Identification of Parameters Influencing the Response of Gas Storage Wells to Hydraulic Fracturing With the Aid of a Neural Network

Performing hydraulic fractures on gas storage wells to improve their deliverability is a common practice in the eastern part of the US. Most fields used for storage in this region are old, and the reservoir characteristic data necessary for most reservoir studies and hydraulic fracture design and evaluation are scarce. This paper introduces a new method by which parameters that influence the response of gas storage wells to hydraulic fracturing may be identified in the absence of sufficient reservoir data. Control and manipulation of these parameters, once identified correctly, could enhance the outcome of frac jobs in gas storage fields. The authors conducted the study on a gas storage field in the Clinton formation of northeastern Ohio. They found that well-performance indicators before a hydraulic fracture play an important role in how good the well will respond to a new frac job. They also identified several other important factors. The identification of controlling parameters serves as a foundation for improved frac job design in the fields where adequate engineering data are not available. Another application of this type of study could be the enhancement of selection criteria among the candidate wells for hydraulic fracturing. To achieve the objective of thismore » study, the authors designed, trained, and applied an artificial neural network. The paper will discuss the results of the incorporation of this new technology in hydraulic fracture design and evaluation.« less

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