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Rosenhagen, Nicolas M. (Colorado School of Mines) | Nash, Steven D. (Anadarko Petroleum Corporation) | Dobbs, Walter C. (Anadarko Petroleum Corporation) | Tanner, Kevin V. (Anadarko Petroleum Corporation)
Abstract The volume of stimulation fluid injected during hydraulic fracturing is a key performance driver in the horizontal development of the Niobrara formation in the Denver-Julesburg (DJ) Basin, Colorado. Oil production per well generally increases with stimulation fluid volume. Often, operators normalize both production and fluid volume based on stimulated lateral length and investigate relationships using "per-ft" variables. However, data from well-based approaches commonly display such wide distributions that no useful relationships can be inferred. To improve data correlations, multivariate analysis normalizes for parameters such as thermal maturity, depth, depletion, proppant intensity, drawdown, geology and completion design. Although advancements in computing power have decreased cycle times for multivariate analysis, preparing a clean dataset for thousands of wells remains challenging. A proposed analytical method using publicly available data allows interpreters to see through the noise and find informative correlations. Using a data set of over 5000 wells, we aggregate cumulative oil production and stimulation fluid volumes to a per-section basis then normalize by hydrocarbon pore volume (HCPV) per section. Dimensionless section-level Cumulative Oil versus Stimulation Fluid Plots ("Normalization" or "N-Plot") present data distributions sufficiently well-defined to provide an interpretation and design basis of well spacing and stimulation fluid volumes for multi-well development. When coupled with geologic characterization, the trends guide further refinement of development optimization and well performance predictions. Two example applications using the N-Plot are introduced. The first involves construction of predictive production models and associated evaluation of alternative development scenarios with different combinations of well spacing and completion fluid intensity. The second involves "just-in-time" modification of fluid intensity for drilled but uncompleted wells (DUC's) to optimize cost-forward project economics in an evolving commodity price environment.
Abstract The successful development of unconventional resources requires an integrated approach using multiple data sets to characterize and optimize recoveries. This study evaluates geological and completion drivers impacting well productivity within the Denver-Julesburg (DJ) Basin of eastern Colorado and southeastern Wyoming, focusing on the Core Wattenberg and Peripheral regions. Understanding these complex relationships is required to explain well performance variability across the play and optimize economics. Over 4,000 producing horizontal wells were analyzed across the study area. Completion data including lateral length, fluid intensity, proppant intensity, stage spacing and vertical well density were put though a comprehensive scrubbing process to identify and remove outliers. Oil and gas type curves were built for individual wells using RS Energy Group's proprietary software. Geological parameters for individual wells were derived from maps generated from over 2,000 digital wells logs, including TVD, hydrocarbon pore volume, isopach, geothermal gradient, clay volume and mud weight-derived pressure gradient, using bottomhole locations. Bayesian, boosted decision tree, linear and decision forest multivariable regression models were tested, and the model with the highest coefficient of determination was used. Permutation feature importance (PFI) method was used to rank variables by impact on recovery. The decision forest regression model was selected for the Core Wattenberg Niobrara and Core Wattenberg Codell sub-regions, whereas the boosted decision tree was chosen for the Peripheral DJ Niobrara and the linear regression model was selected for the Peripheral DJ Codell sub-region. Based on the selected models, fluid intensity and formation TVD were the highest-ranked variables across the entire basin. Introduction DJ Basin breakevens are competitive among North American plays, averaging $35/bbl WTI in 2017, behind the Midland and Delaware basin averages of $32/bbl and $33/bbl. Data analytics using a multidisciplinary approach offers both qualitative and quantitative techniques to characterize variables that influence well productivity. By integrating geological and completion data sets, regional trends are observed along with the key drivers that yield better recoveries. This analysis highlights optimal geological and completion parameters within the Niobrara and Codell formations that drive recoveries in both the Core Wattenberg and Peripheral DJ regions.