Decision-Oriented Geosteering and the Value of Look-Ahead Information: A Case-Based Study
- Kanokwan Kullawan (University of Stavanger) | Reidar B. Bratvold (University of Stavanger) | Camilo M. Nieto (University of Stavanger)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- June 2017
- Document Type
- Journal Paper
- 767 - 782
- 2017.Society of Petroleum Engineers
- geosteering decisions, well placement optimization, geosteering, value of information, decision-making
- 2 in the last 30 days
- 362 since 2007
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Geosteering techniques have been widely implemented in the oil and gas (O&G) industry for well-placement operations. These techniques allow the operators to apply real-time information in precisely controlling the wellbore direction to stay within desired reservoir zones. The industry has mainly focused on technological improvements of real-time technologies.
Nonetheless, previous work has indicated that the conventional approach for making geosteering decisions leaves much to be desired. Normal geosteering operations involve drawing inferences from behind-the-bit information to ahead-of-the-bit reservoir uncertainties and making decisions under unresolved uncertainties to optimize a single objective or multiple objectives. On the basis of a large body of research, the conventional approach—which is heavily driven by intuitions, educated guesses, and approximate methods (rules of thumb)—is unlikely to identify the optimal courses of action.
Geosteering decisions are sequential decisions made under dynamic uncertainties. A sequence of well-trajectory decisions arises as the well penetrates the formation and real-time data are gathered. To optimize decision making in such an environment requires considering future decisions and uncertainties, along with the flexibility to take action as new information is learned.
In this work, we demonstrate how sequential geosteering decisions can be optimized by use of the discretized-stochastic-dynamic-programming approach (DSDP). DSDP exploits the benefits of stochastic dynamic programming to optimize multistage, interrelated decisions under uncertainties. At the same time, the computational time is minimized so it can be applied to geosteering decisions.
Through case studies, we illustrate the application of DSDP in various reservoir structures. The results suggest that the technique could significantly improve the final results of the wells, especially if the reservoir boundaries change rapidly or in a faulted reservoir. The results are expressed as substantial increases in resulting reservoir contacts as well as reductions in well-construction costs.
Finally, we illustrate and discuss the use of DSDP to assess the value of look-ahead information. We demonstrate that merely increasing the look-ahead capability (ahead-of-the-bit distance that the tool can measure) is not sufficient. The values created from look-ahead are also strongly affected by the measurement accuracy and the flexibility to respond rapidly by making large directional changes.
|File Size||1 MB||Number of Pages||16|
Al-Hajari, A., Soremi, A., Ma, S. M. et al. 2009. Proactive Geosteering in Thin Reservoir Bound by Anhydrite in Saudi Arabia. Presented at the International Petroleum Technology Conference, Doha, Qatar, 7–9 December. IPTC-13304-MS. https://doi.org/10.2523/IPTC-13304-MS.
Alkhatib, A. M. and King, P. R. 2014. The Use of the Least Squares Probabilistic Collocation Method in Decision Making in the Presence of Uncertainty for Chemical EOR Processes. Presented at the SPE Annual Technical Conference and Exhibition, Amsterdam, 27–29 October. SPE-170587-MS. https://doi.org/10.2118/170587-MS.
Bickel, J. E. and Smith, J. E. 2006. Optimal Sequential Exploration: A Binary Learning Model. Dec. Anal. 3 (1): 16–32. https://doi.org/10.1287/deca.1050.0052.
Bond, C. E. 2015. Uncertainty in Structural Interpretation: Lessons to be Learnt. J. Struct. Geol. 74 (May): 185–200. https://doi.org/10.1016/j.jsg.2015.03.003.
Bond, C. E., Gibbs, A. D., Shipton, Z. K. et al. 2007. What Do You Think This Is? “Conceptual Uncertainty” in Geoscience Interpretation. GSA Today 17 (11): 4–10. https://doi.org/10.1130/GSAT01711A.1.
Bratvold, R. B. and Begg, S. H. 2010. Making Good Decisions. Richardson, Texas: Society of Petroleum Engineers.
Chadwick, P. K. 1975. A Psychological Analysis of Observation in Geology. Nature 256 (5518): 570–573. https://doi.org/10.1038/256570a0.
Chugh, D. 2004. Societal and Managerial Implications of Implicit Social Cognition: Why Milliseconds Matter. Soc. Justice Res. 17 (2): 203–222. https://doi.org/10.1023/B:SORE.0000027410.26010.40.
Constable, M. V., Antonsen, F., Olsen, P. A. et al. 2012. Improving Well Placement and Reservoir Characterization with Deep Directional Resistivity Measurements. Presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, 8–10 October. SPE-159621-MS. https://doi.org/10.2118/159621-MS.
Etzioni, A. 2001. Humble Decision Making. In Harvard Business Review on Decision Making, 45–57. Boston: Harvard Business School Press.
Goodwin, P. and Wright, G. 2004. Decision Analysis for Management Judgment, third edition. Hoboken, New Jersey: John Wiley & Sons.
Heimer, A., Cohen, I. and Vassiliou, A. A. 2007. Dynamic Programming For Multichannel Blind Seismic Deconvolution. Proc., 2007 SEG Annual Meeting, San Antonio, Texas, 23–28 September, 1845–1849.
Jafarizadeh, B. and Bratvold, R.B. 2009. Taking Real Options into the Real World: Asset Valuation Through Option Simulation. Presented at the SPE Annual Technical Conference and Exhibition, New Orleans, 4–7 October. SPE-124488-MS. https://doi.org/10.2118/124488-MS.
Jafarizadeh, B. and Bratvold, R. B. 2013. Sell Spot or Sell Forward? Analysis of Oil-Trading Decisions With the Two-Factor Price Model and Simulation. SPE Econ & Mgmt 5 (3): 80–88. SPE-165581-PA. https://doi.org/10.2118/165581-PA.
Kahneman, D. 2011. Thinking, Fast and Slow. New York City: Farrar, Straus and Giroux.
Kullawan, K., Bratvold, R. B. and Bickel, J. E. 2014. A Decision Analytic Approach to Geosteering Operations. SPE Drill & Compl 29 (1): 36–46. SPE-167433-PA. https://doi.org/10.2118/167433-PA.
Kullawan, K., Bratvold, R. B. and Bickel, J. E. 2016. Value Creation with Multi-Criteria Decision Making in Geosteering Operations. International Journal of Petroleum Technology 3 (1): 15–31. https://doi.org/10.15377/2409-787X.2016.03.01.2.
Kullawan, K., Bratvold, R. B. and Bickel, J. E. In press. Sequential Geosteering Decisions for Optimization of Real-Time Well Placement. Decision Analysis (submitted 3 August 2016).
Natsir, M., Hasani, N., Tubagus, N. et al. 2012. Real-Time Bed Boundary Mapping And Formation DIP Image To Navigate Horizontal Well At Channel Sand Case Study: Niru Field - Pertamina UBEP Limau. Presented at the IADC/SPE Asia Pacific Drilling Technology Conference and Exhibition, Tianjin, China, 9–11 July. SPE-155056-MS. https://doi.org/10.2118/155056-MS.
Parnell, G. S., Bresnick, T. A., Tani, S. N. et al. 2013. Handbook of Decision Analysis. Hoboken, New Jersey: John Wiley & Sons.
Polson, D. and Curtis, A. 2010. Dynamics of Uncertainty in Geological Interpretation. J. Geol. Soc. 167 (1): 5–10. https://doi.org/10.1144/0016-76492009-055.
Rankey, E. C. and Mitchell, J. C. 2003. That’s Why It’s Called Interpretation: Impact of Horizon Uncertainty on Seismic Attribute Analysis. The Leading Edge 22 (9): 820–828. https://doi.org/10.1190/1.1614152.
Saleh, A. D., Yousef, M. A. S., Mohsin, H. A. H. et al. 2007. Proactive 3D Geosteering Boosts Productivity and Recovery in Complex Clastic Environment. Presented at the SPE/IADC Middle East Drilling and Technology Conference, Cairo, 22–24 October. SPE-106790-MS. https://doi.org/10.2118/106790-MS.
Tetlock, P. and Gardner, D. 2015. Superforecasting: The Art and Science of Prediction. New York City: Crown Publishers.
Thomas, P. and Bratvold, R. B. 2015. A Real Options Approach to the Gas Blowdown Decision. Presented at the SPE Annual Technical Conference and Exhibition, Houston, 28–30 September. SPE-174868-MS. https://doi.org/10.2118/174868-MS.
Tversky, A. and Kahneman, D. 1974. Judgment Under Uncertainty: Heuristics and Biases. Science 185 (4157): 1124–1131. https://doi.org/10.1126/science.185.4157.1124.
Wen, Z., Durlofsky, L. J., Van Roy, B. et al. 2011. Use of Approximate Dynamic Programming for Production Optimization. Presented at the SPE Reservoir Simulation Symposium, The Woodlands, Texas, 21–23 February 2011, SPE-141677-MS. https://doi.org/10.2118/141677-MS.
Willigers, B. J. A. and Bratvold, R. B. 2009. Valuing Oil and Gas Options by Least-Squares Monte Carlo Simulation. SPE Proj Fac & Const 4 (4): 146–155. SPE-116026-PA. https://doi.org/10.2118/116026-PA.
Willigers, B. J. A., Begg, S. and Bratvold, R. B. 2011. Valuation of Swing Contracts by Least-Squares Monte Carlo Simulation. SPE Econ & Mgmt 3 (4): 215–225. SPE-133044-PA. https://doi.org/10.2118/133044-PA.