Physics-guided proximal policy optimization for dynamic correction of multiphase flow production profiles in deep wells

Conventional production logging interpretation methods exhibit substantial inaccuracies in deep high-temperature, high-pressure (HTHP) wells due to dynamic flow-pattern transitions and the depth-dependent accumulation of measurement errors, posing a critical challenge for accurate reservoir evaluation. To address these limitations, a novel Physics-Guided Proximal Policy Optimization (PG-PPO) framework is developed to construct an intelligent downhole correction agent. The proposed approach reformulates the depth-wise correction problem as a Markov Decision Process (MDP), enabling systematic optimization under physical constraints. Its key methodological innovations include: (1) the incorporation of a physics-informed action clipping layer within the policy network to enforce the empirical fluid-dynamics relationship Cv = f(Re) in real time; (2) a constraint-prioritized experience replay mechanism to improve learning efficiency under sparse and high-cost physical constraints; and (3) the integration of a wellbore heat-transfer model with dynamic correction factors to enhance sensitivity in low-flow regimes. Validation using field production logging data from 12 producing wells in a continental oilfield in China demonstrates that the proposed PG-PPO framework reduces the average flow-rate calculation error to 5.2%, corresponding to reductions of 72.2% relative to the traditional Reynolds-number chart method and 39.5% compared with the TD3 algorithm. Meanwhile, the framework maintains a low constraint violation rate of 3.8% and achieves high correction coefficient stability (1.7 × 10 −3 ). This research provides an intelligent optimization solution for multiphase flow production profile interpretation, representing a significant step toward overcoming the accuracy limitations of traditional methods in extreme HTHP environments.