Long horizontal wells in high-permeability fault-block reservoirs may intersect multiple faults, leading to complex pressure-transient responses, strong parameter coupling in conventional well-test interpretation, inefficient manual history matching, and pronounced non-uniqueness in fault-property identification. To address these challenges, this study proposes a physics-regularized neural inversion framework based on a PINN parameterization and low-weight physics regularization for well-test parameter inversion in long horizontal wells intersecting multiple faults. The proposed method takes the multiple-fault pressure response of a long horizontal well as the target problem. Both the pressure–drawdown curve and the pressure–drawdown derivative curve are used as data constraints. At the same time, parameter scaling and stage-wise training are introduced to jointly invert the reservoir permeability, fault transmissibility coefficient, skin factor, and effective producing length of the horizontal well. Considering that the simplified line-source forward model is not fully consistent with the two-dimensional pressure-diffusion equation and the fault-interface residuals, a physics-loss consistency test is performed to determine safe weighting ranges for the PDE residual and the fault-interface residual. These residuals are then incorporated into the training process as low-weight physics regularization terms to improve the physical plausibility of the inversion results. Results from the base case, different fault types, multiple-fault combinations, noise-robustness tests, ablation experiments, and method comparisons show that the proposed method can stably fit pressure–drawdown and pressure–drawdown derivative curves and effectively identify key well-test parameters in single-fault cases and some multiple-fault cases. In single-fault cases, the order of magnitude of the fault transmissibility coefficient can be identified stably. Reliable inversion performance is obtained for medium- to high-transmissibility faults and some multiple-fault combinations. In contrast, ambiguity remains between sealing faults and strong-baffle faults in multiple low-transmissibility fault combinations. The results further indicate that, under multiple random initializations, the physics-regularized neural inversion framework provides improved inversion stability in the tested synthetic low-transmissibility multiple-fault cases compared with the traditional least-squares method. Therefore, the proposed framework can serve as an intelligent auxiliary tool for well-test parameter inversion and fault-connectivity evaluation in complex fault-block reservoirs. Nevertheless, fine discrimination of low-transmissibility faults and interpretation of highly noisy field data still require joint constraints from geological, seismic, and production-dynamic information. A preliminary reduced field PINN fitting test using the well X falloff event further provides an engineering-scale applicability check for real pressure-transient data, with a pressure NRMSE of 2.457% for the extracted shut-in response.

This paper demonstrates the comprehensive research of the target Middle Eastern carbonate oilfield on waterflooding technologies, including geological characteristics, integrated research, and principal development techniques. Geological research reveals that the Mishrif Formation in the B Oilfield is a gentle-sloping carbonate platform, with granular limestone serving as the primary reservoir rock and micrite limestone serving as the secondary reservoir rock. In addition, based on understandings drawn from geological characteristics and numerical simulation, the water flooding mode of IBPT, which can take full use of the gravity effect, has been proven to yield better sweep efficiency in the context of a thick and heterogeneous reservoir. Furthermore, a large-scale physical model experiment is designed to investigate the fluid migration between the producer and injector and indicates that the injected water migration is mainly divided into four phases, including a two-peak advance phase, a gravitational differentiation phase, a secondary bottom water phase, and a wellbore water coning phase. Subsequently, the principal techniques and corresponding optimized production responses of water flooding development are systematically illustrated, which consist of well type optimization, differentiated water injection strategies, injection pattern conversion, unstable water injection, selective well perforation, as well as tracer surveillance methodology. The outcomes of this study are directly derived from field performances and could provide concrete practical experiences for water flooding technology in the Middle East.