Enhancing Production Profiles Using Distributed Fiber-Optic and Production Logging Data: A Case Study of a Fractured Three-Phase Well

Abstract Accurate production profiling is essential for determining zonal contributions within a well, optimizing short-term production strategies, and supporting long-term reservoir and resource management. Traditionally, Production Logging Tools (PLTs) have been the primary method for acquiring production profiles. However, Distributed Fiber-Optic (DFO) sensing—specifically Distributed Temperature Sensing (DTS) and Distributed Acoustic Sensing (DAS)—has gained momentum as a complementary or alternative approach, offering continuous measurements with greater spatial and temporal resolution. This study presents a comprehensive comparison of production profiling based on PLT, DTS, and DAS data acquired simultaneously in a fractured, three-phase production well. Using an industry-standard production profiling software platform, three independent analyses were conducted to evaluate the capabilities and limitations of each method. PLT data provided direct measurements of flowrate and holdup, offering high accuracy when properly calibrated. DTS enabled detection of temperature anomalies associated with fluid entry, supporting flow profiling through thermal modeling, though it is sensitive to uncertainties in geothermal background, especially in fractured systems. DAS data proved highly responsive to fluid movement, allowing identification of inflow zones via acoustic energy signatures, although current models for deriving flowrate and holdup from DAS alone remain less mature. To overcome the limitations of individual methods, a hybrid approach was implemented. Completion data, temperature gradients from DTS, and DAS signal intensities were used to identify inflow zones. Flowrates were primarily calibrated using spinner data, while holdups were constrained using PLT fluid identification probes and refined with thermal modeling. A global optimization algorithm was applied to integrate all datasets into a unified, physics-based wellbore model. The integrated analysis produced a more detailed and accurate production profile than any single method alone. This study demonstrates the value of combining PLT, DTS, and DAS data, establishing a robust workflow for improved production profiling in complex well environments.