Completion design optimization using machine learning and big data solutions

Abstract

Systems and methods for generating and storing completion design models in a central data repository of models for completion design, well bore (such as fracturing or drilling) or other operations is shown. In one embodiment, the methods comprise identifying parameters of a hydraulic fracturing operation within a subterranean formation; generating a completion design model based on the parameters of the hydraulic fracturing operation; storing the completion design model in a central data repository of models; generating the central data repository of models; wherein the data repository is based on previously generated models for one or more other subterranean formations having varying levels of uncertainty for expected output; reducing the level of uncertainty for the expected output based on completion parameters, wherein the completion parameters are used to update the central data repository of models; accessing the central data repository of models to predict results expected for a data set based at least in part on the central data repository of models, wherein the results comprise a prediction as to a level of output for the dataset, further wherein the prediction comparison results in an identification of the optimized completion design for the dataset.