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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.

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Pattern Recognition Techniques in Petroleum Geochemistry

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

  2. 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.

  3. 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.

  4. 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.

  5. GC/MS Mass ChromatogramsTricyclic Terpanes m/z=191

  6. GC/MS Mass ChromatogramsSteranes m/z=217

  7. 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

  8. 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.

  9. 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).

  10. KNN Method to Classify Unknown Marine Lacustrine

  11. SIMCA Method to Qualify Unknown Marine Lacustrine

  12. Oil Classification Schematic Oil Sample Paralic/Deltaic Terrestrial Coal/Resinitic Shale Aquatic Marine Marl/Carbonate Evaporite Fresh Water Lacustrine Saline Water

  13. 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 • • • •

  14. 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.

  15. Crude Oils from Two Reservoir Systems n-C12 n-C15 n-C17 n-C19 Pr Ph

  16. Marker Compounds Between n-C15 and n-C16

  17. Normalizing the Chromatograms to Accentuate Differences

  18. 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 . . .

  19. 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.

  20. Well 696 - Chromatograms 1994 Production Pre-Stimulation 1995 Production Post-Stimulation Are the differences in hydrocarbon distribution significant?

  21. 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.

  22. 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.

  23. 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

  24. 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

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