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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.
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.
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 submitt ed 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 Codell formation is a low permeability, clay rich, late Cretaceous agesandstone within the Wattenberg field of the DJ Basin. Since 1997 over 1500Codell wells have been restimulated. Results on the past 200 refractured Codellwells using a reduced CMG polymer fluid system have yielded incremental monthlyproduction results in excess of 1900 BOE/well or approximately 80% of theoriginal initial production. Success of this program is believed to be thecombination of stringent well selection criteria, high fluid quality controlguidelines and effective operational field practices. It is believed that dueto this recent success in restimulating the Codell, over 4000 additional wellswithin the DJ Basin may be restimulated with economic benefits.
This paper will discuss completion history of the Codell formation and howcriteria from candidate selection to fluid quality may impact the success ofsuch a program.
Records indicate that the first Codell completion in the Wattenberg field ofthe Denver-Julesburg basin occurred in 1955. It was not until the early 1980'sthat the Codell became a major gas play. Since that time thousands of Codellwells have been developed within an area of approximately 100,000,000acres.1 The DJ basin shown in Figure 1 is an asymmetricalbasin just north of Denver, Colorado with the axis of the basin runningparallel to the Front Range uplift.
The Codell, described as Type 2 sandstone by Weimer andSonnenberg2, is a member of the Upper Cretaceous Carlile shale. Itis a bioturbated, reworked fine-grained marine shelf sandstone without acentral bar facies and is laterally continuous across the field area. Figure2 is a stratigraphic sequence of formations bounding the Codell within theDJ Basin. Over the years, many wells have included the Niobrara formation bymeans of separate completions on both the Niobrara and Codell or simultaneouslystimulating both formations with limited entry techniques.3Production from the Niobrara often times has proved to be limited due tonanodarcy matrix permeability. Due to the Niobrara inconsistency, a greatnumber of wells were completed solely in the Codell interval.
These reservoirs were initially over pressured with a pore pressure gradientof ±0.60 psi/ft.4 Pay thickness in the Codell can range from 14 to20 ft. within the central portion of the basin at typical depths of 7000 - 7200feet. Bottom hole temperatures are generally between 230-250° F BHST. It is aclay rich sandstone (15-25% by volume) with pore filling and pore lining mixedlayer illite/smecite clays. Permeability is low (i.e., <0.1 millidarcies),with Density log measured porosity ranging from 8 to 20%.
Aggressive exploration and development of the Codell led to severalexperimental completion techniques throughout the years. Wells were stimulatedwithout regard to geological and lithologic variations. A wide range of fluidtypes and treatment designs were implemented in order to achieve an economicalrate of return.
Original Codell Completions
An obvious contributor to the potential for restimulations is the originalcompletion. The Codell formation has a history that begins in the middle 1950'sand continues today with the continued drilling of acreage within the DJ Basin.Original completion techniques and stimulation fluids utilized have affectedthe results of restimulation programs.
This paper presents geologic and reservoir parameters of the Niobrara Formation In Weld County, Colorado. With the use of computer generated contour maps, It is possible to predict favorable areas of profitable possible to predict favorable areas of profitable Niobrara pay. This predictability Is further enhanced when combined with Scanning Electron Microscopy (SEM) analysis and historical production analysis.
The SEM results show that the Niobrara In this region is a micrite and not a true chalk. The porosity Is, therefore, lower than would be expected in a chalk.
The thickness of the second bench and the pore volume appear to have a better relationship to known faults In the Niobrara than present day structure. These parameters were analyzed in order to predict areas of parameters were analyzed in order to predict areas of faulting and fracturing, since these areas are known to have the best potential for Niobrara production. Use of these techniques Indicates that the northern portion of the study area has the highest potential portion of the study area has the highest potential for successful Niobrara wells.
Based on the limited amount of production history available in this region and current market oil and gas prices, the average Niobrara well in this region appears to be uneconomic unless supported by additional production from other horizons. However, computer mapping suggests that current production Is not located in the most promising areas. Greater Niobrara production potential may be found In local areas characterized by greater porosity, thicker benches, and proximity to faults.
The development of cost effective predictive techniques for petroleum exploration has been a continuing quest in the petroleum geology industry. This paper presents one such technique found useful in the Weld County, Colorado, Niobrara Formation of the Denver-Julesburg Basin. More specifically, the data were derived from the Second Chalk Bench of the Niobrara. This bench was selected as It constitutes the most continuous bench across the study area and has the highest potential for commercial hydrocarbon development. The geographical study area consists of 56 townships located in Weld County, Colorado, T1-7N, R61-68W (Figure 1).
The purpose of this paper Is to Identify favorable areas for Niobrara hydrocarbon exploration. Seven critical variables from publicly available well logs were input Into a computer and used to generate a series of contour maps showing present day structure, paleostructure, porosity, thickness, and pore-volume. paleostructure, porosity, thickness, and pore-volume. Two further techniques, Scanning Electron Miscroscopy (SEM) analysis of sidewall coresand historical production analysis, were employed to assist In production analysis, were employed to assist In interpreting and predicting potential reserves. Evaluation of the computer maps, SEM data, and historical production data provided the basis for predicting favorable areas for hydrocarbon exploration predicting favorable areas for hydrocarbon exploration in the Niobrara Formation.
The Niobrara Formation was deposited during the Late Cretaceous Period in the Western Interior Seaway. The Niobrara is divided Into two members: the Smoky Hill Chalk and the Fort Hays Limestone. The upper member, the Smoky Hill Chalk, consists of gray to white chalky shale with three locally massive chalk benches, referred to as benches 1, 2, and 3 (from top to bottom). The Fort Hays Limestone, the lower member, is composed of 25 to 85 feet of chalk and shaly chalk interbedded with thin beds of chalky shale (see Figures 2 and 3),
The Niobrara produces gas from low-relief structures on the east flank of the Denver-Julesburg Basin and the north flank of the Las Animas Arch in Colorado, Kansas, and Nebraska (Smagala, 1981). In Yuma County, Colorado, and portions of the Las Animas Arch, Bench 1 is a high porosity, low permeability reservoir. This differs from the correlative chalk bench in the Weld County Denver-Julesburg Basin which is of lower porosity. This reduced porosity is thought to be due porosity. This reduced porosity is thought to be due to greater depth of burial.
The Niobrara produces biogenic gas In low volumes ranging from 20 to 300 thousand cubic feet of gas per day (MCFGPD) (Lockridge, 1978). Niobrara wells are commonly stimulated with a foam fracture treatment.