C&P: Development of the Brittle Shale Fracture Network Model
This paper discusses the workflow for the development of a brittle shale model using a data mining approach. A database of more than 1,000 fracture stages and associated microseismic mapping results in the Barnett Shale was assembled. The fracture database is comprised of fracture design parameters including treatment volumes, rates, proppant mass and size, perforation length, fracture pressure, surface pressure trend and fracture dimensions on horizontal well bores. The goal of this analysis is to establish the relationship between frac design, pressure and frac network geometry. Data mining techniques are used on this complex database to find possible hidden relationships to explain the nature of the data. The outcome of this study is to develop a predictive model for fracture networks in shale. Also, using the predictive model, improvements in the current fracture design in Barnett shale are made.
Various aspects of this dataset are examined using data modeling and mining techniques including self-organizing maps (SOM). SOMs are unsupervised artificial neural networks that can cluster large amounts of data into two dimensional maps. Using SOM, frac design parameters are clustered and studied in depth. Then, a forward predictive neural network model is trained with fracture design parameters as inputs and fracture network length, width, height, and fracture volume as outputs. The network is trained with the help of genetic algorithm (GA). Sensitivity study on the trained network demonstrates the effect of different parameters on the fracture geometry. For example, an increase in slick-water volume will have a positive effect on fracture network width and length and negative effect on height. On the other hand, higher injection rates tend to accelerate height growth. Perforation length is also having a negative impact on the total stimulated or affected reservoir volume and tighter perforation designs are preferred. The results of this work potentially helps understanding of the development of fracture networks in shale reservoirs and the recommendations on improving stimulated reservoir volume. This will potentially help operators on more effective treatment designs and reducing the operational costs associated with fracturing in a brittle shale environment.
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