The research on intelligent evaluation methods for non-sealing faults in reservoirs based on well test analysis

In practical reservoirs, there exist a large number of non-sealing faults with flow capacity, which significantly influence oilfield development patterns and wellbore pressure transient behavior. The quantitative evaluation of fault sealing capacity is of great importance for characterizing remaining oil distribution and reconstructing flow fields. However, existing fault analysis methods are primarily qualitative, with limitations in the quantitative characterization of fault sealing. Traditional numerical well-test interpretation models do not account for fluid flow within faults, leading to significant deviations in well-test data interpretation, increased model-solving difficulties, and challenges in achieving quantitative analysis of reservoir sealing. Therefore, based on the fault, fluid, and reservoir property characteristics of the X reservoir, this study establishes a composite reservoir well-test interpretation mathematical model considering skin effects and solves the model using the Boltzmann transformation. By applying the “partial” mirror superposition principle, the dynamic response characteristics of typical curves under different fault boundary conditions are analyzed, and a quantitative sealing evaluation method suitable for non-sealing faults is developed. Furthermore, by integrating XGBoost multi-output regression and PSO algorithms, an intelligent hybrid inversion framework for identifying non-sealing faults in composite reservoirs is constructed: the XGBoost model predicts initial fault characteristic parameters, while the PSO algorithm performs global optimization to refine XGBoost parameters, ultimately inverting the fault connectivity coefficient ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mrow><mml:mi>f</mml:mi><mml:mi>D</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> ) and effective connected thickness ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m2"><mml:mrow><mml:msub><mml:mi>h</mml:mi><mml:mi>b</mml:mi></mml:msub></mml:mrow></mml:math> ). The results indicate the presence of a non-sealing fault F1 between Well B30Y and Well B1A, with inverted values of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m3"><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mrow><mml:mi>f</mml:mi><mml:mi>D</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mn>0.73</mml:mn></mml:mrow></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m4"><mml:mrow><mml:msub><mml:mi>h</mml:mi><mml:mi>b</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mn>20.44</mml:mn></mml:mrow></mml:math> , demonstrating strong fault connectivity. Additionally, the fitting trend of bottom-hole flowing pressure during shut-in periods in both wells verifies the validity and stability of the proposed model. The method presented in this study enables rapid, quantitative, and precise evaluation of non-sealing fault closure, providing robust technical support for subsequent remaining oil potential exploitation and development strategy optimization.