Randy Lafollette

Characterization of Hydraulic Fracture Geometry in Shale Gas Reservoirs Using Early Production Data

Drilling long horizontal wellbores and completing wells using multistage fracturing are common practices in shale play development. One of the keys to enhancing production of these ultratight reservoirs is creation of a complex fracture system with very high surface area. Bi-wing fracture geometry parameters (length, height, width, and conductivity), are not sufficiently detailed to describe complex fractures. Instead, fracture density, unpropped and propped fracture conductivity, and stimulated reservoir volume (SRV) may be more appropriate parameters to consider in both fracture design and production modeling. Characterizing these parameters is challenging due to the uncertainty of natural fracture distribution, local stress changes, and the lack of granular reservoir description in three dimensions. Results of the current study show that posttreatment production data exhibit distinct features associated with various fracture systems and should be able to aid in describing the complex fracture system. The primary objective of this work was to find correlations between early-time production signatures and the fracture network. First, production simulation models were set up with various combinations of secondary fracture distribution, primary fracture conductivity, and different sizes of SRV. Those models were used to generate synthetic production and load recovery data for different scenarios. Secondly, the generated production data were analyzed with diagnostic plots to identify characteristic features for different fracture scenarios. Peak production, earlier production decline rate, and time to reach peak production were also evaluated and correlated to various fracture geometries. Results indicated that peak production correlated well with both SRV and secondary fracture density. Early-time decline rate was affected significantly by secondary fracture density. Time to reach peak production is impacted by fracture density, unpropped and propped fracture conductivity, and SRV. Diagnostic plots showed interesting features for various fracture scenarios, which may indicate complex flow regimes. This result needs further investigation.

Application of Multivariate Statistical Modeling and Geographic Information Systems Pattern-Recognition Analysis to Production Results in the Eagle Ford Formation of South Texas

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.

Application of Multivariate Analysis and Geographic Information Systems Pattern-Recognition Analysis to Production Results in the Bakken Light Tight Oil Play

Even after the drilling of several thousand horizontal Bakken wells in Montana and North Dakota, geologists and engineers question whether their companies are using the appropriate completion and stimulation parameters for their reservoir in their particular location. Answers to this fundamental question can be difficult to come by using univariate statistical techniques. Thus, more advanced and integrated methods of analysis may be needed to achieve sound interpretations. The purpose of this paper is to apply multivariate statistical modeling in conjunction with Geographic Information Systems (GIS) pattern recognition work to update and expand previous Bakken data-mining efforts. The investigation began by updating Bakken Formation 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, were selected and merged with the other data. Final data sets were then subjected to analysis in both an open-source statistical analysis environment and a commercial (GIS) application. The 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 completion and stimulation parameters on the production outcome by averaging out the impact of all other variables in the system. This work is believed to be unique in its combination of multivariate statistics and GIS pattern recognition to address questions of well optimization in unconventional reservoirs and that is its application. It is significant in that it expands the scope of prior studies that did not take full advantage of multivariate statistical methods.