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Production Analysis Couples Multivariate Statistical Modeling and Pattern Recognition

Approximately 5,000 horizontal Eagle Ford wells have been completed in south Texas. Still, geologists and engineers question whether their companies are using the most appropriate operating practices. Multivariate statistical analysis of larger data sets offers sound interpretation across larger geographic areas, with the provision that correlations need to be scaled to local conditions. The purpose of this paper is to apply multivariate statistical modeling in conjunction with geographic-information-systems (GIS) pattern-recognition work to the Eagle Ford.

Introduction

With approximately 5,000 horizontal Eagle Ford wells now completed, large sets of public and proprietary data are available for production and completion-/stimulation-optimization studies. However, the processes of gathering data, quality control, learning what questions to ask of the data sets, and learning how to ask these questions in robust statistical fashion contain many challenges. Data sets involving large well counts contain many variables that are not ideally distributed or have missing or bad values.

This work uses particular data-mining methods, particularly GIS mapping and boosted-tree regression modeling, to attempt to overcome some of the challenges with the available data sets to better understand the effect of key well, completion, and stimulation parameters on productivity and production efficiency.

In this work, the Eagle Ford formation of south Texas was divided into three major producing areas that were subsequently studied with mapping techniques and that were individually modeled by use of boosted trees.

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 168628, “Application of Multivariate Statistical Modeling and Geographic-Information-Systems Pattern-Recognition Analysis to Production Results in the Eagle Ford Formation of South Texas,” by Randy F. LaFollette, SPE, Ghazal Izadi, SPE, and Ming Zhong, SPE, Baker Hughes, prepared for the 2014 SPE Hydraulic Fracturing Technology Conference, The Woodlands, Texas, USA, 4–6 February. The paper has not been peer reviewed.
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Production Analysis Couples Multivariate Statistical Modeling and Pattern Recognition

01 March 2014

Volume: 66 | Issue: 3

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