Investigation of wettability and IFT alteration during hydrogen storage using machine learning.

Reducing the environmental impact caused by the production or use of carbon dioxide (CO) and other greenhouse gases (GHG) has recently attracted the attention of scientific, research, and industrial communities. In this context, oil production and enhanced oil recovery (EOR) have also focused on using environmentally friendly methods. CO has been studied as a significant gas in reducing harmful environmental effects and preventing its release into the atmosphere. This gas, along with methane (CH) and nitrogen (N), is recognized as a 'cushion gas'. Given that hydrogen (H) is considered a green and environmentally friendly gas, its storage for altering wettability (contact angle (CA) and interfacial tension (IFT)) has recently become an intriguing topic. This study examines how H can be utilized as a novel cushion gas in EOR systems. In this research, the role of H and its storage in altering wettability in the presence of other cushion gases has been investigated. The performance of H in changing the CA and IFT with other gases has also been compared using machine learning (ML) models. During this process, ML and experimental data were used to predict and report the values of IFT and CA. The data used underwent statistical and quantitative preprocessing, processing, evaluation, and validation, with outliers and skewed data removed. Subsequently, ML models such as Random Forest (RF), Random Tree, and LSBoost were implemented on training and testing data. During this process of modeling and predicting IFT and CA, the hyperparameters were optimized using Bayesian algorithms and random search (RS) methods. Finally, the results and performance of the modeling were evaluated, with the LSBoost modeling method using Bayesian optimization reporting R values of 0.998614 for IFT and 0.986999 for CA.