A Holistic Approach to Big Data and Data Analytics for Automated Reservoir Surveillance and Analysis

Abstract

Analyses have been widely applied in production forecasting of oil/gas production in both conventional and unconventional reservoirs. In order to forecast production, traditional regression and machine learning approaches have been applied to various reservoir analysis methods. Nevertheless, these methods are still suboptimal in detecting similar production trends in different wells due to data artifacts (noise, data scatter, outliers) that obscure the reservoir signal and leading to large forecast error, or fail due to lack of data access (inadequate SCADA systems, missing or abhorrent data, and much more). Furthermore, without proper and complete integration into a data system, discipline silos still exist reducing the efficiency of automation. This paper describes a recent field trial conducted in Australia's Cooper Basin with the objective to develop a completely automated end-to-end system in which data are captured directly from the field/SCADA system, automatically imported/processed, and finally analyzed entirely in automated system using modern computing languages, modern devices incl. IoT, as well as advanced data science and machine learning methods. This was a multidisciplinary undertaking requiring expertise from petroleum, computing/programming, and data science disciplines. The back-end layer was developed using Wolfram's computation engine, run from an independent server in Australia, while the front-end graphical user interface (GUI) was developed using a combination of Wolfram Language, Java, and JavaScript – all later switched to a Python-React combination after extensive testing. The system was designed to simultaneously capture data real-time from SCADA Historians, IIoT devices, and remote databases for automatic processing and analysis through API's. Automatic processing included "Smart Filtering" using apparent Productivity Index and similar methods. Automated analysis, including scenario analysis, was performed using customized M/L and statistical methods which are then applied to Decline curve analysis (DCA), flowing material balance analysis (FMB), and Water-Oil-Ratio (WOR). The entire procedure is automated, without need for any human intervention.