Enhancing Reservoir Understanding by Utilizing Business Intelligence Workflows

Abstract

Abstract Saudi Aramco Upstream data volumes have been exploding exponentially over the past few years. Reservoir engineers need to quickly analyze huge volumes of multidisciplinary data coming from multiple sources scattered across the upstream business, such as simulation, seismic, corporate database and real time data from intelligent fields. In addition, it is crucial to capture the scattered multidisciplinary experience that Saudi Aramco has across the upstream business. Multidisciplinary professionals are utilizing many expert systems that require specialized experience in their areas. It is very important to break these silos and bridge the gaps to capture all the scattered knowledge and experience in an integrated media to build shared understanding within multidisciplinary teams. Business intelligence provides a platform for guided processes and workflows that help to overcome these challenges. The developed reservoir engineering business intelligence workflows will help engineers to analyze, visualize and report reservoir simulation results, production development scenarios and economics to enhance decision making process. This paper will discuss the Saudi Aramco methodology to develop reservoir engineering business intelligence workflows that utilize advanced data mining, visual analytics, and predictive analytics techniques. In addition, we will review several workflows that improve the process of history matching and prediction by rapidly identifying trends, anomalies, outliers and patterns in the reservoir simulation results.