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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, Inc. Computer-Assisted Geochemistry.

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pattern recognition techniques in petroleum geochemistry
Pattern Recognition Techniques in Petroleum Geochemistry
  • L. Scott Ramos and Brian G. Rohrback Infometrix, Inc.
  • Daniel M. Jarvie
  • Humble Instruments & Services, Inc.
computer assisted geochemistry
Computer-Assisted Geochemistry
  • 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 migration or to assess the degree of communication among reservoirs.
  • Computerized pattern recognition (aka chemometrics) is an efficient way to exploit the information richness of the data without sacrificing speed or accuracy.
an overlay of chromatograms
An 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 geochemical evaluations
Example: Automation of Geochemical 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.

traditional geochemistry

1.6

TraditionalGeochemistry

1.4

1.2

1.0

C29/C30 Hopane

Carbonate

0.8

Marl

0.6

Coal/Resin

Lacustrine

0.4

Marine Shale

Paralic/Deltaic

0.2

0.0

0.5

1.0

1.5

2.0

C22/C21 Tricyclic Terpane

slide8

Construction of a Geochemical Library

Source Rock Type # of OilsMarine Shale 146Paralic/Deltaic Marine Shale 26Marine Carbonate/Marl 157Evaporite/Hypersaline Marls 11Coal/Resinitic Terrestrial Source 29Lacustrine, Fresh 35Lacustrine, Saline 20

The issue here is to assemble data on a sufficient number of oils to make the library valuable.

assembly of a library
Assembly of a Library
  • x11 x12 x13 ... x1m
  • x21 x22 x23 ... x2m
  • ... ... ...
  • xn1 xn2 xn3 ... xnm

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 classify
KNN Method to Classify

Unknown

Marine

Lacustrine

simca method to qualify
SIMCA Method to Qualify

Unknown

Marine

Lacustrine

oil classification schematic
Oil Classification Schematic

Oil Sample

Paralic/Deltaic

Terrestrial

Coal/Resinitic

Shale

Aquatic

Marine

Marl/Carbonate

Evaporite

Fresh Water

Lacustrine

Saline Water

automation of a hierarchical classification
Automation of a Hierarchical Classification
  • • • •
  • elseif All == 3
  • load knn model from ‘aquatic.mod’
  • G3 = predict
  • if G3 == 1
  • load knn model from ‘marine.mod’
  • predict
  • elseif G3 == 2
  • load knn model from ‘lacustr.mod’
  • predict
  • end
  • • • •
example reservoir oil fingerprinting
Example: Reservoir Oil Fingerprinting

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.

example monitoring yield from multiple reservoirs in open hole completions
Example: Monitoring Yield from Multiple Reservoirs in Open 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 . . .

slide19

Production Well 696

30

25

Well

Stimulation

20

15

10

5

0

1

3

5

7

9

11

13

15

17

19

21

23

25

27

29

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 chromatograms
Well 696 - Chromatograms

1994 Production

Pre-Stimulation

1995 Production

Post-Stimulation

Are the differences in hydrocarbon distribution significant?

well 696 oil profile
Well 696 - Oil Profile

Zone C

  • 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 new zone, deeper than the A or B zones already characterized.

Zone B

Zone A

Some other wells also seem to show Zone C input.

slide22

Zone Apportionment Well 696

Well

Stimulation

Zone C

Zone B

Zone A

Yield by Zone in the latest 30 production intervals (bbl/day)

We have an implied interpretation based on the geochemical differences in the chromatograms.

slide23

Field Production Characteristics

Well 696, Region 4

Production 23 bbls/day

Water 85%

13% Zone A; 17% Zone B; 70% Zone C

Perhaps the best way to display the interpretation is by color-coding a map.

A Zone Dominates

C Zone Significant

B Zone Dominates

Injection Wells

conclusions
Conclusions
  • Source of a crude oil: Chemometric pattern matching is effective in routine geochemical evaluations and multi-step classification procedure is preferable (minimizes classification errors)
    • GC, GC/MS, GC/MS plus isotopes
  • Reservoir fingerprinting: The techniques can determine if a reservoir is connected to its neighbors, evaluate reservoir mixing and flag unusual samples
    • GC on peak tables or whole chromatograms