Pattern Recognition Techniques in Petroleum Geochemistry L. Scott Ramos and Brian G. Rohrback...
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Pattern Recognition Techniques in Pattern Recognition Techniques in Petroleum GeochemistryPetroleum Geochemistry
Pattern Recognition Techniques in Pattern Recognition Techniques in Petroleum GeochemistryPetroleum Geochemistry
L. Scott Ramos and Brian G. Rohrback L. Scott Ramos and Brian G. Rohrback Infometrix, Inc.Infometrix, Inc.
L. Scott Ramos and Brian G. Rohrback L. Scott Ramos and Brian G. Rohrback Infometrix, Inc.Infometrix, Inc.
Daniel M. JarvieDaniel M. JarvieDaniel M. JarvieDaniel M. JarvieHumble Instruments & Services, Inc.Humble Instruments & Services, Inc.Humble Instruments & Services, Inc.Humble Instruments & Services, Inc.
InfoMetrixInfoMetrixInfoMetrix
Computer-Assisted GeochemistryComputer-Assisted GeochemistryComputer-Assisted GeochemistryComputer-Assisted Geochemistry
The emphasis in production geochemistry is to match oils to source The emphasis in production geochemistry is to match oils to source rocks and to correlate one crude oil to others. We do this to trace rocks and to correlate one crude oil to others. We do this to trace migration or to assess the degree of communication among migration or to assess the degree of communication among reservoirs.reservoirs.
Computerized pattern recognition (aka chemometrics) is an efficient way Computerized pattern recognition (aka chemometrics) is an efficient way to exploit the information richness of the data without sacrificing to exploit the information richness of the data without sacrificing speed or accuracy.speed or accuracy.
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An Overlay of ChromatogramsAn Overlay of ChromatogramsAn Overlay of ChromatogramsAn Overlay of Chromatograms
By overlaying chromatograms we can look both at the similarities and the differences in the crude oils. Software can use this underlying pattern to build quantitative and objective models.
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Example: Automation of Example: Automation of Geochemical EvaluationsGeochemical EvaluationsExample: Automation of Example: Automation of Geochemical EvaluationsGeochemical Evaluations
Source rock typing can be done by using GC, GC/MS and stable isotopes on crude oils.
We employ a series of chemometric models to first separate the samples based on gross characteristics (I.e., lacustrine versus marine) and then use fine tuning models to further characterize samples.
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GC/MS Mass ChromatogramsGC/MS Mass ChromatogramsTricyclic Terpanes m/z=191Tricyclic Terpanes m/z=191
25 30 35 40 45 50
C19C20 C21
C22
C23
C24
C25
Tet
C26
25 30 35 40 45 50
C19C20 C21
C22
C23
C24
C25
Tet
C26
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GC/MS Mass ChromatogramsGC/MS Mass ChromatogramsSteranes m/z=217Steranes m/z=217
50 55 60 65 70
S1
S2
S3
S4
S5
S6
S7 S8
S9S10
S11
S12
S13
S14S15
50 55 60 65 70
S1
S2
S3
S4
S5
S6
S7 S8
S9S10
S11
S12
S13
S14S15
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TraditionalTraditional GeochemistryGeochemistry
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
0.0 0.5 1.0 1.5 2.0
C22/C21 Tricyclic Terpane
C29
/C30
Hop
ane
CarbonateMarlCoal/ResinLacustrineMarine ShaleParalic/Deltaic
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Source Rock TypeSource Rock Type # of Oils# of OilsMarine ShaleMarine Shale 146146Paralic/Deltaic Marine ShaleParalic/Deltaic Marine Shale 26 26Marine CarbonateMarine Carbonate//MarlMarl 157157Evaporite/Hypersaline MarlsEvaporite/Hypersaline Marls 11 11Coal/Resinitic Terrestrial SourceCoal/Resinitic Terrestrial Source 29 29Lacustrine, FreshLacustrine, Fresh 35 35Lacustrine, SalineLacustrine, Saline 20 20
Construction of a Geochemical Construction of a Geochemical LibraryLibrary
Construction of a Geochemical Construction of a Geochemical LibraryLibrary
The issue here is to assemble data on a sufficient number of oils to make the library valuable.
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Assembly of a LibraryAssembly of a LibraryAssembly of a LibraryAssembly of a Library
xx1111 x x1212 x x1313 ... x ... x1m1m
xx2121 x x2222 x x2323 ... x ... x2m2m
... ... ...... ... ...
xxn1n1 x xn2n2 x xn3n3 ... x ... xnmnm
A data matrix is constructed based on geochemically significant ratios drawn from the GC, GC/MS and stable carbon isotopes (saturate and aromatic).
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KNN Method to ClassifyKNN Method to ClassifyKNN Method to ClassifyKNN Method to Classify
•
Unknown
Marine Lacustrine
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SIMCA Method to QualifySIMCA Method to QualifySIMCA Method to QualifySIMCA Method to Qualify
Marine
Lacustrine
•
Unknown
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Oil Classification SchematicOil Classification SchematicOil Classification SchematicOil Classification Schematic
Oil SampleOil Sample
Aquatic
Terrestrial
Marine
Lacustrine
Paralic/Deltaic
Coal/Resinitic
Fresh Water
Saline Water
Shale
Marl/Carbonate
Evaporite
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• • •• • •
elseif All == 3elseif All == 3
load knn model from ‘aquatic.mod’load knn model from ‘aquatic.mod’
G3 = predictG3 = predict
if G3 == 1if G3 == 1
load knn model from ‘marine.mod’load knn model from ‘marine.mod’
predictpredict
elseif G3 == 2elseif G3 == 2
load knn model from ‘lacustr.mod’load knn model from ‘lacustr.mod’
predictpredict
endend
• • •• • •
Automation of a Hierarchical Automation of a Hierarchical ClassificationClassification
Automation of a Hierarchical Automation of a Hierarchical ClassificationClassification
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Example: Reservoir Oil Example: Reservoir Oil FingerprintingFingerprinting
Example: Reservoir Oil Example: Reservoir Oil FingerprintingFingerprinting
Chromatography allows us to determine if one reservoir is linked to another by looking at marker peaks that show between the normal alkanes. This process can be done either by choosing an appropriate set of marker peaks ahead of time or by evaluating the whole chromatographic pattern.
GC is usually the technique of choice due to the lower cost of analysis and faster turnaround time.
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Crude Oils from Two Reservoir SystemsCrude Oils from Two Reservoir SystemsCrude Oils from Two Reservoir SystemsCrude Oils from Two Reservoir Systems
PrPh
n-C15
n-C17
n-C19
n-C12
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Marker Compounds Between Marker Compounds Between n-Cn-C1515 and n-C and n-C1616
Marker Compounds Between Marker Compounds Between n-Cn-C1515 and n-C and n-C1616
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Normalizing the Normalizing the Chromatograms to Chromatograms to
Accentuate DifferencesAccentuate Differences
Normalizing the Normalizing the Chromatograms to Chromatograms to
Accentuate DifferencesAccentuate Differences
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Example: Monitoring Yield Example: Monitoring Yield from Multiple Reservoirs in from Multiple Reservoirs in
Open Hole CompletionsOpen Hole Completions
Example: Monitoring Yield Example: Monitoring Yield from Multiple Reservoirs in from Multiple Reservoirs in
Open Hole CompletionsOpen Hole Completions
We can use chromatographic patterns to determine the relative yield from more than one reservoir even where there is no casing.
In this example, the field is undergoing water flood to drive the oil to producing wells. One of the producing zones is significantly more porous than the other. Because pumping water is the primary cost, knowing the relative yields from each reservoir is important.
Pattern recognition also can flag the unusual . . .
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Production Well 696Production Well 696Production Well 696Production Well 696
0
5
10
15
20
25
301 3 5 7 9 11
13
15
17
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29
WellStimulation
Production in the latest 30 production intervals (bbl/day)
After closing Well 696 in and pressurizing the reservoir system, an increase in production was noted.
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Well 696 - ChromatogramsWell 696 - ChromatogramsWell 696 - ChromatogramsWell 696 - Chromatograms
1994 ProductionPre-Stimulation
1995 ProductionPost-Stimulation
Are the differences in hydrocarbon distribution significant?
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Well 696 - Oil ProfileWell 696 - Oil ProfileWell 696 - Oil ProfileWell 696 - Oil Profile
Production in Well 696 has changed in composition significantly since stimulation Production in Well 696 has changed in composition significantly since stimulation work was done. The interpretation is that the well is now producing from a work was done. The interpretation is that the well is now producing from a new zone, deeper than the A or B zones already characterized.new zone, deeper than the A or B zones already characterized.
Zone B
Zone C
Zone A
Some other wells also seem to show Zone C input.
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Zone Apportionment Well 696Zone Apportionment Well 696Zone Apportionment Well 696Zone Apportionment Well 696
0
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15
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25
301 3 5 7 9
11
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WellStimulation Zone C
Zone A
Zone B
Yield by Zone in the latest 30 production intervals (bbl/day)
We have an implied interpretation based on the geochemical differences in the chromatograms.
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C Zone Significant
Injection Wells
A Zone Dominates
Field Production CharacteristicsField Production CharacteristicsField Production CharacteristicsField Production Characteristics
Well 696, Region 4Production 23 bbls/dayWater 85% 13% Zone A; 17% Zone B; 70% Zone C
Perhaps the best way to display the interpretation is by color-coding a map.
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ConclusionsConclusionsConclusionsConclusions
Source of a crude oil: Chemometric pattern matching is Source of a crude oil: Chemometric pattern matching is effective in routine geochemical evaluations and effective in routine geochemical evaluations and multi-step classification procedure is preferable multi-step classification procedure is preferable (minimizes classification errors)(minimizes classification errors)
GC, GC/MS, GC/MS plus isotopes
Reservoir fingerprinting: The techniques can determine Reservoir fingerprinting: The techniques can determine if a reservoir is connected to its neighbors, evaluate if a reservoir is connected to its neighbors, evaluate reservoir mixing and flag unusual samplesreservoir mixing and flag unusual samples
GC on peak tables or whole chromatograms