The Marcellus Shale play has attracted much attention in recent years. Our full understanding of the complexities of the flow mechanism in matrix, sorption process and flow behavior in complex fracture system (natural and hydraulic) still has a long way to go in this prolific and hydrocarbon rich formation.
In this paper, we present and discuss a novel approach to modeling and history matching of hydrocarbon production from a Marcellus shale asset in southwestern Pennsylvania using advanced data mining & pattern recognition technologies. In this new approach instead of imposing our understanding of the flow mechanism, the impact of multi-stage hydraulic fractures, and the production process on the reservoir model, we allow the production history, well log, and hydraulic fracturing data to force their will on our model and determine its behavior. The uniqueness of this technique is that it incorporates the so-called "hard data" directly into the reservoir model, such that the model can be used to optimize the hydraulic fracture process. The "hard data" refers to field measurements during the hydraulic fracturing process such as fluid and proppant type and amount, injection pressure and rate as well as proppant concentration.
The study focuses on part of Marcellus shale including 135 wells with multiple pads, different landing targets, well length and reservoir properties. The full-field history matching process was completed successfully. Artificial Intelligence (AI)-based model proved its capability in capturing the production behavior with acceptable accuracy for individual wells and for the entire field.