Multivariate Analysis and Data Mining of Well-Stimulation Data by Use of Classification-and-Regression Tree With Enhanced Interpretation and Prediction Capabilities

Abstract

The well-treatment program is an important part of the fielddevelopment plan, and certain variables, such as job-pause time (JPT) and fracture screenout, can affect its efficiency. JPT is the time during which pumping is paused between subsequent treatments of a job. Screenout occurs because of a sudden restriction of fluid flow inside the fracture and through the perforation. The objectives of this work are to investigate whether, from existing data, it is possible to find patterns in significant variables that affect the extreme values of JPT in a particular region, and what is the most critical variable causing screenout. The answers are sought by applying a classification-and-regression tree (CART) to both categorical and continuous variables in the database. The practical application of CART is presented by use of case studies containing JPT and screenout. Significant variables are found that affect the response variables, and predictor variables are ranked in the hierarchal order of their importance. Such information can be used to control predictor variables that cause high JPT or screenout.The results are outlined in an intuitive way, including categorical, continuous, and missing values. Because CART is a datadriven, deterministic model, one cannot calculate the confidence interval of the predicted response. The confidence in results is purely because of historical values, and the accuracy of the result produced by a tree model depends on the quality of recorded data measured in terms of volume, reliability, and consistency. The prediction capability of CART is enhanced by use of the normalscore transform and by dividing the data set into smaller groups by use of clustering. The approach presented in this paper analyzes a data set under limited information and high uncertainty and should lead to developing methodology for generating proxy models to find future success indices (e.g., one for drilling efficiency or production from a fracture). This could standardize stimulation and generate decision practices to save costs in field development and the optimization process.

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