Hydraulic fracture design and optimization of gas storage wells

Conventional hydraulic fracture design and optimization involves the use of two- or three-dimensional hydraulic fracture simulators. These simulators need a wealth of reservoir data as input to provide users with useable results. In many cases, such data are not available or very expensive to acquire. This paper provides a new methodology that can be used in cases where detail reservoir data are not available or prohibitively expensive to acquire. Through the use of two virtual intelligence techniques, namely neural networks and genetic algorithms, hydraulic fracture treatments are designed using only the available data. The unique design optimization method presented here is a logical continuation of the study that was presented in two previous papers [McVey et al., 1996. Identification of parameters influencing the response of gas storage wells to hydraulic fracturing with the aid of a neural network. SPE Computer Applications Journal, Apr., 54–57; Mohaghegh et al., 1996b. Predicting well stimulation results in a gas storage field in the absence of reservoir data, using neural networks. SPE Reservoir Engineering Journal, Nov., 54–57.]. A quick review of these papers is included here. This method will use the available data on each well, which includes basic well information, production history and results of previous frac job treatments, and provides engineer with a detail optimum hydraulic fracture design unique to each well. The expected post-hydraulic fracture deliverability for the designed treatment is also provided to assist engineers in estimating incremental increase in recovery to be used in economic calculations. There are no simulated data throughout this study and all data used for development and verification of all methods are actual field data.