How Significant Is the P90 Value as a Measure of the Reserves' Downside?
- Sandeep Gupta (University of Western Australia) | Ritu Gupta (Curtin University of Technology) | Jan van Elk (Curtin University of Technology) | Kaipillil Vijayan (University of Western Australia)
- Document ID
- Society of Petroleum Engineers
- SPE Economics & Management
- Publication Date
- January 2012
- Document Type
- Journal Paper
- 51 - 57
- 2012. Society of Petroleum Engineers
- 5.7.4 Probabilistic Methods
- 0 in the last 30 days
- 774 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 10.00|
|SPE Non-Member Price:||USD 30.00|
Probabilistic methods for reserves estimation, including uncertainty quantification and probabilistic aggregation, have gained widespread acceptance in the oil and gas industry, since the first comprehensive guidelines were issued by the Society of Petroleum Engineers (SPE) in 2001. The probabilistic methods now used in the oil industry, as proposed in these guidelines, are similar to those also used in portfolio theory and risk management by the finance industry. A significant amount can be learned from the extensive experience with probabilistic methods and quantification of risk with measures [e.g., value-at-risk (VAR)] in financial risk management. Especially, the guidelines issued by the Basel II Accord (Bank for International Settlements 2006) and the discussions since the 2008 financial crisis contain important lessons.
In this paper, we examine a fundamental question: "Is the P90 reserves value an appropriate measure for quantifying the reserves' downside?" For the P90 reserves value to be considered a good measure of the reserves' downside, it needs to possess a number of basic characteristics involving P90 reserves for each field and the probabilistically aggregated P90 reserves for the portfolio of fields. Analogous to the definition of a coherent risk measure used in the finance industry, we define these characteristics for P90 reserves.
The P90 reserves are as good a risk measure as VAR used in the financial industry. However, like VAR, it is not a coherent risk measure. A possible uncertainty scenario, in which one of these necessary characteristics does not hold, is given. An alternative measure of risk for quantifying the reserves' downside, defined as the average reserves over the confidence interval higher than P90, is presented. This is a coherent risk measure.
In this paper, we highlight the appropriateness and limitations of using the P90 reserves estimate as a measure of the reserves' downside. Understanding of the limitations posed by using the P90 reserves value is vital in management of reserves risk.
|File Size||423 KB||Number of Pages||7|
Artzner, P., Delbaen, F., Eber, J.-M., and Heath, D. 1999. Coherent Measuresof Risk. Mathematical Finance 9 (3): 203-228. http://dx.doi.org/10.1111/1467-9965.00068.
Bank for International Settlements (BIS). 2006. Basel II: InternationalConvergence of Capital Measurement and Capital Standards: A Revised Framework -Comprehensive Version, http://www.bis.org/publ/bcbs128.htm.
Cook, J.D. 2010. Determining distribution parameters from quantiles. WorkingPaper 55, Department of Biostatistics, UT MD Anderson Cancer Center/TheBerkeley Electronic Press (January 2010), http://www.bepress.com/mdandersonbiostat/paper55.
Demirmen, F. 2007. Reserves Estimation: The Challenge for the Industry. JPet Technol 59 (5): 80-89. SPE-103434-PA.
Jorion, P. 2007. Value at Risk: The New Benchmark for Managing FinancialRisk, third edition. New York: McGraw-Hill.
Purvis, D.C. 2003. Judgment in Probabilistic Analysis. Paper SPE 81996presented at the SPE Hydrocarbon Economics and Evaluation Symposium, Dallas,5-8 April. http://dx.doi.org/10.2118/81996-MS.
SPE/WPC/AAPG/SPEE. 2007. Petroleum Resources Management System--2007, http://www.spe.org/industry/docs/Petroleum_Resources_Management_System_2007.pdf.
SPEE-Calgary and Petroleum Society of CIM. 2002. Canadian Oil and GasEvaluation Handbook (COGEH) Volume 1: Reserves Definitions and EvaluationPractices and Procedures, Sec. 5. Calgary, Alberta: Society of PetroleumEngineers Canada.
Swinkels, W.J.A.M. 2001. Aggregation of Reserves. In Guidelines for theEvaluation of Petroleum Reserves and Resources: A Supplement to the SPE/WPCPetroleum Reserves Definitions and the SPE/WPC/AAPG Petroleum ResourcesDefinitions, Chap. 6, 53-71. Richardson, Texas: SPE.
Taleb, N.N. 2010. The Black Swan: The Impact of the HighlyImprobable, second edition. New York: Random House Trade Paperbacks.
US Securities and Exchange Commission. 2009. Modernization of Oil andGas Reporting. Final Rule, 17 CFR Parts 210, 211, 229, and 249, [Release Nos.33-8995; 34-59192; FR-78; File No. S7-15-08], RIN 3235-AK00, US SEC,Washington, DC (14 January 2009). Federal Register 74 (9):2157-2197, http://www.sec.gov/rules/final/2009/33-8995fr.pdf.
van Elk, J.F., Gupta, R., and Wann, D. 2010. Probabilistic Aggregationof Oil and Gas Field Resource Estimates and Project Portfolio Analysis. SPERes Eval & Eng 13 (1): 82-94. SPE-116395-PA. http://dx.doi.org/10.2118/116395-PA.
van Elk, J.F., Vijayan, K., and Gupta, R. 2000. Probabilistic Additionof Reserves--A New Approach. Paper SPE 64454 presented at the SPE Asia PacificOil and Gas Conference and Exhibition, Brisbane, Australia, 16-18 October. http://dx.doi.org/10.2118/64454-MS.