To achieve carbon dioxide (CO₂) storage through enhanced oil recovery, accurate forecasting of CO₂ subsurface storage and cumulative oil production is essential. This study develops hybrid predictive models for the determination of CO₂ storage mass and cumulative oil production in unconventional reservoirs. It does so with two multi-layer perceptron neural networks (MLPNN) and a least-squares support vector machine (LSSVM), hybridized with grey wolf optimization (GWO) and/or particle swarm optimization (PSO). Large, simulated datasets were divided into training (70%) and testing (30%) groups, with normalization applied to both groups. Mahalanobis distance identifies/eliminates outliers in the training subset only. A non-dominated sorting genetic algorithm (NSGA-Ⅱ) combined with LSSVM selected seven influential features from the nine available input parameters: reservoir depth, porosity, permeability, thickness, bottom-hole pressure, area, CO₂ injection rate, residual oil saturation to gas flooding, and residual oil saturation to water flooding. Predictive models were developed and tested, with performance evaluated with an overfitting index (OFI), scoring analysis, and partial dependence plots (PDP), during training and independent testing to enhance model focus and effectiveness. The LSSVM-GWO model generated the lowest root mean square error (RMSE) values (0.4052 MMT for CO₂ storage and 9.7392 MMbbl for cumulative oil production) in the training group. That trained model also exhibited excellent generalization and minimal overfitting when applied to the testing group (RMSE of 0.6224 MMT for CO₂ storage and 12.5143 MMbbl for cumulative oil production). PDP analysis revealed that the input features “area” and “porosity” had the most influence on the LSSVM-GWO model's prediction performance. This paper presents a new hybrid modeling approach that achieves accurate forecasting of CO₂ subsurface storage and cumulative oil production. It also establishes a new standard for such forecasting, which can lead to the development of more effective and sustainable solutions for oil recovery.

With the growth of deep drilling and the complexity of the well profile, the requirements for a more complete and efficient exploitation of productive formations increase, which increases the risk of various complications. Currently, reagents based on modified natural polymers (which are naturally occurring compounds) and synthetic polymers (SPs) which are polymeric compounds created industrially, are widely used to prevent emerging complications in the drilling process. However, compared to modified natural polymers, SPs form a family of high-molecular-weight compounds that are fully synthesized by undergoing chemical polymerization reactions. SPs provide substantial flexibility in their design. Moreover, their size and chemical composition can be adjusted to provide properties for nearly all the functional objectives of drilling fluids. They can be classified based on chemical ingredients, type of reaction, and their responses to heating. However, some of SPs, due to their structural characteristics, have a high cost, a poor temperature and salt resistance in drilling fluids, and degradation begins when the temperature reaches 130 ℃. These drawbacks prevent SP use in some medium and deep wells. Thus, this review addresses the historical development, the characteristics, manufacturing methods, classification, and the applications of SPs in drilling fluids. The contributions of SPs as additives to drilling fluids to enhance rheology, filtrate generation, carrying of cuttings, fluid lubricity, and clay/shale stability are explained in detail. The mechanisms, impacts, and advances achieved when SPs are added to drilling fluids are also described. The typical challenges encountered by SPs when deployed in drilling fluids and their advantages and drawbacks are also discussed. Economic issues also impact the applications of SPs in drilling fluids. Consequently, the cost of the most relevant SPs, and the monomers used in their synthesis, are assessed. Environmental impacts of SPs when deployed in drilling fluids, and their manufacturing processes are identified, together with advances in SP-treatment methods aimed at reducing those impacts. Recommendations for required future research addressing SP property and performance gaps are provided.

Carbonate reservoirs generally achieved relatively low primary resource recovery rates. It is therefore often necessary to clean those reservoirs up and/or stimulate them post drilling and later in their production life. A common and basic carbonate reservoir cleanup technique to remove contaminating material from the wellbore is acidizing. The efficiency of acid treatments is determined by many factors, including: the type and quantity of the acid used; the number of repeated treatments performed, heterogeneity of the reservoir, water cut of the reservoir fluids, and presence of idle zones and interlayers. Post-treatment production performance of such reservoirs frequently does not meet design expectations. There is therefore much scope to improve acidizing technologies and treatment designs to make them more reliable and effective. This review considers acid treatment technologies applied to carbonate reservoirs at the laboratory scale and in field-scale applications. The range of acid treatment techniques commonly applied are compared. Differences between specific acid treatments, such as foamed acids, acid emulsions, gelled and thickened acid systems, targeted acid treatments, and acid hydraulic fracturing are described in terms of the positive and negative influences they have on carbonate oil production rates and recovery. Opportunities to improve acid treatment techniques are identified, particularly those involving the deployment of nanoparticles (NPs). Due consideration is also given to the potential environmental impacts associated with carbonate reservoir acid treatment. Recommendations are made regarding the future research required to overcome the remaining challenges pertaining to acid treatment applications.