Pattern Recognition Techniques in Petroleum Geochemistry L. Scott Ramos and Brian G. Rohrback Infometrix, Inc. Daniel M. Jarvie Humble Instruments & Services,

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.


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.


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.


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


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


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


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.


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).


KNN Method to ClassifyKNN Method to ClassifyKNN Method to ClassifyKNN Method to Classify

•

Unknown

Marine Lacustrine


SIMCA Method to QualifySIMCA Method to QualifySIMCA Method to QualifySIMCA Method to Qualify

Marine

Lacustrine

•

Unknown


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


• • •• • •

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


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.


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


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


Normalizing the Normalizing the Chromatograms to Chromatograms to

Accentuate DifferencesAccentuate Differences

Normalizing the Normalizing the Chromatograms to Chromatograms to

Accentuate DifferencesAccentuate Differences


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 . . .


Production Well 696Production Well 696Production Well 696Production Well 696

0

5

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25

301 3 5 7 9 11

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27

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.


Well 696 - ChromatogramsWell 696 - ChromatogramsWell 696 - ChromatogramsWell 696 - Chromatograms

1994 ProductionPre-Stimulation

1995 ProductionPost-Stimulation

Are the differences in hydrocarbon distribution significant?


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.


Zone Apportionment Well 696Zone Apportionment Well 696Zone Apportionment Well 696Zone Apportionment Well 696

0

5

10

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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.

InfoMetrixInfoMetrixInfoMetrixB Zone Dominates

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.


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

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Pattern Recognition Techniques in Petroleum Geochemistry L. Scott Ramos and Brian G. Rohrback Infometrix, Inc. Daniel M. Jarvie Humble Instruments & Services,