Data-driven approach for evaluation of formation damage during the injection process

Abstract

AbstractWaterflooding is among the most common oil recovery methods which is implemented in the most of oil-producing countries. The goal of a waterflooding operation is pushing the low-pressure remained oil of reservoir toward the producer wells to enhance the oil recovery factor. One of the important objects of a waterflooding operation management is understanding the quality of connection between the injectors and the producers of the reservoir. Capacitance resistance model (CRM) is a data-driven method which can estimate the production rate of each producer and the connectivity factor between each pair of wells, by history matching of the injection and production data. The estimated connectivity factor can be used for understanding the quality of connection between the wells. In the waterflooding operation, the injected water always has the potential of causing formation damage by invasion of foreign particles deep bed filtration (DBF), mobilization of indigenous particles (fines migration), scale formation, etc. The formation damage can weaken the quality of connection (connectivity factor), between the injectors and producers of the field, increasing the skin of injection well. In this paper, DBF is used for creation of formation damage in synthetic reservoir models. Then, it has been tried to find the existence and amount of formation damage by evaluating the connectivity factor of CRM. Finally, the results of that have been used for prediction of skin variation in a real case by using the connectivity factor of CRM.