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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- 188 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|
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