A CONVERGENCE-CONTROLLED ITERATIVE ALGORITHM FOR ZONAL PRODUCTION ALLOCATION IN COMMINGLED INTELLIGENT WELLS USING PERMANENT DOWNHOLE GAUGE DATA

Abstract

Intelligent well systems integrate permanent downhole gauges (PDG) and inflow control valves (ICV) to achieve real-time monitoring and control of oil reservoir production. However, in commingled production, where multiple reservoir layers produce simultaneously through a single wellbore, determining the production contribution of each individual layer remains challenging. This study investigates hierarchical production calculation methods for intelligent wells based on PDG monitoring data. Two methods are analyzed and compared: an analytical production allocation model based on productivity index theory, and a revised time-series iterative production splitting algorithm incorporating explicit convergence control, production mismatch correction, pressure update calculations, normalization correction, and mass balance verification. Both methods were implemented in MATLAB and validated using a synthetic intelligent well production dataset comprising 1000 records from a three-layer commingled well system. The iterative algorithm achieved significantly higher accuracy than the analytical approach, reducing the relative production error from an initial value exceeding 10% to a final average error of approximately 0.15%. The iterative method continuously updates the flowing bottomhole pressure using dynamic monitoring data until the calculated total production converges toward the measured production within a tolerance of 1%. The findings confirm that correct interpretation of PDG measurements—specifically using annulus pressure as the flowing bottomhole pressure—is essential for accurate production allocation. The proposed methodology provides a mathematically complete, numerically stable, and physically consistent framework for intelligent well production allocation analysis under realistic multilayer operating conditions.