Predicting fracture clusters, cluster spacing and perforations of horizontal wells using data analytics

This work offers a predictive model as a success-of-completion strategy (treatment) for a major successful shale play (Wolfcamp). The predictive model may be used to evaluate spacing between fracture clusters and the number of clusters and perforations, and to guide future selective optimum completion for the shale play. Many important parameters that control behavior of producing wells have been analyzed, including number of days on production; depth; fluids in bbl; horizontal well completion configurations; stages per well; fracture type; average water requirement; proppant type; fluid type; hydraulic horsepower (HHP) per stage; lb/ft² of proppants per stage; number of stages, and lateral length (completed interval) of horizontal wells,. We analyzed the performance of thousands of horizontal wells from the Wolfcamp formations for which data were available. The analysis of the data identified key parameters (depth, bbl. of fluids, type and amount of proppant, fluid type, and initial production (IP30)) in defining number of stages, clusters and perforations. Production performance from private and public data was used as a separate criterion to determine predictivity of the models. Various datasets from Wolfcamp are investigated. The procedure for exploring the data can be used as a decision criterion in similar cases for number of clusters, perforations and clusters spacing and additional factors in optimum resource development. A multivariate linear regression predictive model is advised for number of fracture clusters, cluster spacing, and perforations. Testing of the models shows relatively good in-sample predictions using public data.