PPA volumetrics - 2.pdf

© 2005 PetroSkills LLC, All Rights ReservedINSERT FILE NAME – Insert on Master Slide

2 Key Questions2 Key Questions

Are any commercial O & G Are any commercial O & G Are any commercial O & G fields presentfields presentfields present???

What is the probability?What is the probability?What is the probability?

How much O & G is How much O & G is presentpresent??

Average expected amountRange of reserves expected

P. 2-27


Prospect Hydrocarbon Volume Prospect Hydrocarbon Volume

Predicted volume is product Predicted volume is product of:of:

closure areanet thickness of the reservoirporosityhydrocarbon fill of trap volumerecovery factor

P. 2-56


Volumetric determinationVolumetric determination

1. NR = GBV *N/G * Ø (1 - Sw)Where:

NR = hydrocarbons in place at reservoir conditionsGBV=Gross Bulk Volume of reservoirN/G = Net to Gross ratioØ = Porosity, fractionSw = Water saturation, fraction

2.2. Conversion to surface volume Conversion to surface volume -- oiloilShrinkage factor (1/FVF – formation volume

factor)3.3. Times recovery factorTimes recovery factor


Typical exploration workflowsTypical exploration workflows

1.1. Map the critical prospect factorsMap the critical prospect factors(trap type and size, reservoir presence, porosity,

source capability, drive mechanism, recoverability, etc)

2.2. Select ranges for factors that describe Select ranges for factors that describe prospect conditionsprospect conditions

3.3. Combine factors to derive an Combine factors to derive an assessment curveassessment curve

Describes sizes that can occur given local conditions

4.4. Perform a risk assessment on the Perform a risk assessment on the projectproject

Mean = 50.00

25.00 37.50 50.00 62.50 75.00

Effective thickness

TRAP, SEAL, TIMING 0.72Closure volume 0.8Seal - top.lateral,no serious leakage by faults or fractures 0.9Timing - Relative to migration 1

RESERVOIR, POROSITY, PERMEABILITY 0.8Adequate reservoir thickness 0.8Porosity 1Permeability, Continuity 1

SOURCE, MATURATION, MIGRATION 0.9Organic quantity/quality 1Maturation (adequate time, temperature, pressure) 1Migration (primary, secondary, source to trap) 0.9

PRESERVATION, HC QUALITY, RECOVERY 1Preservation (no bad flushing, biodegradation) 1HC Quality and concentration 1Recovery (drive, pressure, depth) 1


How Much O & G?How Much O & G?Several methods usedSeveral methods usedVolumetrics & HC charge Volumetrics & HC charge recommended for recommended for prospectsprospectsField number and size Field number and size recommended for recommended for playsplays

P. 2-27

Provide selection Provide selection priorities for choices priorities for choices among prospects within among prospects within organization. Review organization. Review current and past current and past evaluations to develop evaluations to develop internal consistency in internal consistency in application.application.

Combine reservoir Combine reservoir parameters to produce parameters to produce statistically correct statistically correct assessment curve. assessment curve. Determine ranges of Determine ranges of values for reservoir values for reservoir parameters, from multiple parameters, from multiple sources and ranges of sources and ranges of uncertainty of each touncertainty of each to

Describe techniques of Describe techniques of assessing trap volumes assessing trap volumes and calculating statistical and calculating statistical ranges of expected ranges of expected volumesvolumes

2. Prospect Volume 2. Prospect Volume CalculationCalculation

P. 2-27





P. 2-56


Prospect Volume ElementsProspect Volume ElementsTrap volumeTrap volume

Reservoir thicknessAreal extent

Reservoir propertiesReservoir propertiesNet/gross ratioAverage porosityAverage HC saturationPercent of trap filled (HC fill)Shrinkage or volume factorRecovery factorOil or gas fraction of HC volume

P. 2-28


Prospect AssessmentProspect Assessment

SuccessSuccess -- meeting or exceeding meeting or exceeding minimum economic sizeminimum economic sizeSteps in assessment process Steps in assessment process

1.1.1. Define minimum economic sizeDefine minimum economic sizeDefine minimum economic size2. Select ranges for individual factors3.3.3. Combine factors to derive Combine factors to derive Combine factors to derive

assessment curveassessment curveassessment curve4.4.4. Estimate adequacy of achieving Estimate adequacy of achieving Estimate adequacy of achieving

minimum economic sizeminimum economic sizeminimum economic size

P. 2-28


Assessment MethodsAssessment MethodsGeologic AnalogyGeologic AnalogyDelphiDelphiAreal & volumetric yieldAreal & volumetric yield**Field number and sizeField number and size**Geochemical yields (Material Geochemical yields (Material Balance)Balance)SummationsSummations**ExtrapolationsExtrapolations P. 2-28

* Used in this course


Geologic AnalogyGeologic AnalogyIf A looks like B, then they must If A looks like B, then they must have similar valueshave similar valuesAdvantagesAdvantages

Ties to experienceEasier to sell prospect

DisadvantagesDisadvantagesMiss key factorMay use only one factor

Useful for individual factorsUseful for individual factorsP. 2-29


RESERVOIR INFORMATION

SOURCERegional environment Continuity Geometry

Rock properties

Fluid properties

Depletion technology

Well Pattern Economics

Geologic model xx x xx xGeophysics xx xx xx x xOutcrop studies xx xx xx xxWell logging x x xxx xxCore samples xxx xxDrilling history x xx xxFluid sample xx x xxx xx xWell test xxx x xx xx xx xProduction history xxx x x xxx xxx xx xxxAnalogy x x x x x x xx xx

Legend:x = indicatorxx = qualitative

Sources of dataSources of data

xxx = quantitativeP. 2-29


DelphiDelphiAverage of several expertsAverage of several expertsAdvantagesAdvantages

Fuller range of possibilitiesEasy to use, but time consuming

DisadvantagesDisadvantagesNo scaling factorsParadigm blindness

Useful judgment checkUseful judgment checkP. 2-30


Areal/Volumetric YieldsAreal/Volumetric Yields

Yield per unit area/volumeYield per unit area/volumeAdvantagesAdvantages

QuickEasy

DisadvantagesDisadvantagesNo third dimensionTough to estimate

Useful in combination with other Useful in combination with other methodsmethods

P. 2-30


Field Number and SizeField Number and Size

Like analogy with more dataLike analogy with more dataAdvantagesAdvantages

Deals with prospects & fieldsDisadvantagesDisadvantages

Large amount of data neededSubtle traps difficult

Useful in play assessmentUseful in play assessmentP. 2-30


Material BalanceMaterial BalanceSpecial form of volumetric Special form of volumetric methodmethodAdvantagesAdvantages

Covers numerous genetic factorsDisadvantagesDisadvantages

Time reconstruction difficultIgnorance of geochemical processes

Useful as supplementary methodUseful as supplementary methodP. 2-31


Summation of Prospects and PlaysSummation of Prospects and Plays

Totals individual assessmentsTotals individual assessmentsAdvantagesAdvantages

Combines ranges of possibilitiesDisadvantagesDisadvantages

Requires much dataCan’t be used for individual prospect

Useful in play & basin assessmentUseful in play & basin assessment

P. 2-31


Extrapolation of Discovery Rates Extrapolation of Discovery Rates

Useful for resource assessmentUseful for resource assessmentAdvantagesAdvantages

Ties to realityDisadvantagesDisadvantages

Can’t be used for prospects or playEconomic/ technical factors may change

Useful as supplementary methodUseful as supplementary methodP. 2-31




1. Define minimum economic size2. Select ranges for individual factors3. Combine factors to derive

assessment curve4. Estimate adequacy of achieving

minimum economic size

P. 2-21


Measures of UncertaintyMeasures of Uncertainty

Always remember that there is a single Always remember that there is a single truth to the factor that we are modelingtruth to the factor that we are modelingUncertainties frequently expressed in Uncertainties frequently expressed in various manners:various manners:

Single valueMin, ML, MaxStatistical description

Geostatistical approachesGeostatistical approachesSingle models of complex data setsMultiple simulations (probabilistic approach)

P. 2-32


DefinitionsDefinitions

Deterministic solutionDeterministic solution− Single (best?) solution to problem/conditions

Probabilistic solutionProbabilistic solutionMultiple simulations or probabilities that fit conditions

Continuous probability distributionContinuous probability distributionA probability distribution that describes uninterrupted values over a range.

Discrete probability distributionDiscrete probability distributionA probability distribution that describes distinct values, usually integers, with no intermediate values. P. 2-32


Statistical DistributionsStatistical Distributions

Exceedance/CumulativeExceedance/Cumulative**Normal (gaussian or bellNormal (gaussian or bell--shaped)shaped)LognormalLognormalHistogramHistogramEqualEqualRectangularRectangularTriangular*Triangular*LogLog--triangular*triangular* P. 2-32

Symmetrical DistributionsSymmetrical Distributions

05

10

1520

1 2 3 4 5 6 7 8 9 10 11 12

HISTOGRAM

0.0

20.0

40.060.0

80.0

100.0

120.0

1 3 5 7 9 11

0

5

10

15

20

1 2 3

020406080

100120

1 2 3

P. 2-33

7

127

12


Normal DistributionNormal Distribution

Describes many natural phenomena Describes many natural phenomena (IQ's, people's (IQ's, people's heights, the inflation rate, or errors in measurements).heights, the inflation rate, or errors in measurements).

Continuous probability distribution.Continuous probability distribution.Parameters are:Parameters are:

MeanStandard deviation.

Some value is the most likely (the mean of the Some value is the most likely (the mean of the distribution). distribution). The unknown variable could as likely be above or below The unknown variable could as likely be above or below the mean (symmetrical about the mean). the mean (symmetrical about the mean). The unknown variable is more likely to be close to the The unknown variable is more likely to be close to the mean than far awaymean than far away

Approximately 68% are within 1 standard deviation of the meanP. 2-34


Normal DistributionsNormal Distributions

1 Standarddeviation

P. 2-34


Cumulative CurveCumulative Curve

0

20

40

60

80

100

120

9 18 27 36 45 54 63 72 81 90

Economic Threshold - 40'

Cumulative frequency Cumulative frequency distributiondistributionA chart that shows the A chart that shows the

number or proportion (or number or proportion (or percentage) of values percentage) of values less thanless than or equal to a or equal to a given amount.given amount.

P. 2-35


Exceedance CurveExceedance Curve

0

20

40

60

80

100

120

9 18 27 36 45 54 63 72 81 90

Economic Threshold - 40'

Exceedance Exceedance distributiondistributionA chart that shows the A chart that shows the

number or proportion (or number or proportion (or percentage) of values percentage) of values greater thangreater than or equal to or equal to a given amount.a given amount.

P. 2-35

Sand Distribution

0

5

10

15

20

25

30

9 18 27 36 45 54 63 72 81 90Economic Threshold - 40'Histogram and

Frequency Curve Displays

P. 2-36


Lognormal DistributionLognormal DistributionWidely used in situations where values are Widely used in situations where values are positively skewedpositively skewed (where (where most of the values occur near the minimum value)most of the values occur near the minimum value)

Financial analysis for security valuationReal estate for property valuationDistribution of reserves in a play

Continuous probability distribution. Continuous probability distribution. Financial analysts have observed that the stock prices are usualFinancial analysts have observed that the stock prices are usually ly positively skewed. positively skewed.

Stock prices exhibit this trend because the stock price cannot fall below the lower limit of zero but may increase to any price without limit.

The parameters for the lognormal distributionThe parameters for the lognormal distributionMeanStandard deviation

Three conditions underlying a lognormal distribution are:Three conditions underlying a lognormal distribution are:1. The unknown variable can increase without bound, but is confined to a finite value

at the lower limit. 2. The unknown variable exhibits a positively skewed distribution. 3. The natural logarithm of the unknown variable will yield a normal curve.

P. 2-36


Triangular DistributionTriangular Distribution

Shows number of successes when you know Shows number of successes when you know the the minimum, maximumminimum, maximum, and , and most likelymost likelyvalues. values. Continuous probability distribution.Continuous probability distribution.The parameters for the triangular distribution The parameters for the triangular distribution are are minimum, maximum, and likeliestminimum, maximum, and likeliest

For example, you could describe the number of cars sold per week when past sales show the minimum, maximum, and most likely number of cars sold

Three conditions:Three conditions:1. The minimum number is fixed. 2. The maximum number is fixed. 3. The most likely number falls between the minimum and maximum

values, forming a triangular shaped distribution, which shows that values near the minimum and maximum are less likely to occur than those near the most likely value.

P. 2-37

NORMAL TRIANGLE

(e.g., 2 - 4 - 6) MOST LIKELY = (MIN+MAX) / 2 = (2 + 6) / 2 = 4 MINIMUM = 2 ML - MAX = 2 x 4 - 6 = 2 MAXIMUM = 2 ML - MIN = 2 x 4 - 2 = 6 MEAN = (MIN + ML + MAX) / 3 = ML (IF SYMMETRICAL) P. 2-37

LOG TRIANGLE (e.g., 2 - 4 - 8)

MOST LIKELY = MIN x MAX = 16 = 4 MINIMUM = ML2 / MAX = 16 / 8 = 2 MAXIMUM = ML2 / MIN = 16 / 2 = 8 SYMMETRICAL LOG TRIANGLE MEAN = ML + 0.06 (MAX - ML) *

*DERIVED BY W. R. JAMES

P. 2-38


Uniform (Rectangular) DistributionUniform (Rectangular) Distribution

All values between the minimum and All values between the minimum and maximum are equally likely to occurmaximum are equally likely to occurContinuous probability distribution.Continuous probability distribution.The parameters for the uniform The parameters for the uniform distribution are distribution are minimumminimum and and maximummaximum. . Three conditions:Three conditions:

1. The minimum value is fixed. 2. The maximum value is fixed. 3. All values between the minimum and maximum

are equally likely to occur. P. 2-38


Definitions Definitions -- 22

MeanMeanThe arithmetic average of a set of numbers

ModeModeThat value which, if it exists, occurs most often in a data set.

Standard deviationStandard deviationThe square root of the variance of the numbers in a sample set of size n. The standard deviation is the average amount a set of numbers deviate from the mean

VarianceVarianceAverage of the squared differences between a number of observations in a sample set of size n and their mean

SkewnessSkewnessMeasure of the degree of deviation of a curve from the norm. Thegreater the degree of skewness, the more points of the curve lie to either side of the peak of the curve. A normal distribution curve, having no skewness, is symmetrical in shape

P. 2-39


Typical exploration workflowsTypical exploration workflows

1.1. Map the critical prospect factorsMap the critical prospect factors(trap type and size, reservoir presence, porosity,

source capability, drive mechanism, recoverability, etc)

2.2. Select ranges for factors that describe Select ranges for factors that describe prospect conditionsprospect conditions

3.3. Combine factors to derive an Combine factors to derive an assessment curveassessment curve

Describes sizes that can occur given local conditions

4.4. Perform a risk assessment on the Perform a risk assessment on the projectproject

Mean = 50.00

25.00 37.50 50.00 62.50 75.00

Effective thickness

TRAP, SEAL, TIMING 0.72Closure volume 0.8Seal - top.lateral,no serious leakage by faults or fractures 0.9Timing - Relative to migration 1

RESERVOIR, POROSITY, PERMEABILITY 0.8Adequate reservoir thickness 0.8Porosity 1Permeability, Continuity 1

SOURCE, MATURATION, MIGRATION 0.9Organic quantity/quality 1Maturation (adequate time, temperature, pressure) 1Migration (primary, secondary, source to trap) 0.9

PRESERVATION, HC QUALITY, RECOVERY 1Preservation (no bad flushing, biodegradation) 1HC Quality and concentration 1Recovery (drive, pressure, depth) 1


2. Select Ranges for Individual Factors2. Select Ranges for Individual Factors

MinimumMinimum values are those that values are those that are critical to achieve minimum are critical to achieve minimum economic accumulationeconomic accumulationRanges reflect assessment of Ranges reflect assessment of potential sizes for each factorpotential sizes for each factorBest estimate for each factor is Best estimate for each factor is most likelymost likelyFactors combined to achieve Factors combined to achieve meanmean for each factorfor each factor P. 2-39


Min, ML, Max DefinitionsMin, ML, Max Definitions

Min (Minimum)Min (Minimum)Largest “risk free” (certain) value orValue needed to reach economic minimum accumulation−Risk will need to be accounted for

ML (Most Likely)ML (Most Likely)What you really think the value is - your best interpretation−Probably not “risk free”

Max (Maximum)Max (Maximum)Largest value reasonably expected

P. 2-39


Case Against ML Case Against ML Rose, 2001Rose, 2001

Triangular distributions are poor proxies for the Triangular distributions are poor proxies for the lognormal frequency distributionslognormal frequency distributionsMost prospectors donMost prospectors don’’t recognize how severely t recognize how severely skewed natural distributions are skewed natural distributions are Process:Process:

Postulate tentative high-side and low-side outcomesplot at P10 percent and P90 percent pointsevaluate the plausibility of the consequential P1 percent, P50 percent, P99 percent and Mean outcomes

Iterate and reiterate the cumulative probability Iterate and reiterate the cumulative probability distribution until a distribution until a ““best fitbest fit”” is obtained is obtained

P. 2-40


Prospect Volume ElementsProspect Volume ElementsTrap volumeTrap volume

Reservoir thicknessReservoir thicknessReservoir thicknessAreal extentAreal extentAreal extent

Reservoir propertiesReservoir propertiesReservoir propertiesNet/gross ratioNet/gross ratioNet/gross ratioAverage porosityAverage porosityAverage porosityAverage HC saturationAverage HC saturationAverage HC saturationPercent of trap filled (HC fill)Percent of trap filled (HC fill)Percent of trap filled (HC fill)Shrinkage or volume factorShrinkage or volume factorShrinkage or volume factorRecovery factorRecovery factorRecovery factorOil or gas fraction of HC volumeOil or gas fraction of HC volumeOil or gas fraction of HC volume

P. 2-41

P. 2-41

P. 2-41


Trap Volume perspectivesTrap Volume perspectives

Assessment starts with the volume of the Assessment starts with the volume of the traptrapRemember to model the trap initially, Remember to model the trap initially, DO DO NOT INFER ANY HC FILL AT THIS NOT INFER ANY HC FILL AT THIS STAGE!STAGE!Recommended approach is to use depth / Recommended approach is to use depth / volume plot (demonstrated later)volume plot (demonstrated later)Modern 3D data sets and work stations Modern 3D data sets and work stations make this much easiermake this much easierAdjust volumes with geometry factorsAdjust volumes with geometry factors

Assure that your workstation handles this correctly

Edge Water ModelEdge Water Model

Bottom Water ModelBottom Water Model

Which requires more correction by Which requires more correction by geometry factor? Why?geometry factor? Why?

How does your work station know to choose the lesser of closure height or reservoir thickness – or does it need to?


Mapping ExerciseMapping Exercise

1.1. Draw contours for sand Draw contours for sand thickness. thickness.

2.2. Estimate the Min, ML, and Max Estimate the Min, ML, and Max for locations A and B. for locations A and B.

3.3. Estimate chance of adequacy Estimate chance of adequacy (exceeding the Minimum)(exceeding the Minimum)

4.4. Economic minimum sand Economic minimum sand thickness thickness –– 5050’’

P. 2-42

© 2005 PetroSkills LLC, All Rights ReservedINSERT FILE NAME – Insert on Master Slide P. 2-43

ML ML ““School School AnswerAnswer

Min?Min?

Max?Max?

P. 2-35


Prospect Volume ElementsProspect Volume ElementsTrap volumeTrap volumeTrap volume

Reservoir thicknessAreal extentAreal extentAreal extent

Reservoir propertiesReservoir propertiesReservoir propertiesNet/gross ratioNet/gross ratioNet/gross ratioAverage porosityAverage porosityAverage porosityAverage HC saturationAverage HC saturationAverage HC saturationPercent of trap filled (HC fill)Percent of trap filled (HC fill)Percent of trap filled (HC fill)Shrinkage or volume factorShrinkage or volume factorShrinkage or volume factorRecovery factorRecovery factorRecovery factorOil or gas fraction of HC volumeOil or gas fraction of HC volumeOil or gas fraction of HC volume

Ref. P. 58+

65%35%




Reservoir propertiesReservoir propertiesReservoir propertiesNet/gross ratioNet/gross ratioNet/gross ratioAverage porosityAverage HC saturationPercent of trap filled (HC fill)Percent of trap filled (HC fill)Percent of trap filled (HC fill)Shrinkage or volume factorShrinkage or volume factorShrinkage or volume factorRecovery factorRecovery factorRecovery factorOil or gas fraction of HC volumeOil or gas fraction of HC volumeOil or gas fraction of HC volume

P. 2-44

Multiple realizations of permeabilityMultiple realizations of permeability

P. 2-44

P. 2-45

Cou

nts

Cou

nts

Cou

nts

Cou

nts

Cou

nts

Intrafossilporosity

Moldicporosity

Interparticleporosity

Low-porosity,cemented rocks

Microporosity

(A) (B)

(C) (D)

(E) (F)a)

b)

c)

d)

e)

12

12

0

0

0

0

0-1000 0 0-1000 +1000 +2000+1000 +2000

Mean value andstandard deviation

Velocity deviation (m/s)Velocity deviation (m/s)

Cou

nts

Cou

nts

Cou

nts

Cou

nts

Cou

nts

Intrafossilporosity

Moldicporosity

Interparticleporosity

Low-porosity,cemented rocks

Microporosity

(A) (B)

(C) (D)

(E) (F)a)

b)

c)

d)

e)

12

12

0

0

0

0

0-1000 0 0-1000 +1000 +2000+1000 +2000

Mean value andstandard deviation

Velocity deviation (m/s)Velocity deviation (m/s)

0

200

400

600

800

1000

0

200

400

600

800

1000

0

200

400

600

800

1000 0

200

400

600

800

1000

0

200

400

600

800

1000

0

200

400

600

800

1000

0

200

400

600

800

1000

1.0

0

1.0

0

1.0

0

1.0

0

1.0

0

1.0

0

1.0

0

10

0

10

0

20

0

30

0

15

0

20

0

16

0

Cum

ulat

ive

Prob

abili

ty

Cum

ulat

ive

Prob

abili

tyC

umul

ativ

ePr

obab

ility

Freq

uenc

yFr

eque

ncy

Freq

uenc

y

Permeability (md)

Permeability (md) Permeability (md)

Cpc Cpf Cxd

Cxp Cs Cf

Cgu

Matrix

Clasts

P. 2-46

P. 2-47

Distributions in Various Lithofacies - Porosity (%)

-0.04-1.33-0.38-1.21Kurtosis

-0.80-0.17-0.67-0.53Skewness

0.460.510.120.45CV

4.218.463.298.98Std. Dev.

-27.00-27.00Mode 2

7.007.0027.007.00Mode 1

8.2016.6027.3523.20Median

9.1716.5226.5519.91Mean

18.8028.7032.5032.50Maximum

2.802.6019.302.60Minimum37.0038.0078.00153.00

Points

MuddyMuddy-Granular

GranularAll Lithofacies

Uthmaniyah field, Saudi ArabiaUthmaniyah field, Saudi ArabiaSaner and Sahin,1999Saner and Sahin,1999

P. 2-47

ALL 153 SAMPLES

GRANULARFACIES

MUDDY-GRANULARFACIES

MUDDY-FACIES

ALL 153 SAMPLES

GRANULARFACIES

MUDDY-GRANULARFACIES

MUDDY-FACIES

(A)

(C)

(E)

(G)

(B)

(D)

(F)

(H)

NU

MB

ER O

F SA

MPL

ES

NU

MB

ER O

F SA

MPL

ES

POROSITY % Log-0 PERMEABILITY (md)0 8 16 24 32 40 -2 -1 0 1 2 3 4 5

5040

3020100

2520

151050

2520

151050

108

6420

30

20

10

0

15

10

5

015

10

5

0

15

10

5

0

P. 2-48

Solid line shows mean permeabilities

CORING STATIONS

PER

MEA

BIL

ITY

(md)

1,600

1,400

1,200

1,000

800

600

400

200

0

1 3 5 7 9 11 13 15

ExerciseExercise

P. 2-49


Rock and fluid properties from geophysicsRock and fluid properties from geophysics

AmplitudesAmplitudesPhase changesPhase changesInterval travel times between Interval travel times between eventseventsFrequency variationsFrequency variationsCrossCross--plotsplotsAlgorithms based on geostatistical Algorithms based on geostatistical conceptsconceptsVelocity ratios (Vp/Vs)Velocity ratios (Vp/Vs)

P. 2-50


Seismic Attribute Analysis Seismic Attribute Analysis (Hart, 1999 OGJ)(Hart, 1999 OGJ)

PurposePurposePhysical basis of relationships between well and seismic dataMethods of predicting inter-well reservoir parameters

AttributesAttributes1. Amplitude2. Complex trace attributes (instantaneous phase,

instantaneous frequency)3. Time-derived (structure, isochron)4. Horizon-derived (dip, azimuth)5. Coherency6. Others

P. 2-50

P wave vs. Bulk DensityP wave vs. Bulk DensityGartner and Gartner and SchlagerSchlager, AAPG, 1999, AAPG, 1999

P. 2-51


Attribute Analysis MethodologyAttribute Analysis Methodology1.1. Define/measure/interpret property for all wellsDefine/measure/interpret property for all wells2.2. Extract values of attributes at xExtract values of attributes at x--y locations of y locations of

wellswells3.3. Correlate well data and attribute(s)Correlate well data and attribute(s)

Statistically significant correlation (regression, geostatistics, neural networks, etc.)

4.4. Populate grid with derived dataPopulate grid with derived data5.5. Test for validityTest for validity

Exclusion testingHistory match

6.6. Verify physical mechanism for relationshipVerify physical mechanism for relationshipRock physics, locally calibrated, properly applied P. 2-51


Test for ValidityTest for ValidityHigher possibility of invalid Higher possibility of invalid (coincidental) relationship with:(coincidental) relationship with:

Greater number of attributes consideredFewer wells used for control

Factors:Factors:Random chanceAcquisition and processing parametersSpatially variable surface conditionsBiased sampling of wells P. 2-52

Attribute Case StudyAttribute Case Study (Hart, 1999 OGJ)(Hart, 1999 OGJ)

1.1. 8 well8 well2.2. Multiple pay zones Multiple pay zones 3.3. Used production indicatorUsed production indicator4.4. Decades of historyDecades of history5.5. Fuzzy correlations Fuzzy correlations -- used used

neural network neural network (Fig. A)(Fig. A)(Correlation coefficient (Correlation coefficient -- 0.96)0.96)

ResultsResultsProduction extremes not Production extremes not

sampled by wellssampled by wellsFracture control observedFracture control observedRejected mapRejected map, , used fracture used fracture

attribute (Fig. B)attribute (Fig. B) P. 2-52


Prospect Volume ElementsProspect Volume Elements

Trap volumeTrap volumeTrap volumeReservoir thicknessReservoir thicknessReservoir thicknessAreal extentAreal extentAreal extent

Reservoir propertiesReservoir propertiesReservoir propertiesNet/gross ratioNet/gross ratioNet/gross ratioAverage porosityAverage porosityAverage porosityAverage HC saturationAverage HC saturationAverage HC saturationPercent of trap filled (HC fill)Shrinkage or volume factorShrinkage or volume factorShrinkage or volume factorRecovery factorRecovery factorRecovery factorOil or gas fraction of HC volumeOil or gas fraction of HC volumeOil or gas fraction of HC volume

P. 2-53

P. 2-54

How to choose ? : Min, ML, Max


HC Fill perspectivesHC Fill perspectives

Frequently a critical element in assessmentFrequently a critical element in assessmentAs always, local knowledge vitalAs always, local knowledge vitalBest way to estimate is through HC ChargeBest way to estimate is through HC ChargeML fill fraction should be related to trap ML fill fraction should be related to trap volumevolumeML Possibilities:ML Possibilities:

Lognormal (0.32)Normal ((0.55)Maximized (1.0)

P. 3-25




Reservoir propertiesReservoir propertiesReservoir propertiesNet/gross ratioNet/gross ratioNet/gross ratioAverage porosityAverage porosityAverage porosityAverage HC saturationAverage HC saturationAverage HC saturationPercent of trap filled (HC fill)Percent of trap filled (HC fill)Percent of trap filled (HC fill)Shrinkage or volume factorRecovery factorOil or gas fraction of HC volume


Relative volumesRelative volumes




1. Define minimum economic size2. Select ranges for individual factors3. Combine factors to derive

assessment curve4. Estimate adequacy of achieving

minimum economic size P. 2-56

Provide selection priorities Provide selection priorities for choices among prospects for choices among prospects within organization. Review within organization. Review current and past evaluations current and past evaluations to develop internal to develop internal consistency in application.consistency in application.

Combine reservoir Combine reservoir parameters to produce parameters to produce statistically correct statistically correct assessment curve. Determine assessment curve. Determine ranges of values for reservoir ranges of values for reservoir parameters, from multiple parameters, from multiple sources and ranges of sources and ranges of uncertainty of each to uncertainty of each to combine for volumetric combine for volumetric calculation.calculation.

Describe techniques of Describe techniques of assessing trap volumes and assessing trap volumes and calculating statistical ranges calculating statistical ranges of expected volumesof expected volumes

2. Prospect Volume 2. Prospect Volume CalculationCalculation





P. 2-56


3. Combine Factors to Derive Assessment Curve3. Combine Factors to Derive Assessment Curve

Factors multiplied to achieve Factors multiplied to achieve assessment assessment curvecurve for all potential size accumulations for all potential size accumulations that meet defined circumstancesthat meet defined circumstancesUsually combined through Usually combined through Monte Carlo Monte Carlo methodsmethodsMinimumMinimum (P100 of curve) should be equal (P100 of curve) should be equal to minimum economic sizeto minimum economic sizeMeanMean = average of potential outcomes= average of potential outcomes

P. 2-56


Building an Assessment CurveBuilding an Assessment CurveCurve represents our best interpretation of the prospect Curve represents our best interpretation of the prospect sizesizeMost if not all of these factors are represented by ranges of Most if not all of these factors are represented by ranges of valuesvaluesStatistically validStatistically valid potential sizes for the combination of potential sizes for the combination of valuesvaluesy axis showsy axis shows exceedance probabilitiesexceedance probabilities (percentage of all of (percentage of all of the potential sizes larger than the value plotted)the potential sizes larger than the value plotted)Keep in mind that for each accumulation we assess there Keep in mind that for each accumulation we assess there is a unique solutionis a unique solutionIf we assess carefully and consistently, most volumes for If we assess carefully and consistently, most volumes for successful cases will fall near the average predicted successful cases will fall near the average predicted volumes (volumes (meanmean))Predicted values most frequently combined using aPredicted values most frequently combined using a Monte Monte CarloCarlo computer programcomputer program P. 2-56

Mean = 50.00

25.00 37.50 50.00 62.50 75.00

Effective thickness

Actual Size Found


Building an Assessment CurveBuilding an Assessment CurveCurve represents our best interpretation of the prospect Curve represents our best interpretation of the prospect sizesizeMost if not all of these factors are represented by ranges of Most if not all of these factors are represented by ranges of valuesvaluesStatistically valid potential sizes for the combination of Statistically valid potential sizes for the combination of valuesvaluesy axis showsy axis shows exceedance probabilitiesexceedance probabilities (percentage of all of (percentage of all of the potential sizes larger than the value plotted)the potential sizes larger than the value plotted)Keep in mind that for each accumulation we assess there Keep in mind that for each accumulation we assess there is a unique solutionis a unique solutionIf we assess carefully and consistently, most volumes for If we assess carefully and consistently, most volumes for successful cases will fall near the average predicted successful cases will fall near the average predicted volumes (volumes (meanmean))Predicted values most frequently combined using aPredicted values most frequently combined using a Monte Monte CarloCarlo computer programcomputer program P. 2-56

Assessment CurveAssessment Curve1.0

0.8

0.6

0.4

0.2

100 200 300 400MILLION BARRELS POTENTIAL

UNRISKED MEAN - 140

MINIMUM - 20

P. 2-59

ALPHA PROSPECTESTIMATES

1ST CASE 2ND CASE 3RD CASE 4TH Case 5TH Case

Closure area - acresAvg. reservoir thickness - ft.% HC fill of trapRecovery (Bbl/ac. ft.)

Absolute Product (MM BO)

Volume Factor Estimates - Alpha Prospect 1 2 3 4 5 6

Closure area - acres 2500 3000 4000 4000 4700 5500 Avg. reservoir thickness - ft. 10 30 50 50 70 90

% HC fill of trap 0.2 0.4 0.6 0.6 0.8 1 Recovery (Bbl/ac. ft.) 400 450 500 500 550 600

ExerciseExercise -- Monte Carlo Demonstration Monte Carlo Demonstration Reserves (MMBO) = [area (acres) x thickness (ft.) x HC fill (%)Reserves (MMBO) = [area (acres) x thickness (ft.) x HC fill (%) x recovery x recovery

factor (Bbl./ac. ft)]/10factor (Bbl./ac. ft)]/1099

P. 2-58

0102030405060708090

100

0 50 100 150 200

Assessment CurveAssessment Curve1.0

0.8

0.6

0.4

0.2

100 200 300 400MILLION BARRELS POTENTIAL

UNRISKED MEAN - 140

MINIMUM - 20

P. 2-59

P100

P50

P0

100 tests, 20 successes, 80 dry holes

Chance of adequacy =Chance of adequacy =0.200.20

Understanding risked reservesUnderstanding risked reserves

Probability of each Probability of each potential size for potential size for prospect prospect –– Successful Successful cases onlycases only

Size of a discovery if Size of a discovery if average results average results achieved achieved –– mean mean reservesreserves

Probability of Probability of potential sizes potential sizes –– includes all includes all dry hole dry hole possibilitiespossibilities

P20

Remember, Remember, Only Only one result is possibleone result is possible. . These illustrations These illustrations offer probabilities of all offer probabilities of all potential outcomes potential outcomes based upon our based upon our assessment knowledgeassessment knowledge

Risked Assessment CurveRisked Assessment Curve

P. 2-60

MINIMUM 20

UNRISKED MEAN140 MAXIMUM

POTENTIAL420

MILLION BBL POTENTIALLY RECOVERABLE

CHANCEGREATER

THAN

0 100 200 300 400

1.0

.8

.6

.4

.2

0

RISKED MEAN

35

RISKED ASSESSMENT CURVE

V - 397

V - 381

V V –– 397397200 BCFG200 BCFG

V V –– 381381Mean reservesMean reserves: : 120 BCFG120 BCFGAdequacyAdequacy: : 0.200.20Risked ReservesRisked Reserves::24 BCFG24 BCFG

Play EconomicsPlay Economics::100 miles offshore100 miles offshoreMinimum Minimum EconomicsEconomics::30 BCFG30 BCFG


Cumulative Curve ExerciseCumulative Curve Exercise

Write exceedance chancesWrite exceedance chancesPlot pairsPlot pairsPlot additional intermediate Plot additional intermediate pointspointsCalculate a mean value for the Calculate a mean value for the distributiondistributionPlot a riskPlot a risk--discounted curvediscounted curveP. 2-62


SwansonSwanson’’s Rules Rule

P90___________ x 0.3 = ____________

P50___________ x 0.4 = ____________

P10___________ x 0.3 = ____________

SUM = MEAN _________ P. 2-62

Median645

Mean689

Risked Mean345

1.0

.05

0.0200 600 1000 1400

Max2202

P. 15-3

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