Catch fish optimization algorithm (CFOA) is a newly proposed meta-heuristic algorithm based on human behaviors. CFOA shows better performance on multiple test functions and clustering problems. However, CFOA shows poor performance in some cases, and there is still room for improvement in convergence accuracy, getting rid of local traps, and so on. To further enhance the performance of CFOA, a multi-strategy improved catch fish optimization algorithm (MICFOA) is proposed in this paper. In the exploration phase, we propose a Lévy-based differential independent search strategy to enhance the global search capability of the algorithm while minimizing the impact on the convergence speed. Secondly, in the exploitation phase, a weight-balanced selection mechanism is used to maintain population diversity, enhance the algorithm’s ability to get rid of local optima during the search process, and effectively boost the convergence accuracy. Furthermore, the structure of CFOA is also modified in this paper. A fishermen position replacement strategy is added at the end of the algorithm as a way to strengthen the robustness of the algorithm. To evaluate the performance of MICFOA, a comprehensive comparison with nine other metaheuristic algorithms is performed on the 10/30/50/100 dimensions of the CEC 2017 test functions and the 10/20 dimensions of the CEC2022 test functions. Statistical experiments show that MICFOA has more significant dominance in numerical optimization problems, and its overall performance outperforms the CFOA, PEOA, TLBO, COA, ARO, EDO, YDSE, and other state-of-the-art algorithms such as LSHADE, JADE, IDE-EDA, and APSM-jSO.

With the introduction of China’s “dual carbon” goals, CO₂ is increasingly valued as a resource and is being utilized in unconventional oil and gas development. Its application in fracturing operations shows promising prospects, enabling efficient extraction of oil and gas while facilitating carbon sequestration. The process of reservoir stimulation using CO₂ fracturing is complex, involving coupled phenomena such as temperature variations, fluid behavior, and rock mechanics. Currently, numerous scholars have conducted fracturing experiments to explore the mechanisms of supercritical CO₂ (SC-CO₂)-induced fractures in relatively deep formations. However, there is relatively limited numerical simulation research on the coupling processes involved in CO₂ fracturing. Some simulation studies have simplified reservoir and operational parameters, indicating a need for further exploration into the multi-field coupling mechanisms of CO₂ fracturing. In this study, a coupled thermo-hydro-mechanical fracturing model considering the CO₂ properties and heat transfer characteristics was developed using the phase field method. The multi-field coupling characteristics of hydraulic fracturing with water and SC-CO₂ are compared, and the effects of different geological parameters (such as in situ stress) and engineering parameters (such as the injection rate) on fracturing performance in tight reservoirs were investigated. The simulation results validate the conclusion that CO₂, especially in its supercritical state, effectively reduces reservoir breakdown pressures and induces relatively complex fractures compared with water fracturing. During CO₂ injection, heat transfer between the fluid and rock creates a thermal transition zone near the wellbore, beyond which the reservoir temperature remains relatively unchanged. Larger temperature differentials between the injected CO₂ fluid and the formation result in more complicated fracture patterns due to thermal stress effects. With a CO₂ injection, the displacement field of the formation deviated asymmetrically and changed abruptly when the fracture formed. As the in situ stress difference increased, the morphology of the SC-CO₂-induced fractures tended to become simpler, and conversely, the fracture presented a complicated distribution. Furthermore, with an increasing injection rate of CO₂, the fractures exhibited a greater width and extended over longer distances, which are more conducive to reservoir volumetric enhancement. The findings of this study validate the authenticity of previous experimental results, and it analyzed fracture evolution through the multi-field coupling process of CO₂ fracturing, thereby enhancing theoretical understanding and laying a foundational basis for the application of this technology.

To augment the accuracy, stability, and qualification rate of wind power prediction, thereby fostering the secure and economical operation of wind farms, a method predicated on quadratic decomposition and multi-objective optimization for ultra-short-term wind power prediction is proposed. Initially, the original wind power signal is decomposed using a quadratic decomposition method constituted by the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Fuzzy Entropy (FE), and Symplectic Geometry Mode Decomposition (SGMD), thereby mitigating the randomness and volatility of the original signal. Subsequently, the decomposed signal components are introduced into the Deep Bidirectional Long Short-Term Memory (DBiLSTM) neural network for time series modeling, and the Sand Cat Swarm Optimization Algorithm (SCSO) is employed to optimize the network hyperparameters, thereby enhancing the network’s predictive performance. Ultimately, a multi-objective optimization loss that accommodates accuracy, stability, and grid compliance is proposed to guide network training. Experimental results reveal that the employed quadratic decomposition method and the proposed multi-objective optimization loss can effectively bolster the model’s predictive performance. Compared to other classical methods, the proposed method achieves optimal results across different seasons, thereby demonstrating robust practicality.

To address the challenges of low accuracy and complex influencing factors in predicting horizontal well fracturing productivity during the development of unconventional oil and gas resources such as tight oil, this paper proposes a productivity prediction framework based on an improved feature selection method and an ensemble learning model. This study employs a fusion analysis using the entropy weight method to combine Pearson correlation analysis and improved gray relational analysis (IGRA) for feature selection. Thirteen machine learning models were tested with six distinct parameter combinations to construct a Stacking-based ensemble learning model, with base models including Random Forest (RF), Ridge Regression (RR), and Artificial Neural Network (ANN). Particle Swarm Optimization (PSO) was employed to optimize hyperparameters, followed by interpretability analysis using SHapley Additive exPlanations (SHAP). The results indicate that the model with fused weights demonstrated optimal performance. The Stacking model achieved significantly improved accuracy after PSO optimization, with the coefficient of determination increasing by 4.9%, outperforming all comparison models. Engineering guidance is provided: Under current geological conditions, sand ratio and displacement fluid volume require fine-tuning to prevent over-treatment. Fracturing design should implement differentiated strategies based on the target sand body thickness. This study not only delivers a high-precision production prediction tool but also offers decision support for efficient unconventional oil and gas field development through its exceptional interpretability.