Horizontal completion fracturing techniques using data analytics: Selection and prediction

This paper targets a comprehensive predictive model to evaluate the key success of completion strategies (treatment) for major successful shale plays and guide future selective optimum completion for each shale play. Many important parameters that control producing well behaviors such as number of horizontal wells, spacing between fractures and wells, horizontal well completion configurations, stages per well, fracture type, average water requirement, depth, proppant type, hydraulic horsepower(HHP) per stage, Lb/ft2 of proppants per stage, number of stages, and lateral length of the horizontal wells, have been analyzed. The proposed analysis is performed on 12 major shale gas and oil plays, for which the data were available. The analysis of the data identified similarity in completion strategies. Learning from these analyses can be used to predict completion strategies in new wells of old or new shale plays. A case study from Niobrara shale (Colorado) is investigated. The procedure used in exploring the case study can be used as a decision criterion for similar cases in deciding stimulation configurations and main important factors that lead to the optimum way of developing these resources. Principal component analysis (PCA) is used to correlate the commonly used completions strategies with geochemical and geomechanical properties of shale rocks.