Evaluacion de Reservas Dificiles
-
Upload
mauricio-sanchez-b -
Category
Documents
-
view
217 -
download
0
Transcript of Evaluacion de Reservas Dificiles
7/27/2019 Evaluacion de Reservas Dificiles
http://slidepdf.com/reader/full/evaluacion-de-reservas-dificiles 1/12
.
.
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.
7/27/2019 Evaluacion de Reservas Dificiles
http://slidepdf.com/reader/full/evaluacion-de-reservas-dificiles 2/12
. s
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
108
7/27/2019 Evaluacion de Reservas Dificiles
http://slidepdf.com/reader/full/evaluacion-de-reservas-dificiles 3/12
.
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
109 ‘
7/27/2019 Evaluacion de Reservas Dificiles
http://slidepdf.com/reader/full/evaluacion-de-reservas-dificiles 4/12
.
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
7/27/2019 Evaluacion de Reservas Dificiles
http://slidepdf.com/reader/full/evaluacion-de-reservas-dificiles 5/12
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,
111
7/27/2019 Evaluacion de Reservas Dificiles
http://slidepdf.com/reader/full/evaluacion-de-reservas-dificiles 6/12
7/27/2019 Evaluacion de Reservas Dificiles
http://slidepdf.com/reader/full/evaluacion-de-reservas-dificiles 7/12
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, -
113
7/27/2019 Evaluacion de Reservas Dificiles
http://slidepdf.com/reader/full/evaluacion-de-reservas-dificiles 8/12
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
7/27/2019 Evaluacion de Reservas Dificiles
http://slidepdf.com/reader/full/evaluacion-de-reservas-dificiles 9/12
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.
115
7/27/2019 Evaluacion de Reservas Dificiles
http://slidepdf.com/reader/full/evaluacion-de-reservas-dificiles 10/12
—
. 0%’ , I f- u
—
J-
<
zoz<z L5
7/27/2019 Evaluacion de Reservas Dificiles
http://slidepdf.com/reader/full/evaluacion-de-reservas-dificiles 11/12
q
. ,.
Si a
Ina 0
—
t!”
-.Ill ! I I I I I
1II I I I [ I I I
. . x. . . . . . . . .,,.X .: . .
L.........................y............................... ~~~~~~~~....................... ..
1’.
L.
I
. . .. . .:.:.
,,.;.. .
.
. . . ,. .
I I I I [! I 1 I I I I I I 1 I I I 1 I
0a
.
. .
~“:
,[
J I
0—
i1 \
—
Ill I I I I I I 1!11 I I I I I 11111 I I I II
a
xm
x, ...:..:.: . .,....’:. . . . . . . . . I
mkl HI-12
7/27/2019 Evaluacion de Reservas Dificiles
http://slidepdf.com/reader/full/evaluacion-de-reservas-dificiles 12/12
SPE 22025 “
RU5T1N CHFI LK tUIRIZONTRL HELL S} HULRTICW
PEt lRSRLL F i EL D S11J 71 RFIER
\’1 1 *
100i [ i I 1
.i
:/
90
80
7D
Em
. .K1
,;: ,
0. 5+3. I@). 150. 200. 250. 3CXJ. 350. Uoo. Wo. 50Q
H3bl,
Scotia Systems. Ltd ICI 1908
,, *F Ril[SIH ITtll : Resm-vss Sitmla tm-
FIGullll S AUSTINCHALKllORIZONI’ALWELLSlMUL4T10NP-ALL FIELll STUDYAREA
INPUT DRTR CIISTRIBUT(3NS
FRRC TURE RECOVERT IFIBOI FRRCTURE 5PRCI ff i IFCR t
Ho u“200.0 ?0.0 1s00.0 800.0
HORIZ13NTRL LENGTN IF t ] PERCENT OEPLETEO
Kuqooo. o 2s00.0 0.65 0.20
PERCENT l 19TER F IL LEO
b
o.20-
0.40 0.10
SJ.JMMFII?Y13F RESULTS
CUHUL
mm IE!-UE ~“w
10 230. IS Hinimm 1 7. LW
20 iBU.55 Ha. irn.m U29. 16
30 153.69 Range U12.10
v“ 131. qo tlcdi.3n llq.53
50 llq.63 tkan 127.98
60 96.73 Std Oevlation 71.65
70 79.58 Skevnees 0.93
80 EW.11 Kw-tosio 0.27
90 ~8,65 Oata Points 3000.
Prirmi pal Hydrocarbon: OIL
:,