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• W4285008262 endingPage "138022" @default.
• W4285008262 startingPage "138022" @default.
• W4285008262 abstract "Surfactants could react with adsorbates on carbonate rock surface to alter wettability from oil-wetness to water-wetness, which is effective to enhance oil recovery. Surfactant huff-puff treatment is mostly applied for this purpose and the resulting surfactant performance is the outcome of complex interfacial processes. Currently, the effect of important parameters on surfactant performance is not completely reported and the contribution of each parameter to surfactant performance is hard to be quantified. Traditional methods to optimize surfactant performance are time-consuming and show strong dependency on extensive experiments. In this paper, we address these problems from machine learning (ML) perspectives. Several ML models are established to predict surfactant performance and Random Forest (RF) model presents better accuracy. Based on RF model, we apply Shapley additive explanations (SHAP) approach to interpret modeling results to obtain new insights and provide solutions to unsolved problems. Results show that when interfacial tension is lower than a critical value or oil API gravity is higher than a critical value, surfactant performance shows obvious improvements. In general, porosity, permeability, and surfactant concentration are positively associated with surfactant performance. The trend becomes less obvious when parameter value exceeds a certain level. In addition, SHAP value could effectively decompose surfactant performance into individual effect of each parameter. This analysis is valuable to indicate certain parameters to be optimized. It is shown that surfactant concentration in a proportion of samples is kept in a low level and complete wettability alteration is not achieved. To optimize surfactant concentration, an innovative procedure integrating RF prediction model with Powell’s method is proposed. This procedure is effective to avoid deficient and superabundant surfactant concentration. With optimization, the average surfactant concentration is increased from 0.37 wt% to 0.93 wt%. The average probability of high-oil-recovery class is improved from 0.38 to 0.50. The incremental oil recovery is improved from low-oil-recovery class to high-oil-recovery class. Our work promotes the understanding of wettability alteration by surfactants and provides a fast framework to predict, analyze, and optimize surfactant performance. This framework could greatly save experiment time and cost." @default.
• W4285008262 created "2022-07-12" @default.
• W4285008262 creator A5027520226 @default.
• W4285008262 creator A5051253342 @default.
• W4285008262 creator A5054674388 @default.
• W4285008262 creator A5067654590 @default.
• W4285008262 creator A5084746750 @default.
• W4285008262 date "2023-01-01" @default.
• W4285008262 modified "2023-10-18" @default.
• W4285008262 title "Insights to surfactant huff-puff design in carbonate reservoirs based on machine learning modeling" @default.
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• W4285008262 doi "https://doi.org/10.1016/j.cej.2022.138022" @default.
• W4285008262 hasPublicationYear "2023" @default.
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