Production Profiling of Multi-Fractured Horizontal Wells in Tight Oil Reservoir from Distributed Optical Fiber Temperature Sensing

An inversion model utilizing the simulated annealing (SA) algorithm was developed to analyze distributed temperature sensing (DTS) data, thereby addressing the persistent challenge of quantitatively characterizing production profiles in tight oil reservoir (TOR) horizontal wells. Initially, a coupled thermal model was established to characterize downhole thermal behavior. Along the TOR horizontal well, the simulated temperature distribution displays an irregular serrated profile, where the temperature rise (ΔT) at individual perforation clusters shows a direct proportionality to their respective inflow rates. Orthogonal experimental analysis verified the sensitivity of the temperature profile to multiple parameters, revealing fracture half-length and cluster permeability as the dominant variables, which were consequently chosen as inversion targets for DTS data interpretation. A synthetic case was employed to assess the feasibility and computational efficiency of the proposed SA inversion model for production profiling utilizing downhole DTS data. In the final phase, the SA inversion model was implemented in a field case, successfully yielding accurate inversion results consistent with field measurements. The interpreted production profile exhibited favorable agreement with PLT results, and the model's reliability was further verified by an average inversion error below 9.97% in the inflow rate across all fracture stages. In addition, from the interpreted production profile, the major water-producing layer was identified. The determined half-lengths of the effective hydraulic fractures provide critical guidance for re-fracturing design, which aims to balance the production profile and boost overall productivity.