Geothermal Electric Submersible Pump Virtual Parameters Optimize Well Performance Through Real-time Monitoring and Machine Learning Diagnostics

Geothermal energy is a highly reliable, eco-friendly, sustainable, and clean energy that has proven to be a game-changer in the residential and industrial sectors. It can be developed from hot rocks saturated in geologically favorable reservoirs in which an electric submersible pump (ESP) produces water at temperatures greater than 120°C from a depth of up to 4 km. Once its heat is converted to electricity in the power plant, the water is cooled and reinjected into the reservoir. Due to the required flow rates, high-enthalpy fluids, and harsh downhole conditions of geothermal wells, a real-time well manager system is necessary to improve the ESP design, operation, reliability, and well performance. This paper details the operating conditions of a high-efficiency geothermal ESP system in Germany and the geothermal ESP well manager system built upon in-house developed machine learning models which predicted pump intake pressure, motor temperature, fluid temperature, flow rate, and overall operating parameters with less than 3% error. The virtual parameters and real-time total dynamic head were analyzed together to indicate potential scale buildup within the flow meter, organic deposition on the motor housing, and changes in fluid composition. As a result, our advanced geothermal well manager system can obtain virtual measurements, visual operating indices, vibrations tracking, real-time pump and well performance evaluation, electrical unbalance tracking, and scale detection. A thorough assessment made by continuously monitoring (24/7/365) the physical and digital aspects of the system enable recommendations for improving efficiency and increasing the lifespan of the ESP.