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SPE 139114 Innovative Simulation Techniques to History Match Horizontal Wells in Shale Gas Reservoirs
Waleed Fazelipour, Emerson/Roxar

Copyright 2010, Society of Petroleum Eng

ineers This paper was prepared for presentation at the SPE Eastern Regional Conference held in Morgantown, West Virginia, USA, 12–14 October 2010. This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.

Abstract Shale-gas plays are the current rage in the US energy industry and have started a new era for hydrocarbon production, worldwide. Drilling is expanding; shale-gas wells are not hard to drill, but difficult to complete. The challenge is to release gas from rock as impermeable as concrete. Horizontal drilling and stimulation are enabling technologies that make development of unconventional shale-formations economically viable. Rocks around wellbores must be fractured before wells can produce significantly. Multistage hydraulic-fracturing treatments create complex conductive networks, represented by stimulated reservoir volumes (SRV) which have been effectively contacted and contribute to higher production profiles. Reservoir characterization/modeling and simulation offer the best techniques to evaluate well performance and estimate the ultimate recovery. History matching or replicating past well behavior is the first, crucial step in simulation and is very difficult; however, as complex as it is, history matching, which calibrates the simulation-model is required to generate convincing forecasts. Main challenges to simulate past/future production include accurately describing the SRV, its geometry and position; fractures intensities and characterizing matrix/fracture attributes. This paper presents innovative techniques, which previously were impossible to perform, in order to history-match horizontal wellbores by focusing on the mentioned matrix/fracture challenges to sensitize the complex growth and attributes of hydraulic-fractures. The techniques played an important role in understanding the stimulated shale volumes. Key conclusions of achievements include the capability to generate more reliable forecasts/predictions that are highly critical if it is aimed to understand well performance and optimize its productivity. This research has led to a game-changing methodology for the global E&P industry that enables operators to develop an early understanding of shale-gas wells performance where such detailed knowledge is vital to optimizing exploitation economics and estimation of reserves and resource potential. Introduction Shale resource plays are the current rage in the E&P industry and have opened a new era for oil and gas production. Organically rich shales, once ignored by drillers seeking easier plays and faster returns on their investments, are now boosting the fortunes of midsized producers across the United States. The hottest trend in the United States oil and gas industry over the last several years has been in shale gas. Factors fueling the interest in shale gas are the abundance of the shale deposits and recent technological advances in drilling and completion, making the shale gas plays economical. The interest has also been fueled by higher gas prices and the push by the United States government for cleaner burning fuels and greenhouse gas reductions. Numerous operators including super majors, midsized producers, and small independents continue to identify new plays and add incremental production in existing shale resources. Globally, shale-gas resources are estimated to exceed 16,000 Tscf and it is clear that tremendous potential exists for future growth (Jenkins and Boyer, 2008). Across the United States, from the West Coast to the Northeast, 21 geographic basins (Figure 1) are recognized sources of shale gas which continues to be one of the hottest plays in the United States. While broadly distributed, North American shale gas basins generally follow a trend of thrust belts and a Mississippian/Devonian shale fairway from Western Canada and into the Western, Southern and Eastern United States. The Laramide Thrust Belt bounds the Horn River and Montney Play in British Columbia and Alberta, as well as many of the Western US shale gas fields, including Jonah and Pinedale. Starting in

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South Texas, the Ouachita Thrust Belt bounds Southern US gas basins including: Eagle Ford, Barnett, Woodford, Haynesville and Fayetteville. Finally, the merger of the Ouachita and Appalachian Thrust Belts define the broad extents of the Marcellus shale gas basin.

Figure 1 – Shale gas basins in North America (Source: American Clean Skies Foundation)

Gas production in the United States has increased over the last decade, largely due to increased unconventional shale gas development. As shown in Figure 2, North American shale gas reservoirs currently rank as 6 of the largest 22 global gas fields, based upon estimated recoverable reserves, with average recovery factors of about 20%. Drilling is expanding rapidly in shale resource plays, especially in the south-central United States, the Appalachian basin, and numerous Rocky Mountain basins. On the operation side, shale wells are not hard to drill, but they are difficult to complete. The challenge is to release hydrocarbon from rock as impermeable as concrete.

Figure 2 – Estimated recoverable gas reserves in the world (in Tcf; Source: Energy Information Administration)

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Horizontal drilling and stimulation treatments are the key enabling technologies that make development and recovery of unconventional shale formations economically viable. Long a dream for the E&P industry, horizontal drilling came into widespread use during 1990s. Horizontal drilling has been an efficient way of removing gas from conventional reservoirs and is currently being used by drillers to enhance recovery rates in the ultralow permeabilities they encounter in shale resource plays (Wright, 2008). The rock around the laterals must be hydraulically fractured before the wells can produce significant amounts. Various well placement and hydraulic fracturing treatment schemes are performed.

Figure 3 – Modeling of a horizontal well with multistage hydraulic fractures in a shale gas reservoir

Longer horizontal wells are drilled and massive multistage, multicluster hydraulic fracturing treatments are executed. The more fractures in the shale around the wellbore, the faster the production; fractures are the main key to good production. Because of shale’s extremely low permeability, the best fracture treatments are those that expose as much of the shale as possible to the pressure drop that allows the gas to flow. Reservoir characterization and simulation can offer valuable insight for designing the best horizontal drilling and stimulation strategy, completion practices and re-stimulation plans (Figure 3) to maximize the effective stimulated reservoir volumes throughout the life-cycle of the shale resource play and ultimately a field development plan and best practices for well spacing and reserves estimation. Hydraulic Fracturing and Stimulated Reservoir Volume Wells drilled in shales present a new and unique challenge to operators throughout their lifetime and are usually a mixture of horizontal wells with heavy multistage hydraulic fracturing completions and extremely complex vertical wells. The ability to determine the key performance drivers affecting the well and reservoir properties from very early transient data, as well as, which hydraulic fracturing completions are contributing and their effective flow properties, are all critical factors to insuring the profitability of shale wells. Aside from the wells, in general, understanding the performance of very tight, low perm, low porosity resource plays, such as shales and other major unconventional plays introduces new challenges to the E&P industry. This is due to the effect of multistage hydraulic-fracturing treatments in creating stimulated reservoir volumes (SRV) with a very complex and unpredictable growth patterns (Cipolla et al., 2009). Figure 4 illustrates the various types of growth of hydraulic fractures ranging from simple bi-wing fractures to very complex fracture networks. Multistage and multicluster hydraulic-fracturing treatments create complex fracture networks that can be represented by large stimulated reservoir volumes that have been effectively contacted and contribute to higher production profiles. More hydraulic-fracturing stages with more clusters and longer laterals will increase the size of the SRV and the fracture networks.

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Figure 4 – Schematic representation of the complex growth patterns of hydraulic fractures (Warpinski et al., 2008)

The location and geometry of the SRV created by hydraulic fracturing is complex but microseismic has played an important role in understanding an initial estimation of the SRV and the fracture intensities (Figure 5) to properly guide the reservoir modeling and simulation efforts. The SRV is mainly dependent on the fracture network size and the fractures intensity.

Figure 5 – Schematic representation illustrating location and geometry of SRV around hydraulic fractures (Mayerhofer et al., 2008)

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Microseismic fracture mapping results also indicate that fracture growth is complex and unpredictable and its size is related to the stimulation treatment volume. Larger treatment sizes will result in larger fracture networks and will add to the complexity of the fracture growth in the stimulated reservoir volumes. However, no matter how complex the size and geometry of the SRV might be, it is not the only contributing factor in well performance calculations. The conductivity of the hydraulic fractures within the stimulated reservoir volumes is another very important contributing factor that will directly impact the hydrocarbon production. Microseismic fracture mapping is useful but it does not provide much information on the conductivity of the complex fracture networks. The effect of the conductivity of hydraulic fractures on production from shale gas reservoirs is further discussed in the technical literature as listed in the References section. Reservoir Characterization and Simulation of Shale Gas Reservoirs Reservoir characterization/modeling and simulation offer the best techniques to help understand the shale resource plays; evaluate the performance of wells (Fazelipour et al., 2008); and estimate the field ultimate recovery. Reservoir modeling and simulation technologies have been proven to be the most cost-effective ways of understanding the behavior of various reservoirs under multiple drive mechanisms, changing fluid behaviors, and completion configurations whose performance may change with time. These technologies provide the information oil and gas companies need to make quicker and better field development decisions (Fazelipour et al., 2009). For shale-gas reservoirs involving horizontal wells and multistage hydraulic fracturing treatments, simulation results can play an important role in understanding the stimulated reservoir volumes. This research intends to first model a typical shale-gas reservoir, and consequently simulate, history match and forecast the performance of a horizontal well. Creating a Proper Reservoir Model for the Shale Gas Zone of Interest. The very first step in an integrated reservoir characterization and simulation workflow is describing the area of interest of the shale-gas reservoir or building a proper geologic model. The geologic model of the shale gas will be compiled from various sources including petrographic, core, wireline and production data. A horizontal well with a number of multistage hydraulic fractures was selected for this study. A structure map indicating the top of the shale formation and thickness data along with wireline data were used to determine the average thickness (180 ft) and elevation (9600 ft) over the study area. The horizontal well is located in a shale-gas reservoir. Figure 6 shows a model of the horizontal well intersecting the tilted zone of interest in the shale-gas reservoir.

Figure 6 – Modeling of a horizontal well intersecting a tilted shale-gas zone of interest

Based on the structure of the zone of interest and the location of the horizontal well, a grid was created. Several options were evaluated to select a proper grid for this study. Available core data for a typical shale-gas reservoir were used to populate the created grid with formation properties including porosity, permeability and water saturation. The next set of figures summarizes the main characteristics over the area of interest for the shale gas reservoir. Figure 7 shows the grid structure used to model the shale gas zone along with the porosity array of the grid.

Figure 7 – Example of porosity distribution used in the model for the shale-gas reservoir (unit: frac)

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Figure 8 shows the permeability array of the grid used to model the shale-gas zone. Figure 9 shows the water saturation array of the grid used to model the shale-gas zone. In some cases, a correlation might be available for these two parameters and thus it can be incorporated in the modeling.

Figure 8 – Example of permeability distribution used in the model for the shale-gas reservoir (unit: mD)

Figure 9 – Example of water saturation distribution used in the model for the shale-gas reservoir (unit: frac)

Creating an Initial Simulation Model for the Shale-Gas Zone and the Stimulated Horizontal Well. The next step in the reservoir modeling and simulation workflow after building a reservoir model that can be considered as a proper representation of the geology of the area of interest is building a dynamic simulation model that is capable of reproducing the production profile of the horizontal well. The impacts of gas desorption in shale-gas reservoirs on well productivity and gas recovery were considered in this simulation study. Values representing different Langmuir parameters were obtained from a series of experiments carried out in a lab. Langmuir parameters specified in this study include the Langmuir pressure, the Langmuir volume and Langmuir time. These values which vary across the field were set up using grid arrays for various depth intervals; these values may also be set to vary from cell to cell if that data is available. The initial desorption pressure was specified as a grid array, as well. The flowing bottom-hole pressure in the simulation model is calculated based on the flowing casing pressure over a range of values that the well is producing with. The initial simulation results indicated that significant improvement in gas production from the shale reservoir is all due to the large stimulated reservoir volumes that were created by the hydraulic fractures. Modeling the Multistage Multicluster Hydraulic Fractures in the Simulation Model. A series of hydraulic fracturing stimulations had been performed on the well over a relatively short period. The reservoir simulation results can provide important insights into the effectiveness of the stimulation treatment. The well was cased and cemented with 5 perforation clusters along a lateral of ~2800 ft in length. In most shale-gas plays of the United States and in this case as well, the multistage hydraulic fractures are oriented perpendicular or transverse to the dominant direction of the lateral section of the horizontal well. This will result in more production.

Figure 10 – Top view of initial orientation, geometry and location of the SRV in the simulation model

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The initial SRV design for the stimulated volumes will be presented next. The term “initial” refers to a value that was used to create the first or base simulation model. The base model will go through a rigorous process for validation, as will be described later in this work. The hydraulic fracturing treatment creates stimulated reservoir volumes around the multicluster stages. The discussion that we had earlier leads us to the idea that due to the complexity of the growth of fractures in shale gas reservoirs, it is reasonable to represent the effect of each multicluster stage in terms of stimulated volumes that might have different dimensions in terms of length, width and height and also a variable position in XYZ space. Figure 10 which is a top view of the zone of interest, illustrates the initial orientation, geometry and location of each individual SRV built around each hydraulic fracturing stage in the simulation model. The initial length of each stimulated volume is equal to 2xf where xf is the fracture half-length; the initial width of each stimulated volume is wt where w is the width of each cluster fracture and can be obtained from well completion data for each stage; and finally the initial height of each stimulated volume is equal to h, the fracture height. The initial position of the SRV which is driven by the depth of each stage and its areal extent can be calculated based on the data provided for each treatment including the MDL, lower measured depth, and MDU, upper measure depth for each cluster. Figure 11 shows a side view of the initial orientation, geometry and location of the SRVs in the simulation model.

Figure 11 – Side view of initial orientation, geometry and location of the SRV in the simulation model

The conductivity of the network of hydraulic fractures for each stage that is represented by a stimulated volume can be taken into account by enhanced and improved transmissibility terms in the simulation model. The transmissibility terms for each stimulated volume can vary with time as well; this will enable the user to model the fact that the effectiveness of the hydraulic fractures will fade away with time. The initial transmissibility terms can be calculated based on an estimation of kf , the fracture permeability and k, the matrix permeability. Reservoir simulation results indicate that due to the extremely low permeability of shale reservoirs, the flow behavior is dominated by the extent, configuration and conductivity of the hydraulic fractures. All the initial values will be sensitized on, in the upcoming sections. History Matching or Calibrating the Shale-Gas Simulation Model History matching or replicating the past well behavior is the primary step in reservoir simulation and is very difficult; however, as complex as it is, history matching for simulation-model calibration is required to generate convincing forecasts. The challenges in reservoir modeling and then simulating past/future production profiles of shale resource plays are properly describing the stimulated volumes, the geometry of the fractures and their location; fracture intensities and characterizing the matrix/fracture attributes.

Figure 12 – Side view of a sample orientation, geometry and position of the SRV of a typical simulation run

Daily gas production rate, water production rate and top-hole casing pressure historical data for the horizontal well were available for matching against the simulator calculated quantities to calibrate the simulation model. The permeability of the

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stimulated reservoir volumes, is the main driver for the gas production; heterogeneous gas production rates sometimes indicate permeability heterogeneity. This can be further explained in the next section. Solution Approach. A new and innovative computer-assisted procedure was used to facilitate the history match of the shale-gas simulation model. This process is a very smart iterative technique that utilizes state-of-the-art algorithms without which this effort is impossible to perform. Before the process of history matching is started, as shown in the previous sections, a base simulation case is defined and possible ranges of uncertain input parameters, as well as, the total number of simulations to be run and the number of simulations per each step of the process is specified. In this case, the uncertain input parameters include the geometry and position of the SRVs and their associated transmissibility.

Figure 13 – Top view of a sample orientation, geometry and position of the SRV in one simulation run

Figure 14 – Top view of a sample orientation, geometry and position of the SRV in another simulation run

Simulation runs are executed one after another and at the end of each run the proxy that the computer-assisted procedure is utilizing to achieve fast history matching results will be updated. Each simulation run will place the SRV in a different location and uses different dimensions to describe its geometry. Figure 12 shows a side view of a test orientation, geometry and position for the SRVs in a typical simulation run and can be compared to the initial configuration used. Figure 13 and Figure 14 show a top view of a test orientation, geometry and position of the SRVs in separate simulation runs.

Figure 15 – Initial simulation runs aiming to match the casing pressure

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Besides focusing on sensitizing the SRV attributes including its position and geometry, this innovative methodology also takes into account the enhanced conductivity of the stimulated volume by sensitizing the transmissibility terms. The history match runs are compared to the actual history data including the gas production rate, the water production rate and the casing pressure.

Figure 16 – Initial simulation runs aiming to match the water production rate

The water in the system was considered in the simulations by defining a 2-phase gas-water model since ignoring it will cause the simulator to calculate higher and earlier gas production rates. Figure 15 and Figure 16 show the initial simulation runs aiming to eventually match the casing pressure history and the water production rate history for the horizontal well.

Figure 17 – Comparison of measured and simulated gas production rate (history: green)

Several options were evaluated to determine which SRV location and conductivity value resulted in the best agreement with the available production history. The final match for gas production rate is shown in Figure 17. The simulation results for the gas rate are in an excellent agreement with the historical data of the horizontal well since the simulation model is not calculating much earlier or higher gas production rates. The match for casing pressure is shown in Figure 18 and, as can be seen, the simulation results are in good agreement with the history. The match for water production is shown in Figure 19. The match for the casing pressure and water production rate were more difficult due to the complexity of the growth of the hydraulic fractures and the attributes of the stimulated reservoir volumes.

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Figure 18 – Comparison of measured and simulated casing pressure (history: green)

Figure 19 – Comparison of measured and simulated water production rate (history: green)

Overall, the results of the history match are very satisfactory. Following a successful history match workflow which calibrated the reservoir simulation model, a prediction workflow can be utilized and gas production for the horizontal well can be forecasted under any development scenario the operator wishes to proceed with. This will allow the operator to investigate the effect of uncertain parameters on the forecasted values. Figure 20 shows the cumulative gas production forecast for the horizontal well after 30 years for a specific development scenario. Summary and Conclusions An integrated methodology/workflow was developed for shale-gas reservoir modeling and simulation that utilizes innovative techniques, which previously were impossible to perform, in order to history-match horizontal wellbores by focusing on a number of stimulation-related uncertainties consisting of the matrix/fracture attributes. A typical horizontal well drilled and stimulated in a shale-gas reservoir was chosen for this study. The main challenging objective was to have a proper understanding of the stimulated reservoir volumes created by the complex growth of fracture networks. This objective was achieved by sensitizing on the complex growth and attributes of hydraulic-fractures. The techniques played an important role in understanding the stimulated shale volumes. Key conclusions of achievements include the capability to generate more reliable forecasts/predictions that are highly critical if it is aimed to understand the performance of a well and optimize its productivity. This research has led to a game-changing methodology for the global E&P industry that enables operators to develop an early understanding of shale-gas wells performance where such detailed knowledge is vital to optimizing exploitation economics and estimation of reserves and resource potential.

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Figure 20 – Total gas production forecast for the horizontal well after 30 years

Acknowledgements The author of this paper would like to thank Emerson/Roxar for supporting the publication of this work and the shale-gas reservoir modeling and simulation studies by providing the state-of-the-art tools. Special thanks to the members of the development teams of various modeling and simulation tools at Emerson/Roxar for their dedication and support. Nomenclature frac fraction h height of a hydraulic fracture , L k matrix permeability, L2 kf fracture permeability, L2 mD 10-3 Darcy, L2 MDL lower measured depth of each cluster, L MDU upper measure depth of each cluster, L Mscf 103 standard cubic feet, L3 MMscf 106 standard cubic feet, L3 P well or reservoir pressure, m/Lt2 psia pounds per square inch, m/Lt2 Q rate, L3/t Qg gas rate, L3/t scf standard cubic feet, L3 stb stock tank barrel, L3 stb/day liquid rate, L3/t SRV stimulated reservoir volume, L3 Tcf 1012 standard cubic feet, L3 THP top hole/casing pressure, m/Lt2 xf half length or wing of a hydraulic fracture, L width of each hydraulic fracture cluster, L w wt total width of each hydraulic fracture stage, L References
Cipolla, C.L., Lolon, E.P., Mayerhofer, M.J. and Warpinski, N.R. 2009. Fracture Design Considerations in Horizontal Wells Drilled in Unconventional Gas Reservoirs. Paper SPE 119366 presented at the SPE Hydraulic Fracturing Technology Conference, Woodlands, 19–21 January. Cipolla, C.L., Lolon, E.P. and Dzubin, B. 2009. Evaluating Stimulation Effectiveness in Unconventional Gas Reservoirs. Paper SPE 124843 presented at the SPE Annual Technical Conference and Exhibition, New Orleans, 4–7 October. Cipolla, C.L., Lolon, E.P., Erdle, J.C. and Tathed, V. 2009. Modeling Well Performance in Shale-Gas Reservoirs. Paper SPE 125532 presented at the SPE/EAGE Reservoir Characterization and Simulation Conference, Abu Dhabi, 19–21 October.

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Daniels, J., DeLay, K., Waters, G., Le Calvez, J., Lassek, J. and Bentley, D. 2007. Contacting More of the Barnett Shale Through an Integration of Real-Time Microseismic Monitoring, Petrophysics, and Hydraulic Fracture Design. Paper SPE 110562 presented at the SPE Annual Technical Conference and Exhibition, Anaheim, 11–14 November. Decker, A.D., Hill, D.G., and Wicks, D.E. 1993. Log-based Gas Content and Resource Estimates for the Antrim Shale, Michigan Basin. Paper SPE 25910 presented at the SPE Rocky Mountain Regional/Low Permeability Reservoirs Symposium, Denver, 12–14 April. Du, C., Zhang, X., Melton, D., Fullilove, D., Suliman, B., Gowelly, S., Grant, D., Le Calvez, J. 2009. A Workflow for Integrated Barnett Shale Gas Reservoir Modeling and Simulation. Paper SPE 122934 presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, Cartagena, 31 May–3 June. Fazelipour, W., Pope, G. and Sepehrnoori, K. 2008. Development of a Fully Implicit, Parallel, EOS Compositional Simulator to Model Asphaltene Precipitation in Petroleum Reservoirs. Paper SPE 120203 presented at the SPE Annual Technical Conference and Exhibition, Denver, 21–24 September. Fazelipour, W., Shirdel, M. and Sepehrnoori, K. 2009. Development of an EOS Compositional, Coupled, Wellbore-Reservoir Simulator to Model Asphaltene Deposition in Vertical Oil Wells. Paper SPE 124825 for the SPE Annual Technical Conference and Exhibition, New Orleans, 4–7 October. Jenkins, C.D. and Boyer, C.B. 2008. Coalbed- and Shale-Gas Reservoirs. JPT, February: 92–99. Lolan, E.P., J.R. Shaoul, J.R. and Mayerhofer, M.J. 2007. Application of 3-D Reservoir Simulator for Hydraulically Fractured Wells. Paper SPE 110093 presented at the SPE Asia Pacific Oil and Gas Conference and Exhibition, Jakarta, 30 October–1 November. Mayerhofer, M.J., Lolan, E.P., Warpinski, N.R., Cipolla, C.L. and Walser, D. 2008. What is Stimulated Reservoir Volume (SRV)?. Paper SPE 119890 presented at the SPE Shale Gas Production Conference, Fort Worth, 16–18 November. Schepers, K.C., Nuttall, B., Oudinot, A.Y. and Gonzalez, R. 2009. Reservoir Modeling and Simulation of the Devonian Gas Shale of Eastern Kentucky for Enhanced Gas Recovery and CO2 Storage. Paper SPE 126620 presented at the SPE International Conference on Capture, Storage, and Utilization, San Diego, 10–11 November. Shen, C. 2009. Reservoir Simulation Study of an In-Situ Conversion Pilot of Green-River Oil Shale. Paper SPE 123142 presented at the SPE Rocky Mountain Petroleum Technology Conference, Denver, 14–16 April. Warpinski, N.R., Mayerhofer, M.J., Vincent, M.C., Cipolla, C.L. and Lolon, E.P. 2008. Stimulating Unconventional Reservoirs: Maximizing Network Growth while Optimizing Fracture Conductivity. Paper SPE 114173 presented at the SPE Unconventional Reservoirs Conference, Keystone, 10–12 February. Wright, J.D. 2008. Economic Evaluation of Shale Gas Reservoirs. Paper SPE 119899 presented at the SPE Shale Gas Production Conference, Fort Worth, 16–18 November.


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