Abstract
Formation pressure is a critical formation condition that affects the efficiency and the economy of drilling operations. The knowledge of the formation pressure is significant to control the well. It will assist in avoiding problems associated with drilling operation and decreasing the cost of the drilling operation. It is essential to predict formation pressure accurately prior to the drilling process to prevent circulation loss and kick. Many methods are used to estimate the formation pressure either from log information or drilling parameters. These methods need pressure trends such as abnormal or normal to estimate the formation pressure. Only two papers used artificial intelligence (AI) to estimate the formation pressure by applying only two AI techniques. In this paper, actual field measurements such as log data (formation density (RHOB), porosity (ϕ), and compressional time (Δt)) and drilling parameters (weight on bit (WOB), rotary speed (RPM), penetration rate (ROP), mud density (MW)) were utilized by five techniques of AI to predict the pore pressure. These AI techniques are artificial neural networks (ANN), radial basis function (RBF), fuzzy logic (FL), support vector machine (SVM), and functional networks (FN). In addition, comparative analyses were performed to determine the best combination of AI technique to predict the pore pressure with high accuracy. All artificial intelligence techniques predicted the formation pressure with high performance. In addition, the developed AI methods can be used to estimate the formation pressure without the need for normal pressure trends. The comparative analysis results show that support vector machine has the feature of formation pressure estimation by its short time prediction and high accuracy (a coefficient of determination of 0.995 and an average percentage error of 0.14%). It is recommended to apply SVM to predict the pore pressure in field operations.
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