Application of Artificial Intelligence Techniques to Estimate the Static Poisson's Ratio Based on Wireline Log Data

Static Poisson's ratio (nu(static)) is a key factor in determine the in-situ stresses in the reservoir section. nu(static) is used to calculate the minimum horizontal stress which will affect the design of the optimum mud widow and the density of cement slurry while drilling. In addition, it also affects the design of the casing setting depth. nu(static) is very important for field development and the incorrect estimation of it may lead to heavy investment decisions. nu(static) measurements of nu(static) will take long time and also will increase the overall cost. The goal of this study is to develop accurate models for predicting nu(static) for carbonate reservoirs based on wireline log data using artificial intelligence (AI) techniques. More than 610 core and log data points from carbonate reservoirs were used to train and validate the AI models. The more accurate AI model will be used to generate a new correlation for calculating the nu(static). The developed artificial neural network (ANN) model yielded more accurate results for estimating nu(static) based on log data; sonic travel times and bulk density compared to adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) methods. The developed empirical equation for nu(static) gave a coefficient of determination (R-2) of 0.97 and an average absolute percentage error (AAPE) of 1.13%. The developed technique will help geomechanical engineers to estimate a complete trend of nu(static) without the need for coring and laboratory work and hence will reduce the overall cost of the well.