,899
1,694,063
322,925
959,938
193,119
18,556,404
1,749,464
Estimated inferred
plus indicated
reserves
56,123
160,600
1,921,049
99,196
1, 38 1, 841
275,783
29,797
57,727
569,969
94,235
291,962
4,438,648
823,751
325,646
310,779
22,959
77 '564
1)662,711
219,263
1,077,633
378,860
0
84,281
22,290
0
150,629
375,020
53,979
1,749,746
2,202,642
205,748
3,071,156
632,676
22,824,237
1,120,347
1�1isc ellaneous includes: Arizo,-,a, Florida, Missouri, !-levada, New York, South Dakota,
Te nnesse, Virginia, and \4ashingr.<:ln.
'I -Gulf of �texico offshore is included in Te�as and Louisiana above, but is also listed
as separate entry here to be consistent with American Petroleum Institute practic e.
85
f l t t I
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Table F-2.--Estimated infeT'r>ed r>eser>ves of naturo.Z. gas in fieZ.ds discover>ed
in the United States exclusive of AZ.aska as of Dec. JlJ 1978
[Ta bulated values are compiled from American Gas Association data (American
Petroleum Institute and others, 1967-1979). Values shown are in million cubic feet.
Asterisk, *, indicates that the offshore is included]
Es timated ultimate
production
from kno�1 fields
Reporting area (including growth)!
Alabama................... 1,316,002
Arkansas.................. 6,122,064
California:
*Coastal •. � .•......•...
:
*Los Angeles basin •••••
San Joaquin basin •••••
Colorado ....•.•........... 1<a. nsas •... � .............. .
Kentucky ................... .
Louisiana:
North.� ........•.•.•..
*South •••••••••••••••••
}1ichigan ••....•......•.•..
Mississippi •••••••••••••••
�ton tana ••.•.•..•..•..••...
New Mexico:
Northwest •••••••••••••
Southeast •••••••• •••••
New York ••••••••••••••••••
North Dakota ••••••••••••••
Ohio .••.•.•.••• �··········
Oklahoma ••••••• -. e •••••••••
Pennsylvania ••••••••••••••
Texas districts:
R Rl •••••••••••••• o ••••
* RR2 ••••••• ,_ •••••••••••
*RR3 ••••••••••.••••••• o
*RR.4 •• o••••••••••"•••••
RR5 •• .., ••••••••••••••••
RR6 •••••••••• , ••••••••
RR7 B ••••••••••••••••••
RR7C ••••• "* ••••• •••••••
RR8 •••••••••••••••••••
RR8A-•••• $ ••••••••••• •
RR9 •...................
RRlO •••••••••••••• ,.�.
Utah ••• , ••••••••••••••••••
Vi r gi ni a ••••••••••••• �••••
Wyoming ••••••• �····•••••••
West Virginia1 ••••••••••••
Miscellane ous ••••••••••••
Total United ��ates exclusive
of Alaska •• �·········•• Gulf of Mexico •••••••••••
6,379,027
7,695,404
23,484,657
6,405,314
40,041,628
4,612,374
28,098,933
�10�152,586
3,311,682
8,183,810
3,061,443
23,569,883
25,633,059
817,645
11656,389
7,401,222
70,646,599
11,941,277
4,910,716
29,145,776
64,459,134
59,874,283
3, 924,810
24,482,339
6,615,519
9,338,267
46,860,926
7,519,520
6,360,188
57,966,49:
2,613,725
232,347
15,649,368
18,803,430
3,025,407
852,513,245
112,074 ,200
Cumulative
production
204,468
3,398,326
4,879,920
6,992,565
16,30ii,916
3,249,739
25,089,559
3,370,691
22,536,432
122,196,727
1,090,611
5,290,880
1,713,400
11,235,671
17,973,482
564,978
832,045
5,502,148
49,145,070
9,20 3,489
2,831,532
20,258,807
39;286,732
39,109,643
2,606,787
16J617,900
4,793,943
5,866,211
30,194,839
5,255,155
4,378,382
47,713,768
1,380,774
99,056
8,195,597
15,060,994
2,321,781
556,749,018
49,744,133
Proved
reserves
751,219
1,601,544
643,020
258,729
3,819,537
1,930,275
12,175,595
583,199
2,164,930
47,188,603
1,169,746
1,319,244
834,462
9,641,299
3,592,202
150,213
411,485
1,177,511
11,205,421
1,511,256
! ,067,071
4,325,822
13,178,205
10,527,441
658,827
4,220,103
836,029
1,677,331
8,294,307
1,075,564
976,416
7,510,570
696,557
79,064
4.262,841
2,301,271
223,741
164,040,650
35,635,006
Estimated
inferred
reserves
360,315
1,122,194.
856,(187
444,110"�·
3,358,204 •:.
1, 225, 3oo··i:
2,776,474 -
658,484 �
3,397,571 �
40,967,256
1,051,325
1,573,686
513,581
2,692,913
4,067,375
102,454
412,859
721,563
lO '296, 108
1 t 2 2 6 , 53 2 ·�
1,012,113
4,561,147
11,994,197
10,237,199
659,196
3-,644,336
985,547
1,794,725
8,371,780
1,188,801
1,005,390
2,742,154
536,394
54,227
3,190,930
1,441,165
479,885
t31,723,577
26,695,061
1 Miscellaneous includes: Arizona, Florida, Illinois, :tissouri, �ebraska,
South Dakota, 'tennessee, and i�ashin gton.
2 Gulf of Mexico offshore is included in Texas and Louisiana above, but is
also listed as a separate e nt ry here to be consistent with American Petroleum Institute prac tic e .
86
.� -� E
..... I w w u.. u iil � u .... I! 0 ,..... z z l-� 0 I '
a: 0
I
I
I
�
120
100
80
60
40
20
0 13 12 11 10 9 8 7 6 !5 4 3 2
NUMBER OF YEARS OF DATA
Figure F4.--Projected total gro�th in estimates
of the amount of ultimately
recoverable natural gas in fields
discovered before 1979 in the
contermi�ous United States versus
the numbet" of years of data used.
Thirteen years of data are from
1966 to 1978; two years of data are
from 1977 to 1978 (American
Petroleum Institute and others,
1967-1979).
data are too erratic to permit the calculation of growth factors on a State by State basis, the
table of inferred reserves for the States (table
F2)-was calculated by apply ing the growth
factors calculai:"eCi tor fhe conterminous unrted
States as a whole -co the--state natural�gas
discovery data. tlie-g-r-o'Qtl\· curve �(fig. F4) for
recoverable natural gas was calculated by the
same method that was used for recoverable oil,
except that for gas, nine years of data--1 970-
1978--were used instead of ei,�ht. Figure F4
shows how varying the length of the data series
changes the estimate of growth of gas reserves. Again, the length of the data series
was chosen to give an average amount of
growth. Inferred reserves for natural gas were
calculated in a manner similar to that for oil.
For both oil and gas, the growth factors
were calculated for a particular data series,
and to apply the factors to another data series,
for example, to individual field data, would be
87
inappropriate. Lote reporting of _discoveries
probably is respvnsible for much of th�_apparent
grow�!!.·· the ·rirs·t· two or three seaq; af;er
discoverv. If this is true, then those fields d1at hav"e be_el!. �7ported gfo�t much fes�_.iQ.�.n the
calculated growth curves migJ:lL?L.li.�L i.�.JL.Qrle
t,9_p.tij.ev.e:·· -�-----··
REFERENCES CITED
American Petroleum Institute, American Gas
Association, and Canadian Petroleum
Association, 1967-1.979, Reserves of crude
oil, natural gas liquids, and natural gas
in the United States and Canada [annual
volumes for the years 1966-1978]: New
York, American Petroleum Institute.
Arrington, J. R.., 1960, Size of crude reserves
is key to evaluating exploration
programs: Oil and Gas Journal, v. 58,
no. 9, Feb. 29, p. 130-134.
Hubbert, M. l<.., 1974, U.S . energy resources, a
review as of 1972, pt. 1, i11 A national
fuels and e1.ergy policy sturiy: U.S. 93rd.
Congress, 2d session, Senate Committee on
Interior and Insular Affairs, Committee
Print, Serial No. 93-40 (92-75), 267 p. �Iarsh, G. R., 19il, How much oil are we really
finding: ·Oil and Gas Journal, v. 69, no. 14, April 5, p. 100-104. �last, R. F., and Dingler, Janet, 1975, Estimates
of inferred + indicated reserves for the
United States, in Miller, B. M., Thdmsen , H. L., Dolton, G. L., Co ury, A. B.,
Hendricks, T. A., Lennartz, F. E., Powers,
R. B. 1 Sabl e , E. G. 1 and Varnes, K. L.,
Geological estimates of undiscovered
recoverable oil and gas resources in the
United States: U.s. Geological Survey
Circular 725, p. 73-78,
Pelto� C. R.1 1973, Forecasting ultima�e oil
recovery, i� Symposium on petroleum
economics and evaluation: Society of Petroleum Engineers, American Institute of
�lining and Metallurgical Engineers, Dallas
section, SPE 4261, p. 45-52.
Wh i t e 1 D. A • , Ga r r e t t , R. W • 1 J r • , Marsh , �. R. ,
Ba ker, R. A., and Gehman, H. M., 1975,
Assessing regional oil and gas potential, in Haun, J. D., ed., Methods of estimating
the volume of undiscovered oil and gas
resources: American Association of
Petroleum Geologists, Studies in geology,
no. 1, p. 143-159.
U.S. GO\ 7:RNMENT PRINTING OFFICE: 576-034/'117-1962
EXHIBIT 3 -Estimate oi gas reserves in Alaska by the Alaska Oil
& Gas Conservation Commission.
SOURCE: 1981 Statistical Report.
ESTIMATE OF GAS RESERVES IN ALASKA
FIELD
Beaver Creek
Beaver C re e k (�4G) Beluga R ive r
Birch Hill (SI)
Falls Creek (SI)
Granite Point (AG)
Ivan River (SI)
Kenai
Kuparuk River (AG) (ll�tJ )/ry.t "
Lewis River (SI)
McArthur River
McArthur River (AG)
Middle Ground Shoal (AG)
Nicolai Creek (SI)
North Cook Inlet
North Fork (SI) Prudhoe Bay (AG)(N _�,. �� 1
E ast Barrow (N·S�p�;
So uth Barrow ( N. 5/u ;r: )
Sterling
Swanson R iv e r (AG)**
Trading Bay
Trading Bay (AG)
tvest Foreland (SI)
Wes t Fork
Total
Reserves* (BSCF)
January 1, 1982
239 ... 1·
742 / 11
13 -
26
26 ..
1,109-'.
206
22 ./
63.
27 14 17 ._/ 951 .. 12/
28,778 12
� 23"' 259 �
10
3
20·
6 .,.·
3·2,60l
Total A las ka n gas reserves as of January 1, 1982 are almost 33 trillion
standard cubic feet . The.se reset:"ves are included in 24 s eparate accum ulations.
The largest accumulation is in the Prudhoe Bay Field, Pr udh oe Oil Pool which
accounts for almos t 90% of the Total.
Close to 650 billion cubic feet of gas was produced alon g �nth t he oil from
P ru dh oe Bay Field during 1981. Of this volume of gas almost 600 billion cubic feet were reinjected into the gas cap. The o th er 50 b illion cubic feet were
either u se d in the field as fu el or s ol d.
Gas reserves in the Prudhoe Bay Field are termed associated gas s ince they are
associated with an oil accuD'tulation. This is also true of reserves in s e ve n other fie l ds that have been de s ig na t ed as "AG". Discovered fields that have
not produced or are not pr,.!sently producing have be en des.ignated as "SI" f o r
shut-·in � 'The remalnder of the r·eserves are termed as dry gas since they are
not asso c ia ted with an o il accumulation.
* See footnote pilg e .2.3. ** Primarily inj e c t ed gas for pr e ssu re maintenance.
-24-
BELU�\ RIVER GAS FIELD COOK INLET BASIN, ALASKA
CHEVRON U.S.A. INC., OPERATOR
DISCOVERY WELL
DISCOVERY DATE
DEEPEST TEST
PRODUCING FORMATIONS
GAS POOL
TYPE WELL Method of Operation
Gas Producer
RESERVOIR DATA
F l owi n g
Shut-in
TOTAL
Reference Datum -Feet Below Se� Leve l
Original Pres s ure -psia
Pressure avg. 12/31/81 -psia
Gas Specific Gravity
Temperature -"F
Net Pay Thickness -Feet
Porosity - % Swi -%
Developed Area -Acres
Standard Oil Co. of California
Bel uga River Unit 212-35
December 18, 1962
Standard Oil Co. of California
Beluga River Unit 212-35
16 ,429' MD,TVD
Sterling
Undef ined
No. of Wells
6
0 6
3300
1635
1540
0.556
94
107
31
37
5115
Beluga
4500
2215
1750
0.556
106
106 24 42
4826
EXHIBIT 4 -Representative Gas Field Data
- 23?-
I I I I
I I
I
I I
I
I
I
I
I
I
I
I
I
I
I I
' j I I ·!
I
I I
I ..
l
I
�
�
m
�
�
� m
�
� � '
I
BELUGA RIVER GA S FIELD
COOK INLET BA SIN, ALA SKA CHEVRON U.S.A. INC., OPERATOR
DISCOVERY WELL
DISCOVERY DATE
DEEPE ST TEST
PRODUCING FORMATION S
GA S POOL
TYPE WELL Method of Operat ion
Gas Prod ucer
RE SERVOIR DATA
Flowing
Shu t-in
TOTAL
Reference Datum -Feet Below Se a Level
Original Pres sure -psia
Pressure avg. 12/31/81 - psia
Gas Specific Gravity
Temperature -°F Net Pay Thickness -Feet
Porosity -%
Swi -%
Developed Area -Acres
Standard Oil Co. of California
Beluga River Unit 212-35
December 18, 1962
Standard Oil Co. of California
Belug a River Unit 212-35 16,429' MD,TVD
Sterling
Undefined
No. of Wells
6
0 6
3300
1635
1540
0.556 94
107
31
37
5115
Beluga
4500
2215 1750
0.556
106 106 24
42 4826
EXHIBIT 4 -Representative Gas Field Data
-237-
I
I
I
I
I
I
I
I
I
I
I
I
�
r.
� f• �
�
bl
Gl
" 17
20
• 29
L
.6
21
·;
I
.5 R 10 W
�. 22
27
�233-27
-.'}Z21-23
-'
14
t 2l4-13
R 9 W � 212-18
2.3
¢1-212-24
-<?-14-19 " :/
/fJ-
18
19 \.
T
13 .3C N
/, ..._ BELUGA RIVER UNIT / : OPERATED BY CHE'.t;tOH USAIMC·
36 .31
241-34i}
�232-4
J.... 244-4 --� .. ........
9 II
ALASK A OIL AND GAS
CONSERVATION COMMISSION·:-:. . '� .... ANCHORAGE,ALASKA �
Tj 12
N
BELUGA RIVER GAS FitLD ·,.�,'\'
z ooo' o SCALE '1
�.f�t "·� �·
JAN· I, 1982
-..-. ••• .-......
";�
Reserves (1)
Production (2)
Accumulated Production
Reserv� Estimate (3)
.�·� . ....,. � -�j!§ ·am � ,_.,...,._.;..,�A� ' --.-;.�, ... .:11
EXHIBIT 5
Cook Inlet Reserves -1978-1982
(Volume in TCF)
1979
3.519
Base
0
3.159
1980
3.785
0.185
0.185
3.97
YEAR
1981
3.594
0.199
0.384
3.98
.iW!Y 11@ J!M
1982
3.422
0.202
0.586
4.01
!¥§ ;ge L'!!
(1) As of Jan. 1, of following year. From Alaska Oil and Gas Conservation Commission Statistical Reports for
years indicated.
(2) Gas pr oduced (and consumed) for indicated yea r. From "Historical and Projected Oil and Gas Consumption,
Jan. 1983," State of Alaska, Dept. of Natural Resources, Division of Mineral and Energy Management, table
2.8.
(3) Reserves pl us accum ulated production.
�Y!I
� le:¥1¥§ �� ,:JI!'t!� �� � ..,_ ,. �� � � � '&49 ----·��-J-,_.:___ � ----.,__ --" ,,� ... --..._....' ,:..._.....� ..>.,.,;;.r.-.. ::-.. �-«:1 ,
EXHIBIT 6
U.S.G.S. Estimates of Alaska Gas Reserves -TCF
1975 Estimate(1)
Measured Reserves Inferred Reserves
Onshore
Offshore
Total
Onshore
Associated
Non-AssiQciated
Offshor�
Associated
Non-associated
Total
31.70
0.15
31.85
26.1
3.5
0.2 1.7
31.5
14.7
0.1
14.8
1980 Estimate(2)
2.1
2.3
(2) 1.1
5.5
(1) Estimate not divided into associated and non-associated gas (2) Negligible
Sources: 1975 -Circular 725; 1980 Circular 860
d!9 :?!'!!!
Total
46.40
0.25
46 .65
28.2
5.8
0.2 2.8
37.0
111!1111 ..._ � .ffS ;.;....;..;,;;.� , ...... ...,;!'� ... �-�
J
" ' l l
� "1
•• ,r l
ll !
If, pt,,
� I \ j
�ETHODOLOGY OF RESOURCE APP��ISAL
Review of general methods
Many methods have been developed for
estimating undiscovered petroleum resoui:ces. The methods differ greatly with respect to strengths, weaknesses, amount of informc:ttion needed, and applicability of results. Thc'e are various amounts of overlap between the methods and sometimes several methods are used in conjunction. The five major categories of resource appraisal methods are as follows:
II.
These �ethods use statistical procedures to
predict future discoveries by extrapolation of past performances. The most commonly
used historical statistics are finding
rates, that relate the d1scovered volume oE
hydrocarbons to the exploratory footage
drilled, or the number of exploratory wells,
or time. Hubbert (1974) and �loore (1966)
used techniques of this category.
These methods involve the calcul ation of
amounts of discovered hydrocarbon per unit
area or volume of rock in well-explored
dist�icts and application of these ratios to
areas or volumes of rock in le ss-explored
districts. Variation among these approaches
is due pri�arily to different assumptions
conc�rnl.:1� dbtrict� • ·lnd Kle::�me
!:!le .t:-�u!•)£ies between . ·:eer<:s (1951!), Hendricks (t965) 96 1i di.:>CU$S :he!?e procedures.'
v.
This i.s ,1 specL1l type or volumetric method
in ;..:hich �stim a tes .1re made oE the amounts
.,f hydr<Jcarbon generated, migrated, and
traj)ped. ThL; dpproach has b een utilized
'lluinl.y by Soviet "5eologists (for example,
�:eruchev, 1964),
In these methods the amount of hvdrocarbon
in .:1 13lay or prospect is det"ermi ;ed . by u �e t)[ a rese r'voir engineering equation, taking into dccount--ge.ologic.r"isk faCtOrs. Often
the input for each variable (such as
thickness-of reservoir rocks and porosity)
of this equation is in the form of a
?robdbility distribution. that is known ·or eSITffiate<f:·-:tonte Carlo methods commonly are used to-generate a probability distribution
for the amount of hydrocarbon. The study of
the �ational Petroleum Reserve in Alaska by
the U.S. Department of the Interior (1979)
used a play analysis.
::i"'ea.t Sub,iea.tive .4ssessr:ent \!ei�ods
In these methods, the quantity of resource is estimated directly on a subjective basis by an expert or team of experts. Geological
information, and, generally, results of
analyses by one or more of the other four
methods, are reviewed and weighed. Delphi
techniques commonly are employed. A di�t
subjective assessment method was used in the
pr.esent report, as well as in �tiller and
n.thers (197;?).
EXHIBIT 7 _Methods used in estimating undiscovered gas resources.
...
�1
�
I} Pt ,:
,,
�
su:&.$4
EXHIBIT 8
A Play Approach to Hydrocarbon Resource Assessment and Evaluation
L. P. White
Of fice of Minerals Policy and Research Analysis
I. Introduction
U.S. Department of the Interior
May 14, 1979
This paper presents a methodology developed by the U.S. Department
of the In terior to simulate oil and gas exploration activity for petroleum
provinc es. The analytical construct ls an integral part of a larger
microeconomic si mulation model of petroleum exploration, development,
production, transportation, and distribution activities which will be us�d to
evaluate public policy al ternatives for the National Petroleum Reserve in
Alaska.
The N&tiona.l Petroleum Reserve in Alaska (NPRA ) is an area of
approximately 37,000 square miles located on the northern coast of Alaska
west of Prudhoe Bay. The Naval Petroleum Reserves Production Act of
1976 requires the Pres ident to conduct a study to determine the best
overaJJ procedures to be used in the development, production, transportEl
tion, and distribution of any petroleum ·resources in the Reserve and the
-
I '
. �-
l �� ' i
�' ' l
.,. �-;-�---·-·----·--
2
ec ono mic and environmental consequences of alternative procedure s. The
Office of Minerals Pol icy and Research An alysis has been dire-cted to
develop an economic and policy analysis in support of the legislative
requ irement and has des igned a pe troleum process and decision model
wh ich probabilistic ally sim ulates th e major activities involved in oil and
gas explorat ion, deve lopmen t, productio n, transportation, an d distribut ion.
Public policy alte rn atives are te sted by the model to eva lu ate their
econo mic consequences over an ex tended ti me period (e.g., 50 year s). The
modeling philosophy is process and decision or len ted ln order to captu re the
important inte rdepe nde ncies among the va rious petroleum activit ies and
the sensitivity of economic dec ision making to various public polic ies. 1
The model allows partitioning the surface area of la rge basins or
provinces into ac tivity areas (delinea te d surface areas of arbitrary size,
shape and numbe r) that serve as the bas ic geograp hic frame of reference
for analyzing petroleum or oth er land use activ ities in th e prov ince. The
size, shape, number, lan d use classif icatio n, connect ing transportatio n
co:l"ridor�, and availtibBity sequence of activ ity areas are majo r public
policy optlo'ls for which a.!ternat ive policies and pr ocedures may be te!:ted.
For examp le, a province may be divided into two sets of activity areas1 one
set represent ing areas closed to petroleum act ivity and the other set repre ...
sentlng areas that could be opened for petroleum activity acc ording to a
particular sche dule. (Figure 1 presents an example se t of activity areas for
the National Petrole um Reserve in Ala ska.)
-
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Among t h e ac tivities the in tegra te d model explicitly �imu1ate s are
two of the major economic decis ions that occur repea tedly ln the develop
ment of a petroleu m province. The first decision involves the determina
tion of whe ther a particular prospect merits testing with an explora tory
well; the second, wheth er the reso urces contained in a dis covered de posit ,
or combination of depos its, merit developmen t, produ ction, tran sportation
and dist ribution to market. This paper addresses the procedure s developed
to simulate the ex plorat ion decis ion. In the following sections an overvi ew
of the play app roach is presented, followed by a discussion of alternative
approaches. A geology model and an explorat ion model ba sed on the play
approa ch are then developed.
II . Overview of the Play A pproach
The explora tion process is simulated through the integration of twc
indepe ndent subm odels: a geology model that is based upon a probabilist ic
assessm ent of the most important geologic para meters l n a province, and
an explorat ion model that simulates the search for oil and gas in the
p rovince. The geologic model genera tes a list of prospec ts (poten tial
dri lling ta·:-gets) and a resource appraisal of the oil and gas in place us ing
�ub jecti ve probability distrib utions devel opeti by experts familiar wlth the
geology of the area. The ex plo rat ion model si mulates the economic
evaluation of prosp ec ts by an explore r and the drilli ng de cision, generating
a sequence of di scoveries that for m an inven tory of pools to be evaluated
for de velop ment.
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These two sub models are i n t e g r a ted in a Mon te Carlo simulation of
ex plora Lory act i v ity over an ex tended ti me p e r i od. Each Monte Ca rlo pass
begins wit h the g eol o g ic submodel sa mpH11g fiom proba bi li ty distributions .
for the i m p o r tan t g eolog i c p ara m e t e rs to sim u la te a poss ible state of
g e o l o gic na ture for t h e provi nce. Th is s t a te of g e o l o g i c nature is composed
of a part i c ula r number of pro spects, so me of which are s i mula te d as actual
deposit s of oil and gas with th e re m aining ones d r y . After s i mula ti n g th e i r
e x p e c ted size, these prospects are ranked accord ing to e x p e cte d volu me to
form a simulated target list for th e discovery process.
The discovery process is t h e n represen t ed, on a y e a r by year basis, as
the seq uen tial evaluation of prospects o n the ta rget list. The st a t u s (i.e., a
deposit or dry) of the pro spects is unkn own to the si m u la te d ex p lo re r . If
the expec ted economic val ue of a p art i c u l ar p r o sp e c t just i fies d r i lli n g an
expl o r a to ry well the si mulated decision is to test it an d de termine whether
it con tains hy drocarb ons. This proc edure con tinues each year in the Monte
Ca rlo pass or un til all p ro s p e cts have been teste d. The lear ni n g process in
ex ploration is simul a te d by using the d rill i n g re sults each y ea r to u p da t e
the simula ted ex plorer's perc e i ve d state of g e o l o g ic natu re. The output of
the explora tion m o d e l each year is a list' of dry we lls and discov ered
depos its o f oi l and gas. The discove red deposits are a dde d to an inven tory
of pools to be c on s i d e red for develop ment. A l a rg e nu mber of Monte Ca r l o
passes are made to gene rate f requ e ncy distribut ions for tne i mpo rtant
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output variables such as to ta l oil and gas re sour ces in pla c e, dis cov e re d
reserves, an d p r odu ct i o n .
III. Existing A pp r oa c h es to Resource Appra isal and Exploration Modeling ·
Ex isti ng ap p r o a c hes to resource appr a isa l and ex p l o ra t ion m o deling
g en e r ally fall into one of t h re e ca te g o r ie s. A brief desc r ip t io n a n d
discuss ion of their usefulness to situa t io n s similar to the National Petro-
leum R e s e rve in Alaska (NPRA) follows.
Historically , volu metric an alysis has been th e prim ary assessment
p r oc e dure app lie d to a la r g e province. First, an area t ha t has received
s u b st a n t i a l e x plor atio n and develop ment and that is geologically an a log o us
to the area of in terest is identif ie d. T h e n, the results of the an a l o g area
per unit of se d i m ent a ry volume are used as a su rro g a te for the hy drocarbon
p o te nt ial per unit volume in the area of i n ter es t . A good exa mple of the
v olu m e t ric app roach may be found in J o n e s (1975).
Two shortcomings exist with this a p p r o ac h when used to estimate the
econo mic valu e of the pot e n t i a l resources of a frontie r p r ovi n c e such as
the NPRA. F i r s t, since t he d egr e e to whi�h the a n a log area m a tc h e s the
target area is difficult to assess, it is diffi cult to quantitatively estimate
the geo l ogi c uncer taJ:"ty an d, in turn, the economic u nce rta i nty for the
area of intere st. Second, the level of g e o logic informa tion is far too
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limited to be usefu l for econo mic analysis . Many of the geo log i c
characteristics critic al in deter mining econom ic value are lo s t (e.g.,
number of deposits, sizes of deposit s).
Prospect analysis, a se c on d app roach to th e a s se ss men t of an area,
involves the identif ication and relatively detailed ev aluation of all the
p o t e n ti al targets for ex p l o r a tory drilling th a t exist in an area. This
ap proach is widely used and well developed approach; see for exa mple
Wansb rough (1976) or Ne we ndorp (197 5).
Again, there are two shortcomings in applying this tech nique to an
area s u c h as the Nat ional Petroleum Reserve in Alask a. First, t he levels of
effort and data req ui red in a prospect a n a l y s is are substantial and gen eralJ y
are not av ailable for the initial econo mic ev aluat ion of pr ovinces or bas ins.
Second, prospect analy sis typicall y treats each prospect i n dependent ly and
.
ignores any r eg ion al cor relation of geo l o g i c cha racterist ics across pros-
pects. The probability that a particula r prosp ect is act u a ll y a depo si t -t h e
dry hole risk facto r--is co m monly used to in dependently risk �ach prospec t.
In essence, this i m p li e s that the presence of oil or gas in on e prosp ect is
totally in d epen de nt of its presence in other deposit s , even though the
I
prospects exist in a reJa tlv ely homogeneous geologic setting. Therefore,
while p r o spe c t analysis is app r op r i a te for certain appl icat ions, t h e amount
of infor mation necessa ry for its appli c a t i o n and the fact that it i g no r e s
regional geologic c�:-relat ion make it unatt ra ct ive for the assess men t of
la r ge areas such as the NPR A.
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li m ited to be usefu l for econo mic an alysis. Many of the geologic
characteristics critical in deter mining ec onom ic value ar(� lost (e.g.,
nu mber of deposits� siz es of deposits).
Pr ospect an alysis, a sec ond app roach to the asse ss ment of an area,
invo lves the identif icat ion and relat ively de tai led evaluation of all the
potential targets for exploratory drilling tha t exist in an area. This
approach is widely used an d well developed approach; see for exa mple
Wansb rough (1976) or Newendorp (197 5).
Again, there are two shortcomings in applying this techniq ue to an
area such as the National Petroleum Reserve in Alaska. First, the levels of
effort and data required in a prospect an alysis are su bstantial and generally
are not av ai lable for the init ial econo mic evaluat ion of pr ovinces or basin s.
Second, prosp ect an alysis ty pic ally treats each prospect independently and .
ignores any regional correlation of geologic characterist ics across pros-
pects. The prob ability that a particular pros pect is actually a deposit-the
dry hole risk factor--is com monly used to independently risk �ach prospect.
In essen ce, this lmplies tha. t the presence of oil or gas in one prospec t is
totally in dependent of its P' ese nce in other depos its, even thou ;,h the
prospects exist in a relatively homogeneous geologic setting. Therefore,
while pro spe ct an alysis is appropriate for certain applicat ions, the amount
of infor mation necessa ry for it s application and the fact that it ignores
region al geologic correlation make it unatt ractive for the assessment of
large areas such as the NPRA.
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A more recent app roac h to modeli ng the exploration process uses the
early resu lts of e�.plorat ion to es timate the returns to future drilling. In
general, initial discoveries are used to estimate the para meters of a
statist ical model of the discovery process. Two examples of thj� ap proach
may be found in papers by Kaufman (197 9) and Drew (197 9). A shortcoming
of this a pproa c h, in its pre s ent state of development, is that is req�ires
information that is no t av ailable un til after some actual discoveries have
been made in an area.
IV. A Geologic .\11odel Based on the Play Approach
The geologic assessment procedure developed fo r app licat ion to the
National Pe troleum Reserve ln Alaska focuses on the play--a st ratigraphic
unit of relat ive ho mogeneous geology--as the basic unit of geolog ic
analysis. 2 A fundamental assumption is that the geologic characteristics
wtthin the play are signlf lcantly co rrelated but show substantially le ss
corre lation ac ross plays. In particula r, if all the reg ior.a l geologic
cha racteri st ics necessa ry for the occu rrence of trapped hy t.koca rbons are
present in th e play area, it ' is li kely that the play will contain deposits of
oil or gas. How ever, if one or more of these regional cha racterist ics is .
m1ssmg or unfavorable, it is likely that all the prospects within th e play
will turn out to be dry. 3
The play approach divides th e geolo gical characteristics of a paten-
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tial depos it into three categories : play specific, pro spect specif ic, an d
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re servoir specific attr ibutes. Play sp ecific attributes con sist of geologic
characterist ic.s co mmon to the play as a unit and include h�rdroca rbon
source, timing, mig ration, reservoir rock, and the number of prosp ects.
T h e occur rence of these attributes is a neces sary, but not s uff icien t ,
condition for the ex istence of oil or g�s deposit s in the play.
Pro spect speci fic att ributes are the geologic characterist ics co mmon
to the ind ividual prospects within the play and inclu de the ex istence of a
trappi n g mech anism, minimum ef fective poros ity, an d hy droca rbon accu-
mulation. Cond itional on the ex iste nce of the necessa ry play characte r-
iMics, the si multaneous occurre nce of these th ree pro spect attributes
results in the presence of oil or gas in a prosp ect.
Reservoir specif ic attributes are the reservoir characterist i cs of an
individual deposit of oil or gas in the play and inclu de the area of closure,
reservoir th ickness, ef fect ive poros ity, trap f.ill, rese rvoir depth, water
satuiation, and hydr ocarb o n ty p e (i .e., oil or dry gas). These reservoir
attributes joi ntly determine the volume of oil or gas present in a depos it.
Probab ility judgments concerning eac h of these three sets of charac
terist ics for the play are developed by exper ts fam illar with the g e o l o gy of
the area of interest. The experts first iden t ify the major p la y s within the
basin or provi nc e, review all ex isting data re !t:van.t to the ev aluat i on, an d
then make sub jective probab ility judgments conc:erning the three s e t s of
attrib utes for each ide ntif ied play. 4
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The play specific attr ib u te s are assessed in the follow ing manner.
Firs t, a pr obability distribution is developed for the number of potentially
drillable prospects that may ex ist in th� play area. Second , a prob ability of
existence or occurrence is assigned to each regional play cha racterist ic.
For exam p le , the probab ili ty that a hy drocarbon so urce ex ists for t he p lay
may be assessed as .7 5, t h e probability that timing in the play area has
been favorab le at .8, th e probability that hy drocarbons could have su ccess-
fully migrated from the sou rce to traps in the play at .9, and the
probability that tr1e play contains reservoir grade rock at .84. The product
of these four pr obabilit ies is t e r m e d the marginal play probabi lity--the
joint prob ability that all the regional geologic characteris tics necessary lor
the accumulat ion of hy droca rbons in the play area are simultaneously
favorable. For th e above e x a m p le th e marginal play probabili ty would
eq ual .4 54.
The s e c o n d se:t of probab ility judgments re quired for t h e assessm ent
concerns the likelihood that each of the three prospects at tributes is
presen t in a prospect. These prob a bility judgments are made on th e
con dit ion that all of the play speci fic attr ibutes are favorable. The
produ c t of the three prosp ec t sp ecific attrib ute probabilities is the joint
pr obab ility that a prospect is a depos it an d. is called the conditional dep osit
probability (conditiona l on favorable play geo logy).
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The third set of probability judgments req uired involves assessments
of the range of values for the reservoir cha racte ristics of an individual
deposit within the play� Given that a prospect ac tually contains oil or gas,
each cha racteristic is assessed as a probabilb:y distribution, with th-=
exception of the hy droc arbon mix which is assessed as a point estimate of
the likelih ood that a deposit is oi l ra ther than dry gas.
These three basic sets of prob ability judgments are made for each of
the identified plays and co mprise the bas ic geologic input data to the
geologic model. Fig ure 2 presents an example data form for recording
these judgmen ts for a particular play.
At the beginning of each Monte Carlo pass the geology model uses
the three sets of probabilit ies for each play tv simulate one poss ible state
of geologic nature. The geolo gic model proc eeds in the foil owing manner
for each identif ied play in each pass.
Step 1. The probability distribution for the number of potentiallr
dri llable prospects is sa mpled to determ ine the number of prospects that
wilJ be si mulated as existing in the play du ring the particu lar pass.
Step 2. Each of the reservoir volume dist ributions is sampled fo r
each of the prospects to simula te the amount of oil or gas present should
the prospect be a simulated depos it during the pass.
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S t e p 3. The marginal play p r ob abil ity is sam ple d to de te r m i n e
w he t he r o r n o t t he p l ay will be s i m u la ted as d ry or a s po t e n t ial l y
p r o d u c t i v e dur i n g the pass.
St ep 4. For e ac h prosp ec t in a p r o d u c tiv e play the c on dit iona l
d e p osi t probability is sa m p le d to de te r m i ne whether it will be simulated
d urin g the pass as a d ep o s i t or as dry. All p r o spec ts in a dry play are
simulated as dry.
The p a r t ic u l a r state of g e o l ogi c nature dete rmined in each pass in the
geology model ls not made known to the si mulated ex plorer, who must
p r oce e d with an e x plo r at ion d r i lli ng progra m in or d e r to le arn the st atus of
the in d ividu a l prospects and th us make discoveries.
An imp o r ta n t i n p u t to t h e ge o logy mod el is the percentage of
pro spects from each play that are likely to fall w i t hin e a c h a ctiv i t y area.
It may be dir ect l y assessed by the g e o l o gist or it may be e st im ate d as the
percent of t h e play covered by the a c t i v ity area. The geology mode l uses .
this estimate to a ss ign eac h sp ec ific p r osp ect g en e rate j at the b e g i n n i n g of
a Monte Carlo pass to a p art icula r a c t i v i ty area. The r e s u lt is a projection
of the three dimensional geologic colu mr� in which p l a y s are vertically
stacked into a t w o dimens ion al re a liz a t io n at the su rface, with each
ac tiv i ty are a assigned the numb e r of p r ospec t s estimate d to unde r li e it.
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The l ist of p r osp ec ts f o r e ac h ac t i v it y area is the primary output of
t he g e o l og ic sub model; a s ec ond m a j o r output, af ter completion of all
Monte Carlo passes, is an apprai sal of the oil and ga s reso urces for the
ar( .:1. The resource a p p r a i s a l is g e n e r a ted by cumulating the simulated
deposit s for eac h Monte C a rlo pass, over a la r g e nu mber of p a s s es, to I
deve lop a pr ob a b i li t y distribution for oi l a n d gas resources. F i gure 3
presents a flowchart of the Geology Model.
V. _t.n Exploration M ode l Based on the Play Approach
The explorat ion model simulates the sea rch for oi l and gas deposits as
a dy n a m i c discovery process, i n tegra t i n g the im p o r t a n t ele ments of both
the geo logy and the econo mics ap p r o pr i a te to t h e province. The pri mary
func tion of the ex ploration m o del is to take the prospects gener a t e d by the
geology m o d e l at the b eg inn i n g of e a c h Monte Ca rlo pass and simulate
their evaluation and drilli n g by an explore r. E x p l o r a t i o n is modeled as a
decis ion process under uncertain ty in that t h e s i m u la ted ex p l orer does not
know prior to drilling if a p a rticul ar prospect has been simul ated as a
depos it or as dry by the geology mode l. That is, perfect information about
the si mulat ed sta t e of the geologic wo rld is not available at the ex plora tion
decision poin t. Rather, the evaluation o:£ a prospect and the dec i s ion
whether or not to te st it with an ex p lor a to r y well are based on an
impe rfect percep tion of the geology derive d fro m the origin al probability
distributions. Given that his decision is to test a particular prospect, the
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GEOLOGY MODEL
[ lEG IN A MONTE CARL(' PASS.
SELECT A 'LAY.
SAM,LE ONCE FROM THE NUMBER OF PROSPECTS DISTRIBUTION TO
I&M ULAT E THE NUMBER OF PROS·
'ECTI IN THE PLAY.
SIMULATE THE CONDITIONAL
,..--.-4�VO lUM E FO R A PR OSPECT BY
SAMPliNG ONCE FROM EACH OF
TH E R£SE�WO IR DISTRIBUTIONS
AND CALCU L.TING THt '!OLUME.
NO
I ·a tfiiAL PLAY PR OIABILITV AND LAI!L fMI 'LAY AI PRO DUCTIVE fl DIIV.
<'!. • ·-. ,., ... -.. -" ..
IUM THE VOLUMES OF THE DEPOS.
ITI FO R USE IN GENERATING A
. RES OURCE APPRA �SA l.
NO
TH E PLAY �E EN LAB ELED'f
�;
NO
SA MPLE ONCE AGAINST THE CON·
DITIOi'IAL DEPOSIT PROBA BILITY
AN D LAB EL THE PROSPECT AS A
DEPOSIT OR AS DRY.
SELECT A PROSPECT.
YES
FIGU RE 3
ALLOCATE TH
PROSPECTS TO
ACTIVITV
AREAS •
15
-
IEGIN T>!f EX"-OAATiO r4 MOCel.
LAIEL A&.L PROSPECn AS DRY.
!
-"" --- .
� l,
,. '
: ,,
,
F
ri l i r ' ' [n.i i' 'i L·"'.' ·' '
['1 ,,
---'fo1-�·
cost of an exploratory we ll is char g ed and the st atus of the prospec t (a
depos it or d r y) i s r e v eale d to the ex plo r e r .
The discovery process continues each year unti l all prospects p e r -
16
ceived to be economic hav e been tested or until the ter m ination yea r for
the particular Monte Ca rlo pass has been re ached. The ou tco m e, in terms
of d i sc o v e ries and dry p r o spec ts, represents the returns to ex ploratory
drilling for o n e particular re aliz ation of g e o log i c and econ o mic cunditi o ns.
A la rge n umb e r of such passes are made, each time using the ge o l o g i c
model tv gen erate a p o ss i ble state of g eolo g i c natu re against which the
exp loration m o d e l simulat€5 the discovery p roces s . Outcomes of interest
(e.g., total oil and gas in place, to tal oil and gas discovere d) from each pass
may be accu m ulated and frequency distribut ions deve lope d as outputs of
the Monte Carlo simulation.
Once a p ar t i cula r g e olo g i c st ate of nature has been generated for
each activ ity area in the geology m o del the year by year ex plo r a t i o n
process for the particular Monte Carlo pass is simulated in the ex ploration
model. Associated with each act ivity area is the year that ex plo r a t io n is
scheduled to com mence. More than one a c ti vty area may be s c h ed u l e d for
initial exp l o rat i on in the same year while so me activity areas may never be
scheduled for exp lora tion. This flexibility pe rmits test i ng the ec onomic
consequences of alternat ive configurations f o r Fede ral lease sa les in a
province.
I I
I -1 . l I
. r
�l ! i .
rl 1 [l � : l
17
The e x p loratio n model fol lows a four step procedu re simulating the
discove ry p r oc ess over time in activ ity areas o pen to explo rat ion (F i gu r e 4
presents a more detai led flow chart).
Step 1 -A target list con t a i ning all untested prospects in ac tiv ity
areas open to ex ploration is for med by rar.l<ing the !)r e sp ects accord-
ing to e x pec ted (risk ed) volume.
St ep 2 -Prosp ec ts on the ta rget li st are economic ally evaluated,
taking into a ccou nt the pe rceived g e o logi c risks (the ma rginal play
and condit ional deposit probabilities) .
Step 3 -The decis ion on w h ic h prospects, if any, to test with an
explorato ry we ll is made based on expecte� v alu e , drilling .c.?st s , and
an ex plorat ion bu_Qg_�L�PQ�_�rain t. --
Step 4 -The explore r's g eo l o g i c perception is updated� by re vising the
marginal play probabi lities he uses in esti mating expected volume an d
econo mic value, to reflect p r evi ous drill ing re sults.
The four steps are repeated each y e a r that exp l o rat i o n is allowed
until either the te rmination year is reached or all of the p r o s p e c t s
perceived to be economic have been t e sted.
r
-
. I l'l r
r r
, f
r
r . '
� J.: r., · ..
r� ' J
r. J I
'
r· #) 1
r. !.,, ' . ' . t J··�, l ' . ·'I
18
EXPLORATI ON MODEL
IEG IN THE NEXT YEAR IN TH E
MOWTE CARLO PASi.
AGC ftEIATE THE UNTESTlD PMlln:cTI F O R AI.L ACTIVITY IAft�:· OPEN TO IXPlO RATI ON.
�
/�, /�HE�� ArtY NO ( UNTESTED >---� �ROIPECTS7
COMPUTE T H E EXPE CTED VOLUME FO R !ACH UNTEST ED PROSPECT IY Rtlr.IN G TH E CONDITIONAL VOLUME IY IT'S MA RGINAL PLAY
AND CONDITIONAL D EPOS IT PROB·
AI IUTIES.
CO�Si "t;CT A TA"GET L I ST 8,. O�DERING THE PROSPECTS BY
EXPECTED VOLUME.
CALC ULATE THi CONDITIONA� (UN JUtK EO) tCOHOMIC VALU! Of 'tHE ���IT UNTIITEO ftROIPECT o• jH t TA ftiE1 LilT.
----=·· ---·
'
DETERMINE WH ICH DEPOSITS IN
TH E PO Ol I NVE NT O R Y TO PRO· DUCE AND TRANSPO RT.
RECORD THE RESU LTS FO R ALL ACT IVITIES IN TH E CU RRENT PE R IOD.
EXPLO RATION IS FINISHED FO R
TH E CU RRENT P E R I O D : UPDATE
THE MA RG INAl. PlAV PROIA BILI·
TIES.
YES
NO
f=
YES
CTOfl
�0
DO ANY
UNTESTEu
REMAIP47
/ /·
. I
LABEL TH E PROSPECT AS TE¥TIT;l AN D SUBTRACT TH E COST OF AN f
EXPLORATO RY WE LL FROI.\ THE i BUDGET CO NSTRAINT; If A b!S·I
COVERY, PLACE IN ntE fiOOL i
INV ENTO RY. i
YES iSTH E� ' EXPECT ED VAL\J£:""' GREAT ER THII.� .. ". THE COS1 OF . // AN EXPLORATOftY/ WELL? ,/ /
_,
--___ !;. .........
' j . ·-t J
FIGURE 4
,.. _ .... _...,..4 ..... . .... . ... � ..
'. .,
! '
r� ·" l I r
�."
! ' --
r ,, � '1
r
• I ; �
' '
r I
� � l.' j' �
r 1 l
f< r� . ; ; ' .
�� .. ' ·' [1 ; j ' �� l
r· I .. ' r, J;i j r, 1 ! 'I
r· ' d ·)
r ·:·:
�.�; J �'l . -'!
�
I i -. ... J. , ... aw..... .YB t
19
Step 1 in the explora t ion model is the formation of a ta rget li st. All
the untested prospects allocate d to activity areas open to ex ploration are
aggregated and the expected volu me of petroleum for each is computed by
risk ing the con dition al volume with the appropriate margin al play an d
condit iona l deposit prob abilities. The ta rget list is formed by ra nking the
pro spects according to expet.::ted volume an d represents the order in which
the prospects will be ev aluated for exploratory drilling in Ste ps 2 and 3.
Figure 5 presents an example.
Step 2 involves the econo mic evaluation of a prosp ec-t on the target
lis t. The expected ne t present value of the pro spect, conditional upon its
actt:ally being a deposit of oil or gas, is estimated by using a production
model, a t r a nspo r t a tio n model, and a distribution model. The production
model estimate s a sim plifi ed time profile of production condit ional on the
pro spect 's beco ming a developed discovery. The tran sportation and distri-
bution models estimate the cost of moving oil or gas to market, conditiona l
on any ex ist ing transportetion network serving the area. The transporta-
tion model dyna mically de velops a pipeli ne network for the area as
discoveries are made and placed in production. Field deveiopment7
transportation, and dist ribution costs are est imated by the production,
transpor tat ion, and distrib ution models to ·arrive at a time profile of the
costs that must be incurred to mark�t the oil or gas that the prospect
mlght contain. The associated revenue stream is generated by matching
the potential produ ction profi le with a time profi le of market prices (an
-
...
Prospect Rank Play Activ i�y Area
l
2
3
4
5
6
7
8
9
�o ,\ .
Alpha
Beta
Al pha
Al pha
Alpha
Beta
Alpha
Beta
Beta
Alpha
1. Margina! Play Probability
2. Condit ional Deposit Prob abil ity
H
A
B
B
H
F
A
H
F
1\
>, • -- -�--'-' ·-..--,-<---�.-4 �.-.---�--··"-•-·<<<•<��·�,.., .......... """""'�-----------�··-;.____.,_.,__�.� -��""".-"-�U�--·'""• � --�-·--,_,_. __ .. �.•-<<-«�> . ..-,,_ �-----�� ............... � ---,-�-�·�·-··
·
'
�
-
-
.
<••'---��-.....___-< •--.�-. -' ,-·-·�--------�--"" � -�:;;....::..._�
EXAM PL E TA RG ET LIST
Condit iona l Volume MPP l ----
2000 .4 511
3000 .650
1500 .4 54
1000 .11 5ll
700 .11 54
1200 .6 50
600 .4 511
900 .6 50
850 .650
400 .4 511
Fi gure 5
CDP 2 ----
.31 5
.120
.3 15
.3 15
.3 15
.120
.3 15
.120
.120
.3 15
�xpected Volume
286
234
215
143
100
94
86
70
66
57
Status
Dry
Dry
Deposit
Dry
Dry
Dry
Deposit
Dry
Dry
D r y
N 0
J
f r
f r
f
� • . .... '::� ' •• -. . "' • • .• ' f • : • . . .. • • \, � • .• � •.• ··: � . +"' • .;. • • .. . .... • •• "' • ' '
21
exogenous input to the model) a n d the p re sen t value of net revenue is
computed us ing an a p p rop r iate <ll scount rate.
It ls necessary to inco r porat e geologic risk into th e e c o n o mic
calculations since up to this p o i n t the p r o s pect has been economically
evalu ated conditional that it is a deposit. The geologic ri sk consists of two
componen ts: the risk that the r e g i onal geo logy of a play is unfavorable and
the risk tha t, even in cases in which the overall play ge olog y is favo rab le,
the geo logy specif ic to t h e particular p r o sp ect is flawed. T h e r e for e , it is
necessary to risk the prospect by multiplying i t s conditional net presen t
value by the marginal p la y p r·';)b abllit y and the approx imate conditional
d epos i t probabi lity. The r:;-sl�!• ls the ex p e c t ed economic value of the
p r o s pec t .
Step 3 in the explor a t i o n model is the application of a decision rule to
de termine w he the r or not a prospect merits testin g with an ex ploratory
well. The decision rule uses a c om par i s o n of expected net present val u e s
and a budge t constrain t. The budge t c o nstrain t se ts an upper bound on the
nu mber of exploratory we lls t h a t may be d r i l l ed ln any particula r p e r io d .
The expected economic v a l u e of the prospect is compared to the cost of a n
exploratory we ll and on l y if the e x p ecte d val ue equals or ex cee ds the cost
of testing the pro spect will an ex p lor a to ry well be simulated as dri lled.
..
•• 01 ' '
�r �
!;
< >:
,,
�
� ! �
� � fl
ri r· ['
. � J
'l
r: [T
r J .. ':
r �·· r·�
f.,':
22
During e ach time per i o d the process of comparing pro$pect expe<.:ted
values with exp loratory well costs continues dow n the ta rget list ':Inti ! one
of the following th ree even ts occurs: ex ploration in a particular year
ceases when the sum of the co sts :ior wi Idea t wells exceeds the budget
const raint; a prospect is reached for which the wildcat we ll costs ex ceed
its expected ne t present v a lu e ; or when no additional prospects remain on
the t a r get list. At t h is point the d rillin g decis ions are assu med to be
implem en te d, ex ploration co sts reco rded, an d the results of the dri lling
revealed. Any discoverle$ are placed in a p ool in ventory to await a
deve lopment decis ion in following periods.
Step 4 involves upda t i n g the ex plorer's geologic perception to reflect
the re sults of ex plorato ry activ ity in the cur ren t year. Results from all
we-.ds are assu med to b e com e av ailab le simultaneously at the en d of th e
p e riod. Pro spects that have been te sted with an e x p lo r a t o ry well during
the period are re moved from the target list, reduc ing the nu mber of
av ai lable prospects f or explorat ion cons ideration in follow ing periods .
Further, an atte mpt to capture some o f the learning· that takes place
du ring the discove ry process can be made by tak ing advan tage of the way
in which the geo logic risk has been fac.tored into two components. The
marginal play probability fc.lr each p lay in which o n l y d r y pro spects have
been dr illed is revised ac c ordin g to Bayes formula; each t1me a dry
prospect is drilled in a p lay 7 the marginal play probability is reduced as
-
f <" l �
1: I ,: ' I
J <
1: '
( <
[ r
� � <
r < [r <: '
r t� [
u
r -c
r ..
L, c ,.�:�
23
sp ec i f i e d by the formula. (An e xam p le is p rese nt�d in t h e Appendix.) The
first t i me a p ro sp e c t in the p l a y is tested and revealed t o be a s i m u la ted
dep o s i t of oil or gas, the m a r g i n a l p la y p r o b ab i li t y for that play is set e q u a l
to one p e rma n ent l y to refle ct the fact t hat all of the reg i o n a l p l ay
characte ristics must be f a v o r ab l e for an oil or gas deposit to e x is t . T h ese
re vis ions .in the pe rceived m a r g i n a l play prob a bili ty do not af fect t h e
simulated st ate of geologic nature sin ce it has been fixed at the beg i n n i n g
of the Monte Ca r l o pas s; the revisions only inf l u e n c e ex pec t a tio n s , th e
p erc e p tion of nature, and th us simulate the l e arni ng process that will take
place over time during ex ploration.
T h e r e v i sed m a r g i nal prob abilities influence e x p l o r a t b n decisions in
future periods th r ou g h their imp act on the e x p ecte d volumes an d ne t
present values c o m p u t e d .in St e p s 1 and 2. In the case of a play tha t is
y i e l ding an i n it ial series o f dry pro spects the marginal p r ob abil i ty i s
continu ally re vised dow n w a rd . T h us, after a s e r i e s of c!ry we l ls, t h e
ex p e c t e d values of the remaining pro sp e c ts in the play can become too
small to justify fu rther e x p l o r a t i o n in t h e play even thou gh so me of them
may be s i m u la t e d depos its an d c o mme r c i a l if di scove red by the explore r.
In th e case of a play in which a prospect has been te sted wit h an
ex plorato ry well an d de monstrated to be a simulated d e p o s i t , the risking of
re m a i n i n g prospects in the pla y is based on l y on the conditional d epos it
pr obability since the marginal play pr ob abili t y is eq ual to one. Re maini ng
prospects will have, therefore, higher ex pec ted val ues than would be the
case if there had been no d i sc o v e r i e s in t h e play.
-
Fi
.
. i
'1 IJ
(I ·,
t"l
I
r
r
r ,., ._,, �
r
r·
24
The oil and gas discoveries genera ted by the explo ration model form
an invento ry o f p<>ols that repre sents the input to the developmen t
decision. These poc,ls are econo mically evaluated .for possible production,
transportation, and dis tribution an d those combinat ions of pools expected
to be commercial are sim ulated as being produced, transpor ted, and
distributed to ma rke ts.
VI. Conclusion
The procedu re s de veloped here have been designed to achieve an
analytical melding of geology and econom ics; the objective has been to
de velop a resource assessm ent and ec onomic evaluation methodology for a
la rge area, under conditions of substantial uncer tain ty, and in a manne r
conducive to policy analy sis.
A play app roac h to re source assessment and ev alu ation for la rge
bas ins or prov inces has been selected for several reason so First, it pr ovides
a direct assess ment of the geolo gical cha rac terist ics and uncertain ty for
the area of interest. While analogs are cer··:a inly of great use to the
geologist in develop ing his judgments conce rning an area,, his fi nal judg-
ments are tai lored ex pHcitly to the information an d pe rceptions of the
target art::a. Second, the le vel of geologic deta il provided by the play
approach is rich enough to support meaningful econom ic analysis. Further-
more, the re sults of actual exploration in an area are quite easily
inco rporated into the play for mat. Third, while the play approach treats
-
25
ex plora tion as a proce ss of prospect evaluation and de cision, it does not
r eq u ire expl i c it identif icat ion an d substan tial de tail f o r each prospect.
Fourth, the play approach re cognizes a regional component to the geo logy
within a play that causes pro spects to be geol o g i ca l ly co rrelate d. In
essence, the play approach di vides the traditional dry hole risk fac to r into
two co m ponents. The first component is the risk th a t is com mon to all
prospects in the play because they share a common poten tial for source
material, migration, t i m ing , and reservoir rock. The second co mponen t is
the risk that an in div i dual prospect may have a geo logi c a l flaw speci fic to
it and i n d ep e n den t of other pro spects in the play. Fin ally, the ap proac h
does no t req uire actual discoveries in a play for assessment pur pose ;
judgments may be based on wha tever da ta ex ist and can exp licitly ref lect
the uncertainty in the data.
Initial efforts to advance the an a l y s is will focus on the s i m pli f ica
tions that curre n t ly exist in the exp loration m o d e l . For ex ample, the
target list is f o r m e d by ra nking the prospects ac cording to e x pecte d
volume an d only a few prospects are fully evaluated to dete rm ine e x pecte d
economic value. The number of prosp ects fully evalu ated is a func tion of
the budget constraint. In princip le, the ta rget list shou ld be based on
econo mic value b u t volume is used as a surrogate to save computation .
time. Alternative approaches that more closely ref lect econom ic v a lu e
(e.g., pt·oduction rate) without a dding sub stantially to the computation time
are being investigated. The use of a budget const raint to l i m it the numbe r
-
I
1.; I ·J
I
I
r
r
25
ex p loration as a process of prospect ev aluation and decision, it does no t
require explicit iden tification and substantial detail for each prospect.
Fourth, the play approach re c og n i z es a regiona l component to the geology
withi r. a play that causes pro spects to be geologi�a lly c<..,rrelated. In
essence, the play a p proac h divides the traditional dry hole risk factor into
two components. The first c�m ponent is the risk t hat is co mmon t o all
prosp ects in the play because they share a com mon potent i a.! for so urce
m a t erial, migration, timing, and reservoir rock. The second component is
the risk that ar. individual prospe'Ct may have a geolo gical flaw s p ecifi c to
it and in depen den t of o t he r prospects in the p lay . Finally, the appr oa ch
does no t r eq uir e ac tual d iscoveries in a pla y for assessment purpose;
judgme n ts may be b ase d on wha tever da ta exist and can explicitly ref lect
the uncerta int y in the data.
Initial efforts to advance the an alysis will focus on the simp lifi ca-
tions that curr en tly exist in the exp loration model. For ex ampl e, the
target list is formed by ran king th e p ros p ec t s accordi ng to ex pected
volume and only a few prospects are fully eva luated to de termine expected
economic value. The number of prospec ts fully evalu ated is a f u n c t i on of
t h e b u dg e t constralnto In princip le, the ta rget list should be based on
economic value but volume is used as a su rrogate to save c o mputatio n .
time. Alternative appro aches that more closely ref lect econom ic value
(e.g., producti on ra te) without adding substan tially to the computation time
are being invest igated. The use of a budget const raint to limit the number
-
I I
I I I I
� � [ r
;i J
-� . I
26
of exploratory we lls each period is a second simp lification that can be
improved-perhaps with a more explicit model of the invest ment pr_ocessc
-
� •'::· -j
APPEND IX
Bay esian Revi sion of the Margi nal Play Probabi li ty: An Examp lP.
Marginal Play Probabi lity = P [favorable play] = 0.454
Conditional Depos it Probabi lity = P [discovcry ,favo rable pl ay] = 0.315
P {dry we l l J favo rab l e play] = 1.0 -P [discovery,favorable play]� 1.0 -0.315 = 0.685
P [dry we llf unfavorable pl ay ] = 1.0
P [unfavorable pl ay] = 1.0 - P [favorable p l ay] 1.0 -0.454 = 0.546
P [favorable playJ a dry wel l] =
P [dry w:llJ tav�rable play ] *P �favorabl e play] + P [dry ho lefunfavorable pl ay] *P [un favorabl,e pl ay ]
p [favora ble play J one dry we ll] ::sa (. 68 5) (. 4 54 ) = .311 = 0.363
(. 685 ) (. 4 54 ) + (1 .0) (.546) . 31 1 + . 54 6
(. 685) (. 363) = .249 = 0.281 p [favorable pl ayf two dry wel ls) =(.685) (.363 ) + (1 . 0) {. 637 ) .24 9 + .637
P [fa vora ble play Jthr ee dry we lls] = �����(�.6�8_ 5_)��(._2�8_1�)��� (.6 85) (.281 ) + (1 .0) (. 719) =
.192 = 0.211
.192 + .719
P [fa vo rable playJ a discovery) = 1.0
-
I
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I
I
I
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�
�
rn
r: ..;J,
c
(
(�
[�
CJ
r, �.
[�
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28
FOOTNOTES
1. The Office has benefited from nu merous discussions with Professor
Gordon Kaufman of M.I.T. over the last several years; fo r a more detai led
discussion of the .relative merits of a proc es s oriented approach see
K auf man (1975) and Eckbo (1978).
2. Our selection of the play as the appropriate geologic u ni t was in large
part the result of the work of, and discussions, w i t h the Energy Subdivis ion,
Institute of Sedimen tary and Petroleum Geology of th e Geol ogh::al Survey
of Canada. In particular, the NPRP, Study Team is much indebted to Dr.
Robert McCrossan and Dr. Richard Proctor; see Roy (197 5).
3. The im por ta nc e and im plications for modeling of regional geologic
correlation was brought to our attention by Gil Mull of the Alaska Branch,
Geologic Division of the U.S. Geological Survey.
4. For example, the U.S. Geological Survey is responsib le for the
probability judgments for the Nat ional Pe trol eum Reserve i.n Alaska.
-
I
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I
e
�
�
0
D
D
rJ
29
REFERENCES
1. Drew, L. J., Attanasi, E. D., and Root, D. H. (1979) "I m p or tance of
Physical Parameters in Petroleum Supply Models," The Economics of
Exploration for E ne rgy Resources Conference, May 17-18, 1979, New
York University.
2. Eckbo, P ., Jacoby, H., and Smith, J. (1978) "Oil Supply Forecasting :
a Disaggregated Approach," Bell Journal of Economics, Vol. 9, No. 1,
Spring 1978, pp. 218-235.
3. Jones, R. W ., (197 5) "A Qu anti t ative Geolo gi c A pp r oac h to Predic-
tion of Petroleum Resou rces," AAPG Studies in Geo l ogy No� 1, pp.
186-195.
4. Kaufman, G. M., Balcer, Y ., and Kruyt, D. (197 5) "A Probabilistic
Model of Oil an d Gas Discovery," AAPG Studies in Geol ogy No. I, pp.
113-142.
5. Kaufman, G. M., R u ngg al dle r, W., and Livne, z. (1979) "Predicting
t he Time Rate of Supply From a Pe trole um PJay,u The Economics of
Exploration for Energy Resources Conference, May 17-18, 1979, New
York University.
6. Newendorp, P. D., (197 .5) De cisio n Analysis for Pe troleum Explor!:
tion, Petroleum Pub!ishing Company 1 Tulsa.
.... '4 .... • •• • � ... .. ...
-
I
I
I
I
rn I
>
r L
r t��,
[
[
7.
8.
30
Roy, K. J., Procter, R. M., and Mc Cr ossa n , R. G. (1975) uHyd"rocarbon
Assessment Using Subjective Probability", AAPG Research Sympo-
slum, Probability Methods !r!, Oil Exploration, Stanford University,
Aug 20-22, 19751 pp. 56-60.
Wansbrough, R. S., Price, E. R., and Epple r, J. L. (1976 ) "Evaluation
of Dry Hole Probaqility Associated with Exploration Projects," 51st
Annual Fall Technical C onfere n ce and Exhibition of the Society of
Pe tr o l eu m E ngi n eers of AIME, New Orleans, Oct 3-6, 1976, SPE Paper
No. 6081.
-
-,_,
j f
J .
f
! ' i 1 r
! l
.,,..._.,_� h . _I
Total Alaska
Onshore
O f fshore
Total
Total Alaska
Onsho re
Offshore
Total
Cook Inlet(3 )
Onshore
Offshore
Total
(1) Circular 725 (2) Circular 860
(3) Open File Report 82-666A
EXHIBIT 9
U.S.G.S. Estimate of Undiscovered Alaska
Natural Gas (TCF)
1975 Estimate (1)
__ ___;.....;..,;�
Fgs Fs
16 57
8 80
24 1 3 7
1980 Estimate (2)
19.8 62.3
33.3 109.6
53.1 172.0
1.27 7.41
0.85 5.03
2.12 12.44
.. -
Mean �� . l<'
32
44
76
i
p '
36.6
64.6
101.2
3.53
2.19
5.72
.,
ltw Unltrd Sl at•• ��� �t<tl fl'•ttlw"'' I i" off •IM•u•
•oUIMierl•• •lth c1tlwr Statt-11 ,.,_. .. rnt·d. Tlw llno•fl
nn thl• ct .. rt •r• fnr pt�rt•n•o•ll ,.f llh1 .. 1 r.ot lun
Dnly1 e114il 4n 110( -t'C'tl!tarUw fe•fl�rot lhl" rmoltlun
tar wlrv• of 1M Unlrr .. Stah•• with '''"'""''' t" lho•
•o..larw lnvulwtrol.
16
63
r
,--'
r�
.�
I .. r-'
' \ "L.__,_, 66 '
Flaure I.--Index -.p of Alaaka •hovtna province• aaeeeaed. Shading denote• of f •hore
•helf areaa; naaea of provinces are llated by nu•ber In the table.
EXHIBIT 10.
� !,_ __ . _c,, 'J''
Table 2.--�stimates of mini�� economic field sizes offshoPe [Gas-field sizes for the Gulf of Mexico and all oil-field sizes were provided by D. E. Kash, Conservation Division, U.S. Geological Survey, 1980, in a written communication. Non-associat ed gas-field sizes other than for the Gulf of Mexico were derived fr om the oil-field sizes by using a British thermal unit (BTU) equivalent of 6000 cubic feet of gas to one barrel of oil]
Water Offshore areas
Alaskan waters depth Gulf of Atlantic Ocean Pacific Souther r Be ring Chukchi Beaufort (meters) Mexico South Middle North Ocean Alaska Sea Sea Sea
Oil (million barrels)
3-30 0.6-1.0 3-15 10-30 20-50 6-20 40-70 40-100 75-150 100-200 30-100 1.0-2.0 5-25 20-80 30-100 10-45 50-100 50-125 100-200 150-300 100-200 2.0-5.0 20-80 40-100 75-125 20-65 75-150 75-200 150-400 200-450 )200 5-50 80-200 60-500 100-600 )30 125-250 125-300 )300 >400
No n-associated gas (billion cubic feet)
3-30 2-5 18-90 60-180 120-300 36-120 240-420 240-600 450-900 600-1200 30-100 5-15 30-150 120-480 180-600 60-270 300-600 300-750 600-1200 900-1800
100-200 15-30 120··480 240-600 450-750 120-390 450-900 450-1200 900-2400 1200-2700 )200 30-200 480-1200 360-3000 600-3600 )180 750-1500 750-1800 )1800 )2400 1Gulf of Alaska, Kodiak, lower Cook Inlet, <"'lelikof Strait.
EXHIBIT 11
.. -
Probability
Average or expected
99
95
90
75
50
25
10
5
1
(2)
EXHIBIT 12
DNR Estimates of Undiscovered Gas In
The Cook Inlet Basin (FCF) (1)
Gas in Place
.47
.93
1.24
1.98
3.07
4.38
5.84
6.93
9.06
3 .. 36
(1) From letter to Mr. Eric P. Yould, APA from Ross G. Schaf f, DNR, Feb. l, 1983
(2) Probability that quanity 1s at least the value shown.
Economically
Recoverable Gas
.00
o22
.43 .9 3
1.76
2.78
4.04
4.90
6.83
2.04
-
u
r LJ
c
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, L' 1
r� I ' l i
u
L . ...�
L . ...�
f L.
IIBZA·IIASCO SYBJF.CT ---------------FIL! NO. ----
DATE ----SUSITNA JOINT VENTURE -----------------------------
I'll
0.0 0'-
.........
I
�
� ltl
�
� �
I
Q) l) . ...
� �
I I !
I
(;.{))j I
I I I I ! s:ooi
l
I l l
I
I
I l 4.60!
J,()O
0
COMPUTED --------CHECKED ----
EXHIBIT /3
Cook IrJ let Fleet r-/c G e ne.ret.f:/o¥7
Net iur-01. I Cas Sv?p/y I.JS', 0.'a·.s Prt'ce.
I I I
I I
I I Vn Ct>w.l t Led I UY·d/.scove�red I
I .K: e s e. r ve. S' I R.. eso urce.s I
(/, C, TCF) I ( �, 0 TCJ:" @ f ::CJ,ts) I
I I
/. 0 �.o .J.O
(i?va.vu'-6y -TcF
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