Flow Regime-Driven Adaptive Imaging for Oil–Water Two-Phase Flow in Horizontal Wells

Cross-sectional imaging of two-phase oil–water flow in horizontal wells is essential for optimising production, yet conventional deterministic interpolation cannot adapt to varying flow regimes: Kriging smooths chaotic textures while stochastic simulation introduces spurious noise into stable flows. This paper proposes a Flow-Regime-driven Framework for Adaptive Cross-sectional Imaging (FR-FACI) that couples flow-regime identification with image reconstruction. Six physically meaningful features extracted from capacitance (CAT) and turbine (SAT) array signals feed a support vector machine (SVM) classifier that assigns each sampling window to one of three regimes: stratified (SF), stratified-froth (SFF), or froth (FR). A chaos weight derived from the calibrated classifier probability continuously blends detrended ordinary kriging with sequential Gaussian simulation, eliminating hard-switching artefacts. Experiments covering 12 operating conditions yield 95.83% classification accuracy under leave-one-condition-out validation. Variogram ranges differ by more than 26-fold across regimes, confirming the physical necessity of dual-path design. FR-FACI achieves an overall MAE of 0.105 and RMSE of 0.160, matching Kriging in stable flows while recovering chaotic textures that all single-model methods miss. Directions for future work, including uncertainty propagation, field-scale validation, and real-time monitoring integration, are discussed.