Prediction of Pore and Fracture Pressures Using Support Vector Machine
- Abdulmalek Ahmed S. (King Fahd University of Petroleum & Minerals) | Ahmed Abdulhamid Mahmoud (King Fahd University of Petroleum & Minerals) | Salaheldin Elkatatny (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
- 5.3 Reservoir Fluid Dynamics, 7 Management and Information, 7.6 Information Management and Systems, 1.6 Drilling Operations, 5 Reservoir Desciption & Dynamics, 7.6.6 Artificial Intelligence, 5.3.4 Integration of geomechanics in models
- Formation Pressure, Artificial Intelligence (AI), Support Vector Machine (SVM), Pore Pressure, Fracture Pressure
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- 264 since 2007
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Pore and fracture pressures are a critical formation condition that affects efficiency and economy of drilling operations. The knowledge of the pore and fracture pressures is significant to control the well. It will assist in avoiding problems associated with drilling operation and decreasing the cost of drilling operation. It is essential to predict pore and fracture pressures accurately prior to drilling process to prevent various issues for example fluid loss, kicks, fracture the formation, differential pipe sticking, heaving shale and blowouts.
Many models are used to estimate the pore and fracture pressures either from log information, drilling parameters or formation strengths. However, these models have some limitations such as some of the models can only be used in clean shales, applicable only for the pressure generated by under-compaction mechanism and some of them are not applicable in unloading formations. Few papers used artificial intelligence (AI) to estimate the pore and fracture pressures. In this work, a real filed data that contain the log data and real time surface drilling parameters were utilized by support vector machine (SVM) to predict the pore and fracture pressures.
Support vector machine predicted the pore and fracture pressures with a high accuracy where the coefficient of determination (R2) is greater than 0.995. In addition, it can estimate the pore pressure without the need for pressure trends and predict the fracture pressure from only the real time surface drilling parameters which are easily available.
|File Size||1 MB||Number of Pages||10|
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