As of this date, 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. Side-by-side case studies may show value or not, given the challenge of small sample size and hidden influences on outcome. Multivariate statistical analysis of larger data sets offers sound interpretation across larger geographic areas, with the caveat 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.
The investigation began by acquiring Eagle Ford data using both proprietary and public information. The different data sets were loaded into a common database and put through quality control sanity checks. Production proxies, such as maximum oil rate in the first 12 producing months and normalized 12 month cumulative production, were selected and merged with the other data. Final data sets were then subjected to analysis in both an open-source multivariate statistical analysis and visualization code and a commercial Geographic Information Systems (GIS) application.
Similar to other studies in unconventional reservoirs, integration of the two analysis and interpretation methods highlighted the importance of using well location as a proxy for reservoir quality when working with data sets that lack such measurements. The use of multivariate statistical analysis allowed modeling the impact of particular well architecture, completion, and stimulation parameters on the production outcome by integrating out the impact of other variables in the system.
This work is a continuation of the prior work designed to address well optimization in unconventional reservoirs. It is significant in that it takes full advantage of GIS map-based methods and multivariate statistical methods to capitalize on the volume of data available through the public domain.