Economic and Reserve _Evaluation of Coal Bed Methane ReservoirsDhir

7/27/2019 Economic and Reserve _Evaluation of Coal Bed Methane ReservoirsDhir

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S P ESociety of Petroleum Engineers

SPE 22024

Economic and Reserve _Evaluation of Coal bed Methane ReservoirsR. Dhir, * R.R. Darn, and M.J. Mavor, * Resource Enterprises Inc. SPE Members

Copyright 1991, Society of Petroleum Engineers, Inc.

This paper ~ a sprepared lor presentation at the SPE Hydrocarbon Economics and Evaluation Symposium held in Dallas, Texas, Aprll11-12, 1991.

This paper was selected for presentation by an SPE Program Committee following review of information contained in an abstract submitted by the author(s). Contents of the paper,as presented, have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material, as presented, does not necessarily reflectany posiUon of the Society of Petroleum Engineers, its officers, or members. Papers presented at SPE meetings are subject to publication review by Editorial Committees of the Societyof Petroleum Engineers. Permission to copy is restricted to an abstract of not more than 300 words. Illustrations may not be copied. The abstract should contain conspicuous acknowledgmentof where and by whom the paper is presented. Write Publications Manager, SPE, P.O. Box 833836, Richardson, TX 75083-3836. Telex, 730989 SPEDAL.

ABSTRACT

A rigorous procedure for the reserve and economicevaluation of coalbed methane reservoirs is presented.Traditional methods for estimating recoverable reserves arenot applicable to coalbed methane reservoirs. A reservoirsimulator based approach is discussed here. This approachincorporates modern formation evaluation techniques forreservoir characterization along with a numerical coalbedreservoir simulator. Production forecasts from the reservoiri l i d i i d l

Coalbed methane reservoirs are characterized by thenon-conventional nature of gas storage and fluid production,which limits the use of traditional decline curve orvolumetric methods for reserve estimations. To accuratelyaccount for the physical phenomena affecting production, anapproach based on the use of reservoir simulators isrecommended. Applications of this approach for differentreserve categories are presented.

Production from coalbed methane reservoirs is affectedl d l f f h


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2 Economic an d Reserve Evaluation of Coalbed Methane Reservoirs SPE'22024

CONCLUSIONS

The conclusions that have resulted from this study aresummarized in this section. The quantitative basis for theconclusions is presented in the subsequent sections.

Accurate production forecasts and reserve estimates forcoalbed methane reservoirs can be performed with modemformation evaluation and reservoir simulation technology.Due to the complex nature of the physical phenomenagoverning production of natural gas from coalbed reservoirs,conventional decline curve models should not be used for

reserve estimation.

Although some coalbed methane reservoir developmentinvestments can meet economic requirements without taxcredits, the Section 29 non-conventional fuel tax creditgreatly improves the potential profitability for investors withsignificant tax burdens. After tax internal rates of returnwith the credit are almost twice the return without the

credit. Income shelter potential through January 1, 2003 isapproximately four times the initial coalbed methanedevelopment investment for the cases studied. Themagnitude of the credit is so great that it may often exceedthe cash flow generated by a single project.

Based upon statistical analysis of the results of numeroussimulations of reservoir performance, a simple regressionmodel has been developed to estimate either the before orafter tax internal rate of return, discounted profitabilityindex, or recovery factor based upon the permeability of thenatural fracture system, the initial reservoir pressure, the

l l d h h k f h

Design and construction of pipelines, gatheringsystems, processing plants and other surface facilities

Establishing sales contracts and prices

Regulatory body approvals and establishing division ofownership in unitized projects.

Reserve Definitions

In 1987, the Society of Petroleum Engineers (SPE)approved a set of definitions! for Oil and Gas Reserves

which stated that: "Reserves are estimated volumes of crudeoil, condensate, natural gas liquids and associatedsubstances anticipated to be commercially recoverable fromknown accumulations from a given date forward, underexisting economic conditions, by established operatingpractices, and under current government regulations." Basedon the degree of uncertainty of the reservoir and geologicinformation, the reserve estimates are classified as eitherproved or unproved. Proved reserves can be furtherclassified as developed or undeveloped and unprovedreserves can be further classified as probable or possible.Fo r detailed definitions of each of these categories thereader is referred to the Definitions for Oil and GasReserves published by the SPE.l

Methods for Reserve Determination

Various methods are available for estimating reservesfor oil and gas reservoirs.2.3 These can be broadly classifiedas follows:

Analog


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SPE.J2024 Dhir, R., Dem, R.R., an d Mavor, M.J.

of decline curves can yield estimates of initial fluid volumesin place and future production rates that are significantly inerror.

Volumetric methods that estimate recoverable reservesbased on estimates of initial gas-in-place and recoveryfactors have limited applicability as the proper physicalphenomena are not accounted for and as a result themethods have limited accuracy when used to forecastproduction rates. A recently proposed material balancetechnique is based upon a modified p/z approach to accountfor both diffusion and desorption. 6 This approach requiresth e assumption of equilibrium between free and adsorbedgas phases which limits th e applicability to reservoirs withlarge diffusion coefficients. The method is further limited bvth e inability to effectively account for multiphase flow,various completion techniques, and product ion strategies.

Th e proposed method for estimating reserves and futureproduction combines the analog and volumetric approach toenhance the reservoir description. A reservoir simulator isused to forecast production rates.5 The availability ofmicrocomputer based reservoir simulators that effectivelymodel most of the physical phenomena and unique featuresof coalbeds has made this technique accessible to thepracticing engineer. These reservoir simulators have beenvalidated and utilized for reservoir analysis and productionprediction in various coal basins of the United States},S Th e

proposed method can be summarized in the following steps:

Reservoir Characterization: Performance of a geologican d engineering analysis of formation evaluation to develop

proper reservoir characterization through th e gathering andanalysis of accurate an d reliable formation evaluation datais essential. Ga s production and recovery from coalbeds are

most affected by th e following critical properties:9

Gas-in-PlaceNatural Fracture Permeability

Initial Reservoir PressureCoal Desorption and Diffusion Characteristics

Estimates of th e critical properties ar e available fromvarious sources, depending on th e stage of th e developmentof th e project. Reliable estimates of reserves an d productionare often required to evaluate the proposed investmentsbefore drilling. Unfortunately, it is at this time that th e leastinformation about the reservoir is available. Generalbackground information about several basins in the UnitedStates is available from various resource estimate studiesfunded by th e Department of Energy (DOE) and the GasResearch Institute (GRI).10,11,12 Coal structure, stratigraphyan d thicknesses can be estimated from numerous studiesperformed by th e United States Geologic Survey (USGS)and when available, from well logs drilled to deeperhorizons. At this stage of development, estimates of coaldesorption an d diffusion characteristics, natural fracturepermeability and initial reservoir pressure ca n only beestimated by analogy with areas having similar coal rank,composition, overburden and in-situ stress conditions, and

from regional hydrologic studies.

Proper data collection is of prime importance duringboth the exploration an d early development stages for a

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4 Economic and Reserve Evaluation of Coalbed Methane Reservoirs SPE22024

gas content cannot be estimated from wireline log data andmust be determined from reservoir samples.

The desorption phenomena is characterized with alaboratory measurement known as a sorption isotherm.14The sorption isotherm relates the gas storage capacity of thecoal matrix to pressure with the following relationshiporiginally presented by Langmuir.15

pg = gL (1-a


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SPE22024 Dhir, R., Dern, R.R., and Mavor, M,J.

scenarios are the drilling of additional wells, application ofimproved completion and stimulation methods, or changesin surface producing facility conditions. Care should be

taken to include well spacing and allowable productionlimitations in the production forecasts.

Examples of Reserve Evaluation

The above concepts are now illustrated by case studiesfor the proved and unproved reserve categories.

Proved Reserves

Proved reserves are those which can b e estimated withreasonable certainty to be recoverable under currenteconomic conditions. Fo r coalbed methane reservoirs, ahigh degree of certainty in the reserve estimates can beachieved if the primary reservoir properties listed in Table 1are known and if sufficient production data are available todevelop and validate a representative reservoir model.

To classify reserves in this category it is essential to haveopen-hole log data and production test data. In addition tothese, core derived gas content estimates from theformation of interest and estimates of the natural fracturepermeability enhance the accuracy of the reserve estimates.

Consider the following example. Six wells were drilledon five adjacent sections, at a spacing of 320 acres into the

Fruitland Formation (Upper Cretaceous)of

the San JuanBasin of Colorado and northern New Mexico at depthsranging from 1,100 to 1,800 feet. Open-hole log data weremeasured in all the wells. Core gas content data and natural

summarized in Table 2. These estimates were thenincorporated into the reservoir models for both Groups 1and 2. The reservoir models were used to forecast future

production rates, recoverable reserves and recovery factorsthat are summarized in Table 3.

Proved Undeveloped Reserves

Three additional infill wells were proposed on the sameacreage at an average depth of 1,300 feet. Based on thereservoir and geologic information, these locations wereanticipated to have similar reservoir characteristics to thoseof the wells from Group 1. Th e production forecasts fromthe Group 1 wells were used to estimate recoverablereserves and recovery factors for these proposed wells.

Probable Reserves

Probable reserves are those which are less certain thanproved reserves and can be estimated with a degree ofcertainty sufficient to indicate that the hydrocarbons aremore likely to be recovered than no t.l Fo r coalbed methanereservoirs, probable reserves would be those from a coalseam which has been proven to be productive in the samegeologic province. Th e reservoir should appear to beproductive based on log characteristics. Fo r coalbedmethane reservoirs, the use of open-hole density log data toestimate the net coal thickness is recommended. Gascontent estimates may be obtained by analogy, through the

use of reservoir pressure and the sorpt ion isotherm, or fromthe open-hole log data with gas content estimates calibratedfrom offset well core data.13 Estimates of the fracture

s


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6 Economic an d Reserve Evaluation of Coal bed Methane Reservoirs SPE'22024

probable reserves and can be estimated with a low degree ofcertainty, insufficient to indicate whether hydrocarbons aremore likely to be recovered than not.l For coalbed methane

reservoirs, possible reserves would be from a coal seam thathas proven to be productive in the general geologicprovince. Estimates of net coal thickness, structure andstratigraphy should be obtained from well log information.Typically the well control for this classification is less thanfor probable reserves. Gas content estimates may beobtained by analogy or from the open-hole log data bycalibrated core-log relationships from nearby areas, or by

the use of sorption isotherm data. Estimates of naturalfracture permeability can be obtained by analogy. As before,semi-quantitative estimates can be obtained from analysis ofmicrolog resistivity data. Extensions of productiveformations are typically inferred from regional geologicstudies.

Fifteen additional wells were proposed on 6,200 acres ofundeveloped acreage. The wells were to be drilled to

penetrate the Fruitland Formation at depths ranging from2,800 to 3,200 feet. Open-hole logs were obtained from wellsdrilled to deeper horizons. An analysis of public domaininformation11,16,17 revealed that wells producing fromFruitland Formation coal at similar depths in the samegeologic province had proved to be commercial. Estimatesof permeability and secondary reservoir properties wereobtained from data available from research wells funded byGRI.9,11,13,17,18These estimates are summarized in Table6.

Production forecasts were obtained with a reservoir

basis for the revenue stream and water disposal costs in theeconomic model.

Initial Development costsThe initial costs of development include the costs of land

acquisition, drilling and completing the wells, and theinstallation of surface facilities. The drilling costs associatedwith coalbed methane development projects are similar tothose for the development of conventional oil and gasresources. The initial exploration wells may include someadditional costs for core, logs, and drill stem test datarequired for formation evaluation that would not berequired by development wells.

Completion techniques for coalbed methane wells varywith reservoir and geologic characteristics and include singleand multiple zone hydraulic fracture treatments and openhole cavity completions.19 The costs associated with each ofthese completion types also vary with the implementation

procedures.

To facilitate gas production, coalbed reservoirs usuallyhave to be depressurized by water production (dewatering)and the wells are usually produced at low bottomholepressures. The surface facilities must include artificial lift,gas/water separation, and gas compression. In some cases,surface facility costs can be a significant part of the overallexpenditure. Drilling and completion costs for arepresentative well in the northern San Juan Basin areprovided in Table 8.

O i E


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SPE22924 Dhir, R., Dern, R.R., an d Mavor, M J.

Gas Price Assumptions

Forecasts of future natural gas prices are required tomake revenue projections. Fo r securities registration andfinancing purposes, constant gas prices are frequently used.Fo r other economic analysis, a constant price forecast mayprove to be conservative and may result in inaccurateinvestment decisions. For this analysis, the assumed gasprice forecast was based on 1990 average wellhead gasprices of $1.50 per MSCF with a real escalation of 5% ineach following year.

Nonconventional Fuel Tax Credit

Under certain specified conditions, production from acoalbed methane well is eligible for a non-conventional fueltax credit established under Section 29 of the IRC.23 Thiscredit is allocated on the basis of the revenue interests.

Section 29 creates a tax credit for qualified fuels sold by ataxpayer to an unrelated party during the taxable year,provided production of the fuel is attributable to thetaxpayer; the fuel is produced in the United States; the gasis sold at a lawful price; and the fuel is produced from wellsdrilled after December 31, 1979, and before January 1, 1993.Production from valid wells is subject to the tax credit untilJanuary 1, 2003. The value of this credit is anticipated to beapproximately $0.90/MSCF for 1990. Fo r this analysis, itwas assumed that the credit would escalate at 4.3% per yearuntil expiration in January 1, 2003. Implicit in thisassumption is that oil prices would not rise to levels greatenough to result in either a partial or complete phaseout ofthe tax credit.

methods and the method giving the greater tax deductionwas used. The possible effects of Alternative Minimum Tax(AMT) were not considered.

Division of Interest and Taxes

Typical oil and gas investments often involve complexdivision of ownership interest. Fo r this analysis, a simplestructure was assumed with 100% working interest and82.5% net revenue interest. A Severance tax rate of 6%, anAd Valorem tax rate of 5%, and a Federal tax rate of 36%were assumed. No AMT considerations were addressed.

Examples

The production forecasts obtained for the "likely" casesfor the various prospects estimated by reservoir simulationwere incorporated in the economic analysis model alongwith all the investment, recurring expense, gas price and taxcredit assumptions discussed above. Th e economic analysismodel was then used to estimate the economic return fromthe investments. These computations were performed onboth a before and after tax basis and ar e presented in Table9. Analysis of development economics for coalbed methaneprojects has demonstrated that discounted cash flowevaluation serves as an effective measure of the economicviability of coalbed methane projects.9

Discounted Cash Flow Rate of Return and Net Present

ValueThe Discounted Cash Flow Rate of Return (DCFROR)

is defined as the rate of return that makes the present worth

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8 Economic and Reserve Evaluation of Coalbed Methane Reservoirs SPE22024

with the circumstances of the investors.

The net present value of given investment is presented inthis paper in terms of a dimensionless discountedprofitability index (DPI). The DPI is the ratio of the ne tpresent value of the investment plus the net present value ofthe revenue stream cash flow to the net present value of theinvestment. The net present values are computed at aspecified hurdle rate. The DPI values for various projectswere computed on an after-tax basis assuming 100% taxcredit utilization and are presented in Table 9.

Tax Credits and Income Shelter Potential

The 1990 magnitude of the tax credit is estimated to beabout $ 0.90/MSCF. The value of this tax credit can have asignificant effect on the project economics. During thisanalysis it was concluded that the value of this credit canoften exceed the income tax burden generated by the cashflows from the investment. Under such circumstances, theexcess tax credits would constitute additional income shelterpotential for the investor. The magnitude of the surpluscredits and the additional income shelter potential for thecases being analyzed are illustrated in Table 9. These resultsindicate that, for the study cases, the cumulative incomeshelter potential through January 1, 2003, is approximatelyfour times the initial investment. This conclusion issignificant because it demonstrates that the true benefit of acoalbed methane investment is the tax credits if sufficienttax liability is available for sheltering.

REGRESSION ANALYSIS

returns from a coalbed methane project are a function offour primary reservoir properties; the initial reservoirpressure, Pi (psia), the initial gas content, 8c. (SCF/Ton), and

the formation thickness, h, (feet), and the natural fracturepermeability, k, (md). The relationship is expressed in termsof a regression model, of the general form listed below.9

(2)

Where Y the dependent variable is either the recoveryfactor, before and after tax internal rate of return with andwithout tax credits, or the discounted profitability index. Th ecoefficients ao and a 1 are constant for a given value of

permeability. Th e relationship between the constantcoefficients and permeability is expressed as follows.

log a 1 =a10 + a11 log k

(3)

(4)

A unique set of coefficients would define a model foreach of the dependent variables. The coefficients are validonly for the given set economic and cost assumptionspresented in this paper. Fo r ease of illustration, it wasassumed that one set of economic and cost assumptionswould be applicable for all the reserve categories. Theregression model coefficients for the set of reservoir andeconomic conditions discussed here are summarized in

Table 10 for all Y variable possibilities. These relationshipswere then used to study the risk associated with investmentsin San Juan Basin coalbed methane development projects.


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'SPE22024 Dhir, R., Dern, R.R., an d Mavor, M J.

discusses a unique approach which quantifies some of therisks associated with coalbed methane projects. The basicconcepts of this approach are fairly general and could be

applied to the analysis of almost any oil and gas investmentproject.

Analysis Methodology

In the context of economic analysis of an energyinvestment project, risk can be defined as a quantitativemeasure of the uncertainty in the expected economicreturns from the investment. The risk can arise fromuncertainty in market or operating conditions and fromuncertainty in estimates of reservoir and geologicproperties. This analysis concentrates on factors whichcontribute to the geologic risk.

Probability Distributions for Input Variables

To accurately quantify the geologic risk associated withcoalbed methane reservoirs, it is important to quantify thepotential uncertainty in the estimates of the primaryreservoir properties which are then translated intouncertainty in the values of the estimated economic returns.Several models have been discussed for the representationof the uncertainty associated with estimated reservoir andgeologic variables26,27. For this analysis, probabilitydistribution functions were chosen to model uncertainty.These functions consist of a set of possible values for a givenvariable and the probability of occurrence of each of thesevalues. The probability distribution functions can rangefrom simple models where probabilities are assigned

ab zb-1 eazr(z,a,b) = (b- 1)! (5)

where:

meana =

variance (6)

mean2b =

variance (7)

Probability distributions for each reservoir variable canthus be characterized by a mean and variance. Th e mean isthe "likely" value while the variance is a measure of theuncertainty of the estimates. Reservoir properties for eachof the reserve categories discussed previously werecharacterized by such probability distributions. Thesecharacteristics are summarized in Table 11.

Probability Distributions for Economic Returns

The next step in this analysis is to derive a probabilitydistribution for expected economic returns using theprobability distribution for each critical variable. This step isaccomplished by the multiplication rule of probabilitieswhich states that the probability of the simultaneousoccurrence of two or more independent events is simply the

product of their individual probabilities.27 Th e rule can berepresented as follows

9


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-------------------------------------------------------------------------------------10 Economic a nd Reserve Evaluation of Coalbed Methane Reservoirs SPE22024-

n[x] denotes the number of elements, x, in a probabilitydistribution.

The total number of such possible combinations couldbecome fairly large and a detailed analysis using thereservoir simulator and the economic analysis model wouldbe very tedious. However, it was earlier demonstrated thatthe economic returns could be computed directly from givenvalues of the critical reservoir variables using the statisticalmodel described in the section above. The statistical modelwas used to calculate the economic returns corresponding toeach specific combination of reservoir properties (event).Specific events were generated using the probabilitydistributions for the various reserve categories listed inTable 11. The probability of obtaining the return wasestimated using the multiplication rule of probability. Thisprocedure was repeated for all possible combinations togenerate a probability distribution for the economicreturns.28

The probability distribution for the internal rate ofreturn from the investment is illustrated in Figure 4.Interestingly, the results can be approximated by a straightline in a semi-log graph. These probability distributionswere then used to calculate the expected returns bycomputing the sum of the products of the various returnstimes their probabilities. The expected return measures theaverage o r central tendency of the probability of returns andis represented by the following formula.29

nE(r) =I Pi f i

i= l(10)

measuring the uncertainty in the expected returns. Measures for quantifying the risk associated with a given project a rediscussed in this section.

Standard Deviation

The standard deviation is a measure of the variance ofthe data from the mean value. The variability of thedistribution is also indicated by the magnitude o f the slopeof the semilog probability distribution lines illustrated inFigure 4. The standard deviation increases with the degreeof uncertainty in the reserve estimates. The standarddeviation values for the different reserve categories arepresented in Table 12. These values illustrate that thestandard deviation value is the least for the provedproducing reserves category while it is the greatest for theunproved reserve category. The basic mathematicalrepresentation for the standard deviation is as follows. 29

s = ( I Pi (ri - E(r)) 2 )05 (11)i= l

Performance Index (PI)

This measure of risk incorporates both the expectedreturn and the fisk30 and serves as a complete measure ofthe feasibility of a proposed investment. It is defined asfollows.

~ f T \

P I = ~s (12)

Since the basic objective of any investment is tomaximize the economic returns at the lowest risk it is


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SPE22024 Dhir, R., Dern, R.R., an d Mavor, M,J.

a hurdle rate for investment of 10% or greater and that hismaximum risk aversion was 32%, his minimum performanceindex requirement would be 1.0.

Using the performance index and downside probabilitymeasure, potential coalbed methane investments can beranked by risk adjusted expected returns. An explorationbudget ca n then be designed around a portfolio of coalbedmethane prospects whose risk adjusted expected returnexceed th e investors required rate of return.

To accurately quantify the risk associated with a

proposed investment, it is useful to include all of the abovemeasures of risk to make an informed investment decision.

SUMMARY

A rigorous technique for th e estimation andclassification of reserves for coalbed methane reservoirs waspresented in this paper. This technique accounts for th enon-conventional nature of gas production from coalbeds byusing a reservoir simulation to accurately account for th ephysical processes involved in coalbed methane reservoirexploitation. Several examples of the results of th e reservedetermination were presented.

Th e computer simul ation results were used as input toan economic analysis model which computed the economicreturns for proposed investments. Th e effects of the

Sections 29 non-conventional fuel tax credits were analyzedand discussed.

A statistical model was presented which relates th e

aad

ao

aoo

ao1al

a1o

aub

BSCFE(r)gic

icshhsk

ks

logMSCFn[x)

pPi

PI

Pis

PL

NOMENCLATURE

gamma function parameter defined by Equation 6dry, ash content, fraction

regression coefficient






gamma function parameter defmed by Equation 7

billion standard cubic feet of gasexpected value of the random variable rgas storage capacity of coal, SCF To ngas content of coal, SCF Ton

specific value of the gas content of coal, SCF Ton

reservoir thickness, feetspecific value of the reservoir thickness, feet

natural fracture absolute permeability, md

specific value of the natural fracture absolutepermeability, mdbase 10 logarithmthousand standard cubic feet of gasthe number of elements, x, in a probabilitydistribution.pressure, psiainitial pressure, psia, or probability of occurance of the

ith value of the random variable pperformance index, dimensionlessspecific value of the initial pressure, psia

Langmuir pressure, psia

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12 Economic an d Reserve Evaluation of Coalbed Methane Reservoirs SPE22024

4. King, G.R. and Ertekin, T.: "State-of-the-Art in Modelingof Unconventional Gas Recovery," paper SPE 18947presented at the 1989 SPE Joint Rocky Mountain

Regional/Low Permeability Reservoirs Symposium andExhibition held in Denver, Colorado, March 6-8, 1989.

5. McElhiney, J.E., Koenig, R.A. and Schraufnagel, R.A.:"Evaluation of Coalbed-Methane Reserves InvolvesDifferent Techniques," Coalbed Methane, Oil and GasJournal Special Gas Production Report, (Oct., 1989) pp.13-18.

6. King, G.R.: "Material Balance Techniques for Coal Seamand Devonian Shale Gas Reservoirs," paper SPE 20730presented at the 65th Annual Fall Technical Conferenceand Exhibition of the Society of Petroleum Engineers heldin New Orleans, LA , Sept. 23-26, 1990.

7. Zuber, M.D., Kuuskraa, VA. and Sawyer, W.K.:"Optimizing Well Spacing and Hydraulic Fracture Designfor Economic Recovery of Coalbed Methane," FormationEvaluation, Society of Petroleum Engineers, (March 1990)pp. 98-102.

8. Young, G.B., McElhiney, J.E., Dhir, R., and Mavor, M.J.:"Coalbed Methane Production Potential of the RockSprings Formation, Great Divide Basin, SweetwaterCounty, Wyoming," paper SPE 21487 presented at the 1991Society of Engineers Gas Technology Symposium held inHouston, Texas, January 23-25, 1991.

9. Dhir, R., Mavor, M.J. and Close, J.C.: "Evaluation ofFruitland Coal Properties and Development Economics,"1991 : Coalbed Methane .Qf Western North America,

14. Mavor, M.J., Owen, L.B., and Pratt, T.J.: "Measurementand Evaluation of Coal Sorption Isotherm Data," PaperSPE 20728 presented at the 65th Annual Technical

Conference and Exhibition of the Society of PetroleumEngineers held in New Orleans, LA, September 23-26,1990.

15. Langmuir, 1.: Am. Chern. ~ . l .40 (1918) p. 1,361.

16. Zuber, M.D., Sawyer, W.K., Schraufnagel, RA . andKuuskraa, VA.: "The Use of Simulation and HistoryMatching To Determine Critical Coalbed MethaneReservoir Properties," paper SPE/DOE 16420 presentedat the Society of Petroleum Engineers / Department ofEnergy Low Permeability Reservoirs Symposium held inDenver, Colorado, May 18-19, 1987.

17. Ayers, W.B., Jr., Kaiser, W.R., Ambrose, WA., et. al.:Geolodc Evaluation .Qf Critical Production Parameters fru:Coalbed Methane Resources.: Part .1. ~ Juan Basin,Report No. GRI 90/0014.1, Gas Research Institute,Chicago, IL (Jan., 1990).

18. Mavor, M.J., and Close, J.C.: Western Cretaceous ~SsJ.m ~ Evaluation of ~ Cooperative ResearchWell Hamilton . t l Operated bx Mesa Operatin& LimitedPartnership, Report No GRI 90/0040, Gas ResearchInstitute, Chicago, IL (Dec., 1989).

19. Logan, T.L.: "Western Basins Dictate Varied Operations,"Coalbed Methane, Qi l .!lru! Gas lruwW ~ QMProduction ..B&pQrL(Oct., 1989) pp. 26-30.

20. Zimpfer, G.L., Harmon, E.J. and Boyce, B.C.: "Disposal ofProduction Waters from Oil and Gas Wells in the


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SPEl2024 Dhir, R., Dern, R.R., and Mavor, M J.

26. Megill, RA.: An Introduction !Q Rik Analysis, Penn WellBooks, Tulsa, OK, 1984.

'27. Feller: An Introduction !Q Probability ~ !!D!! }!APplications. Volume 1, Third Edition, John Wiley andSons, New York, (1981).

28. Hess, S.W. and Quigley, HA.: "Analysis of Risk inInvestments Using Monte Carlo Techniques," CbemicalEn&ineerin& Symposium ~ ~ Statistics ill!!Numerical Methods in Chemical &&ineering:, AmericanInstitute of Chemical Engineering, New York (1963).

29. Francis, J.C.: Investments Analysis .l!.ru! Mana&eroent,McGraw-Hill Book Company, New York, 1980.

30. Sharpe, W.F. : "Mutual Fund Performances," Im!rnll ofBusiness, Suppl., (Jan., 1966).

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SPE. 22024

TablelSummary of Reservoir Properties for the Proved Reserves Category

Table 1

Data Sources for Primary Reservoir PropertiesPrimary PropertY Uuita Estimated Value

Group 1 Group2Primary Property Data Source Average Depth feet 1,270 1,810

Thickness Open-hole density logsGas Content On-site desorption tests

Average Net Thickness feet 65 55Gas Content sc i /Ton 300 320

Sorptive Capacity Sorption isotherm Langmuir Volume SCF/Ton 500 500Sorption Time On-site desorption tests Langmuir Pressure psia 320 320

Natural Fracture Permeability Open hole drill stem testReservoir Pressure Open hole drill stem test

Sorption Time hours 24 24Natural Fracture Permeability md 85 5

Completion Diagnostics Post-completion production tests Reservoir Pressure Gradient psi/ft 0.52 0.52lnduced Fracture Half-Length feet 150 150

Secondary Property Units Estimated Value

Table3Estimates of Proved Reserves

Initial Water Saturation, % 100Fracture Porosiiv % 2

Pore Volume Compressibility 1/psi 1o-SProperty Uults Estimated Value

Group t Group2Gas-in-Place per Well BSCF 12.1 11.25 Table4

DraiJlll&eArea per Well Acres 320 320Cumulative Gas RecoveiY per Well BSCF 2.95 2.0

Summary of Reservoir Properties for the Probable Reserves Category

Gas RecoveiY Factor % 24.3 17.8 Primary Property Uults Estimated ValueAverage Gas Rate per Well MSCF/D 260 178 Average Depth feet 1,900Average Water Rate per Well B/ D 58 51 Average Net Thickness feet 50

Number of Wells 6 3 Gas Content SCF/Ton 320Recoverable Gas Reserves BSCF 17.6 6.0 ~ u i r V o l u m e SCF/Ton 500

r::a;;:gmuir Pressure osia 320 lncludes 3 Proved Undeveloped Wells and Pilot Well If 1 Sorption Time hours 24

Natural Fracture PermeabilitY md 6.5Reservoir Pressure Gradient psi/ft 0.5

lnduced Fracture HalfLelll!th feet 150

Table 5Estimates of Probable Reserves

Table6Property Units Estimated Value Summary of Reservoir Properties for the Possible Reserves Category

Gas-in-Place per Well BSCF 10.22

SPE 22 02 It


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Table9Summary or Ecoaomic Aaalysb Results

Rauw Groa 1-.ldal Bellx'e-Tu Afte .. T u DiacouotedCateaory IJtYU t .... 1 - n a l R a t e IDCena.lltate ot Rdun . AfterTax

o r ~ t e t. . . . ProAtabDicyJada

Wltlloat Chdk Witll Crcd1& Wltll Cftdlt$1,000 % % % Table 10

Proved 2,115 16.8 15.7 31.1 2.44Develooed

llegessiOA Allai)'sll Re . t

Proved 1,357 1JJ.1 19.8 37.6 2.95 Paraaeter UaitJ aoo .. ll uUndevclooed

Probable 5,190 16.4 15.3 30.7 2.43Befoce Tax Internal Rate of % 1.0871 0.001088 0.029 -a.3868;Z 0.704

Re turnPossible 6,787 24.6 24.0 44.4 3.46 After-Tax ln temal Rate of % 1.0583 0.005751 0 .05 -8..3686 0.69-46

Surplus Tax CreditsReturn

After- Tax Internal Rate nf % 1.1146 0.000279 0.07542 -a.216o'7 0.778 1

Reserve Gross laitial Cuulative Additioaaleateaoey lll'atmeat Tu C n d i U ~

iote..tial

Return with creditDiscounted Profitability 1.0545 0.1191 0.0 -1.1Jm 0.5531

Index After Tax with Credi t

s 1,000 s 1,000 Sl ,000 Recovery Fedor % 1.04213 0.053226 0..0 -a.6857 0.485Proved Developed 2,715 4,319 11,998

Proved 1,357 2,453 6,815Undeveloped

Probable 5,190 8,353 23.203Possible 6,787 13,159 38,219

TatlleUTable l l Resll Frora Risk ADalysllCllaradertslks ofProbabUity Distriballons for VarioasltellnOir Properties Dlswunled Cash now l akmal Rate or Return After Taxes wllb Tax Credit

Raene Permeability Reservoir Pressure GuConteatCaii!IOlY

Resert'e Catepry Eqlected Retua Coatpll&t4 Retlll'JlFrom. Prvbabilib' b t r i ~ M d o a F n Detiled A:aalysLt

Mean Sid Mean Sid Mcao StdDeviation Dc:Yiatiou Deviation

Proved Developed 32.4% 31.1%Proved Undeveloped 37.3% 37.6 %

md md PSia psia SCF(fon SCF(fon Pr ob a ble 32.9% 30.7%Proved 6.15 1.4 750 150 310 30 Possible 41.9% 44.4%

Developed

Proved 8.0 2 635 160 300 100Undev elOP ed

Probable 6 .S 3.3 950 175 320 122Poss ible 7 4.5 1,128 200 350 141

Racrva Catqory Stua rd PerfDrmuce DonsldcDnl81ioa IDcles Prollablllt)'

Proved De veloped 12.7% 2.5 0%Proved 23.0% 1.6 20 %

Undevel opedProbable 25.0 % 1.3 29%l'lmiblc 40.9% 1.0 32 %

seE 22024


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Cumtiallve Frequency

Economic and Reserve _Evaluation of Coal Bed Methane ReservoirsDhir

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Transcript of Economic and Reserve _Evaluation of Coal Bed Methane ReservoirsDhir