A Machine Learning Approach to Modeling Pore Pressure

Machine Learning techniques and applications have lately gained a lot of interest in many areas, including spheres of arithmetic, finances, engineering, dialectology, and a lot more. This is owing to the upwelling of ground-breaking and sophisticated machine learning procedures to exceedingly multifaceted complications along with the prevailing advances in high speed computing. Numerous usages of Machine learning in daily life include pattern recognition, automation, data processing and analysis, and so on. The Petroleum industry is not lagging behind also. On the contrary, machine learning approaches have lately been applied to enhance production, forecast recoverable hydrocarbons, augment well placement by means of pattern recognition, optimize hydraulic fracture design, and to help in reservoir characterization. In this paper, three different machine learning models were trained and utilized to explore the feasibility of forecasting pore pressure of a well. The machine learning algorithms include, Simple Linear Regression, Decision Stump and Multilayer Perceptron (ANN). The predictive accuracies of the algorithm was analyzed using statistical measures. Five (5) parameters were utilized as input variables in the models: hydrostatic pressure, overburden pressure, observed and normal sonic velocities and pore pressure. 80% of the data was used in training while the remaining 20% was used for testing of the models. A sensitivity analysis of the five variable was conducted so as to identify correlations of the variables. Results of the sensitivity analysis revealed that both hydrostatic and overburden pressures appear to have the strongest correlation with pore pressure (0.766) and closely followed by normal compacted sonic velocity (0.753). Meanwhile, observed sonic velocity has the least correlation (0.046).The models were appraised by determining their Relative Absolute Errors. Results indicate that Multilayer Perceptron has the best prediction and least Relative Absolute Error of 5.77%. While the Decision Stump model had a Relative absolute error of 54.41%. The Simple Linear Regression had a relative absolute error of 67.93%. By and large, all three models appear to be suitable for modeling pore pressure but the Multilayer Perceptron is the most accurate.