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The Use of Reservoir Simulation in Deterministic Proved-Reserves Estimation

Authors
Alistair D W Jones (BP Exloration) | Frank R Denelle (Shell Exploration & Production Co) | William John Lee (University of Houston) | David MacDonald (BP Exploration) | Bernard J Seiller (Total S A)
DOI
https://doi.org/10.2118/170669-PA
Document ID
SPE-170669-PA
Publisher
Society of Petroleum Engineers
Source
SPE Reservoir Evaluation & Engineering
Volume
19
Issue
03
Publication Date
July 2016
Document Type
Journal Paper
Pages
358 - 366
Language
English
ISSN
1094-6470
Copyright
2016.Society of Petroleum Engineers
Disciplines
Keywords
Simulation, Reserves, PRMS, Reliable Technology, SEC
Downloads
4 in the last 30 days
655 since 2007
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Summary

This paper proposes an approach for assessing a reservoir-simulation model for use in estimating reserves. A simulation model can integrate complex static data, the physical description of displacement processes, production constraints, and schedules. Hence, it can provide important information for business decisions and reserves estimation. Confidence in simulation predictions depends on the strength of evidence for the input data, quality control of the model, robustness of the history match, and whether there is independent evidence supporting predictions. We explain the principles for evaluating a simulation model and propose requirements for simulation predictions to be considered as proved reserves. This involves evaluation against different strands of evidence, such as static and dynamic characterization, wells and facilities description, reservoir performance, and analogs. Simulation models are often built to support business decisions by use of the best technical estimates for inputs. There can be instances where a simulation model may be reasonable and reliable but it only represents a “best technical” outcome. There may not be sufficient evidence to count the whole predicted recovery as proved reserves. We propose how such a model may be modified to also provide proved-reserves estimates. The approach can be used with different available data and at different stages of field life. It is illustrated through a case study that shows how the principles may be applied.

File Size  590 KBNumber of Pages   9

References

Dromgoole, P. and Speers, R. 1997. Geoscore: A Method for Quantifying Uncertainty in Field Reserves Estimates. Petrol. Geosci. 3 (1): 1–12. http://dx.doi.org/10.1144/petgeo.3.1.1.

Lee, W. J., Sidle, R. E. and McVay, D. A. 2011. Reservoir Simulation: A Reliable Technology? Presented at the SPE Annual Technical Conference and Exhibition, Denver, 30 October–2 November. SPE-146524-MS. http://dx.doi.org/10.2118/146524-MS.

Petroleum Resource Management System (PRMS). 2007. Sponsored by the Society of Petroleum Engineers, the World Petroleum Council, the American Association of Petroleum Geologists, and the Society of Petroleum Evaluation Engineers, http://www.spe.org/industry/docs/Petroleum_Resources_Management_System_2007.pdf

(accessed 12 February 2012).

Rietz, D. C. and Usmani, A. H. 2005. Reservoir Simulation and Reserves Classifications – Guidelines for Reviewing Model History Matches to Help Bridge the Gap Between Evaluators and Simulation Specialists. Presented at the SPE Annual Technical Conference and Exhibition, Dallas, 9–12 October. SPE-96410-MS. http://dx.doi.org/10.2118/96410-MS.

Rietz, D. C. and Usmani, A. H. 2009. Case Studies Illustrating the Use of Reservoir Simulation Results in the Reserves Estimation Process. SPE Res Eval & Eng 12 (1): 149–158. SPE-110066-PA. http://dx.doi.org/10.2118/110066-PA. US Securities and Exchange Commission (SEC). 2008. Modernisation of Oil and Gas Reporting. Conforming Version, 17 CFR Parts 210, 211, 229, and 249 (Release Nos. 33-8995; 34-59192; FR-78; File No. S7-15-08). Washington, DC, December 2008.

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