Data-Driven Optimization of Injection/Production in Waterflood Operations

Temizel, Cenk (Area Energy) | Nabizadeh, Mehdi (International Petro Asmari Company) | Kadkhodaei, Nematollah (International Petro Asmari Company) | Ranjith, Rahul (University of Southern California) | Suhag, Anuj (University of Southern California) | Balaji, Karthik (University of Southern California) | Dhannoon, Diyar (Texas A&M University)

OnePetro 

Abstract

Decision making in waterflooding operations is a crucial process in petroleum oilfield activities where numerous attributes and uncertainties exist in the complete process. This study investigates the reservoir management of waterfloods in terms of injection/production practices. A well-organized historical database that also collects real-time data is especially important in utilization of data-driven methods in the process of determination of optimum injection/production practices for waterfloods that will result in better recovery and sweep, which is illustrated in this paper.

Statistics is a strong tool to turn information or data into knowledge when used with care and physical understanding of the cause-effect relation between attributes and the outcome. Unfortunately, historical data and learnings from the past cannot be used in an efficient way in oilfield decisions due to the lack of systematically organized historical data where there is a huge potential of turning terrabytes of data into knowledge and understanding for improved decisions and results. Historical injection and production data at pattern level is utilized to determine the optimum injection levels in light of significant factors that affect the success of a waterflooding displacement process with commercial data analysis tools.

Analysis of injection/production data at associated injectors and producers reveals the optimum injection levels depending on the significant factors including but not limited to subsurface conformance, number and location of producers, vintage of wells, completion practices and injection history. The optimum injection levels change depending on the changing variables that affect the displacement and injection processes, thus, a real-time data flow from producers and injectors is required to capture and maintain the optimum operating levels.

The significance of each parameter in this process is obtained in a dynamic manner with real-time feed of field data and efficiently used to determine the optimum levels of injection at a specified time. Change of important factors in the process in time is also important by means of adding another dimension on the relative significance of parameters in the process, thereby shedding light on future decisions.