The Role of Machine Learning in Drilling Operations; A Review
- Christine I. Noshi (Texas A&M university) | Jerome J. Schubert (Texas A&M university)
- Document ID
- Society of Petroleum Engineers
- SPE/AAPG Eastern Regional Meeting, 7-11 October, Pittsburgh, Pennsylvania, USA
- Publication Date
- Document Type
- Conference Paper
- 2018. Society of Petroleum Engineers
- 6.3.3 Operational Safety, 7.6 Information Management and Systems, 6.3 Safety, 7 Management and Information, 7.6.6 Artificial Intelligence, 1.2.2 Drilling Optimisation, 1.6.3 Drilling Optimisation, 1.7 Pressure Management, 7.6.4 Data Mining, 1.7.5 Well Control, 3 Production and Well Operations, 1.6 Drilling Operations
- Data Analytics, Big Data, Drilling Real Time, Statistical Analysis, Data Mining Supervised
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Drilling problems such as stick slip vibration/hole cleaning, pipe failures, loss of circulation, BHA whirl, stuck pipe incidents, excessive torque and drag, low ROP, bit wear, formation damage and borehole instability, and the drilling of highly tortuous wells have only been tackled using physics-based models. Despite the mammoth generation of real-time metadata, there is a tremendous gap between statistical based models and empirical, mathematical, and physical-based models. Data mining techniques have made prominent contributions across a broad spectrum of industries. Its value is widely appreciated in a variety of applications, but its potential has not been fully tapped in the oil and gas industry. This paper presents a review compiling several years of Data Analytics applications in the drilling operations. This review discusses the benefits, deficiencies of the present practices, challenges, and novel applications under development to overcome industry deficiencies. This study encompasses a comprehensive compilation of data mining algorithms and industry applications from a predictive analytics standpoint using supervised and unsupervised advanced analytics algorithms to identify hidden patterns and help mitigate drilling challenges.
Traditional data preparation and analysis methods are not sufficiently capable of rapid information extraction and clear visualization of big complicated data sets. Due to the petroleum industry's unfulfilled demand, Machine Learning (ML)-assisted industry workflow in the fields of drilling optimization and real time parameter analysis and mitigation is presented.
This paper summarizes data analytics case studies, workflows, and lessons learnt that would allow field personnel, engineers, and management to quickly interpret trends, detect failure patterns in operations, diagnose problems, and execute remedial actions to monitor and safeguard operations. The presence of such a comprehensive workflow can minimize tool failure, save millions in replacement costs and maintenance, NPV, lost production, minimize industry bias, and drive intelligent business decisions. This study will identify areas of improvement and opportunities to mitigate malpractices. Data exploitation via the proposed platform is based on well-established ML and data mining algorithms in computer sciences and statistical literature. This approach enables safe operations and handling of extremely large data bases, hence, facilitating tough decision-making processes.
|File Size||1 MB||Number of Pages||16|
Ahmadi, M. A. Towards Reliable Model for Prediction Drilling Fluid Density at Wellbore Conditions: A LSSVM Model. Neurocomputing 211: 143–149. https://doi.org/10.1016/j.neucom.2016.01.106.
Al-Yami, A. S., Al-Shaarari, A., Al-Bahrani, H.. 2016. Using Bayesian Network to Develop Drilling Expert Systems. Presented at the SPE Heavy Oil Conference and Exhibition, Kuwait City, Kuwait, 6–8 December. SPE-184168-MS. https://doi.org/10.2118/184168-MS.
Anno, P.D., Pham, S., Ramsay, S.C. 2016. Big Drilling Data Analytics Engine https://www.Google.Com/Patents/US20160333673, Google Patents.
Ambrus, A., Pournazari, P., Ashok, P.. 2015. Overcoming Barriers to Adoption of Drilling Automation: Moving Towards Automated Well Manufacturing. Presented at the SPE/IADC Drilling Conference and Exhibition, London, England, UK, 17–19 March. SPE-173164-MS. http://dx.doi.org/10.2118/173164-MS.
Bakshi, A., Uniacke, E., Korjani, M.. 2017. A Novel Adaptive Non-Linear Regression Method to Predict Shale Oil Well Performance Based on Well Completions and Fracturing Data. Presented at the SPE Western Regional Meeting, Bakersfield, California, USA, 23–27 April. SPE-185695-MS. https://doi.org/10.2118/185695-MS.
Bello, O., Teodoriu, C., Yaqoob, T.. 2016. Application of Artificial Intelligence Techniques in Drilling System Design and Operations: A State of the Art Review and Future Research Pathways. Presented at the SPE Nigeria Annual International Conference and Exhibition, Lagos, Nigeria, 2–4 August. SPE-184320-MS. https://doi.org/10.2118/184320-MS.
Bhandari, J., Abbassi, R., Garaniya, V.. 2015. Risk Analysis of Deepwater Drilling Operations Using Bayesian Network. Journal of Loss Prevention in the Process Industry 38:11–23. https://doi.org/10.1016/j.jlp.2015.08.004.
Bian, X. Q., Han, B., Du, Z. M.. 2016. Integrating Support Vector Regression with Genetic Algorithm for CO2-Oil Minimum Miscibility Pressure (MMP) in Pure and Impure CO2 Streams. Fuel 182: 550–557. https://doi.org/10.1016/j.fuel.2016.05.124.
Bilgesu, H., Cox, Z. D., Elshehabi, T. A.. 2017. A Real-Time Interactive Drill-Off Test Utilizing Artificial Intelligence Algorithm for DSATS Drilling Automation University Competition. Presented at the SPE Western Regional Meeting, Bakersfield, California, USA, 23–27 April. SPE-185730-MS. https://doi.org/10.2118/185730-MS.
Cai, B., Liu, Y., Liu, Z.. 2012. Using Bayesian Networks in Reliability Evaluation for Subsea Blowout Preventer Control Systems. Reliability Engineering & System Safety 108: 32–41. https://doi.org/10.1016/j.ress.2012.07.006.
Castiñeira, D., Toronyi, R., Saleri, N.. 2018. Machine Learning and Natural Language Processing for Automated Analysis of Drilling and Completion Data. Presented at the SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition, Dammam, Saudi Arabia, 23–26 April. SPE-192280-MS. https://doi.org/10.2118/192280-MS.
Chang, Y., Chen, G., Wu, X.. 2018. Failure Probability Analysis for Emergency Disconnect of Deepwater Drilling Riser Using Bayesian Network. Journal of Loss Prevention in the Process Industry 51: 42–53. https://doi.org/10.1016/j.jlp.2017.11.005.
Dursun, S., Tuna, T., Duman, K.. 2015. Real-Time Risk Prediction During Drilling Operations. https://www.google.com/patents/WO2015060865A1?cl=en Google Patents.
Dunlop, J., Isangulov, R., Aldred, W. D.. 2011. Increased Rate of Penetration Through Automation. Presented at the SPE/IADC Drilling Conference and Exhibition, Amsterdam, The Netherlands, 1–3 March. SPE-139897-MS. http://dx.doi.org/10.2118/139897-MS.
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, The Netherlands, 23–25 February. SPE-92194-MS. http://dx.doi.org/10.2118/92194-MS.
Fatehi, M. and Asadi, H. H. 2017. Data Integration Modeling applied to Drill Hole Planning Through Semi-Supervised Learning: A case study from the Dalli Cu-Au porphyry Deposit in Central Iran. Journal of African Earth Sciences 128: 147–160. https://doi.org/10.1016/j.jafrearsci.2016.09.007.
Goebel, T., Molina, R.V., Vilalta, R.. 2014. Method and System for Predicting a Drill String Stuck Pipe Event. https://www.google.com/patents/US8752648 Google Patents.
Hartmann, A., Akimov, O.N., Baule, A.. 2009. System and Method for Real-Time Quality Control for Downhole Logging Devices. https://www.google.com/patents/US20090177404 Google Patents.
Hegde, C. and Gray, K.E. 2017. Use of Machine Learning and Data Analytics to Increase Drilling Efficiency for Nearby Wells. Journal of Natural Gas Science and Engineering 40: 327–335. https://doi.org/10.1016/j.jngse.2017.02.019.
Hernandez, C.A., Johnston, R.E., Rettew, K.M.. 2017. Systems and Methods for Alerting of Abnormal Drilling Conditions. https://www.google.com/patents/US20170241252.
Hoffimann, J., Mao, Y., Wesley, A.. 2017. Sequence Mining and Pattern Analysis in Drilling Reports With Deep Natural Language Processing. https://arxiv.org/pdf/1712.01476.pdf.
Kormaksson, M., Vieira, M. R., Zadrozny, B. 2015. A Data Driven Method for Sweet Spot Identification in Shale Plays Using Well Log Data. Presented at the SPE Digital Energy Conference and Exhibition, The Woodlands, Texas, USA, 3–5 March. SPE-173455-MS. https://doi.org/10.2118/173455-MS.
Lind Y. B. 2012. Parallel Computations in Drilling Process. Journal of Computational Science 8 (6): 456 –459. https://doi.org/10.1016/j.jocs.2012.08.007.
Lind, Y. B. and Kabirova, A. R. 2014. Artificial Neural Networks in Drilling Troubles Prediction. Presented at the SPE Russian Oil and Gas Exploration and Production Technical Conference and Exhibition held in Moscow, Russia, 14–16 October. SPE-171274-MS. https://doi.org/10.2118/171274-MS.
Ludvigsen, H., Seddiki, A. 2015. Planning Drilling Operations Using Models and Rig Market Databases. https://patents.justia.com/patent/9934481.
Lundh, R., Joyce, S., Mansouri, M.. 2017. Method and System for Assigning Tasks to Drill Rigs. https://www.google.com/patents/WO2017058089A1?cl=en Google Patents.
Macpherson, J. D., de Wardt, J. P., Florence, F.. 2013. Drilling Systems Automation: Current State, Initiatives and Potential Impact. Presented at the SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, USA, 30 September–2 October. SPE-166263-MS. https://doi.org/10.2118/166263-MS.
Marx, T., Reid, G.W., Leung, H.. 2014. Methods And Systems for Improved Drilling Operations Using Real-Time and Historical Drilling Data. https://www.google.com/patents/US20140116776 Google Patents.
Mohaghegh, S.D. 2015. System and Method Providing Real-Time Assistance to Drilling Operation. https://patents.google.com/patent/US20150300151.
Morgan, S. 2016. Cybercrime Damages $6 Trillion By 2021. https://cybersecurityventures.com/hackerpocalypse-cybercrime-report-2016. (accessed 23 June 2018).
Ponemon I.LLC. 2017. The state of cybersecurity in the oil & gas industry: United States, https://ics-cert.us-cert.gov/#monitornewsletters.
Pollock, J., Stoecker-Sylvia, Z., Veedu, V.. 2018. Machine Learning for Improved Directional Drilling. Presented at The Offshore Technology Conference, Houston, Texas, USA, 30 April – 3 May. OTC-28633-MS.https://doi.org/10.4043/28633-MS.
Saputelli, L. 2016. Technology Focus: Petroleum Data Analytics. J Pet Technol 68 (10): 66–66. SPE-1016-0066-JPT. https://doi.org/10.2118/1016-0066-JPT.
Saputelli, L., Nikolaou, M., and Economides, M. J. 2003. Self-Learning Reservoir Management. Presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, USA, 5–8 October. SPE-84064-MS. https://doi.org/10.2118/84064-MS.
Sivarajah, U., Kamal, M. M., Irani, Z.. 2017. Critical Analysis of Big Data Challenges and Analytical Methods. Journal of Buisness Research 70: 263–286. https://doi.org/10.1016/j.jbusres.2016.08.001.
Sule, I., Khan, F., Butt, S.. 2018. Kick Control Reliability Analysis of Managed Pressure Drilling Operation. Journal of Loss Prevention in the Process Industry 52: 7–20. https://doi.org/10.1016/j.jlp.2018.01.007.
Tan, M., Song, X., Yang, X.. 2015. Support-Vector-Regression Machine Technology for Total Organic Carbon Content Prediction from Wireline Logs In Organic Shale: A Comparative Study. Journal of Natural Gas Science and Engineering 26: 792–802. https://doi.org/10.1016/j.jngse.2015.07.008.
Temizel, C., Aktas, S., Kirmaci, H.. 2016. Turning Data into Knowledge: Data-Driven Surveillance and Optimization in Mature Fields. Presented at the SPE Annual Technical Conference and Exhibition, Dubai, UAE, 26–28 September. SPE-181881-MS. https://doi.org/10.2118/181881-MS.
Unrau, S., Torrione, P., Hibbard, M.. 2017. Machine Learning Algorithms Applied to Detection of Well Control Events. Presented at the SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition, Dammam, Saudi Arabia, 24–27 April. SPE-188104-MS. http://dx.doi.org/10.2118/188104-MS.
Wessling, S., Moos, D., Macpherson, J.D. 2014. System And Method For Well Data Analysis https://www.Google.Com/Patents/US20140121972, Google Patents.
Winkler, H. and Teasdale, P. 2015. Systems and Methods for Processing Drilling Data. https://www.google.com/patents/US9024778 Google Patents.
Xiao, J. and Sun, X. 2017. Big Data Analytics Drive EOR Projects. Presented at the SPE Offshore Europe Conference & Exhibition, Aberdeen, United Kingdom, 5–8 September. SPE-186159-MS. https://doi.org/10.2118/186159-MS.
Zhao, J., Shen, Y., Chen, W. 2017. Machine Learning–Based Trigger Detection of Drilling Events Based on Drilling Data. Presented at the SPE Eastern Regional Meeting, Lexington, Kentucky, USA, 4–6 October. SPE-187512-MS. https://doi.org/10.2118/187512-MS.