Maleki Mehdi, Dehghani Mohammad Rasool, Akbari Ali, Kazemzadeh Yousef, Ranjbar Ali
Department of Petroleum Engineering, Faculty of Petroleum, Gas, and Petrochemical Engineering, Persian Gulf University, Bushehr, Iran.
Persian Gulf University-Northeast Petroleum University of China Joint Research Laboratory, Oil and Gas Research Center, Persian Gulf University, Bushehr, Iran.
Heliyon. 2024 Sep 30;10(19):e38679. doi: 10.1016/j.heliyon.2024.e38679. eCollection 2024 Oct 15.
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.
减少由二氧化碳（CO）及其他温室气体（GHG）的生产或使用所造成的环境影响，近来已引起科学界、研究界及工业界的关注。在此背景下，石油生产及强化采油（EOR）也聚焦于采用环境友好型方法。CO作为一种在减少有害环境影响及防止其释放到大气中的重要气体，已得到研究。这种气体与甲烷（CH）和氮气（N）一道，被视为一种“缓冲气”。鉴于氢气（H）被认为是一种绿色且环境友好的气体，其用于改变润湿性（接触角（CA）和界面张力（IFT））的储存近来已成为一个引人关注的话题。本研究考察了H如何能在EOR系统中用作一种新型缓冲气。在本研究中，已对H及其储存在其他缓冲气存在时改变润湿性方面的作用进行了研究。还使用机器学习（ML）模型比较了H与其他气体在改变CA和IFT方面的性能。在此过程中，ML和实验数据被用于预测和报告IFT和CA的值。所使用的数据经过了统计和定量预处理、处理、评估及验证，去除了异常值和偏态数据。随后，在训练和测试数据上实施了诸如随机森林（RF）、随机树和LSBoost等ML模型。在对IFT和CA进行建模和预测的这个过程中，使用贝叶斯算法和随机搜索（RS）方法对超参数进行了优化。最后，对建模的结果和性能进行了评估，采用贝叶斯优化的LSBoost建模方法报告的IFT的R值为0.998614，CA的R值为0.986999。
