Data-driven models to predict shale wettability for CO2 sequestration applications

Abstract

The significance of CO2 wetting behavior in shale formations has been emphasized in various CO2 sequestration applications. Traditional laboratory experimental techniques used to assess shale wettability are complex and time-consuming. To overcome these limitations, the study proposes the use of machine learning (ML); artificial neural networks (ANN), support vector machines (SVM), and adaptive neuro-fuzzy inference systems (ANFIS) tools to estimate the contact angle, a key indicator of shale wettability, providing a more efficient alternative to conventional laboratory methods. A dataset comprising various shale samples under different conditions was collected to predict shale-water-CO2 wettability by considering shale properties, operating pressure and temperature, and brine salinity. Pearson’s correlation coefficient (R) was utilized to assess the linearity between the contact angle (CA) value and other input parameters. Initial data analysis showed that the elements affecting the shale wettability are primarily reliant on the pressure and temperature at which it operates, the total organic content (TOC), and the mineral composition of the rock. Between the different ML models, the artificial neural network (ANN) model performed the best, achieving a training R2 of 0.99, testing R2 of 0.98 and a validation R2 of 0.96, with an RMSE below 5. The adaptive neuro-fuzzy inference system (ANFIS) model also accurately predicted the contact angle, obtaining a training R2 of 0.99, testing R2 of 0.97 and a validation R2 of 0.95. Conversely, the support vector machine (SVM) model displayed signs of overfitting, as it achieved R2 values of 0.99 in the training dataset, which decreased to 0.94 in the testing dataset, and 0.88 in the validation dataset. To avoid rerunning the ML models, an empirical correlation was developed based on the optimized weights and biases obtained from the ANN model to predict contact angle values using input parameters and the validation data set revealed R2 of 0.96. The parametric study showed that, among the factors influencing shale wettability at a constant TOC, pressure had the most significant impact, and the dependency of the contact angle on pressure increased when TOC values were high.

An Experimental Study and Machine Learning Modeling of Shale Swelling in Extended Reach Wells When Exposed to Diverse Water-Based Drilling Fluids

“…Lalji et al developed a model that utilizes a multiple degree’s polynomial function to estimate the linear swelling. Ibrahim and Elkatatny , used ML techniques to predict the contact angle of shale, which is an indirect indication of shale wettability and swelling. In our previous study, we employed ML techniques to predict the linear swelling of shale over a limited time of 24 h when exposed to various WBDFs.…”