In order to maintain or enhance deliverability of gas storage wells in the Clinton Sand in Northeast Ohio an annual restimulation program has been in place since the late sixties. The program calls for as many as twenty hydraulic fractures and refractures per year. Several wells have been refractured three to four times while there are still wells that have only been fractured once in the past thirty years.

As the program continues many wells will be stimulated a second, third or fourth time. This paper summarizes an attempt to carefully study the response of the Clinton sand to hydraulic fractures and identify the performance drivers in each series of frac jobs. Do the performance drivers remain the same for the later fractures (second, third and fourth frac jobs) as they were in the first ones? Or do they change? This paper attempts to answer such questions.

Identification of major performance drivers becomes important when new jobs are to be designed. They not only play an important role in enhancing the response of the wells to new stimulation jobs; but also may prove to be an important economic factor in the design of new stimulation procedures. If for instance it is concluded that an increase in proppant volume does not influence the stimulation outcome after the second refracture, then fewer resources can be used for proppant volume and be directed toward parameters that are more influential.

This study employs a combined neural network and fuzzy logic tool to identify the performance drivers.

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