Maximizing the Value of Information of a Horizontal Polymer Pilot Under Uncertainty Incorporating the Risk Attitude of the Decision Maker
- Dominik Steineder (OMV Exploration and Production) | Torsten Clemens (OMV Exploration and Production) | Keyvan Osivandi (OMV Exploration and Production) | Marco R. Thiele (Streamsim Technology and Stanford University)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- May 2019
- Document Type
- Journal Paper
- 756 - 774
- 2019.Society of Petroleum Engineers
- Risk Attitude, Value of Information, Prospect Theory, Polymer Pilot, Value at Risk
- 22 in the last 30 days
- 75 since 2007
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Polymer injection might lead to incremental oil recovery and increase the value of an asset. Several steps must be taken to mature a polymer-injection project. The field needs to be screened for applicability of polymer injection, laboratory experiments have to be performed, and a pilot project might be required before field implementation.
The decision to perform a pilot project can be dependent on a value-of-information (VOI) calculation. The VOI can be derived by performing a work flow that captures the effects of the range of geological scenarios, as well as dynamic and polymer parameters, on incremental net present value (NPV). The result of the work flow is a cumulative distribution function (CDF) of NPV linked to prior distributions of model parameters and potential observables from the polymer-injection pilot.
The effect of various parameters on the CDF of the fieldwide NPV can be analyzed and in turn used to decide which measurements from the pilot have a strong sensitivity on the NPV CDF, and are thus informative. In the case shown here, the water-cut reduction in the pilot area has a strong effect on the NPV CDF of the polymer-injection field implementation. To extract maximum information, the response of the pilot for water-cut reduction needs to be optimized under uncertainty.
To calculate the VOI, the expected-monetary-value (EMV) difference of a decision tree with and without the pilot can be used if the decision maker (DM) is risk neutral. However, if the DM requires hurdle values through a probability of economic success (PES), value functions (VFs) and decision weights according to the prospect theory should be used. Applying risk hurdles requires a consistent use of VFs and decision weights for calculating VOI and the probability of maturation (POM) of projects.
The methodology was applied to assess the VOI for a horizontal-well pilot in the ninth Tortonian Horizon (9TH) Reservoir in Austria for a risk-averse DM. The operating parameters (polymer concentration and water injection) were chosen such that the watercut reduction, which was the most influential parameter of the polymer pilot on the field NPV CDF, was maximized.
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Adepoju, O. O., Hussein, H., and Chawathe, A. 2017. Assessment of Chemical Performance Uncertainty in Chemical EOR Simulations. Presented at the SPE Reservoir Simulation Conference, Montgomery, Texas, 20–22 February. SPE-182596-MS. https://doi.org/10.2118/182596-MS.
Allan, P. D. and D’Arcy, M. 2017. Project Specific Risk Consideration From a Portfolio Perspective. Presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, Jakarta, 17–19 October. SPE-186189-MS. https://doi.org/10.2118/186189-MS.
Azzarone, D. and Bruni, T. 2008. Real Option Theory Complements the Stage and Gate Process: The Value of Information. Presented at the Europec/EAGE Conference and Exhibition, Rome, 9–12 June. SPE-113634-MS. https://doi.org/10.2118/113634-MS.
Ball, B. C. and Savage, S. L. 1999. Holistic vs. Hole-Istic E&P Strategies. J Pet Technol 51 (9): 74–84. SPE-57701-JPT. https://doi.org/10.2118/57701-JPT.
Begg, S. H., Welsh, M. B., and Bratvold, R. B. 2014. Uncertainty vs. Variability: What’s the Difference and Why is it Important? Presented at the SPE Hydrocarbon Economics and Evaluation Symposium, Houston, 19–20 May. SPE-169850-MS. https://doi.org/10.2118/169850-MS.
Bickel, J. E. 2012. Discretization, Simulation, and Value of Information. SPE Econ & Mgmt 4 (4): 198–203. SPE-145690-PA. https://doi.org/10.2118/145690-PA.
Boasson, V., Boasson, E., and Zhou, Z. 2011. Portfolio Optimization in a Mean-Semivariance Framework. IMFI 8 (3): 58–68.
Bratvold, R. B. and Begg, S. H. 2010. Making Good Decisions. Richardson, Texas: Society of Petroleum Engineers.
Bratvold, R. B. and Thomas, P. 2014. Robust Discretization of Continuous Probability Distributions for Value-of-Information Analysis. Presented at the International Petroleum Technology Conference, Kuala Lumpur, 10–12 December. IPTC-17975-MS. https://doi.org/10.2523/IPTC-17975-MS.
Bratvold, R. B., Bickel, J. E., and Lohne, H. P. 2009. Value of Information in the Oil and Gas Industry: Past, Present, and Future. SPE Res Eval & Eng 12 (4): 630–638. SPE-110378-PA. https://doi.org/10.2118/110378-PA.
Buciak, J. M., Fondevila Sancet, G., and Del Pozo, L. 2015. Polymer-Flooding-Pilot Learning Curve: Five-Plus Years’ Experience To Reduce Cost per Incremental Barrel of Oil. SPE Res Eval & Eng 18 (1): 11–19. SPE-166255-PA. https://doi.org/10.2118/166255-PA.
Caldwell, R. H. and Heather, D. I. 2001. Characterizing Uncertainty in Oil and Gas Evaluations. Presented at the SPE Hydrocarbon Economics and Evaluation Symposium, Dallas, 2–3 April. SPE-68592-MS. https://doi.org/10.2118/68592-MS.
Campbell, R., Huisman, R., and Koedijk, K. 2001. Optimal Portfolio Selection in a Value-at-Risk Framework. J. Bank Financ. 25 (9): 1789–1804. https://doi.org/10.1016/S0378-4266(00)00160-6.
Chiotoroiu, M.-M., Peisker, J., Clemens, T. et al. 2017. Forecasting Incremental Oil Production of a Polymer-Pilot Extension in the Matzen Field Including Quantitative Uncertainty Assessment. SPE Res Eval & Eng 20 (4): 894–905. SPE-179546-PA. https://doi.org/10.2118/179546-PA.
Coopersmith, E. M. and Cunningham, P. C. 2002. A Practical Approach To Evaluating the Value of Information and Real Option Decisions in the Upstream Petroleum Industry. Presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, 29 September–2 October. SPE-77582-MS. https://doi.org/10.2118/77582-MS.
Coopersmith, E. M., Cunningham, P. C., and Pena, C. A. 2003. Decision Mapping—A Practical Decision Analysis Approach To Appraisal and Development Strategy Evaluations. Presented at the SPE Hydrocarbon Economics and Evaluation Symposium, Dallas, 5–8 April. SPE-82033-MS. https://doi.org/10.2118/82033-MS.
Demirmen, F. 1996. Use of “Value of Information” Concept in Justification and Ranking of Subsurface Appraisal. Presented at the SPE Annual Technical Conference and Exhibition, Denver, 6–9 October. SPE-36631-MS. https://doi.org/10.2118/36631-MS.
DuBois, J. R. and Quarles, A. I. 2006. Insuring the Portfolio Against Large Project Failure. SPE Res Eval & Eng 9 (6): 674–680. SPE-84331-PA. https://doi.org/10.2118/84331-PA.
Eidsvik, J., Mukerji, T., Bhattacharjya, D. et al. 2015. Value of Information Analysis of Geophyical Data for Spatial Decision Situations. Presented at the 2015 SEG Annual Meeting, New Orleans, 18–23 October. SEG-2015-5925674.
Erdogan, M., Mudford, B., Davis, C. T. et al. 2005. Going Beyond the Efficient Frontier Analysis Using an Integrated Portfolio Management Approach. Presented at the SPE Hydrocarbon Economics and Evaluation Symposium, Dallas, 3–5 April. SPE-94565-MS. https://doi.org/10.2118/94565-MS.
Faya, L. C., Lake, L. W., and Lasdon, L. S. 2007. Beyond Portfolio Optimization. Presented at the Hydrocarbon Economics and Evaluation Symposium, Dallas, 1–3 April. SPE-107709-MS. https://doi.org/10.2118/107709-MS.
Fortenberry, R., Kim, D. H., Nizamidin, N. et al. 2015. Use of Cosolvents To Improve Alkaline Polymer Flooding. SPE J. 29 (2): 255–266. SPE-166478-PA. https://doi.org/10.2118/166478-PA.
Gerhardt, J. H. and Haldorsen, H. H. 1989. On the Value of Information. Presented at Offshore Europe, Aberdeen, 5–8 September. SPE-19291-MS. https://doi.org/10.2118/SPE-19291-MS.
Greenwalt, W. A. 1981. Determining Venture Participation. J Pet Technol 33 (11): 2189–2195. SPE-9556-PA. https://doi.org/10.2118/9556-PA.
Grose, T. and Smalley, P. C. 2016. Risk-Based Surveillance Planning: A Practical Value-of-Information Approach for Data Acquisition in Producing Fields. SPE Econ & Mgmt 9 (1): 1–11. SPE-184409-PA. https://doi.org/10.2118/184409-PA.
Haskett, W. J. 2003. Optimal Appraisal Well Location Through Efficient Uncertainty Reduction and Value of Information Techniques. Presented at the SPE Annual Technical Conference and Exhibition, Denver, 5–8 October. SPE-84241-MS. https://doi.org/10.2118/84241-MS.
He, J., Xie, J., Sarma, P. et al. 2016. Proxy-Based Work Flow for a Priori Evaluation of Data-Acquisition Programs. SPE J. 21 (4): 1400–1412. SPE-173229-PA. https://doi.org/10.2118/173229-PA.
He, J., Sarma, P., Bhark, E. et al. 2017. Quantifying Value of Information Using Ensemble Variance Analysis. Presented at the SPE Reservoir Simulation Conference, Montgomery, Texas, 20–22 February. SPE-182609-MS. https://doi.org/10.2118/182609-MS.
Hite, J. R. and Bondor, P. L. 2004. Planning EOR Projects. Presented at the SPE International Petroleum Conference in Mexico, Puebla, Mexico, 8–9 November. SPE-92006-MS. https://doi.org/10.2118/92006-MS.
Jafarizadeh, B. and Bratvold, R. B. 2009. Strategic Decision Making in the Digital Oil Field. Presented at the SPE Digital Energy Conference and Exhibition, Houston, 7–8 April. SPE-123213-MS. https://doi.org/10.2118/123213-MS.
Kemp, A. and Stephen, L. 2015. The Economics of EOR Schemes in the UK Continental Shelf (UKCS). Presented at the SPE Offshore Europe Conference and Exhibition, Aberdeen, 8–11 September. SPE-175470-MS. https://doi.org/10.2118/175470-MS.
Kolmogorov, A. N. 1933. Sulla Determinazione Empirica di una Legge di Distribuzione. Giorn. Ist. Ital. Attuari. 4: 83–91.
Koninx, J. P. M. 2000. Value-of-Information—From Cost-Cutting to Value-Creation. Presented at the SPE Asia Pacific Oil and Gas Conference and Exhibition, Brisbane, Australia, 16–18 October. SPE-64390-MS. https://doi.org/10.2118/64390-MS.
Le, D. H. and Reynolds, A. C. 2014. Estimation of Mutual Information and Conditional Entropy for Surveillance Optimization. SPE J. 19 (4): 648–661. SPE-163638-PA. https://doi.org/10.2118/163638-PA.
Lüftenegger, M., Kadnar, T., Puls, C. et al. 2016. Operational Challenges and Monitoring of a Polymer Pilot, Matzen Field, Austria. SPE Prod & Oper 31 (3): 228–237. SPE-174350-PA. https://doi.org/10.2118/174350-PA.
Markowitz, H. 1952. Portfolio Selection. J. Finance 7 (1): 77–91. https://doi.org/10.1111/j.1540-6261.1952.tb01525.x.
McVean, J. R. 2000. The Significance of Risk Definition on Portfolio Selection. Presented at the SPE Annual Technical Conference and Exhibition, Dallas, 1–4 October. SPE-62966-MS. https://doi.org/10.2118/62966-MS.
Mishar, S. N. 2012. Improving Major Project Development Through a Front End Loading Management System: Medco. Presented at the Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, 11–14 November. SPE-162254-MS. https://doi.org/10.2118/162254-MS.
Narayanan, K., Cullick, A. S., and Matthew, B. 2003. Better Field Development Decisions From Multi-Scenario, Interdependent Reservoir, Well, and Facility Simulations. Presented at the SPE Reservoir Simulation Symposium, Houston, 3–5 February. SPE-79703-MS. https://doi.org/10.2118/79703-MS.
Orman, M. M. and Duggan, T. E. 1999. Applying Modern Portfolio Theory To Upstream Investment Decision Making. J Pet Technol 51 (3): 50–53. SPE-54774-JPT. https://doi.org/10.2118/54774-JPT.
Puls, C., Clemens, T., Sledz, C. et al. 2016. Mechanical Degradation of Polymers During Injection, Reservoir Propagation and Production—Field Test Results 8 TH Reservoir, Austria. Presented at the SPE Europec/EAGE Conference and Exhibition, Vienna, 30 May–2 June. SPE-180144-MS. https://doi.org/10.2118/180144-MS.
Purdy, G. 2010. ISO 31000: 2009—Setting a New Standard for Risk Management. Risk Anal. 30 (6): 881–886. https://doi.org/10.1111/j.1539-6924.2010.01442.x.
R Core Team. 2017. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
Roy, A. D. 1952. Safety First and the Holding of Assets. Econometrica 20 (3): 431–449. https://doi.org/10.2307/1907413.
Sawiris, R., Howes, C. S., Rodriguez, J. A. et al. 2015. Uncertainty and Risk Management Plans are Critical for Team Alignment and Better Decision Quality. Presented at the SPE Annual Technical Conference and Exhibition, Houston, 28–30 September. SPE-174932-MS. https://doi.org/10.2118/174932-MS.
Scheidt, C. and Caers, J. 2009. Uncertainty Quantification in Reservoir Performance Using Distances and Kernel Methods—Application to a West Africa Deepwater Turbidite Reservoir. SPE J. 14 (4): 680–692. SPE-118740-PA. https://doi.org/10.2118/118740-PA.
Sieberer, M., Clemens, T., Peisker, J. et al. 2018. Polymer Flood Field Implementation—Pattern Configuration and Horizontal Versus Vertical Wells. Presented at the SPE Improved Oil Recovery Conference, Tulsa, 14–18 April. SPE-190233-MS. https://doi.org/10.2118/190233-MS.
Siena, M., di Milano, P., Guadagnini, A. et al. 2015. A New Bayesian Approach for Analogs Evaluation in Advanced EOR Screening. Presented at the SPE Europec 2015, Madrid, Spain, 1–4 June. SPE-174315-MS. https://doi.org/10.2118/174315-MS.
Simpson, G. S., Lamb, F. E., Finch, J. H. et al. 2000. The Application of Probabilistic and Qualitative Methods to Asset Management Decision Making. Presented at the SPE Asia Pacific Conference on Integrated Modelling for Asset Management, Yokohama, Japan, 25–26 April. SPE-59455-MS. https://doi.org/10.2118/59455-MS.
Smirnov, N.V. 1938. On Estimating the Discrepancy Between Empirical Distribution Curves for Two Independent Samples. Byull. Moskov. Gos. Univ. Ser. A 2 (2): 3–14.
Taber, J. J., Martin, F. D., and Seright, R. S. 1997. EOR Screening Criteria Revisited—Part 1: Introduction to Screening Criteria and Enhanced Recovery Field Projects. SPE Res Eval & Eng 12 (3): 189–198. SPE-35385-PA. https://doi.org/10.2118/35385-PA.
Teletzke, G. F., Wattenbarger, R. C., and Wilkinson, J. R. 2010. Enhanced Oil Recovery Pilot Testing Best Practices. SPE Res Eval & Eng 13 (1): 143–154. SPE-118055-PA. https://doi.org/10.2118/118055-PA.
Thiele, M. R. and Batycky, R. P. 2006. Using Streamline-Derived Injection Efficiencies for Improved Waterflood Management. SPE Res Eval & Eng 9 (2): 187–196. SPE-84080-PA. https://doi.org/10.2118/84080-PA.
Thiele, M. R. and Batycky, R. P. 2016. Evolve: A Linear Work Flow for Quantifying Reservoir Uncertainty. Presented at the SPE Annual Technical Conference and Exhibition, Dubai, 26–28 September. SPE-181374-MS. https://doi.org/10.2118/181374-MS.
Tversky, A. and Kahneman, D. 1992. Advances in Prospect Theory: Cumulative Representation of Uncertainty. J. Risk Uncertain. 5 (4): 297–323. https://doi.org/10.1007/BF00122574.
Walkup, G. W. and Ligon, J. R. 2006. The Good, Bad, and Ugly of Stage-Gate Project Management Process as Applied in the Oil and Gas Industry. Presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, 24–27 September. SPE-102926-MS. https://doi.org/10.2118/102926-MS.
Walters, S., Ward, G., Wigston, B. et al. 2016. Justifying Appraisal in a Low Oil Price Environment: A Probabilistic Workflow for Development Planning and Value of Information. Presented at the SPE Asia Pacific Oil & Gas Conference and Exhibition, Perth, Australia, 25–27 October. SPE-182410-MS. https://doi.org/10.2118/182410-MS.
Wassmuth, F. R., Green, K., Arnold, W. et al. 2009. Polymer Flood Application To Improve Heavy Oil Recovery at East Bodo. J Can Pet Technol 48 (2): 55–61. PETSOC-09-02-55. https://doi.org/10.2118/09-02-55.
Welsh, M. B. and Begg, S. H. 2008. Modeling the Economic Impact of Individual and Corporate Risk Attitude. Presented at the SPE Annual Technical Conference and Exhibition, Denver, 21–24 September. SPE-116610-MS. https://doi.org/10.2118/116610-MS.
Wills, H. A. and Graves, R. M. 2004. Information is Costly, But How Valuable is It? Presented at the SPE Annual Technical Conference and Exhibition, Houston, 26–29 September. SPE-90710-MS. https://doi.org/10.2118/90710-MS.