Deep Learning Assisted Doppler Sensing for Hydrocarbon Downhole Flow Velocity Estimation

Abstract

Logging hydrocarbon production potential of wells has been at the forefront of enhancing oil and gas exploration and maximize productivity from oil and gas reservoirs. A major challenge is accurate downhole fluid phases flow velocity measurements in production logging due to the criticality of mechanical spinner-based sensor devices. Ultrasonic Doppler based sensors are more robust and deployable either in wireline or logging while drilling (LWD) conditions; however, due to the different sensing physics, the measurement results may not be equivalent. We present in this work an innovative deep learning framework to estimate spinner phase velocities from Doppler based sensor velocities. Tests of the framework on a benchmark dataset displayed strong estimation results. This allows for the real-time automatic interpretative framework implementation and flow velocity estimations either in conventional wireline production logging technologies (PLTs) and potentially also in LWD conditions, when the well is flowing in underbalanced conditions.