Ahmed S., Abdulmalek (King Fahd University of Petroleum & Minerals) | Mahmoud, Ahmed Abdulhamid (King Fahd University of Petroleum & Minerals) | Elkatatny, Salaheldin (King Fahd University of Petroleum & Minerals) | Mahmoud, Mohamed (King Fahd University of Petroleum & Minerals) | Abdulraheem, Abdulazeez (King Fahd University of Petroleum & Minerals)
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.