Abstract

Shale gas in the United States went almost instantly from a practically invisible resource to massive reserves that challenge the largest conventional gas accumulations in the world. Shale gas success is directly the result of economically managed deployment of petroleum technology, namely horizontal wells .Horizontal drilling and multi-stage stimulation technologies are driving the successful development of shale plays.

Modeling and simulation of shale gas reservoirs poses a unique problem. The geological complexity of shale gas reservoirs—containing both natural and hydraulic fractures—makes accurate modeling a significant challenge. To overcome these challenges and maximize recovery of a shale gas field requires specialized methods and state-of-the-art technology.

In the first part of this paper an integrated workflow, which demonstrates a quantitative platform for shale gas production optimization through capturing the essential characteristics of shale gas reservoirs was discussed. A comprehensive sensitivity studies on key matrix, fracture system and all other shale related properties were performed and the results were presented. This study attempted to show how sensitivity analysis applied to this model can be used to aid in the design and history matching of a complex shale gas system.

In the second part of this paper the state-of-the-art technology using Artificial Intelligence and Data Mining (AI&DM) techniques which is called single well shale surrogate reservoir model (S3) has been built. Shale surrogate reservoir model is new solution for fast track, comprehensive reservoir analysis (solving both direct and inverse problems) using existing shale gas reservoir simulation models. This model was defined as a replica of the shale gas reservoir simulation model that ran and provided accurate results in real-time very fast and can be used for automatic history matching, real-time optimization, real-time decision-making and quantification of uncertainties. The intelligent model was verified using several completely blind simulation runs.

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