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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
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
Reservoir propertiesReservoir propertiesNet/gross ratioAverage porosityAverage HC saturationPercent of trap filled (HC fill)Shrinkage or volume factorRecovery factorOil or gas fraction of HC volume
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
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
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
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
SuccessSuccess -- meeting or exceeding meeting or exceeding minimum economic sizeminimum economic sizeSteps in assessment process Steps in assessment process
1. Define minimum economic size2. Select ranges for individual factors3. Combine factors to derive
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)
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
Exceedance/CumulativeExceedance/Cumulative**Normal (gaussian or bellNormal (gaussian or bell--shaped)shaped)LognormalLognormalHistogramHistogramEqualEqualRectangularRectangularTriangular*Triangular*LogLog--triangular*triangular* P. 2-32
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
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.
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.
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.
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) *
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
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
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
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
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
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?
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
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
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)
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)
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
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
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:
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
SuccessSuccess -- meeting or exceeding meeting or exceeding minimum economic sizeminimum economic sizeSteps in assessment process Steps in assessment process
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
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
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
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.)
% 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
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
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