Intelligent Use of Big Data for Heavy Oil Reservoir Management

Abstract

Abstract In recent years, the application of Big Data technologies and associated analytics has enjoyed significant attention and has seen an enormous growth in the oil industry. This is due to exponential growth of both historical and real-time data being collected. In this article, we present examples of the successful application of these technologies for the management and optimization of large heavy oil reservoirs. These particular reservoirs present a very interesting, yet complex, challenge given the steam assisted gravity drainage recovery mechanism and the thousands of producers, injectors, and observation wells which generate terabytes of data on a daily basis. To handle all this large and high-dimensional information efficiently, we introduced new workflows consisting of operational domain data acquisition, data transfer to business domain, storage in accessible repositories, and ending with data consumption, quality control, visualization and analytics. The paper summarizes how big data is aggregated into smart applications to monitor the reservoirs and observe steamflood recovery development. Examples of high definition DTS data integrated with completion, wellbore equipment, geologic markers, and real-time wellhead pressure showing reservoir heating and/or cooling are presented. Intelligent visualization tools and analytics, such as pattern recognition applied on static and dynamic subsurface data enabled superior heat management. A second example of high efficiency operations enabled by big data is the well integrity during cyclic steam operations. Pressure and injection rate data streams are integrated with analytics to monitor and identify abnormal operating conditions. Lastly, an example of facility reliability and production impact quantification is demonstrated through use of integration of surface system stream data and subsurface well information. The later led to significant business impact in terms of realized production and operation cost savings. The examples presented in this paper demonstrate the business impact and value creation generated by efficient use of the Big Data technologies and associated analytics for heavy oil reservoir management and optimization. The workflows are now used in all Chevron heavy oil fields in San Joaquin Valley.