Author:
Ibrahim Ahmed Farid,Elkatatny Salaheldin
Abstract
AbstractThe 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.
Funder
Deanship of Research Oversight and Coordination (DROC) at King Fahd University of Petroleum & Minerals
Publisher
Springer Science and Business Media LLC
Reference43 articles.
1. Espinoza, D. N. & Santamarina, J. C. Water-CO2-mineral systems: Interfacial tension, contact angle, and diffusion—Implications to CO2 geological storage. Water Resour. Res. 46(7), 8634 (2010).
2. Kaveh, N. S., Barnhoorn, A. & Wolf, K.-H. Wettability evaluation of silty shale caprocks for CO2 storage. Int. J. Greenhouse Gas Control 49, 425–435 (2016).
3. Ksiezniak, K., Rogala, A. & Hupka, J. Wettability of shale rock as an indicator of fracturing fluid composition. Physicochem. Probl. Miner. Process. 51(1), 315–323 (2015).
4. Iglauer, S. et al. CO2 wettability of caprocks: Implications for structural storage capacity and containment security. Geophys. Res. Lett. 42(21), 9279–9284 (2015).
5. Chiquet, P., Broseta, D. & Thibeau, S. Wettability alteration of caprock minerals by carbon dioxide. Geofluids 7(2), 112–122 (2007).
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献