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

Static Poisson's ratio (ν_static) is a key factor in determine the in-situ stresses in the reservoir section. ν_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. ν_static is very important for field development and the incorrect estimation of it may lead to heavy investment decisions. ν_static can be measured in the lab using a real reservoir cores. The laboratory measurements of ν_static will take long time and also will increase the overall cost. The goal of this study is to develop accurate models for predicting ν_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 ν_static. The developed artificial neural network (ANN) model yielded more accurate results for estimating ν_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 ν_static gave a coefficient of determination (R²) 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 ν_static without the need for coring and laboratory work and hence will reduce the overall cost of the well.