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Assessing Subeconomic Natural Gas Resources in the Anadarko and Uinta Basins*

By

K.K. Rose1, A.S.B. Douds2, J.A. Pancake2, H.R. Pratt III2, and R.M. Boswell1

 

Search and Discovery Article #10069 (2004)

 

*Adapted from poster presentation at AAPG Annual Convention, Dallas, TX, April 18-21, 2004.

 

 1Department of Energy, Morgantown, WV (kelly.rose@netl.doe.gov) (rboswe@netl.doe.gov)  

2EG&G Technical Services, Morgantown, WV  (asbdouds@yahoo.com) (james.pancake@eg.netl.doe.gov)

 

Abstract 

Natural gas resource assessments are a commonly used tool by industry, academia, and government to understand the current and near-term recoverability of the nation’s resource base. However, these resource assessments tend to be static pictures of a resource that is, in reality, highly dynamic. Assessment based on gas-in-place (GIP) analysis and iterative modeling of resource recoverability under a variety of technology/policy scenarios provide improved means to identify the most promising approaches to expanded resource recoverability. 

This study collects detailed, spatially distributed, geologic and engineering information on key segments of the nation’s under-utilized gas resource base. Phase one of this study, completed February 2003 for the DOE’s National Energy Technology Laboratory, provided detailed GIP resource assessments for the Greater Green River and Wind River basins. Phase two of this effort focuses on the Tertiary and Cretaceous sections of the Uinta Basin, and the mid-Pennsylvanian and older strata of the deep Anadarko Basin. 

Through the correlation and analysis of hundreds of log suites, and drilling and completion records, key geologic and engineering parameters including depth, potential pay thickness, porosity, pressure, water saturation, and temperature were determined and used to produce detailed characterizations of the GIP for each unit analyzed. In conjunction with DOE modeling efforts and permeability analyses, this study provides a detailed, disaggregated, geologic and engineering database for modeling the impact of different technology scenarios on the future of U.S. natural gas exploration, production, and supply.

 

 

uAbstract

uBackground

  uFigures 1.1-1.4

  uKey points

  uStudies

uMethodology

  uFigures 1.5-1.14

  uUOA definitions

  uUOA parameters

  uWorkflow

  uLog analysis

    uClastics

    uCarbonates

  uData density

  uData sources

uExamples

  uFigures 2.1-3.2

  uMorrow

  uL. Mesaverde

uDataset preparation

  uFigures 3.3-3.9

  uProduction

  uGridding

  uPrevious assessments

  uDisaggregation

  uPermeability

uConclusions

uPostscript

  uInformation transfer

  uDisclaimer

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

uAbstract

uBackground

  uFigures 1.1-1.4

  uKey points

  uStudies

uMethodology

  uFigures 1.5-1.14

  uUOA definitions

  uUOA parameters

  uWorkflow

  uLog analysis

    uClastics

    uCarbonates

  uData density

  uData sources

uExamples

  uFigures 2.1-3.2

  uMorrow

  uL. Mesaverde

uDataset preparation

  uFigures 3.3-3.9

  uProduction

  uGridding

  uPrevious assessments

  uDisaggregation

  uPermeability

uConclusions

uPostscript

  uInformation transfer

  uDisclaimer

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

uAbstract

uBackground

  uFigures 1.1-1.4

  uKey points

  uStudies

uMethodology

  uFigures 1.5-1.14

  uUOA definitions

  uUOA parameters

  uWorkflow

  uLog analysis

    uClastics

    uCarbonates

  uData density

  uData sources

uExamples

  uFigures 2.1-3.2

  uMorrow

  uL. Mesaverde

uDataset preparation

  uFigures 3.3-3.9

  uProduction

  uGridding

  uPrevious assessments

  uDisaggregation

  uPermeability

uConclusions

uPostscript

  uInformation transfer

  uDisclaimer

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

uAbstract

uBackground

  uFigures 1.1-1.4

  uKey points

  uStudies

uMethodology

  uFigures 1.5-1.14

  uUOA definitions

  uUOA parameters

  uWorkflow

  uLog analysis

    uClastics

    uCarbonates

  uData density

  uData sources

uExamples

  uFigures 2.1-3.2

  uMorrow

  uL. Mesaverde

uDataset preparation

  uFigures 3.3-3.9

  uProduction

  uGridding

  uPrevious assessments

  uDisaggregation

  uPermeability

uConclusions

uPostscript

  uInformation transfer

  uDisclaimer

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

uAbstract

uBackground

  uFigures 1.1-1.4

  uKey points

  uStudies

uMethodology

  uFigures 1.5-1.14

  uUOA definitions

  uUOA parameters

  uWorkflow

  uLog analysis

    uClastics

    uCarbonates

  uData density

  uData sources

uExamples

  uFigures 2.1-3.2

  uMorrow

  uL. Mesaverde

uDataset preparation

  uFigures 3.3-3.9

  uProduction

  uGridding

  uPrevious assessments

  uDisaggregation

  uPermeability

uConclusions

uPostscript

  uInformation transfer

  uDisclaimer

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

uAbstract

uBackground

  uFigures 1.1-1.4

  uKey points

  uStudies

uMethodology

  uFigures 1.5-1.14

  uUOA definitions

  uUOA parameters

  uWorkflow

  uLog analysis

    uClastics

    uCarbonates

  uData density

  uData sources

uExamples

  uFigures 2.1-3.2

  uMorrow

  uL. Mesaverde

uDataset preparation

  uFigures 3.3-3.9

  uProduction

  uGridding

  uPrevious assessments

  uDisaggregation

  uPermeability

uConclusions

uPostscript

  uInformation transfer

  uDisclaimer

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

uAbstract

uBackground

  uFigures 1.1-1.4

  uKey points

  uStudies

uMethodology

  uFigures 1.5-1.14

  uUOA definitions

  uUOA parameters

  uWorkflow

  uLog analysis

    uClastics

    uCarbonates

  uData density

  uData sources

uExamples

  uFigures 2.1-3.2

  uMorrow

  uL. Mesaverde

uDataset preparation

  uFigures 3.3-3.9

  uProduction

  uGridding

  uPrevious assessments

  uDisaggregation

  uPermeability

uConclusions

uPostscript

  uInformation transfer

  uDisclaimer

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

uAbstract

uBackground

  uFigures 1.1-1.4

  uKey points

  uStudies

uMethodology

  uFigures 1.5-1.14

  uUOA definitions

  uUOA parameters

  uWorkflow

  uLog analysis

    uClastics

    uCarbonates

  uData density

  uData sources

uExamples

  uFigures 2.1-3.2

  uMorrow

  uL. Mesaverde

uDataset preparation

  uFigures 3.3-3.9

  uProduction

  uGridding

  uPrevious assessments

  uDisaggregation

  uPermeability

uConclusions

uPostscript

  uInformation transfer

  uDisclaimer

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

uAbstract

uBackground

  uFigures 1.1-1.4

  uKey points

  uStudies

uMethodology

  uFigures 1.5-1.14

  uUOA definitions

  uUOA parameters

  uWorkflow

  uLog analysis

    uClastics

    uCarbonates

  uData density

  uData sources

uExamples

  uFigures 2.1-3.2

  uMorrow

  uL. Mesaverde

uDataset preparation

  uFigures 3.3-3.9

  uProduction

  uGridding

  uPrevious assessments

  uDisaggregation

  uPermeability

uConclusions

uPostscript

  uInformation transfer

  uDisclaimer

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

uAbstract

uBackground

  uFigures 1.1-1.4

  uKey points

  uStudies

uMethodology

  uFigures 1.5-1.14

  uUOA definitions

  uUOA parameters

  uWorkflow

  uLog analysis

    uClastics

    uCarbonates

  uData density

  uData sources

uExamples

  uFigures 2.1-3.2

  uMorrow

  uL. Mesaverde

uDataset preparation

  uFigures 3.3-3.9

  uProduction

  uGridding

  uPrevious assessments

  uDisaggregation

  uPermeability

uConclusions

uPostscript

  uInformation transfer

  uDisclaimer

 

 

 

Project Background

Figure Captions (1.1-1.4)

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Key Points  

Resource assessments conducted at NETL support the DOE's natural gas R&D program planning with a focus on:

  • Resources that are key program targets

  • Regional and deep gas accumulations

  • Producing geo-spatially disaggregated databases

The disaggregated databases generated for each assessment are used to analyze the potential of different technologies under a variety of future scenarios.   

The study enables analyses as to why key segments of this gas are currently unrecoverable and provide insight into the R&D needed to accelerate its entry into the resource base. Figures 1.1, 1.2, and 1.3 show the significance of “unrecoverable” gas resources and the importance of technology in their conversion into a technologically recoverable resource.      

 

Current and Completed Basin Studies  

This gas-in-place resource assessment effort is continuing currently in the Anadarko Basin of Oklahoma and Texas and the Uinta Basin of Utah (Figure 1.4). Previous studies included the Wind River Basin and the Greater Green River Basin.

 

Project Methodology 

Figure Captions (1.5-1.14)

Figure 1.5. Generalized stratigraphic and Units of Analysis (UOA) column - Anadarko Basin. 

Figure 1.6. Generalized stratigraphic and UOA column - Uinta Basin.

Figure 1.7. Map showing geographic extent of UOAs in the "deep" Anadarko Basin.   

Figure 1.8. Map showing geographic extent of UOAs in the Uinta Basin. Deep UOA boundaries are indicated by dashed lines  

Figure 1.9. Unita type log, showing UOAs, which are primarily clastics.

Figure 1.10. Southeast Anadarko type log, showing UOAs. Packages highlighted with yellow are dominantly clastics; packages highlighted with blue are dominantly carbonates.

Figure 1.11. Chart showing project workflow.

Figure 1.12. Wireline log suite of interval of clastics, highlighting significant values in determining “potential pay.”

Figure 1.13. Wireline log suite of interval containing carbonates, highlighting significant parameters in determining “potential pay.”

Figure 1.14. Table of data density for the assessments in the Anadarko and Uinta basins. Density decreases with depth. Variable data density = varying degrees of resolution in resource computation

 

Definitions of Units of Analysis (UOAs)  

Units of Analysis (UOAs) were defined for each basin (Figures 1.5 and 1.6), based on the drilling and completion histories of the formations.   

Reservoir properties, such as porosity, permeability, water saturation, and shale-volume, are averaged over the entire UOA for any one data point; therefore, UOA definition is critical.   

Industry practices are taken into account by analyzing drilling and completion histories in the formations.   

In the "deep" Anadarko Basin eight UOAs were identified based on these criteria (Figure 1.5), and in Uinta Basin six UOAs were defined using the criteria (Figure 1.6).  

 

Explanation of UOA parameters  

Once the UOAs were defined based on the drilling/completion history in the basin, UOA extents were defined through correlation of well logs throughout the basins. In the "deep" Anadarko Basin, UOA boundaries (Figure 1.7) were typically delineated by structural features, erosional boundaries, and the 10,000' MD structure contour for each.  

UOAs in the Uinta Basin (Figure 1.8) were separated into shallow and deep zones based on stratigraphic depth and density of data. The deep portion of the UOAs generally contain significantly fewer well penetrations.    

Type logs were generated in the Uinta Basin (Figure 1.9) and southeast "deep" Anadarko Basin (Figure 1.10), to show the lithologic and stratigraphic distribution of the UOAs. Uinta UOAs are primarily clastics, whereas in the Anadarko Basin, there are both clastic and carbonate UOA packages.

 

Project Workflow  

The project workflow, from data collection to technology modeling, is diagrammed in Figure 1.11, which shows the basic model followed for each resource assessment in order to determine GIP. The elements of the model are also listed, as follows:  

  • Obtain best quality, evenly-distributed well log data; objective of 1 well per township

  • Subdivide stratigraphic section into units of analysis that are to be modeled as separate drilling targets.

  • Establish the three-dimensional geometry of each UOA.

  • Establish the distribution of resource-bearing sandstone facies to improve extrapolation of parameters to areas of poor data control.

  • Estimate UOA-average values of porosity, drilling depth, resistivity, shale volume, and potential pay thickness for each well log suite.

  • Estimate pressure and temperature gradients and water resistivity at township or quarter-township scale.

  • Estimate expected matrix permeability and likely natural fracture overprint through reference to field studies and the density of interpreted surfaces and basement features.

  • Distribute scattered well data to regular grid filling UOA area. Add control wells to provide interpreted values in areas of poor data.

  • Compile fully defaulted and formatted database for model input. Remove all areas of significant historical production.

  • Conduct GSAM analyses to determine gas-in-place, and the impact of technology/cost scenarios on economically- and technically-recoverable volumes.  

 

Log Analysis  

After correlation of the well logs, log analysis is performed in order to collect reservoir properties for the UOAs and determine the variation within the basin of those properties. The properties collected during log analysis include:

(1) Drilling depth 

(2) Net thickness (shale-volume < 50%)** 

(3) Average shale resisitivity

(4) "Potential pay" thickness

**Note: This property was not used to calculate GIP. These values are mapped to create the Isochore/Isolith maps indicating sandstone distribution throughout the basins.  

 

        Clastics 

Generally, the following parameters were used by the geologist when determining "potential pay" of clastics (Figure. 1.12):

  • water saturation < 70%

  • porosity > 4%

  • shale-volume < 75%

  • minimum bed thickness cutoff = 4ft 

 

The following properties are collected over the "potential pay" interval:

(5) Average shale-volume

(6) Average porosity

(7) Average resisitivity 

 

Parameters 1 and 3-7 are entered into either the Simandoux or Archie equations to calculate water saturation. Water saturations >70% are considered wet for this analysis.  

 

Carbonates   

Generally, the following parameters were used by the geologist when determining "potential pay" of carbonates (Figure 1.13):

  • water saturation < 70%

  • density porosity > 2%

  • shale-volume < 50%

  • minimum bed thickness cutoff = 4ft 

 

The following properties are collected over the "potential pay" interval:

  • Average shale-volume

  • Average bulk density, compensated density, and neutron porosities

  • Average resisitivity 

 

These parameters are entered into the Archie equation to calculate water saturation. Water saturations >70% are considered wet for this analysis. 

 

Data Density  

A target data density of one well per township was sought for each UOA. However, in most UOAs, data density decreases with increased drilling depth, because the number of well penetrations decreases with depth (Figure 1.14). As such, resolution in those resource calculations decreases with increasing depth.

 

Data Sources  

Below is a list of references for data used in the assessments

Well Logs: A2D, MJ Systems, In-house Microfiche

Well Data (location and production): IHS Energy, Texas RRC

Core Data: Oklahoma Geological Survey, IHS Energy

Rw Data: USGS Produced Rw Database, SPE Survey of Rw Data in Oklahoma,1988

Temperature Data:

P.K. Cheung, 1975, " The Geothermal Gradient in Sedimentary Rocks in Oklahoma", M.S. Thesis Oklahoma State Univ. (OSU); BHT data collected from log headers; IHS Energy

Pressure Data:

OSU "Pressure Data on the Anadarko Basin", OSU website

IHS Energy   

 

Example Maps and Cross Sections for the Anadarko and Uinta Basins

Figure Captions (2.1-3.2) 

Figure 2.1. Morrow UOA map illustrating the drilling depth required to reach the mid point of the UOA.

Figure 2.2. Morrow UOA isolith map showing the geographic distribution of the gross interval thickness.

Figure 2.3. Morrow UOA potential pay map showing the geographic distribution of sandstone porosity >4% for the UOA. Note that "potential pay" does not equal "economically recoverable."

Click to view sequence of Morrow UOA maps.

Figure 2.4. W-E diagrammatic structural cross section C - C', in the southeasternmost part of the basin, showing UOAs..

Figure 2.5. W-E stratigraphic cross section A-A', showing UOAs. Stratigraphic datum = Top of Morrow.  

Figure 2.6. Lower Mesaverde UOA map illustrating the drilling depth required to reach the top of the UOA.

Figure 2.7. Lower Mesaverde UOA isolith map showing the geographic distribution of the gross interval thickness.

Figure 2.8. Lower Mesaverde UOA potential pay/H map showing the geographic distribution of sandstone with porosity > 4% for the UOA. Areas with Sw >= 70% are viewed as 0 potential pay/H. Note that "potential pay" does not equal "economically recoverable."

Click to view sequence of Lower Mesaverde UOA maps.

Figure 3.1. W-E diagrammatic structural cross section, AA’, Uinta Basin, showing UOAs.

Figure 3.2. N-S diagrammatic structural cross section B-B’, Uinta Basin, showing UOAs.

 

Anadarko Basin

Morrow UOA (Figures 2.1, 2.2, and 2.3)

Anadarko Basin Cross Sections (Figures 2.4 and 2.5)

 

Uinta Basin

Lower Mesaverde UOA (Figures 2.6, 2.7, and 2.8) 

Uinta Basin Cross Sections (Figures 3.1 and 3.2)

 

Dataset Preparation 

Figure Captions (3.3-3.10) 

Figure 3.3. A. Map of Anadarko Basin showing Morrow production and UOA boundary. B. Map of area contained within Morrow UOA boundary, with areas and grid cells excluded from GIP analysis.

Figure 3.4. Example of gridding, with chart of data density (Figure 1.14). Computer interpolates drilling depth from well data for nine 2,560-acre cells per township. Grid cell size is based on the data density for the play. There is identical gridding for remaining volumetric parameters (thickness, porosity, Sw, pressure).

Figure 3.5. Technically recoverable gas assessments by USGS for the Anadarko Basin.

Figure 3.6. Gas Assessments for the Uinta Basin by USGS, 2002.  

Figure 3.7. Schematic representation of resource distribution by depth in the dataset resulting from this study. A. Uinta Basin UOAs. B. Anadarko Basin UOAs.

Figure 3.8. Gas-in-place and average volumetric parameters, in tabular form for Uinta Basin and Anadarko Basin UOAs. Average values refer only to the potential pay in each grid cell. For example, 7% porosity means that the average porosity of the zones identified as potential pay over all grid cells is 7%. Total values are the aggregate values for all grid cells.  

Figure 3.9. A. Map of Deese UOA, Anadarko Basin, showing candidate wells used for permeability analysis. B. Determination of permeability by type curve matching of production history. C. Table of averaged values for the Deese UOA (High, Average, Low).

Figure 3.10 Example of a structural complexity map from the Greater Green River Basin.

 

Areas of Historical Production  

After values for potential pay thickness have been determined for every grid cell, areas of significant historical production or grid cells that are interpreted as wet due to interpolation of log analysis values will be extracted from the dataset (Figure 3.3, as an example). This will result in a database that reflects the remaining gas-in-place potential for all UOAs studied in the basins.

 

Data Density and Gridding  

UOAs will be gridded at the township level (Figure 3.4). Grid spacing is designed to correlate with the data density. Gridding the unevenly spaced data results in an even distribution of points with a value in the center of the cell.

 

Previous Assessments    

In the past decade the USGS National Assessment quantified technically recoverable resources for most major US basins, including the Anadarko and Uinta Basins (Figures 3.5 and 3.6). In basins where gas-in-place assessments have been completed, data indicate that only 2% of the existing GIP is technically recoverable. The remaining 98% represents one of the nation's largest untapped natural gas resources.  

Expanded access to this gas will require the development of advanced technologies specifically tailored to unlock this segment of the resource. In order to identify the most promising R&D opportunities, NETL uses sophisticated computer models of the national natural gas E&P system. To increase the reliability of the results, this study produces model databases which capture the geologic variety of resource occurrences.  

 

Stratigraphic Disaggregation and Volumetric Results 

The results can be characterized by the amount of resource contained at specific drilling depths. However, the stratigraphic and geographic disaggregation of the resource gives a more accurate picture of how the resource is distributed among the UOAs of the basin (Figures 3.7 and 3.8).  

 

Permeability Analysis

(G. Koperna, G. Bank, T. Graham, ARI)

 

ARI was tasked with deriving bulk permeability estimates for each UOA. Methodology, as illustrated in Figure 3.9 for the Anadarko Basin, is:

1) Identify producing fields for each UOA.

2) Identify low, average, and high-productivity wells using estimated 30-year EUR by field/area (Figure 3.9A).

3) Analyze logs to calculate H for perforated zones in suitable candidate wells.

4) Type curve match production history to determine permeability for the wells (Figure 3.9B).

Table in Figure 3.9C shows the averaged values for the 10 wells in each category.

 

The bulk permeability results provided by ARI will be used in conjunction with structural complexity mapping to determine permeability for each UOA.  Structural complexity will be determined through analysis of existing structural features and surface and subsurface lineament data from each basin (as illustrated in Figure 3.10).

 

Conclusions 

DOE’s National Energy Technology Laboratory is modeling the recoverability of major untapped resources in an effort to identify the most promising R&D opportunities.   ·  

Through log based analyses, NETL's resource assessment of the "deep" Anadarko Basin seeks to produce a detailed, disaggregated dataset of the basin's tight, deep, and unconventional formations.  

This dataset will assist DOE modelers in the identification of future technologies that can unlock GIP resources and help shift them to a technically or economically recoverable status.  

Previous model datasets have been upgraded through new studies of the gas-in-place in the Greater Green River Basin and Wind River Basin that capture the full natural variety in key parameters. 

Basins currently under investigation are Anadarko Basin and Uinta Basin. 

 

Postscript 

Information Transfer 

COGA presentation of GGRB &WRB Assessments, Denver, CO, August 2002  

GGRB & WRB Assessments published in Gas TIPS, Summer 2002  

Assessing the Technology Needs of Unconventional Gas Resource, May 20, 2003
SPE National Capital Section Unconventional Gas Resources Assessment Symposium
Ray Boswell, Ashley Douds, Kelly Rose, Skip Pratt, Jim Pancake, and Jim Dean
National Energy Technology Laboratory Energy and Environmental Solutions  
 

Presentation of the Anadarko Assessment at the "Unconventional Energy Resources in the Southern Midcontinent" OGS workshop, March 2004   

GGRB & WRB assessments presented at the 2003 national AAPG convention   

Poster presentation of the Anadarko and Uinta assessments at the 2004 national AAPG convention, after acceptance of abstract   

Abstract accepted for presentation of the Uinta Assessment at the August, 2004, RMAG-COGARMR AAPG joint meeting, Denver, CO   

GGRB & WRB Final Report CD:
Over 400 cd's distributed at the 2003 AAPG National Convention
Numerous CD's distributed via NETL's online library at: www.netl.doe.gov
Various requests from independents and majors for the digital data files associated with the final report
  

The maps, cross sections, and other data generated in this study will be available in a CD format in the Fall of 2004.  

For more information regarding the National Energy Technology Laboratory visit the website at: www.netl.doe.gov.

 

Disclaimer  

This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, expressed or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference therein to any specific commercial product, process or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed therein do not necessarily state or reflect those of the Unites States Government or any agency thereof.

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