Rate of Penetration Prediction in Shale Formation Using Fuzzy Logic
- Abdulmalek Ahmed S (King Fahd University of Petroleum & Minerals) | Salaheldin Elkatatny (King Fahd University of Petroleum & Minerals) | Abdulwahab Z Ali (King Fahd University of Petroleum & Minerals) | Mohamed Mahmoud (King Fahd University of Petroleum & Minerals) | Abdulazeez Abdulraheem (King Fahd University of Petroleum & Minerals)
- Document ID
- International Petroleum Technology Conference
- International Petroleum Technology Conference, 26-28 March, Beijing, China
- Publication Date
- Document Type
- Conference Paper
- 2019. International Petroleum Technology Conference
- 7.6.6 Artificial Intelligence, 7.6 Information Management and Systems, 1.11 Drilling Fluids and Materials, 7 Management and Information, 1.6 Drilling Operations
- Artificial Intelligence (AI), Fuzzy Logic (FL), Rate of penetration (ROP)
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- 132 since 2007
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Rate of Penetration (ROP) means how fast the drilling bit is drilling through the formations. It is known that in the oil and gas industry, most of the well cost is taken by the drilling operations. So, it is very crucial to drill carefully and improve the drilling processes. Nevertheless, it is hard to know the influence of every single parameter because most of the drilling parameters depend on each other, and altering an individual parameter will have an impact on the other. Due to the difficulty of the drilling operations, up to the present time, there is no dependable model that can estimate the ROP correctly. Consequently, using the artificial intelligence (AI) in the drilling is becoming more and more applicable because it can consider all the unknown parameters in building the model.
In this work, a real filed data that contain the real time surface drilling parameters and the drilling fluid properties were utilized by fuzzy logic (FL) to estimate the rate of penetration. The achieved results proved that fuzzy logic technique can be applied effectively to estimate the rate of penetration accurately with R = 0.97 and AAPE = 7.3%, which outperformed the other ROP models. The developed AI models also have the advantage of using less input parameters than the previous ROP models.
|File Size||1 MB||Number of Pages||9|
Alarifi, S., AlNuaim, S., & Abdulraheem, A., (2015, March 8). Productivity Index Prediction for Oil Horizontal Wells Using different Artificial Intelligence Techniques. Society of Petroleum Engineers. doi:10.2118/172729-MS.
Anifowose, Abdulazeez Abdulraheem. (2011, Julay 12) Fuzzy logic-driven and SVM-driven hybrid computational intelligence models applied to oil and gas reservoir characterization, In Journal of Natural Gas Science and Engineering, Volume 3, Issue 3, 2011, Pages 505–517, ISSN 1875-5100, https://doi.org/10.1016/j.jngse.2011.05.002.
Arabjamaloei & S. Shadizadeh (2011) Modeling and Optimizing Rate of Penetration Using Intelligent Systems in an Iranian Southern Oil Field (Ahwaz Oil Field), PetroleumScience and Technology, 29:16, 1637–1648, DOI: 10.1080/10916460902882818
Bilgesu, H. I., Tetrick, L. T., Altmis, U., Mohaghegh, S., & Ameri, S., (1997, January 1). A New Approach for the Prediction of Rate of Penetration (ROP) Values. Society of Petroleum Engineers. doi:10.2118/39231-MS
Cuddy, S. J., & Putnam, T. W. (1998, January 1). Litho-Facies and Permeability Prediction from Electrical Logs using Fuzzy Logic. Society of Petroleum Engineers. doi:10.2118/49470-MS.
Fuzzy Logic Toolbox., (2010). User's Guide. MathWorks, Inc, September. www.mathworks.com/help/pdf_doc/fuzzy/fuzzy.pdf.
Gharbi, Ridha B.C.; Mansoori, G. Ali. (2005, December 15). An introduction to artificial intelligence applications in petroleum exploration and production, Journal of Petroleum Science and Engineering. Retrieved November 22, 2017, from www.deepdyve.com/lp/elsevier/an-introduction-to-artificial-intelligence-applications-in-petroleum-vy98JMoCo3.
Jang, J.S.R. (1993). "ANFIS: adaptive-network-based fuzzy inference system". IEEE Transactions on Systems, Man and Cybernetics 23 (3). doi:10.1109/21.256541.
Xian Shi, Gang Liu, Xiaoling Gong, Jialin Zhang, Jian Wang, and Hongning Zhang (2016). "An Efficient Approach for Real-Time Prediction of Rate of Penetration in Offshore Drilling," Mathematical Problems in Engineering, vol. 2016, Article ID 3575380, 13 pages,. doi:10.1155/2016/357538.