Pore Pressure Prediction While Drilling Using Fuzzy Logic

Abstract

Abstract Formation pressure is the main function that affects drilling operation economically and efficiently. Knowing the pore pressure and the parameters that affect it will help to reduce the cost of the drilling process. Many empirical models reported in the literature were used to calculate the formation pressure based on different parameters. Some of these models used only drilling parameters to estimate pore pressure. Other models predicted the formation pressure based on log data. All of these models required different trends such as normal or abnormal to predict the pore pressure. Few researchers applied artificial intelligence (AI) techniques to predict the formation pressure by only one method or a maximum of two methods of AI. The objective of this research is to predict the pore pressure based on both drilling parameters and log data namely; weight on bit (WOB), rotary speed (RPM), rate of penetration (ROP), mud weight (MW), bulk density (RHOB), porosity (ϕ) and compressional time (Δt). A real field data is used to predict the formation pressure using Fuzzy Logic (FL) which is one technique of AI. Fuzzy Logic (FL) tool was compared with different empirical models. FL method estimated the formation pressure with a high accuracy (high correlation coefficient (R) of 0.998 and low average absolute percentage error (AAPE) of 0.234%). FL outperformed all previously published models. The advantage of the new technique is its simplicity, which represented from its estimation of pore pressure without the need of different trends as compared to other models which require a two different trend (normal or abnormal pressure).