Low-permeability reservoirs play a crucial role in global energy supply, yet their efficient development is hindered by complex seepage mechanisms and strong nonlinear flow behavior. This study systematically investigates the characteristics of oil–water relative permeability curves and the associated non-Darcy flow phenomena in low-permeability sandstone reservoirs. Through unsteady-state water flooding experiments on native cores with permeabilities ranging from 2.99 to 34.40 mD, we analyzed the influence of permeability on relative permeability curves and categorized the water-phase curves into concave-downward and linear types. A dynamic quasi-threshold pressure gradient model was established, incorporating the corrected permeability and water saturation. Furthermore, a novel relative permeability calculation model was derived by integrating the threshold pressure gradient into the non-Darcy flow framework. Validation against the traditional Johnson–Bossler–Naumann (JBN) method demonstrated that the proposed model more accurately captures the flow behavior in low-permeability media, showing lower oil-phase permeability and higher water-phase permeability. The findings provide a reliable theoretical basis for optimizing water flooding strategies and enhancing recovery in low-permeability reservoirs.

In low-permeability reservoirs, studying reservoir sensitivity is crucial for optimizing water flooding, as it identifies detrimental mineral-fluid interactions that can cause formation damage and reduce injection efficiency. However, existing diagnostic methods for sensitivity-induced damage rely on post-facto pressure monitoring and lack a quantitative relationship between sensitivity factors and water injectivity impairment. Furthermore, correlating microscale interactions with macroscopic injectivity parameters remains challenging, causing current models to inadequately represent actual injection behavior. This study combines microscopic techniques (e.g., SEM, XRD, NMR) with macroscopic core flooding experiments under various sensitivity-inducing conditions to analyze the influence of reservoir mineral composition on flow capacity, evaluate formation sensitivity, and assess the dynamic impact on water injectivity. The quantitative relationship between clay minerals and injectivity impairment in low-permeability reservoirs is also investigated. The results indicate that flow capacity is predominantly governed by the type and content of sensitive minerals. In water-sensitive reservoirs, water injection induces clay swelling and migration, leading to flow path reconfiguration and water-blocking effects. In salt-sensitive formations, high-salinity water promotes salt precipitation within pore throats, reducing permeability. In velocity-sensitive formations, fine particle migration causes flow resistance to initially increase slightly and then gradually decline with continued injection. Acidizing generally enhances pore connectivity but induces pore-throat plugging in chlorite-rich reservoirs. Alkaline fluids can exacerbate heterogeneity and generate precipitates, though appropriate concentrations may improve connectivity. Under low effective stress, rock dilation increases porosity and permeability, while elevated stress causes compaction, increasing flow impedance.

The implementation of CO₂ huff-n-puff in unconventional oil reservoirs represents a green development technology that integrates oil recovery and carbon storage, emphasizing both efficiency and environmental protection. A rational well selection method is crucial for the success of CO₂ huff-n-puff development. This paper initially identifies eight parameters that influence the effectiveness of CO₂ huff-n-puff development and conducts a systematic analysis of the impact of each factor on development effectiveness. A set of factors for well selection decisions is established with seven successful CO₂ huff-n-puff cases. Subsequently, the influencing factors are classified into positive, inverse, and moderate indicators. By using an exponential formulation, a method for calculating membership degrees is calculated to accurately represent the nonlinearity of each parameter’s influence on development, resulting in a dimensionless fuzzy matrix. Furthermore, with the oil exchange ratio serving as a pivotal parameter reflecting development effectiveness, recalibration of weighting factors is performed in conjunction with the dimensionless fuzzy matrix. The hierarchical order of weighting factors, from primary to secondary, is as follows: porosity, reservoir temperature, water saturation, formation pressure, reservoir thickness, crude oil density, crude oil viscosity, and permeability. The comprehensive decision factor and oil exchange ratio exhibit a positive correlation, affirming the reliability of the weighting factors. Finally, utilizing parameters of the Ordos Basin as a case study, the comprehensive decision factor is calculated, with a value of 0.617, and the oil exchange ratio is predicted as 0.354 t/t, which falls between the Chattanooga and Eagle Ford reservoirs. This approach, which incorporates exponential membership degrees and recalibrated weighting factors derived from actual cases, breaks the limitations of linear membership calculation methods and human factors in expert scoring methods utilized in existing decision-making methodologies. It furnishes oilfield decision-makers with a swifter and more precise well selection method.