Evaluacion de Reservas Dificiles

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SPE

E 22025

ow To Evaluate Hard-to-Evaluate ReservesH. Caldwell and D.1,Heather, The Scotia GroupMembers

yri ght 1991, Soci ety of Petrol eum Engi neers, Inc.

papar was prepared for presentahon at the SPE Hydrocarbon Economtcs and Evalual!on Sympomum held m Dallas, Texas, April 11-12, 1991.

paper was seleclad for presental,on by an SPE Program Committee fol[owmg rewew of information contained m an abstract submtfed by the author(s). Con!en!s of the paper,

presented. ha$e not bean reviewed by the Socle!y of Petroleum Engmaera and sre aubjecl 10 correction by the aulhor(s). The ma!eriril, as presented, does not necessarily reflect

p.x!hon of the Sociely of Patrolaum Engineers, Its offtcers, cr members. Papers presented al SPE meatmga are subject 10 publlcalmn rewew by Eddonal Commdleas of the SocIely

et rol eum En gi neer s. Per mi ss ion t o c op y i s r es tr ic ted t o an ab st rs ct 0! n ot m or e t han 3#3 w or ds . Il lu at rat !o ns may n ot b e c op red. Th e ab st rac t s ho ul d c on lar n c on sp ic uo us ac kn ow led gmen t

here and by whom the paper is presented, Wnle Publications Manager, SPE, P,O. Box 833s36, Rrchardaen, TX 75083-3S36. Telex, 730989 SPEDAL,

aditional reserve evaluations in the United

ates are based on tried and tested engineering

inciples, a wealth of local and generalperience, and a set of reserve definitions thatve evolved to become an indust~ standard.r the most part they work well. However, for

me of the emerging technology plays, sometimesferred to as statisticid plays, where individualell performances are characterized by significantriability of recoveries, application of these

initions alone is insufficient,

e problem for evaluation engineers is how to

st evaluate such technology plays: tight sands,

albed methane, Devonian shales, horizontalilling in fractured reservoirs, redevelopmentpleted fields, all being typical examples.

eferences and illustrations at end of paper.

of

This paper presents a method for evaluating playsthat involve a significant variability (uncertainty)

component. The method which employs

probability analysis is not new and has indeedbeen formalized to a stage of definitions of

proven, probable and possible reserve categories,The method is in use in many parts of the world.

Use in U,S.-based reserve evaluations has to datebeen virtually non-existent.

Case histories are presented illustrating thecomparison of reserve evaluation methods in two

hard-to-evaluate U.S. plays, specifically the AustinChalk horizontal drilling. play and the San JuanBasin coalbed methane play. These case historiesillustrate the benefit of complementing classical

deterministic techniques with probability analysis

so that uncertainty is expressed in a consistentand meaningful manner.



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HOW TO EVALUATE HARD TO EVALUATE RESERVES SPE 22025

he reserve definitions most commonly used in

he U.S. are those published by the Securities and

xchange Commission* (SEC) and the Society ofetroleum Engineers2 (SPE) in conjunction with

he Society of Petroleum Evaluation Engineers

SPEE). The most recent version published byhe SPE in May, 1987 is comprehensivelyescribed in SPEE Monograph 13 dated

ecember, 1988. Definitions are subdivided as

CATEGORY STATUS

Proved Prodiicing

Probable Shut-InPossible Behind Pipe

Undeveloped

or any estimate, the assignment of ca~egoryeflects the degree of certainty of the estimate

ince the definitions of proved, probable orossible are based on applying a test of

easonable certainty.” The status assignment

rovides an indirect confidence measure since the

lassifications at the top of the list will benefitrom hard production data, while those lower on

the list will rely on more inferential data andassumptions in order to derive an estimate.hese definitions are strictly deterministic. Thatis, a single figure is estimated as to the future

recovery of oil and gas from a well lease field orfor a company as a whole. The fact that such

estimates are imprecise is acknowledged by allprofessional reserve evaluators. In the words of

the SPEE, “In the final analysis, the reliability ofreserve estimates is the direct function of the

available data and the confidence and integrity ofthe estimator.”

In the SEC and SPE reserve definitions, no

mention is made of the use of probability analysisin reserve evaluations. In the SPEE monograph,

the use of probability analysis is explicitly rejected

unless specifically requested by the client. In

contrast, in 1983 a set of reserve definitions wasissued by the World Petroleum Congress4 (WPC).

This report discussed the use of probabilisticreserve definitions, particularly where the degreeof uncertainty associated with the estimate waslarge. These definitions may be summarized as

follows:

q Proven (P) Reserves: Are those thathave a probability of existence greater

than 85% to 95% (a 90% value is usedin subsequent discussion).

q Probable (P+ P) Rese&es: Are the

quantities added to proven reserves thatextend the overall probability of existenceto more than 50%.

@ Possible (P+ P+P) Reserves: Are thequantities added to proven and probablereserves that extend the probability of

overall existence to more than 5% to15% (a 10% value is used in subsequentdiscussion).

Please note that there are terminology differencesin that the word proven is used for the highest

confidence catego~ in contrast to the wordproved. As is discussed later in this paper, thehighest confidence categories are not equivalent.

The evolution of these reserve categories was

influenced by the need of the oil companies tohave a better idea of ultimate potential recovery

than could be gained using deterministicdefinitions alone. Such information was especiallynecessary where substantial capital investmentdecisions were required with only limitedreservoir information. A typical “example of this

would be a development decision in the NorthSea. As such, in certain areas of the world, the

use of probabilistic analysis in reserve evaluation

is commonly accepted both by the oil companiesand regulatory authorities (e.g.; the London StockExchange).

It is the authors’ opinion that reserve evaluation

using SEC/SPE/SPEE deterministic reserve

definitions is the most appropriate approach forthe majority of U. S. oil and gas plays. Where

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SPE 22025 ROBERT H, CALDWELL AND DAVID I. HEATHER 3

reserve evaluation is required for technology orstatistical plays which by their nature yield widevariations in individual well performance, and as

a consequence are more suited to significantmulti-well development program commitment, the

probabilistic approach enhances traditional reservedetermination methods. Specifics and casehistories are outlined below.

TEC1-INOLOGY/STATISTICAL PLAYS

Technology/statistical plays are not new.

‘Volatility in oil price, the tax reform act in 1986,Section 29 tax credits and the unrelated highpotentials of certain horizonal well developments,have led to an emphasis on such plays in therecent years, In many cases, these plays are

characterized by a few excellent wells togetherwith a substantial number of average and

marginal wells, Frequently, there is only a partial

understanding as to what makes a good well andwhat makes a marginal well, As such, theoperator must make a strztegic commitment tomulti-well programs in order that he is exposed

to the few good wells which make the play viable,

A]~plyingtraditional reserve definitions in certainor these technology plays, for example tight sands,coalbed methane and horizontal drilling in

fractured reservoirs, can be a difficult exercisebecause:

1, A considerable variation in recoveries from

well-to-well is the norm. Even with alltechnical data (drilling, completion,stimulation) being identical, productionperformance can vary considerably.

2, The normal technical data that is collectedin support of a reserve estimate may benon-definitive or even misleading.

3, Operator-specific drilling and completiontechniques may substantially affect wellrecoveries,

As a result, for a new area or for assignment ofreserves to proved undeveloped locations evenwith the application of all evaluation methodsnormally used in a deterministic reserveevaluation can still leave doubt in the mind of

the estimator as to remaining reserve. In manycases the approach then becomes one of applyingdiscount factors to result in a more conservative

estimate or, at the very least, low-balling thevarious parameters that affect the estimate, In

other words, the original intention of the reserve

definitions (to impart levels of confidence), has

been compromised by a risk weighting procedurethat arbitrarily adjusts reserve volumes to allowassignment to higher confidence categories, Thisjudgmental and very human reaction is the resultof the lack of a definition for “reasonable

certainty” within the reserve definitions themselvesand has the effect of adding an arbitrary andpotentially highly misleading component to the

estimation process.

EVALUATION METHODOLOGIES

The three principal methods used in reserve

evaluations are analogy, volumetric andperformance analysis and such methods apply foreither deterministic or probabilistic analyses, Asrecognized by the WPC, when uncertainty is high

the probabilistic methods become moremeaningful and the deterministic methods less

meaningful. As a result, probabilistic methodsutilize analogy and volumetric as primary tools,

since once a persistent performance history hasbeen accumulated uncertainty is usually not

dwelling in the reserves area but rather in theare of costs, prices, etc. affecting the economiclimit and hence remaining recoverable volume.

Figure 1 is a familiar graphic illustrating howevaluation methods and confidence in resultschange during the life of a producing reservoir,The normal clich6 is that the ultimate reserves

are not known with certainty until the last dropof oil bas been produce(i. While a tritestatement, it is worthwhile looking at the relative

levels of uncertainty in more detail. Duringinitial field development, analog and volumetric

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4 HOW TO EVALUATE HARD TO EVALUATE RESERVES SPE 22025

techniques are used, with performance taking overas production history is accumulated. Note thatthe range of estimates is large at first anddecreases with time. “To the casual observer this

graphic would imply that analog and volumetricestimation techniques are inherently imprecise

and inferior to performance estimates, Not SO.

The data available simply improves as productionhistory accumulates and allows fine-tuning of bothmethods as time passes. The performanceprojection is preferred since it is real, it is simple

to generate and to explain, and requires less workto generate than volumetric. Hence volumetricand analogs are usually abandoned as reserveestimation techniques once a persistent decline is

established and provided a non-water drivereservoir is being considered. However,considering the range of reserve estimates with

time and their classification into P, P+ P andP+ P + P, illustrates an interesting phenomenon.

At initial development, volumetric indicate arange of estimates with corresponding P, P+ Pand P+P+P. As production history isaccumulated, estimates are refined as areas of

uncertainty are eliminated. This has the effect ofbunching the range so that the remaining reserve

estimate is increasingly dominated by proven, witha diminishing contribution from probables and

possibles. This can be thought of as losingprobables and possibles both through revision as

uncertainty decreases and by production, to resultin the last drop of oil being proven.

In contrast with a deterministic estimate whichwill asymptotically converge on the ultimate

reserve figure from a high, low or middle position

based on the validity of the very first estimate,the probabilistic estimate based on the same

dataset will converge from the low end of theestimation range and grow towards the ultimaterevision and by production of lower confidencecategories. This contrast describes the conceptual

difference between a probabilistic and adeterministic approach.

Considering the so called hard to evaluatereserves with horizontal drilling in fractured

Austin Chalk and San Juan Basin coalbedmethane being examples developed herein, there

is a further conceptual hurdle. This involves themarked variability of recoveries from well-to-wellthat is noted in such plays. This single factmakes any estimate of reserves on a well-by-well

basis very difficult and such difficulties areequally shared by both the deterministic and theprobabilistic approaches. In order to embrace the

variability of these situations, it is incumbent to

consider reserves at the larger scale (field, lease,play) level and then allocate back to the smaller

scale (well level) rather than visa versa, This is

a major conceptual difference of approach and

bears further examination.

Consider a coalbed methane lease where aninitial round of drilling has resulted in a few

excellent wells, a few more average wells and

even more sub-average wells resulting in a typical

and expected lognormal distribution. The patterndrilled qualifies edge and infill locations asproved undeveloped (PUD) under normal reservedefinitions, The coal is shown regionally to be

continuous and structural elevation considerations

are not relevant. Consider the different

approaches to assigning reserves to the PUDlocations:

1. The Deterministic Approach, Well-by-Well: Existing wells on the lease plus

offset producers are analyzed andEstimated Ultimate Recoveries (EURS)

developed based on volumetric and onproduction history to date. EURS are

assigned to PUD locations based onsome form of averaging of EUR’S of the

closest offsets. Allowances are made in

the volumetric for drainage areadifferences and interference.

2. The Probabilistic Approach, Lease Level:Identical analysis work to the above isperformed but, rather than assigning

EURS on a well-by-well basis, the

distribution of EURS is used to develop

110



PE 22025 ROBERT H. CALDWELL AND DAVID I. HEATHER 5

an EUR model for all the PUD locations.Sampling this distribution at the 90%, 50%and 10% confidence levels yields the P,P+P and P+P+P values. The sameallowances are made for drainage areadifference and interference. The PUDlocations are then treated as a groupexpectation rather than as individualentities.

he advantages of the probabilistic approach can

e summarized as follows:

. Well Level Error: Assignment of EURSindividually based on offset performance is

subject to considerable error in statisticalplays. The best wells are given the bestoffset PUD’S and most engineers will notsign off on such estimates without applyinga healthy dose of conservatism since theyknow that the variation will seldom workin favor of their estimates. Similarly, forthe poor producers, most engineers will bevery hesitant to give more reserves to aPUD location than are indicated in the

direct offsets, In other words, the PUDSadjacent to the better wells are arbitrarilydown graded or even worse, carried atidentical values to the offsets while PUD’Sadjacent to the poorer wells are not given

the benefit for the potential to performbetter than their neighbors. Theprobabilistic approach recognizes thatvariation is a key characteristic of thelease, develops an EUR distribution forthe existing wells and another for thePUD’S, and assigns reserve values for the

PUD’S as a whole based on this

distribution.

Acknowledgment of Variability: The use

of a reserve distribution model for thePUD’S not only acknowledges the expected

variation, it utilizes this variation as thebasis for assigning reserve values. Theweakness of the deterministic approach

becomes the strength of the probabilisticapproach.

3. A More Conservative and DefendableApproach: Since the P value is definedas the 90% confidence figure, it willrepresent a lower figure than a normalproved estimate in most cases, while still

acknowledging the upside in the form ofthe PI- P and P+ P+P figures. Theconventional proved estimate will usuallylie somewhere between the P and P+Pvalues prior to the application ofadjustment factors by an individual

estimator. Using the most likely valuesfor a volumetric calculation and calling

that value Proved will result inequivalence to the P+ P value derivedfrom the same volumetric input using

normal distributions around the mostlikely values for each variable.

4. Orderly Reserve Revisions: As thePUD’S are drilled and production historyis accumulated, the volumetric and

analogy based estimates will be replacedby performance estimates. At first such

performance estimates will be tied to theoriginal estimate but, as historyaccumulates, the original estimate will be

abandoned and revised to the

performance related estimate. Thepotential for major reserve revisions willthus occur in the first few years ofproduction with the major revisions beingcentered on the lower confidence

categories. This is the logical way for arevision process to work.

CASE HISTORY: SAN JUAN BASINOALBED METHANE

To illustrate the evaluation problems associated

with coalbed methane gas reserves, a twotownship area comprising the best San Juancoalbed methane production was chosen. Thisarea, T30N-R6W and T30N-R7W, contains manyof the best coalbed wells and some of the longestproduction histories available, dating back to themid-1980’s. This area is characterized by thick

coal sequences, up to 70 feet, is over-pressured,

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SPE 22025 ROBERT H. CALDWELL AND DAVID I. HEATHER 7

on the margins, As development drillingcontinues, this pattern becomes moreclearly defined, This can be attributed toa variety of factors including the natural

permeability development or lack thereof,interference between wells and localdewatering, and depletion of the reservoirwith production. Ile following figuresillustrate this phenomenon:

EUR’SVERSUSTIME(B(W)

Phase1 Phase2Phase3

Avmge Wctl 7.1 5.7 4.9W%CIxdidcncc 1.7 1.2 0.9Wo Ccmfidcncc S.8 4.s 4.410%Cmfidcnm 18.0 10,0 7.8

Considering the adjustments described above, areserves distribution model for remaining PUDlocations is developed with the followingcharacteristics:

().8 13CF @ P or 90% confidence level3,3 BCF @ Pi-P or 50!??0confidence leveland6.5 BCF @ P+ P + P or 10% confidencelevel

The approach to developing a probabilisticreserves model is identical to the process for

developing a deterministic one. The difference isthat by basing the reserve definitions onprobability or confidence levels, the natural

tendency to risk weight is accomplished by thedefinitions rather than by an arbitrary process.This example is extreme in that enormousreserve variation from 320-to-320 acre location isobserved, This highlights the other majorcontrast to the deterministic approach, ThePUD model developed herein refers to thePUD’S as a whole and are not location-specific.

The model recognizes that while it may be

unrealistic to assign reserves to specific locationsclue to the observed variability, a developmentprogram should honor the model as a whole,

CASE HISTORY: AUSTIN CHAL>KI-]C)RIZOPITAI. DRILLING

The second example considered is the evaluation

problem fiaced for horizontal drilling in thefractured Austin Chalk, South Texas. Spurred onby recent success, particularly the Pearsall Fieldarea, the Austin Chalk is experiencing ahorizontal drilling boom. Horizontal wells are

being drilled on leases containing depletedvertica I chalk wells and intersecting untappedfracture systems.

For development of this l~xample, an area in the

Pearsall Field comprising 55 leases was chosen.In contrast to the previous example where a

“sweet spot” was chosen, the Pearsall study area

is mediocre in terms of horizontal wellperformance. This area contains 154 vertical

chalk wells that have combined EUR’S of 5,224MBO (34 MBC1/well) and were producing at 141BOPD (4 BOPD/active well) as of September,1990. A total of 19 horizontal wells have beencompleted on the subject leases and these wellsare producing 3,264 BOPD (172 BOPD/activewell) as of September, 1990,

Examination of vertical well EUR’S (Figure 4)

shows two populations, one with E~JR’s less than

20 MBO (40% of the wells) and another withEUR’S greater than 20 MBO (60% of the wells).The former represents kilure to communicatewith a fracture system (matrix population) while

the latter represents wells that have encounteredor frac’d into a fracture swarm (fracturepopulation), Horizontal drilling has the effect of

substantially improving not only the chance ofencountering a fracture swarm but also severalfracture swarms may be encountered in a single

wellbore, EUR’S for the horizontal wells in thestudy area average 119 M130 while the fracturepopulation vertical wells average 55 MB(3, This

can be thought of as two average fracture systemsper average horizontal well, although therelationships are more complex as described

below, -

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8 HC9W To EVALUATE HARD TO EVALUATE RESERVES SPE 22025

In order to utilize available vertical well EUR

data on the same leases to model likely horizonalwell recovery, the following horizonal wellrecovery model was developed.

HR = VR X HL X (l-D) X (l-W) / FS

Where:HR = Horizontal well recovery (MBO)VR = Vertical well recovery (MBO)HL = Horizontal length (feet)D = Fraction of fractures depleted

w = Fraction of fractures water filledFS = Fracture system spacing (feet)

This model uses the distribution of recoveries

from vertical wells (using the fracture populationas opposed to the matrix population), calculatesthe number of fractures intersected (HL/FS), anddeducts depleted and water filled fracture systems,The model is calibrated by matching to actual

horizontal well results and can be used tosimulate depletion of the undrilled locations by

offsetting production by increasing the water filled

and depleted fractures. Using the parameters

specified in Figure 5, the following expectationper well for horizontal exploitation of theundrilled locations on the studied leases results:

49 MBO (@P or 90% confidence level115 MBO @ P+ P or 507’ confidence level and230 MBO @ P+ P+ P or 10% confidence level

This compares with the most likely deterministicestimate of 73.5 MBO before consideration of

any adjustment factors based on the Modal valuesspecified in Figure 5.

Again, the above results are representative of the

expectation for all undrilled locations on theleases and are not well-specific. The probabilisticreserve definitions successfully bracket the

anticipated range of outcomes based on history to

date while honoring volumetric considerations.

SUMMARY AND CONCLUSIONS

Probabilistic reserve estimates utilizing thedefinitions described herein offer advantages overtraditional deterministic definitions in evaluation

situations where uncertainty is a key issue. Suchsituations are not restricted to “exotics” such asthe examples cited herein, but also include many

evaluations where volumetric or analogyrepresent the primary evaluation approach.

For properties with an established production

performance history and an established decline,

the conventional deterministic approach is tried,tested and preferable.

Where uncertainty is an issue, the probabilistic

reserve definitions offer the following advantages:

1.

2.

3.

4.

5.

Uncertainty, quantified by a reserves

distribution and associated probabilitiesis the basis for the reserve definitions,

thus representing the strength of themethodology.

The methodology offers a consistent way

of handling uncertainty thus avoiding thenormal human reaction of applyingarbitrary adjustment factors in order to

qualify reserves into higher confidencecategories.

The definitions quanti& upside potentialin the form of P+P and P+P+P

estimates, having important exploitationplanning implications.

The P or highest confidence category

provides a solid, defendable and

conservative estimate.

The process of reserve revisions isorderly and smooth with less impact fromthe occasional bad initial estimate.

114



22025 ROBERT H, CALDWELL AND DAVID I. HEATHER 9

with deterministic reserve estimates,abilistic reserve estimates and distributiondels on which they are based are only as goodthe quality of input data, rigor of theluation and analysis, and competence and

grity of the estimator will allow. Badmisleading data and bogus

mations will give just as bad an estimatependent of whether probabilistic orrministic reserve definitions are used.

ption of probabilistic reserve definitions for

use in the U.S. has severalificant hurdles to overcome:

Lack of linkage to deterministic definitionswill cause not only confusion but will open

the door for misuse. Provision ofguidelines by an authoritative body such as

the SPEE would alleviate this concern.

Since the probabilistic P value is normallymore conservative than a proved figure

and because much of the reserveestimation activity is centered onstandardized reporting (SEC cases), mostoil companies will hesitate to takeadvantage of the knowledge provided byP+ P and P+ P+ P due to the confusion

created by a proven (P) versusconventional proved reserve figure. Aconsensus that “reasonable certainty”

equates to (say) the 75940confidence level(between P and P+P) would providelinkage between the systems and removemuch confusion.

When should it be applied and when

should it not be applied? While theauthors believe that certain applications

are obvious, the development of guidelines

to suit all circumstances is required.

ACKNOWLEDGMENTS

The authors wish to thank Wayne Beninger and

the staff of The Scotia Group for data extractionand preparation for the examples used in this

paper as well as for manuscript review andcritical commentary.

REFERENCES

1.

2.

3.

4,

5.

Securities and Exchange Commission,

Reserve Definitions as shown in Bowne

& Co., Inc. pamphlet dated March, 1981as Regulation S X, Rule 40-10--FinancialAccounting and Reporting of Oil andGas Producing Activities.

“Reserve Definition Approved,” Journal ofPetroleum Technolow, May, 1987, 576-

578.

Monograph I, Guidelines For Application

Of the Definitions For Oil and GasReserves, Society of PetroleumEvaluation Engineers, December, 1988.

Eleventh World Petroleum Congress,

London 1933 and 1983, Study GroupReport, “Classification and Nomenclature

Systems for Petroleum and PetroleumReserves,” 1984.

“Hydrocarbon Classification Rules

Pr;posed,” Oil & Gas Journal, August 13,1990, 62.

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Evaluacion de Reservas Dificiles