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Abstract 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.
Abstract Accelerating the learning curve in the development of the Vaca Muerta utilizing lessons learned in North American unconventional resource plays is the focus of this paper. Reducing completion costs while maintaining high productivity has become a key objective in the current low-price environment. Completion diagnostics have been demonstrated to optimize stimulation and completion parameters that have shaped successful field developments. The paper reviews stimulation diagnostic data from wells completed in the Tuscaloosa Marine Shale, Eagle Ford, Wolfcamp and Niobrara shale formations. Case histories are presented in which proppant and fluid tracers were successfully employed in completion optimization processes. In the examples presented, diagnostic results were used to assess the stimulation of high productivity intervals within a target zone, evaluate various completion methods, and optimize stage and cluster spacing. The diagnostic data were compared with post-frac production rates in an effort to correlate completion changes with well performance. Results presented compare first, engineered perforations versus conventional geometrically spaced perforations to drive up effectiveness in cluster stimulation. Second, new chemistries, such as nanosurfactant, versus conventional chemistries to cut either completion cost or prove their profitability. Third, employing an effective choke management strategy to improve well productivity. Last, as in any stacked pay, determining fracture height growth in order to optimize well density, well spacing, field development and ultimately the recovery of the natural resources. Completion effectiveness is shown to be improved by landing laterals in high productivity target intervals, increasing proppant coverage across the lateral by utilizing the most effective completion methods, optimizing cluster spacing and decreasing the number of stages to reduce completion costs while achieving comparable production rates. Cluster treatment efficiency (CTE), in particular, has become a critical metric when optimizing hydraulic fracturing treatment designs based on current and future well densities. It can be used to rationalize well performance as well as to identify possible candidates for a refrac program. Using completion diagnostics, successful completion techniques were identified that led to production enhancements and cost reductions in prolific plays such as the Tuscaloosa Marine Shale, Eagle Ford, Wolfcamp and Niobrara.
Abstract In unconventional resource plays, constructing a sound geological model that ties various well information is imperative for properly extracting and integrating well and seismic information and for predictive and prescriptive analytic workflows. Unlike conventional plays, unconventional plays that span basins have potentially tens of thousands of wells. Constructing geological models to include all wells and then updating them as additional ones become available can be a daunting task. When constructing large cross sections, regional stratigraphic patterns are easily discernible visually. Converting these geologic events and spatial patterns to digital information using the power of the computer and new machine learning techniques is becoming more important than ever as geoscientists attempt to "keep up" with all this information. This paper will cover a modern technology toward that end. Introduction Previous attempts have been made to pick geologic well tops automatically using expert systems (Olea et al.), neural networks (Luthi et al.), and dynamic programming (Lineman et al., Inazaki, Zoraster et al., Fang et al.). While these previous efforts have been helpful in defining the problems and building blocks to solve well-log correlation automatically, they have clearly been much less successful than has been observed in seismic picking algorithms that started in the 1980's. This is mainly owing to the nature of seismic data. Seismic traces are band-limited, closely spaced (on the order of meters) with neighboring traces almost identical to each other, and are consistent with the same start and ending times, sample rates, and vertical representation. These traits make correlating neighboring peaks, troughs and zero-crossings reasonably easy as compared to well logs, which are more widely spaced (on the order of hundreds to thousands of meters), have inconsistent depth ranges with possible gaps, and may be from highly non-vertical well bores. As more oil companies transition from exploration to resource recovery optimization and the number of new wells in well-known basins dramatically increases, geologic cross sections across these basins begin to take on more of a seismic look, as shown in Figures 1 and 2 below. When logs are hung on stratigraphic datums, as Figure 2 shows, geologic intervals are readily evident across many tens, if not hundreds or thousands of wells. Not only is the lateral consistency of strong events evident, such as the Codell in this case, but patterns of finer detail in the sequence stratigraphy (flooding surfaces, onlap, thickening and thinning from changing accommodation and sediment supply) become more visually apparent. Further refined picking of associated events is warranted but could prove tedious and time consuming if done manually.
ABSTRACT: Heterogeneity of an unconventional reservoir is one of the main factors affecting production. Well performance depends on the size and efficiency of the interconnected fracture “plumbing system”, as influenced by multistage hydraulic fracturing. A complex, interconnected natural fracture network can significantly increase the size of stimulated reservoir volume, provide additional surface area contact and enhance permeability. The purpose of this study was to characterize the natural fracture patterns occurring in the unconventional Niobrara reservoir and to determine the drivers that influenced fracture trends and distributions. Highly fractured areas/fracture swarm corridors were identified and integrated into a reservoir model though DFN (Discrete Fracture Network) application for further prediction of reservoir performance using reservoir simulations. The predictive capability of DFN models can aid in improved reservoir performance and hydrocarbon production through optimized well spacing, re-frac stage locations planning for existing wells as well as completion strategies design for new wells.
Summary ConocoPhillips built its position in the D-J Basin Niobrara play between 2010 and 2012 with current leasehold of approximately 130,000 net acres south east of Denver. Envisaged as a new play extension to Wattenberg Field, based on a thermal anomaly in Arapahoe County, early production expectations were based on the aggregate of Niobrara horizontal wells across the basin. Production results from early COP horizontal wells were difficult to compare with aggregate D-J basin production, and demanded a more granular approach to analogue selection. With the goal of identifying geologically comparable well groups, ConocoPhillips' Niobrara Exploration team assembled a database of 800+ horizontal wells and 1500+ vertical producers, paring down potential analogues by thermal maturity, API gravity, fractured reservoir, petrophysical properties, lateral length and completion type. Petrophysical parameters were mapped across the basin in > 3000 wells, and extrapolated to all horizontal producers to enable cross-plotting with production rates, EUR estimates from decline curve analysis and fluid properties. Sensitivity analysis revealed meaningful trends in well orientation, API gravity, proppant volumes, target horizon and the relevance of oil in place, which helps focus the appraisal program and identify critical uncertainties to test. Selecting analogue wells based on reservoir properties and comparable well design was critical to calibrate reservoir simulation models to better forecast production from horizontal wells in the area of interest.
The Upper Cretaceous Niobrara Chalk in the Sand Wash Basin is characterized by having more terrigenous components than the Niobrara Chalk further to the east. This difference in lithology affects reservoir quality and the potential of the chalk as a matrix-producing reservoir. The degraded reservoir does not appear productive as a shaleoil reservoir, but may be productive as a shale-gas reservoir in the deeper and hotter parts of the Sand Wash Basin. The major objective of this paper is to present a preliminary characterization of the Niobrara Chalk as a shale-gas system in the northwest Sand Wash Basin.
Summary Using a good quality northern Pennsylvania (PA) Analog 3D survey, available well data, published outcrop data and subsurface information as well as production data available from the state, we are able to demonstrate that wide-azimuth seismic is sensitive to variations in fracturing at the scale of individual pads or even individual wells. This variation in fracturing begins to explain why production varies significantly, even locally, within the Marcellus play. Rose diagrams from quantitative fracture analysis using azimuthal seismic velocity volumes are compared to published data from Appalachian black shale outcrops and subsurface fracture models proposed in various papers in order to validate the results from subsurface data. While it has long been understood that natural fracture systems are essential for achieving the best production in Marcellus shale gas wells, methodologies for understanding the heterogeneities in these fracture systems in the subsurface are less well understood. Analysis of wide-azimuth P-wave seismic velocity attributes at the reservoir level, and for specific laterals or proposed laterals, can provide this insight. Although anisotropy, measured as azimuthal variations in velocity, can reflect rock fabric or stress, we show evidence that the likely source of these anisotropies is the presence of systematic joints.
Summary Calcite forms variable proportions of source-rock reservoirs ("shale plays"). Although calcite content can be quantified via petrophysical analyses, XRD, XRF and other techniques, the amount of calcite, by itself, is not enough information to predict the likely importance of these minerals for reservoir and completions quality. Four principle types of calcite can be recognized:Pelagic components, mostly foraminifera and coccoliths, form a large component of the Eagle Ford and Niobrara but other types of pelagic carbonates (e.g., tentaculitids) are common in Paleozoic source-rock plays such as the Marcellus, Carbonate "event beds" (turbidites, storm deposits, etc.) are present in the Avalon, Barnett, Vaca Muerta and other plays, In situ benthic carbonates (bivalves, corals) are present in some plays (e.g., Eagle Ford, Marcellus), and Diagenetic calcites (pore filling cements, fracture fills, replacements, etc.) are present to varying degrees in perhaps most source-rock plays. Detailed core descriptions and petrographic observations are critical for assessing the origin of the calcite. Similar concepts apply to other mineral and organic components of mudstones.
Copyright 2013, Unconventional Resources Technology Conference (URTeC) This paper was prepared for presentation at the Unconventional Resources Technology Conference held in Denver, Colorado, USA, 12-14 August 2013. The URTeC Technical Program Committee accepted this presentation on the basis of information contained in an abstract submitted by the author(s). The contents of this paper have not been reviewed by URTeC and URTeC does not warrant the accuracy, reliability, or timeliness of any information herein. All information is the responsibility of, and, is subject to corrections by the author(s). Any person or entity that relies on any information obtained from this paper does so at their own risk.
The Niobrara is characterized by sweet spots that are isolated and small in size compared to the expansive area of the play. A new approach developed in the mineral industry that considers Niobrara sea floor chemistry and the effects of underlying basement lithology and fault reactivation, has the potential to augment seismic and help identify sweet spots earlier in the exploration process. Although the relatively soft Niobrara is characterized by variable structure, reservoir quality, and rock properties, sweet spots are associated with reactivated basement faults, heat, and fluids. Identification of basement lithology and faults using potential fields data in light of far-field stress modeling of basement fault reactivation, overlying sedimentary rock deformation, and heat and fluids predicted by specific basement lithologies, identifies areas favorable for sweet spot development. Geochemical analysis of drill cuttings and surface soils reveal specific geochemical fractionation sequences that are interpreted to reflect paleo-sea floor patterns of chemical deposition of groups of elements and hydrocarbons (HC‘s) that traveled together leaving =chemical trails‘ from a source to a depositional site on the sea floor. These patterns can be mapped and =vectored‘ because they provide directional and magnitude information. They are characterized by proximal anomalies that lie within and above petroleum accumulations and the distal anomalies that lie adjacent to and distant from petroleum accumulations. They can also be modeled in light of kinematic analysis of basement fault reactivation and overlying sedimentary rock deformation.