Using data-driven modeling to understand multi-fractured, horizontal Marcellus completions
A data-driven, neural network model was developed to quickly and economically evaluate completion effectiveness for Marcellus shale wells. This model was used to identify significant opportunity to improve production for new wells by modifying completion and frac design. According to the model, geology and reservoir quality dominate Marcellus production. However, controllable contact and conductivity-related parameters are also significant. The number of frac treatments and the amount of proppant used in the completion rank first and second in significance. This is followed by perforation design, fluid volume and treatment rate.
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