New Insight into Integrated Reservoir Management using Top-Down, Intelligent Reservoir Modeling Technique; Application to a Giant and Complex Oil Field in the Middle East

Abstract

Abstract This paper demonstrates the validity of a recently developed reservoir modeling technique called "Top-Down, Intelligent Reservoir Modeling" (will be referred to Top-Down Modeling for short). This new modeling technology integrates reservoir engineering analytical techniques with Artificial Intelligence & Data Mining (AI&DM) in order to arrive at an empirical, cohesive and spatiotemporally calibrated full field model. The model is used to predict reservoir performance in order to recommend field development strategies. One of the distinctive features of this technology is its data requirement for analysis. Although Top-Down Modeling can incorporate almost any type and amount of data that is available in the modeling process, it only requires monthly production rate and some well log data (porosity, initial water saturation and thickness) in order to start the analysis and provide a full field model. Presence and incorporation of other types of data such as core analysis, pressure data, reservoir characteristics and seismic data can and will increase the accuracy and validity of the developed model. In this work, we apply Top-Down Modeling to a large and complex Oil field in the Middle East that produces from a combination of clastic and carbonate formations. Production rates and well log data from 210 wells have been analyzed and imported into the Top-Down Modeling software to develop a new empirical reservoir model and make predictions on new well performance and potential infill locations. Results from Top-Down Modeling analyses are compared with results concluded from a comprehensive reservoir management study (that included use of large amount and various types of data and a commercial numerical reservoir simulator) performed by an IOC. Analytical reservoir engineering techniques used in the Top-Down Modeling presented in this study include production decline analysis, volumetric reserve and recovery factor estimations and are integrated with Voronoi graph theory, geostatistics, two-dimensional Fuzzy Pattern Recognition (FPR), and discrete, data driven predictive modeling. The resulting full field Top-Down Modeling is used to identify the distribution of the remaining reserves, sweet spots for infill locations and under-performer wells and also identify the locations in the reservoir where gas cap is formed. Furthermore, the fullfield model is used to predict new well’s performance.