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Top-Down Intelligent Reservoir Modeling of New Albany Shale

Although the New Albany Shale of the Illinois Basin has been estimated to contain approximately 86 TCF of natural gas in place, the full development of this potentially large resource has not yet occurred. The intent of this study is to reassess the potential of New Albany shale using a novel integrated workflow, which incorporates field production data and well logs using a series of traditional reservoir engineering analyses with artificial intelligence & data mining techniques. The model developed using this technology is a full filed model and its objective is to predict future reservoir/well performance in order to recommend field development strategies. In this integrated workflow unlike traditional reservoir simulation and modeling, we do not start from building a geo-cellular model. Top-Down intelligent reservoir modeling(TDIRM) starts by analyzing the production data using traditional reservoir engineering techniques such as Decline Curve Analysis, Type Curve Matching, Single-well History Matching, Volumetric Reserve Estimation and Recovery Factor. These analyses are performed on individual wells in a multi-well New Albany Shale gas reservoir in Western Kentucky that has a reasonable production history. Data driven techniques are used to develop single-well predictive models from the production history and the well logs (and any other available geologic and petrophysical data). Upon completion of the abovementioned analyses a large database is generated .This database includes a large number of spatio-temporal snap shots of reservoir behavior. Artificial intelligence and data mining techniques are used to fuse all these information into a cohesive reservoir model. The reservoir model is calibrated (history matched) using the production history of the most recent set of wells that have been drilled in the field. The calibrated reservoir model is utilized for predictive purposes to identify the most effective field development strategies including locations of infill wells, remaining reserves, and under-performer wells. Capabilities of this new technique, ease of use and much shorter development and analysis time are demonstrated as compared to the traditional simulation and modeling.