Shale oil and gas development is shifting from single stimulation methods toward integrated recovery strategies that combine flow-mechanism understanding, enhanced oil recovery, and carbon utilization and storage. Based on the discussions in Session “Shale Oil and Gas Flow Mechanisms and Enhanced Oil Recovery” of the second “International Geo-Energy Frontier Forum”, this work summarizes recent advances in thermally assisted CO₂ huff-n-puff, supercritical CO₂ flow and multiscale CO₂ foam simulation, in-situ upgrading and thermal conversion, micro/nanobubble injection, dual geological-engineering sweet-spot identification, and shut-in optimization. The major bottleneck is no longer the lack of individual stimulation methods, but the insufficient integration among pore-scale mechanisms, fracture-matrix interactions, field-scale simulation, and carbon storage accounting. Future research should focus on mechanism-informed pilot design, lithology-specific upscaling models, CO₂-thermal-chemical coupled processes, and standardized evaluation workflows linking recovery efficiency with carbon sequestration performance.

The recovery factor of tight oil reservoirs is comprehensively controlled by formation geological properties, well spacing, and fracturing intensity. Insufficient formation drainage or fracture-hits between wells can lead to low recovery factors. The integrated optimization of well spacing and fracturing intensity is a crucial approach to improving recovery factors in tight oil reservoirs. This work analyzes the production characteristics of tight oil wells and identifies the main factors affecting productivity applying machine learning methods. An integrated workflow combining fracturing modeling, reservoir simulation, and production optimization is developed and applied to a real field reservoir block to explore the optimal matching relationship between well spacing and fracturing intensity. The research results indicate that the fractured horizontal well productivity is highly correlated with the stable water cut and is influenced by reservoir geological parameters and fracturing intensity. The material balance method can quickly reconstruct water saturation field after fracturing, which improves history matching. Moreover, simple machine learning method, response surface experiments, is used along with our integrated workflow to generate well spacing and fracturing intensity matching charts under different reservoir permeability conditions and provided the corresponding recovery factor prediction correlations. This study offers a fast and efficient method for developing tight oil reservoir with optimized well spacing and fracturing intensity.