Estimation of underground interwell connectivity: A data-driven technology

Abstract

Water injection into petroleum reservoirs is widely performed around the world for enhancing oil recovery. Understanding the underground fluid path is an important factor in improving reservoir performance under waterflooding operation. This may be used to optimize subsequent oil recovery by changing injection patterns, assignment of well priorities in operations, recompletion of wells, targeting infill drilling, and reduce the need for expensive surveillance activities. Most of the hydrocarbon reservoirs are equipped with sensors that measure the flow rate, pressure, and temperature in the wellbores continuously. Valuable and useful information about the interwell connections can be obtained from the measured data. In the present paper, a novel data-driven approach is developed that contributes to estimating the underground interwell connections through analyzing and processing the injection and production data of a petroleum reservoir. This novel data-driven technology is named Detection of Events (DoE) and combines with the Capacitance Resistance Model (CRM) to quantify the connections between injectors and producers. The proposed approach has been applied to both synthetic and field cases. The results of the method show a good agreement between CRM and DoE for the field case (75%), and present a good insight into the better understanding of the underground connections.