Performance Comparison of Algorithms for Real-Time Rate-of-Penetration Optimization in Drilling Using Data-Driven Models
- Chiranth Hegde (University of Texas at Austin) | Hugh Daigle (University of Texas at Austin) | Ken E. Gray (University of Texas at Austin)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- October 2018
- Document Type
- Journal Paper
- 1,706 - 1,722
- 2018.Society of Petroleum Engineers
- Optimization, drilling, machine learning, meta-heuristic, data-driven
- 16 in the last 30 days
- 359 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 10.00|
|SPE Non-Member Price:||USD 30.00|
Real-time drilling optimization is a topic of significant interest because of its economic value, and its importance increases particularly during periods of low oil prices. This paper evaluates different optimization strategies and algorithms for real-time optimization of an objective function (function to be optimized) specific to drilling. The objective function optimized here is derived from a data-driven (or machine-learning) model with an unknown functional form. A data-driven model has been used to calculate the objective function [rate of penetration (ROP)] because it has been shown to be more efficient in ROP prediction relative to deterministic models (Hegde and Gray 2017). The data-driven ROP model is built using machine-learning algorithms; measured drilling parameters [weight on bit (WOB), revolutions per minute (rev/min), strength of rock, and flow rate] are used as inputs to predict the ROP.
Real-time drilling optimization that is data-driven is challenging because of run-time constraints. This is perceived as a handicap for data-driven models because their functional form is unknown, making them more difficult to optimize. This paper evaluates algorithms depending on their ability to best maximize the objective (ROP) and their time effectiveness. Two simple yet robust algorithms, the eyeball method and the random-search method, are presented as plausible solutions to this problem. These methods are then compared with popular metaheuristic algorithms, evaluating the tradeoff between improvement in the objective (search for a global optimal) and the computational time of run.
Using results from the simulations conducted in this paper, we concluded that data-driven models can be used for real-time drilling despite their computational constraints by choosing the right optimization algorithm. The best tradeoff in terms of ROP increase as well as computational efficiency evaluated in this paper is the simplex algorithm. The ROP was improved by 30% on average with a variance of 2.5% in the test set over 14 formations that were tested.
|File Size||1 MB||Number of Pages||17|
Bingham, M. G. 1965. A New Approach to Interpreting Rock Drillability. Tulsa: Petroleum Publishing Company.
Boyd, S. and Vandenberghe, L. 2004. Convex Optimization. New York City: Cambridge University Press.
Bybee, K. 2011. Real-Time Optimization of Drilling Parameters. J Pet Technol 63 (2): 48–49. SPE-0211-0048-JPT. https://doi.org/10.2118/0211-0048-JPT.
Casella, G. and Berger, R. L. 2002. Statistical Inference, second edition. Boston, Massachusetts: Cengage Learning.
Chang, D. L., Payette, G. S., Pais, D. et al. 2014. Field Trial Results of a Drilling Advisory System. Presented at the International Petroleum Technology Conference, Doha, Qatar, 19–22 January. IPTC-17216-MS. https://doi.org/10.2523/IPTC-17216-MS.
Chapman, C. D., Sanchez, J. L., De Leon Perez, R. et al. 2012. Automated Closed-Loop Drilling with ROP Optimization Algorithm Significantly Reduces Drilling Time and Improves Downhole Tool Reliability. Presented at the IADC/SPE Drilling Conference and Exhibition, San Diego, California, 6–8 March. SPE-151736-MS. https://doi.org/10.2118/151736-MS.
Cui, M., Wang, H. G., Zhao, J. Y. et al. 2015. Optimizating Drilling Operating Parameters With Real-Time Surveillance and Mitigation System of Downhole Vibration in Deep Wells. Adv. Petrol. Explor. Dev. 10 (1): 22–26. https://doi.org/10.3968/7386.
Detournay, E., Richard, T., and Shepherd, M. 2008. Drilling Response of Drag Bits: Theory and Experiment. Int. J. Rock Mech. Min. 45 (8): 1347–1360. https://doi.org/10.1016/j.ijrmms.2008.01.010.
Dupriest, F. E. and Koederitz, W. L. 2005. Maximizing Drill Rates with Real-Time Surveillance of Mechanical Specific Energy. Presented at the SPE/IADC Drilling Conference, Amsterdam, 23–25 February. SPE-92194-MS. https://doi.org/10.2118/92194-MS.
Eberhart, R. and Kennedy, J. 1995. A New Optimizer Using Particle Swarm Theory. In MHS’95, Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, 4–6 October, 39–43, IEEE. https://doi.org/10.1109/MHS.1995.494215.
Gandelman, R. A. 2012. Prediçao da ROP e otimizaçao em tempo real de parâmetros operacionais na perfuraçao de poços de petróleo offshore. PhD dissertation, Federal University of Rio de Janeiro, Brazil.
Hegde, C. and Gray, K. E. 2017. Use of Machine Learning and Data Analytics to Increase Drilling Efficiency for Nearby Wells. J. Nat. Gas Sci. Eng. 40 (April): 327–335. https://doi.org/10.1016/j.jngse.2017.02.019.
Hegde, C., Daigle, H., Millwater, H. et al. 2017. Analysis of Rate of Penetration (ROP) Prediction in Drilling Using Physics-Based and Data-Driven Models. J. Pet. Sci. Eng. 159 (November): 295–306. https://doi.org/10.1016/j.petrol.2017.09.020.
Hegde, C., Wallace, S., and Gray, K. 2015a. Using Trees, Bagging, and Random Forests to Predict Rate of Penetration During Drilling. Presented at the SPE Middle East Intelligent Oil and Gas Conference and Exhibition, Abu Dhabi, 15–16 September. SPE-176792-MS. https://doi.org/10.2118/176792-MS.
Hegde, C. M., Wallace, S. P., and Gray, K. E. 2015b. Use of Regression and Bootstrapping in Drilling Inference and Prediction. Presented at the SPE Middle East Intelligent Oil and Gas Conference and Exhibition, Abu Dhabi, 15–16 September. SPE-176791-MS. https://doi.org/10.2118/176791-MS.
Iman, R. L. 2008. Latin Hypercube Sampling. In Encyclopedia of Quantitative Risk Analysis and Assessment, ed. E. L. Melnick and B. S. Everitt, Vol. 3. Hoboken, New Jersey: John Wiley & Sons.
James, G., Witten, D., Hastie, T. et al. 2013. An Introduction to Statistical Learning, Vol. 112. Springer.
Jones, E., Oliphant, E., Peterson, P. et al. 2001. SciPy: Open Source Scientific Tools for Python, https://www.scipy.org/ (accessed 18 November 2017).
Kaiser, M. J. 2009. Modeling the Time and Cost to Drill an Offshore Well. Energy 34 (9): 1097–1112. https://doi.org/10.1016/j.energy.2009.02.017.
Luke, S. 2009. Essentials of Metaheuristics, Vol. 113. Lulu. http://cs.gmu.ed/~sean/book/metaheuristics.
Lummus, J. L. 1969. Factors To Be Considered in Drilling Optimization. J Can Pet Technol 8 (4): 138–146. PETSOC-69-04-02. https://doi.org/10.2118/69-04-02.
Nelder, J. A. and Mead, R. 1965. A Simplex Method for Function Minimization. Comput. J. 7 (4): 308–313. https://doi.org/10.1093/comjnl/7.4.308.
Self, R. V., Atashnezhad, A., and Hareland, G. 2016. Use of a Swarm Algorithm to Reduce the Drilling Time Through Measurable Improvement in Rate of Penetration. Presented at the 50th US Rock Mechanics/Geomechanics Symposium, Houston, 26–29 June. ARMA-2016-456.
Soares, C., Daigle, H., and Gray, K. 2016. Evaluation of PDC Bit ROP Models and the Effect of Rock Strength on Model Coefficients. J. Nat. Gas Sci. Eng. 34 (August): 1225–1236. https://doi.org/10.1016/j.jngse.2016.08.012.
Storn, R. and Price, K. 1997. Differential Evolution—A Simple and Efficient Heuristic for global Optimization Over Continuous Spaces. J. Global Optim. 11 (4): 341–359. https://doi.org/10.1023/A:1008202821328.
Teale, R. 1965. The Concept of Specific Energy in Rock Drilling. Int. J. Rock Mech. Min. 2 (1): 57–73. https://doi.org/10.1016/0148-9062(65)90022-7.
Theloy, C. 2014. Integration of Geological and Technological Factors Influencing Production in the Bakken Play, Williston Basin. PhD dissertation, Colorado School of Mines, Golden, Colorado.
Wallace, S. P., Hegde, C. M., and Gray, K. E. 2015. System for Real Time Drilling Performance Optimization and Automation Based on Statistical Learning Methods. Presented at SPE Middle East Intelligent Oil & Gas Conference & Exhibition, Abu Dhabi, 15–16 September. SPE-176804-MS. https://doi.org/10.2118/176804-MS.
Yi, P., Kumar, A., and Samuel, R. 2014. Realtime Rate of Penetration Optimization Using the Shuffled Frog Leaping Algorithm. J. Energy Resour. Technol. 137 (3): 032902. https://doi.org/10.1115/1.4028696.