The methodology developed in this study uses several artificial neural networks and genetic algorithm routines to help engineers select restimulation candidates based on available data. The neural networks provide realistic models of the hydraulic frac jobs and chemical treatments in this field. The genetic algorithms provide design optimization and economic analysis (capital investment allocation).
Historically wells in this storage field have been stimulated/restimulated by hydraulic fracturing or by being chemically treated using one, two or sometimes three different chemicals. Several neural network models were developed for different stimulation processes. The first series of genetic algorithm routines are used with each of the neural network models to provide optimum treatment design for each of the stimulation processes. A separate genetic algorithm uses several economic parameters and provides the engineer with an optimum stimulation combination of the candidate wells.
A software tool based on this methodology has been developed for a gas storage field in Ohio. Upon the completion of the analysis, the software tool provides a list of the maximum number of candidate wells. This maximum number is based on the provided stimulation budget for a particular year. The list specifies the type of stimulation for each candidate well - whether it should be refraced or chemically stimulated - and recommends a list of possible parameters to be used during the implementation.