Top-Down Intelligent Reservoir Modeling (TDIRM)

Abstract

Abstract This paper examines the validity of a recently introduced reservoir simulation and modeling technique. The technique, that is named Top-Down Intelligent Reservoir Modeling, TDIRM (not to be confused with BP's TDRM history matching technique), integrates traditional reservoir engineering analysis with Artificial Intelligence & Data Mining (AI&DM) technology in order to arrive at a full field model and to predict reservoir performance in order to recommend field development strategies. The distinguishing feature of this technology is its data requirement for its analysis. Although it can incorporate almost any type and amount of data that is available in the modeling process, it only requires field production rate and some well log data (porosity, thickness and initial water saturation) in order to start the analysis and provide a full field model. Presence and incorporation of other types of data can increase the accuracy and validity of the developed model. In this work three different reservoir models with varying criteria have been generated using a commercial simulator. The models are built to investigate TDIRM's capabilities in predicting different aspects of an oil reservoir. The models include different reservoir saturation conditions (saturated or under-saturated), different number of wells and different distributions of reservoir characteristics. Production rates and well log data from the wells in the simulation model are imported into the TDIRM to develop a new empirical reservoir model and make predictions on new well performance and potential infill locations. The results from the TDIRM analysis are compared to the original simulation models. Investigation and validation of TDIRM's predictive capabilities include identification of gas cap development in the formation, identification of infill locations by mapping the remaining reserves as well as predicting flow performance of the wells that are planned to be drilled in the reservoir.