The Powder River Basin has emerged over the past year as the latest source of oil production growth for the Lower 48. Companies ranging from a reborn Samson Resources to US onshore mainstays Devon, Chesapeake, and EOG are now betting on the basin to become a long-term core asset. Colorado’s industry lacks the size, variety, and Wild West characteristics of Texas, but that is precisely why the Centennial State’s oil production is surging to record levels. This paper describes a comprehensive field study of eight horizontal wells deployed in the stacked Niobrara and Codell reservoirs in the Wattenberg Field (Denver-Julesburg Basin).
The operator piloted a new well-completion design combining inflow-control valves (ICVs) in the shallow reservoir and inflow-control devices (ICDs) in the deeper reservoir, both deployed in a water-injector well for the first time in the company’s experience. In this paper, the authors describe a project to design, field trial, and qualify an alternative solution for real-time monitoring of the oil rim in carbonate reservoirs that overcomes these disadvantages. The authors detail the development of a technique based on surface-to-borehole controlled-source electromagnetics (CSEM), which exploits the large contrast in resistivity between injected water and oil to derive 3D resistivity distributions, proportional to saturations, in the reservoir. This industry is one often considered reactive and overly tradition-bound. These new technologies, however—and, more importantly, the drive of these researchers to harness their capabilities—prove that petroleum engineers remain at the forefront of innovation and discovery.
After a long cooling off period, this dry-gas shale play is once again red hot. Ghawar vs. Permian Basin: Is There Even a Comparison? While some try to put the two enormous oil producers toe-to-toe, the best thing to do might be to understand why they are different. Machine-learning methods have gained tremendous attention in the last decade. The underlying idea behind machine learning is that computers can identify patterns and learn from data with minimal human intervention.
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)
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.
Image processing of high-resolution 3D images to create digital representation of pore microstructures for image-based rock physics simulations remains a highly subjective enterprise, despite the seemly precision associated with improving imaging resolutions and intensive parallel computations. The decisions on how to identify pore space, both macro- and micropores, and various mineral components remain very much dependent upon user choices and biases. This study demonstrates how uncertainty can be quantified for a highly subjective segmentation process. A set of shaly sand samples with significant amounts of authigenic chlorite/smectite that lines larger pores was tested to identify uncertainty quantification (UQ) requirements associated with image-processing steps, segmentation in particular. Much of the porosity in these coarse-grain samples is associated with subresolution micropores that complicates their assignment in any pore-grain segmentation strategy. Two segmentation strategies, a binary segmentation with a linear-threshold and a machine-learning (ML) approach to two-phase segmentation, are employed with different UQ parameter space. The contribution of resolvable macropores in these samples, and their spatial distributions with regard to pore-lining clay mineral with unresolvable microporosity, are iteratively studied over the defined UQ parameter space, and cross-validated by independent NMR and MICP measurements. The pore structure extracted from these different iterations was the basis of simulations for basic petrophysical properties. Upon cross-validation of simulated results with measured core properties, a UQ framework is proposed to assess the differences between the different measurements from three angles: sampling, numerical and physical.
A Sand Wash Basin well was drilled for an unconventional target for which the measured core properties did not match production for the well. The crushed-rock porosity for the core suggested a bulk-volume hydrocarbon (BVH) of 1.5 to 2.0 p.u., indicating that the stimulation would have to be draining at approximately 400 ft vertically. To resolve this incongruity for further field development, we investigated the validity of crushed-rock porosity and nuclear magnetic resonance (NMR) to accurately assess the resource. Initial results using conventional 2-MHz core NMR yielded results similar to those for crushed-rock porosity. Because unconventional rocks have very fast relaxations in NMR, it was then theorized that with the use of a high-resolution 20-MHz machine, the signal/noise ratio would improve and create a more-accurate quantification of porosity components. The results of using a high-resolution 20-MHz NMR showed a porosity increase from 6.5 p.u. using the Gas Research Institute (GRI) methodology (Luffel et al. 1992) to 14 p.u. on an as-received sample, creating a large increase for in-place calculations. As a result, a process termed sequential fluid characterization (SFC) was developed using high-resolution 20-MHz NMR to quantify all components of porosity (i.e., movable fluid, capillary-bound water, clay-bound water, heavy hydrocarbon, residual hydrocarbon, and free water). This method represents an alternative to crushed-rock methodologies (such as GRI and tight rock analysis) that will accurately quantify movable porosity as well as the other components without the errors introduced by cleaning and crushing. After investigating the application of SFC with the high-resolution 20-MHz NMR, it was identified that other unconventional plays (such as Marcellus and Fayetteville) have an average of 45% uplift on in-place calculations using SFC-based movable porosity. Identifying in-place volumes correctly can vastly improve the characterization of fields and prospects for unconventional-resource development, and, as is shown in this paper, SFC can be used to do so with a great effect on volume assessment in unconventional reservoirs.
Mayerhofer, Michael (Liberty Oilfield Services) | Oduba, Oladapo (Liberty Oilfield Services) | Agarwal, Karn (Liberty Oilfield Services) | Melcher, Howard (Liberty Oilfield Services) | Lolon, Ely (Liberty Oilfield Services) | Bartell, Jennifer (Liberty Oilfield Services) | Weijers, Leen (Liberty Oilfield Services)
Michael Mayerhofer, Oladapo Oduba, Karn Agarwal, Howard Melcher, Ely Lolon, Jennifer Bartell and Leen Weijers, Liberty Oilfield Services Summary In the Williston Central Basin, a well-completion design has a significant effect on well productivity and ultimate recovery. More than 12,000 horizontal wells have been drilled and completed while completion practices continue to vary widely across the basin. Several companies have adopted slickwater-only designs, whereas others have dramatically increased proppant mass. Completion strategies have differed depending on the area in the basin. The objective of this paper is to discuss the effect of various completion changes in the Central Basin and determine which particular change delivers the most "bang for the buck" using a metric of dollars spent per barrel of oil (USD/BO). Although MVA has been used by the authors and many others before, statistical models are limited by their ability to provide predictive relationships (mostly simple linear regressions, and unreliable beyond the data range). This paper provides a novel hybrid approach that uses calibrated relationships from physics-based modeling (combination of fracture and numerical reservoir modeling) between completion parameters and production response in combination with statistical MVA results. This model is then coupled with a completion-cost model to determine which completion method is the most effective to lower USD/BO. Many common completion-parameter changes, such as increasing stage intensity, moving to plug-and-perforate cemented-well designs, increasing injection rate, and increasing proppant mass per lateral foot and fluid volume per lateral foot, have a positive effect on production and are advantageous to lower USD/BO in all areas of the Middle Bakken and Three Forks. The new hybrid MVA approach indicates that pumping slickwater treatments with average proppant concentrations of 1 lbm/gal and treatment sizes from 545 to 750 lbm/ft at pump rates approaching 100 bbl/min through a stage length of 200 ft (50 stages for a 10,000-ft lateral) might be the economic optimum, provided there are no significant well-communication issues. Introduction and Background Several of the authors have been involved in Middle Bakken production evaluations of the Central Basin for several years. The analysis methodology presented in this paper builds on this previous work.
A. Alfataierge, J. L. Miskimins, T. L. Davis, and R. D. Benson, Colorado School of Mines Summary The 3D hydraulic-fracture-simulation modeling was integrated with 4D time-lapse seismic and microseismic data to evaluate the efficiency of hydraulic-fracture treatments within a 1 sq mile well-spacing test of Wattenberg Field, Colorado. Eleven wells were drilled, stimulated, and produced from the Niobrara and Codell unconventional reservoirs. Seismic monitoring through 4D time-lapse multicomponent seismic data was acquired by prehydraulic fracturing, post-hydraulic fracturing, and after 2 years of production. A hydraulic-fracture-simulation model using a 3D numerical simulator was generated and analyzed for hydraulic-fracturing efficiency and interwell fracture interference between the 11 wells. The 3D hydraulic-fracture simulation is validated using observations from microseismic and 4D multicomponent [compressional-wave (P-wave) and shear-wave (S-wave)] seismic interpretations. The validated 3D simulation results reveal that variations in reservoir properties (faults, rock-strength parameters, and in-situ stress conditions) influence and control hydraulic-fracturing geometry and stimulation efficiency. The integrated results are used to optimize the development of the Niobrara Formation within Wattenberg Field. The valuable insight obtained from the integration is used to optimize well spacing, increase reserves recovery, and improve production performance by highlighting intervals with bypassed potential within the Niobrara. The methods used within the case study can be applied to any unconventional reservoir. Introduction The Niobrara Formation is an organic-rich, self-sourcing unit composed of carbonate deposits in the form of alternating layers of chalks and marls. The Niobrara resource play is typically compared with the Eagle Ford Shale because of its high carbonate content. Early production can be dated back to 1976 from vertical wells in Wattenberg Field, although development was not deemed commercially viable at the time (Sonnenberg 2013). The shale play has become more attractive because of horizontal drilling and multistage hydraulic fracturing, allowing the Niobrara to be developed with overall success in the Denver-Julesburg Basin since 2009. The Niobrara Formation extends into several basins within the central USA involving Colorado, Wyoming, Nebraska, and Kansas.
Shale hydrocarbon production has become an increasingly important part of global oil and gas supply during the past decade. The life of projects in unconventional plays, such as shale oil and gas, tight oil and gas, coal bed methane etc., heavily depends on the Estimated Ultimate Recovery (EUR). However, the correlation to predict EUR in conventional plays becomes invalid for unconventional plays, which significantly affects the economics of relevant unconventional projects. The objective of this paper is to investigate the correlations between EUR and petrophysics/engineering/production parameters by data regression and interpolation analysis via big data mining from Eagle Ford. Furthermore, a 4-D interpolated EUR database and EUR prediction models are established based on the relevant regression and interpolation results. This study not only helps us understand the physics behind EUR prediction in unconventional plays, but also facilitates determining the viability of projects in unconventional formations from a big data perspective.
In this study, petrophysics/engineering/production data from 4067 wells in Eagle Ford is summarized for analysis. Firstly, a sensitivity analysis is carried out to determine the most sensitive petrophysics and engineering controlling factors. In particular, the physics behind the EUR predictions is discussed in details. Following it, the 2-D nonlinear regression and the multivariate linear regression are applied to evaluate the relationship between EUR and engineering/production data. In addition, a 4-D interpolated EUR database is established to predict EUR based on the petrophysics parameters. The applied nonlinear multivariate interpolation methodology is the Triangulated Irregular Network based Nearest Interpolation Method (3-D). Finally, the 4-D interpolated EUR database are applied to several wells in the Eagle Ford to test its accuracy, confidence and reliability.
Based on the sensitivity analysis results, Vitrinite Reflectance Equivalent (VRE), Total Organic Carbon (TOC) and Resource Density (porosity, hydrocarbon saturation and gross formation thickness) are the most sensitive and important parameters in Eagle Ford shale formation. Based on the data-mining results, effective lateral length has a positive monotonic relation with EUR; EUR increases with more proppant weight and higher true vertical depth. Frac stage and perf per cluster do not have a strong correlation with EUR. In addition, azimuth has a vague relation with EUR while drilling along the North-South orientation is the safest approach in Eagle Ford Shale. The physics behind the correlations is analyzed and discussed in detail. Finally, several DCA EURs of wells from Eagle Ford are used to test the established 4-D interpolated EUR database, and the study results show that the relative errors in EUR predictions are within 30%, indicating that the methodology in this study has great potentials for unlocking more reserves economically in shale formations.
This study offers an insightful understanding of unconventional hydrocarbon production mechanism from a big data perspective, as well as a feasible and accurate method to predict EUR and evaluate projects economic feasibility in Eagle Ford. This methodology can be also applied to other unconventional fields such as Utica, Permian and Bakken Shale plays, if data is available.
In some basins, large scale development of unconventional stacked-target plays requires early election of well targeting and spacing. Changes to the initial well construction framework can take years to implement due to lead times for land, permitting, and corporate planning. Over time, as operators wish to fine tune their development plans, completion design flexibility represents a powerful force for optimization. Hydraulic fracturing treatment plans may be adjusted and customized close to the time of investment.
With a practical approach that takes advantage of physics-based modeling and data analysis, we demonstrate how to create a high-confidence, integrated well spacing and completion design strategy for both frontier and mature field development. The Dynamic Stimulated Reservoir Volume (DSRV) workflow forms the backbone of the physics-based approach, constraining simulations against treatment, flow-back, production, and pressure-buildup (PBU) data. Depending on the amount of input data available and mechanisms investigated, one can invoke various levels of rigor in coupling geomechanics and fluid flow – ranging from proxies to full iterative coupling.
To answer spacing and completions questions in the Denver Basin, also known as the Denver-Julesburg (DJ) Basin, we extend this modeling workflow to multi-well, multi-target, and multi-variate space. With proper calibration, we are able generate production performance predictions across the field for a range of subsurface, well spacing, and completion scenarios. Results allow us to co-optimize well spacing and completion size for this multi-layer column. Insights about the impacts of geology and reservoir conditions highlight the potential for design customization across the play. Results are further validated against actual data using an elegant multi-well surveillance technique that better illuminates design space.
Several elements of subsurface characterization potentially impact the interactions among design variables. In particular, reservoir fluid property variations create important effects during injection and production. Also, both data analysis and modeling support a key relationship involving well spacing and the efficient creation of stimulated reservoir volumes. This relationship provides a lever that can be utilized to improve value based on corporate needs and commodity price. We introduce these observations to be further tested in the field and models.