Production Optimization and Reservoir Monitoring Through Virtual Flow Metering

Abstract

Abstract Development of marginal oil fields having limited production rate are typically very challenging, especially for the fields where reservoir behavior could be very sensitive to the operating envelop. Also, minimizing the operational expenses is the key for economic development of such marginal fields. Bu Haseer is one of the marginal fields in offshore concession area of Al Yasat Petroleum Operations Company. Al Yasat has implemented hybrid virtual flow meter solution in Bu Haseer field, to enhance the reservoir management plan while minimizing the operational expenses. Hybrid virtual flowmeter solution adopted by Al Yasat utilizes first principle based high fidelity multi-phase flow assurance simulator, leveraging its machine learning model for fast and robust flow estimation. Dynamic analysis and data driven approach of the virtual flow meter solution ensures the estimation accuracy and would highlight the system failure, if any. Utilizing available real time data from field, the virtual flow meter estimates the real time oil, water and gas flow rates from producers and expected down-hole parameters, within accuracy acceptable for reservoir surveillance and production optimization. Typically, well performance is being monitored through periodic well tests and downhole monitoring through wireline activities. Frequency of such reservoir surveillance activities could be significant, especially for marginal fields due to reservoir sensitivity. Continuous real time reservoir monitoring through virtual flow meter provides better understanding of reservoir for timely corrective actions and maximizes the oil recovery in the field lifecycle. With the implementation of virtual flow metering technology, physical well testing and downhole monitoring will be limited for periodic validation and calibration of the virtual flow meter. Therefore, operational expenses for reservoir monitoring activities and associated HSE risks are also significantly optimized. Virtual flow measurement using conventional steady state principles of physics is widely used in oil and gas industry. However, advancing the conventional methodology with a combined machine learning & dynamic simulation approach not only enhances the measurement accuracies, but allows the solution to be a tool for analyzing and identifying potential failures and their root causes. This paper presents Al Yasat experience in hybrid virtual flow measurement technology implemented in one of its marginal fields.