The precise determination of minimum miscible pressure is of great importance for CO₂ enhanced oil recovery and storage as it directly influences the efficiency of pore-scale oil displacement and CO₂ trapping. In this study, an interpretable machine learning framework is developed, enabling the reliable evaluation of nano-confined minimum miscible pressure. Four machine learning algorithms (Random Forest, Multi-layer Perceptron, Support Vector Regression, and eXtreme Gradient Boosting) are employed to accurately predict the nano-confined minimum miscible pressure of a CO₂-oil system. The results demonstrate that, excluding support vector regression, the determination coefficients for all models surpass 94%, signifying the robust predictive performance of our model. Subsequently, Shapley Additive exPlanations is used to analyze the feature importance ranking and the impact of each input feature on minimum miscible pressure in these models. Based on the interpretation results, our multi-layer perceptron model is superior in mining the input-output relationship and reflecting the petrophysical laws, rendering it highly suitable for predicting the minimum miscible pressure while considering nano-confinement. In addition, it is found that pore size significantly influences minimum miscible pressure prediction and that minimum miscible pressure decreases with decreasing pore size when the pore size is ≤75 nm. Single-factor sensitivity analysis is applied to validate the trend patterns between input features and minimum miscible pressure in the multi-layer perceptron model.

Enhanced oil recovery draws increasingly interests from the research and development phases to oilfield implementation worldwide. Due to the complexity of the developed reservoirs and requirement of carbon footprint reduction, new innovations are urgently needed to increase enhanced oil recovery efficiency and/or reduce emissions simultaneously. This paper presents the strategies to improve the enhanced oil recovery performance of carbon dioxide flooding, polymer flooding and imbibition in complex reservoirs. Field trials conducted at Mahu reservoirs demonstrated the potential of nanoemulsion imbibition in stimulating tight oil recovery. These results can provide constructive envision for the development and application of enhanced oil recovery technologies for challenging systems.

Despite the promising results obtained from the utilization of interfacial-active additives in enhancing imbibition-based oil recovery from tight reservoirs, the predominant mechanisms governing this process remain inadequately understood. In this work, a meticulously designed workflow is implemented to conduct experiments and modeling focusing on imbibition tests performed on tight sandstone cores while utilizing surfactant and microemulsion. Our primary objective is to investigate the response of oil recovery to these additives and to develop a robust and reliable model that incorporates the intricate interactions, thereby elucidating the underlying mechanisms. Two imbibition fluids are designed, namely, surfactant and microemulsion. A comprehensive investigation is performed to analyze the physicochemical properties of these fluids, encompassing phase behavior, density, viscosity, and wettability alteration, with the aim of establishing fundamental knowledge in the field. Three imbibition tests are carried out to observe the response of oil production and optimize the experimental methodology. A numerical model is developed that fully couples the evolution of relative permeability and capillary pressure with the dynamic processes of emulsification, solubilization and molecular diffusion. The results demonstrate the crucial role of emulsification/solubilization in the imbibition process.

Lignosulfonate, a byproduct of the pulp and paper industry, has been used in the oil-well drilling industry for a significant amount of time. Lignosulfonate and its derivatives serve different roles in the oil-well drilling industry because of their unique structures and properties. This review summarizes lignosulfonate and its derivatives, including lignosulfonate complexed with metal ions, lignosulfonate graft copolymers, lignosulfonate-tannin complexes, and other lignosulfonate-containing composites, in terms of their preparation, properties, and potential applications in oil-well drilling industry. It provides readers with a quick review of existing studies in this area and some inspirations for future studies pertaining to the utilization of lignosulfonate-based materials in the oil-well drilling industry.