Abstract—Various basins in the world comprises of areas with abnormal pore-fluid pressures (higher or lower than normal hydrostatic pressure). Undesirably, predicting pore pressure parameters (depth, extension, magnitude, etc.) in such areas are challenging tasks. The compression seismic travel time converted into sonic logs (DT) is often used as a predictor because it responds to changes in porosity or compaction produced by abnormal pore-fluid pressures. The objective of the paper is to propose a model using an artificial neural network (ANN) to synthetically create wire line logs (sonic logs (DT), Density logs and Resistivity Logs (RIED) by identifying the mathematical dependency between Seismic Travel time and wire line logs of neighboring wells. A neighboring well will be used as a training well to enable the system to learn the relationship among the predictors. Once the system has trained and learnt the relationship, the model will be used to predict the next well’s pore pressure position and magnitude, using only seismic travel time logs.
Index Terms—Abnormal Pore Pressure, artificial neural network (Ann), density log, resistivity log (Reid), seismic travel time, sonic log (DT).
Haravinthan A., Ayob M. R., and Salleh S. are with Faculty of Mechanical Engineering, Universiti Teknikal Malaysia, Melaka (e-mail: email@example.com, firstname.lastname@example.org, email@example.com).
Japper-Jaafar A. is with the Mechanical Engineering Faculty, Universiti Teknologi Petronas Tronoh, Malaysia (e-mail: firstname.lastname@example.org).
Cite: Haravinthan A., Ayob M. R., Salleh S., and Japper-Jaafar A., "A Review on Prediction of Abnormal Geo-Pressure via Seismic Travel Time and Wire Line Log Correlation Modeling Using Neural Network," International Journal of Information and Electronics Engineering vol. 1, no. 3, pp. 195-199, 2011.