TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThis paper discusses how the Horseshoe Canyon coalbed methane (CBM) play in Western Canada was converted from an under-explored, non-commercial resource to a major commercial play through the application and modification of technology and analysis techniques from other basins, and how this play is being developed today. As of December 2004, production from the Horseshoe Canyon CBM play is estimated to be over 100 MMscfd of gas, with future production expected to grow significantly.The first commercial developments were completed in 2003 and 2004, and drilling is increasing and expected to exceed 3,000 wells per year in 2005. 1 The Horseshoe Canyon CBM play covers a large geographic area of 200 miles by 50 miles, and exists in a large, complex vertical section with numerous coal, sand, silt, shale and mudstone layers. In addition, the play is naturally under-pressured, and the coal beds are mostly dry. Because of these complex and unique characteristics, assessment of commercial viability and development optimization can be confusing and only applicable over small parts of the play. MGV Energy, Inc. (a wholly-owned subsidiary of Ft. Worthbased Quicksilver Resources, Inc.) and its initial joint venture (JV) partner, PanCanadian Petroleum Limited (now EnCana Corp.), discovered the techniques to achieve commerciality and pioneered many of the practices used by industry today for Horseshoe Canyon CBM development. In this paper, we discuss identification of the reservoir opportunity, including data acquisition and analysis. We describe geologic and reservoir models, and production forecasting methods. We also cover completion and production practices, spacing optimization and reserves estimation procedures.
In 1996, the Gas Research Institute (GRI) performed a scoping study to investigate the potential for natural gas production enhancement via restimulation in the United States. The results indicated that the potential was substantial, particularly in the tight sands of the Rocky Mountain, Mid-Continent and South Texas regions. However it was also determined that industry's historical experience with restimulation is mixed, and that considerable effort is required in candidate selection, problem diagnosis, and treatment selection/design/implementation for a restimulation program to be successful. As a result GRI initiated a subsequent two-year R & D project with the objectives: 1) to develop efficient, cost-effective, and reliable methodologies to identify wells with restimulation potential, 2) to identify and classify various mechanisms leading to well underperformance, and 3) to develop and test various restimulation techniques tailored to different causes of well underperformance. The approach adopted for the R&D program is a combination of conceptual methodology development, laboratory studies, and actual field testing of restimulation treatments in tight gas sand reservoirs. In total, four test sites are planned for the project; each site consists of about 300 total wells in a contiguous area and completed in a consistent producing horizon, out of which five are selected for actual restimulation. The first two sites are in the Rocky Mountain region, the first being in the Big-Piney/LaBarge Producing Complex of the northern Moxa Arch in the Green River Basin (Frontier Formation), and the second being the combined Rulison, Parachute and Grand Valley fields of the eastern Piceance Basin (Williams Fork Formation). At present, restimulation candidates have been selected, verification testing completed, and two restimulations performed at the first site; preliminary candidate selections have been made at the second site. The results to date from this project show why methodologies to accurately select "high - potential" restimulation candidates have eluded previous investigators. Each of the analytic procedures utilized thus far in the project is providing a different list of candidates, each based upon different criteria and with uncertainty as to the validity (or lack thereof) of each. While the situations being addressed in this project are understandably complex, specifically multilayered, heterogeneous reservoirs that are completed and stimulated in a wide variety of ways, the project experience to date supports that no acceptable methodology currently exists to universally select restimulation candidates across different geologic settings (which is the objective of this project). What is clear is that restimulation potential does appear to exist, as evidenced from the restimulation treatments performed to-date, but that some degree of site-specific knowledge and methodology customization is required. This is supported by the findings from the individual well reviews and laboratory studies. Clearly, more results from this project are needed to better understand the methodologies being investigated, and how they should be integrated to develop the efficient yet robust methodology sought.
In this paper, we present a methodology to predict the performance of horizontal gas wells in unconventional reservoirs using publicly available completion data. Our process combines public domain data with statistical analysis and probabilistic simulation methods to forecast well performance without a detailed reservoir characterization. We have tested our methodology using a 425-well dataset from the unconventional Montney resource play in British Columbia, Canada. We believe this workflow can be applied to other resource plays with similar data.In our SPE Paper 167154 [1], we determined the sensitivity of production performance to completion parameters using multivariate regression analysis on the same 425-well dataset from the Montney formation. We found that the number of fracture stages and the number of perforation clusters per stage were the most influential predictors of well performance. In this paper, we discuss how we combined the regression analysis results with probabilistic methods to predict well performance. The model converts the deterministic regression coefficients into probabilistic distributions to account for parameters not considered in the original regression analysis, including reservoir properties. The results of our study show that by using this model, we can match the range of actual well performance outcomes with a 95% confidence.Considering the importance of shale gas resources to the North American energy supply and the difficulty of characterizing shale gas reservoirs, this methodology offers a distinct advantage by providing a predictive model for well performance without the need for a detailed reservoir characterization. This also could be a beneficial tool to use in scoping studies where high-level, rapid evaluation is required.
Studies by the Gas Research Institute have revealed that improved methods are needed to cost-effectively identify high-potential restimulation candidate wells. subsequent research has had the objective of developing such methodologies, and testing them in the field. The techniques being investigated include production statistics, virtual intelligence, and type-curves. For various reasons, field activities have been slow to implement, limiting the feedback needed to fully test each candidate selection method. Therefore a reservoir simulation study was performed to test the methods. The simulation field model consisted of four reservoir layers of variable properties. Wells were drilled in three rounds over a 12-year period (120 total wells). Completion intervals were varied for each well, as were skin factors for individual layers. Before providing the data to the project team for analysis, noise was added. These model features and noise were incorporated into the exercise to best replicate actual field conditions. Restimulation potential was established by "restimulating" each well in the model and observing the incremental production response. Application of the various candidate selection techniques, and comparing the results to the known answer, has yielded several important conclusions. First, simple production data comparisons are not effective at identifying high-potential restimulation candidates; better producing wells tend to be better restimulation candidates. Virtual intelligence techniques were the most successful, correctly identifying over 80% of the theoretical maximum available potential. The type-curve technique was not as effective as virtual intelligence, but still achieved a 75% candidate selection efficiency. Introduction The Gas Research Institute began investigating the potential of restimulating existing natural gas wells as a source of incremental, low-cost reserves in 1996. Initial studies revealed that the potential was substantial (particularly in tight sand formations), but improved methods were required to cost-effectively and reliably identify high-potential restimulation candidate wells.1 This need was underscored by an observation that 85% of the restimulation potential for a given field appears to exist in only 15% of the wells; identification of that 15% is therefore critical to restimulation economics, but comprehensive field studies specifically for this purpose are too costly to justify. Based on these findings, subsequent research initiated in 1998 has had the objective of developing a cost-effective and reliable restimulation candidate selection methodology, and testing it in the field.2 The techniques being investigated for the methodology include production statistics,3 virtual intelligence4,5,6,7 and engineering-based type-curves.8,9 The techniques were to be applied to four field test sites in the Green River, Piceance, East Texas and South Texas basins, each consisting of 200–300 wells and at which five restimulation treatments were to be performed.10,11 For various reasons, field activities have been slow to implement, limiting the feedback needed to fully examine the effectiveness of and optimize each candidate selection technique. In order to advance methodology development in a timely manner, a different approach was needed to validate the performance of each individual candidate selection technique.
We have used a three-dimensional, multi-phase reservoir model to history match the post-fracture performance data of two separate completions in the complex, low permeability Travis Peak formation. This model consists of a two-layer, two-phase (gas-water) reservoir in communication with the wellbore via a vertical hydraulic fracture. The two-layer, two-phase reservoir model accurately matches post-fracture production data and pressure buildup test data from each Travis Peak completion interval.Our results show that the properties of the hydraulic fractures determined by this reservoir simulation are in good agreement with the fracture properties determined by a three-dimensional (3-D) fracture propagation model. In addition, reservoir simulation history matching determined that a multi-layer reservoir system was necessary to model the performance in the lower Travis Peak completion interval.
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