PROJECT FINAL: Integrated microseismic and 3D seismic interpretations

Team Members – Tom Wilson (PI), West Virginia University, Department of Geology and

Geography.

Project period extended from 5/1/2013 through 5/31/2016

Objective – The integrated microseismic and 3D seismic studies were funded in two phases.

Phase 1 (5/1/2013 – 5/31/2015) study included the following objectives: 1) develop a 3D

seismic interpretation of an actively producing Marcellus shale reservoir in southwestern

Pennsylvania; 2) integrate frac-induced microseismic data observed along laterals in the

field into the subsurface 3D seismic interpretation; 3) determine stratigraphic distribution of

microseismic events within the 3D seismic framework; 4) evaluate influence of seismic-scale

fault networks on microseismic distribution; and 5) incorporate available geophysical logs

and subsurface data into the geophysical characterization and subsurface interpretation.

Phase 2 extension (5/31/2015 through 5/31/2015) included the additional objectives: 1)

development of lithostatic pressure curves and estimates of Sv from 3 wells with density

logs in the local area; 2) estimation of Shmin from individual wells based on instantaneous

shut-in pressures (ISIP) stage-by-stage; 3) evaluation of local variability in ISIPs between

individual wells and between groups of wells on hanging wall and footwall locations; 4)

estimation of Shmax assuming strike-slip neotectonic stress and (coefficient of internal

friction); 5) independent estimation of Shmin for normal fault offsets.

Summary with Key Results

Some of the main outgrowths of the research conducted under this project include:

o Characterization of prominent out-of-zone microseismicity through rupture of

critically stressed fractures which were continually re-ruptured during hydraulic

fracture treatment of wells in the area. These events appeared initially at distances

from about 3300ft to 5000ft from the hydraulic fracture stimulation point. Although

these events extended from about 800ft to 2000ft above the base of the reservoir they

remain more than 6000ft beneath the surface.

o Demonstration that energy density provides a better estimate of stimulated reservoir

volume since it is directly related to fracture surface area ruptured in response to

hydraulic fracture stimulation. Radiated microseismic energy density associated with

hydraulic fracture treatment correlates better to cumulative production than does the

simple stimulated reservoir volume

Appendix I: EFD East Regional Center I.6 Microseismic and 3D Seismic Interpretation (Task 5.4.5)

o Identification of a new class of microseismic events referred to as proximal cross-well

events. This class of events consists of an event or events produced during stimulation

of one well that occur close to events produced during the stimulation of adjacent

laterals. Although areas with a greater number of cross-well events are associated

with greater production, the observations are interpreted to suggest that areas of

clustered cross-well events are associated primarily with re-stimulation or repeated

rupture of the same fracture or fault rather than to additional stimulation.

o Workflow development to reveal subtle seismic discontinuities. These discontinuities

are interpreted to be associated with fracture zones and small faults. They have trends

similar to observed microseismic event cluster trends and are interpreted to

accommodate and enhance hydraulic fracture treatment.

o Estimation of the orientation of SHmax using the orientations of microseismic event

clusters and Mohr-Coulomb failure criteria. The orientation of SHmax is estimated as

that orientation that minimizes the number of cluster trends that fall below the failure

envelope.

o Information gathered from operators in the region that report low stress anisotropy

(SHmax-Shmin) on the order of 400psi. This small difference in the horizontal stresses

leads to more diffuse clusters of microseismic events. The reported stress anisotropy

also implies that shear failure in response to hydraulic fracture stimulation will occur

in the form of normal offset. Normal offset is expected when Sv> SHmax>Shmin.

o Development of discrete fracture network (DFN) representations of the stimulated

reservoir volume. These DFNs are developed using fracture sets inferred from the

microseismic response and Mohr circle analysis. An energy weighted event density

grid is used to distribute fracture intensity in the stimulated reservoir volume. The

intensities of the individual sets are scaled in proportion to their relative occurrence.

The properties of the DFN are upscale into the grid and used to estimate relative

porosity and permeability distribution with in the stimulated region.

Key outgrowths of the research effort are also reflected in numerous publications that

include:

1) Wilson, T., Hart, A., Sullivan, P., 2014, Proximal cross-well microseismicity as a

possible indicator of drainage efficiency and critically stressed strata: SEG Technical

Program Expanded Abstracts 2014, 2738-2742.

2) Wilson, T., Hart, A., Sullivan, P., 2014, Characterization of Marcellus shale fracture

systems for fracture model development using 3D seismic and microseismic data:

SEG Technical Program Expanded Abstracts, 2683-2687.

3) Wilson, T., Hart, A., and Sullivan, P., 2014, Frac creates out-of-zone rupture: Special Report, American Oil and Gas Reporter, August issue, p. 149-152.

4) Wilson, T., A. Hart, P. Sullivan, D. Patchen, 2014, Integrated microseismic and 3D seismic interpretation: Martz Summer Conference, Colorado School of Law. (See

http://www.colorado.edu/law/sites/default/files/WVU.Integrated%20Microseismic%20and%203D%20Seisimic%20Interpretation.pdf).

5) Wilson, T., Hart, A., and Sullivan, P., 2015, Study measures Marcellus frac results: Special Report New Products/Technology, American Oil and Gas Reporter, July issue, 58, 7, 79-85.

6) Hart, Ariel K., 2015, 3D seismic attribute-assisted analysis of microseismic events in

the Marcellus Shale: Master’s Thesis, West Virginia University, 147p

7) Wilson, T., Hart, A., Sullivan, P., 2016, Interrelationships of Marcellus Shale Gas

Production to Frac-Induced Microseismicity, Interpreted Minor Faults and Fractures

Zones, and Stimulated Reservoir Volume, Greene County, Pennsylvania:

Interpretation, v. 4, No. 1, T15-T30.

8) Wilson, T., P. Sullivan, 2016, submitted, Microseismic energy density and event trend

constraints on model DFN development for hydraulically fractured reservoirs:

Marcellus shale, southwestern Pennsylvania, U. S. A.: submitted for presentation in

the 2016 SEG meeting, 5p.

9) Zhu, Y., T. Wilson, P. Sullivan, 2016, submitted, Variations of microseismic b-values

and their relationship to 3D seismic structure in the Marcellus Shale: Southwestern

Pennsylvania: submitted for presentation in the 2016 SEG meeting, 5p.

AAPG online abstracts through Search and Discovery

10) Wilson, T., Hart, A., Sullivan, P., 2014, 3D Seismic Workflows Developed to

Evaluate Out-of-Zone and Stealth-Zone Microseismic Behaviors: Marcellus Shale,

Central Appalachians, USA: AAPG Annual Convention and Exhibition, April 8,

Houston, TX, see

http://www.searchanddiscovery.com/abstracts/html/2014/90189ace/1828336.html

11) Hart, A., Wilson, T., and Sullivan, P., 2014, 3D Seismic Attribute-Assisted Analysis

of Microseismic Events Within the Marcellus Shale: AAPG Annual Convention and

Exhibition, April 8, Houston, TX, see

http://www.searchanddiscovery.com/abstracts/html/2014/90182se/abstracts/hart.htm

*The following report provides an overview of some on some key issues associated with the

model representation of the discrete fracture network created in response to hydraulic

fracture stimulation and is not intended to be comprehensive. Research results are

summarized in the numerous publications and presentations made as part of this project.

Developing a model discrete fracture network of the stimulated reservoir volume

produced in response to hydraulic fracture treatment

Abstract

Results from research conducted under this contract include:

1) Development of novel 3D seismic processing workflows to extract subtle

discontinuities in the 3D seismic reflection response that are interpreted to arise from

zones of increased fracture intensity in the Marcellus shale gas reservoir.

2) Development of methods to use microseismicity induced during hydraulic fracture

treatments to estimate the orientation of SHmax.

3) Development of methods that use radiated microseismic energy to characterize the

stimulated reservoir volume.

4) Development of methods that provide a more reliable prediction of cumulative gas

recovery using microseismic energy density.

5) Development of methods that use microseismic cluster trends to estimate fracture

trends opened in response to hydraulic fracture stimulation.

6) Development of an approach to modeling the stimulated reservoir fracture network that

uses energy density to distribute fracture intensity.

7) Validation of these approaches through general relationships between fracture porosity

and permeability distributions to well production.

Overall, the work conducted under this contract provides approaches for improved

reservoir characterization, outline methods used to better define the state of stress in the

Marcellus, highlight the use of energy density as a better measure of stimulated reservoir

volume, reveal insights into the heterogeneity of fracture systems generated in response to

hydraulic fracture treatment and their relationship to cumulative well production. The

results and value of this work have been recognized in the form of several publications (see

pages 2 and 3) and most recently in the form of an invitation by the journal

INTERPRETATION to develop a special section on Appalachian Shale Gas Field Exploration

and Development: Lessons Learned.

Purpose and structure of the final report

This report focuses on the outgrowths of project research that led to development of a model

discrete fracture network (DFN) of the Marcellus Shale reservoir in the project study area. The

primary focus will be on the development of a model DFN representation of the stimulated

reservoir volume. Data used to constrain fracture set and intensity distribution include 3D

Seismic and microseismic data. Following a presentation of background, we discuss the

development of fracture intensity attributes used to distribute fracture intensity through the

DFN on a set by set basis. This incorporates analysis of microseismic cluster trends and their

relationship to the trend of 3D seismic discontinuities that are interpreted to be associated with

small faults and fracture zones. The relative occurrence of microseismic event trends is used

to scale the intensities of dominant fracture sets inferred from the microseismic and 3D seismic

analysis. The orientation of SHmax in the region is estimated by minimizing the number of

microseismic events that fall below the Mohr Coulomb failure envelope. Two varieties of

DFNs are developed and include 1) the reservoir fracture network activated by hydraulic

fracture stimulation and 2) the reservoir fracture network associated with the reservoir prior to

stimulation. Fracture intensities in the stimulated reservoir DFN are controlled using radiated

microseismic energy density. Fracture intensity, pre-stimulation is estimated using extracted

seismic discontinuities. Estimated porosity and permeability associated with the reservoir

fracture network are determined in a relative sense through upscaling of the model DFN into a

geo-cellular grid. Porosity and permeability provide a visual reference to the heterogeneity in

the stimulated reservoir volume. Distribution in the gridded model is examined and related to

stimulated reservoir volume (SRV). The correlation of energy weighted SRV to production is

reviewed.

Many of the ideas presented in this report were initially developed in some of the papers

published as part of this research effort. This report brings together some of those ideas and

outgrowths coming near the end of the research period. Publications and presentations resulting

from this effort are listed above as Key Outgrowths.

Background

Regional Structural Context -The study area is located in the Central Appalachian foreland of

North America in southwestern Pennsylvania. Deformation in the Central Appalachian tends

to be dominated by the Permo-Pennsylvanian Alleghenian orogeny (Figure 1). However,

earlier tectonic events influence the development of Central Appalachian structure,

particularly in the more distal foreland areas. Extension during the early Cambrian

deformed the Precambrian foreland basement. The extensional basement complex is

known as the Rome trough or Eastern Interior aulacogen (Harris, 1978) that formed during

the opening of the early Paleozoic Iapetus Ocean (Shumaker, 1986; Thomas, 1993).

Subsidence and sedimentation across the area continued through the Paleozoic at varying

rates. Faults in the trough served as zones of weakness along which slight reverse

movements occurred during Paleozoic shelf-loading (Wilson, 2000). Shelf-loading occurred

during three Paleozoic orogenic events, including the Middle-Late Ordovician Taconic

orogeny, Devonian Acadian orogeny and Permo-Pennsylvanian Alleghenian orogeny.

Continued movement across these structures through time influenced current reservoir

conditions in sequentially deposited depositional systems overlying these early structures

(Beardsley and

Late stage reactivation of these earlier structures can create secondary fracture porosity

along trends that cross those of later orogenic events.

Fracture trends reported in the study area include coal cleat trends of approximately N22E

(butt cleat) and N70W (face cleat) just east of the site (Steidel, 1977). The coal cleats are

assumed to have formed prior to Appalachian folding in response to stresses in the early

stages of the Appalachian orogeny (Nickelsen and Hough, 1967). The orientation of the butt

cleat, for example, parallels local folds in the area, which strike approximately N25E.

Outcrop fractures measured to the west have dominant trends of N74W and N26E along

Figure 1: Study are is located in Greene Co., southwestern Pennsylvania. Cable, 1983).

with a less prominent N71E trend (Wilson et al., 2012). Engelder et al. (2009) referred to an

ENE joint set observed in core and outcrop as the J1 set and noted that, in the Marcellus

Formation, the J1 set formed early in response to abnormal fluid pressure during thermal

maturation of organic matter. Joint orientations measured in Eastern gas shale wells to the

west in Ohio are dominated by the east-northeast oriented J1 joint set.

Indirect observations of the reservoir discrete fracture network

As part of this study we had no direct measurements of the fracture network in the

reservoir and bounding strata. Image logs were not available in the field or surrounding

areas. Thus we had to establish a model using indirect evidence for the nature of the pre-

existing local fracture network and the fracture system developed in response to hydraulic

fracture stimulation of the reservoir. Some preliminary developments were reported in

Wilson et al. (2014) and Wilson and Sullivan (2016, submitted). Some of those ideas and

their extension are presented below.

Seismic Discontinuity and microseismic event trends

Post-stack processing of 3D seismic data from this area is described and summarized in

Wilson et al. (2016). They developed a post-stack processing workflow to enhance subtle

seismic discontinuities interpreted to be associated with subtle faults and fracture zones.

Their analysis revealed a bimodal distribution of seismic discontinuities with average trends

of approximately N51E and N45W (Figure 2A). Larger, ~250 foot offset seismic scale faults

cutting through the area have roughly N25E trend: a trend that coincides with the trend of

local surface anticlines. On average in the area, northeast-southwest trending

discontinuities occur with greater frequency than the northwest-southeast trending

discontinuities.

Hydraulic fracture stimulation of the wells in the study area generally produced clusters of

microseismic events dominated by a northeast trending set (Figure 2B). The dominant trend of

microseismic event clusters shares the trend of the seismic discontinuities (Figure 2A). Clusters

of microseismic events with northwest-southeast trend although less pronounced are also

observed. Overall, microseismic events produced during hydraulic fracturing of the wells in

this area do not form tight elongate clusters; in total event clusters become less eccentric and

more diffuse. Their diffuse appearance suggests that the difference between SHmax and Shmin is

relatively small. Local stress perturbations may allow limited failure to occur along northwest-

southeast trending natural fractures and small faults.

The regional average SHmax orientation in this part of the central Appalachian foreland is

~N67oE based on data from the World Stress Map (Heidbach et al. 2008), Wilson et al. (2016)

considered measurements closest to the area along with the interpreted microseismic event

trends and suggested that local SHmax may be approximately equal to N81oE in the vicinity of

the study area.

Figure 2: A) Seismic discontinuity trends and B) microseismic event trends.

We use Mohr-Coulomb failure criterion to classify interpreted microseismic event trends into

two groups: one group consisting of event trends more likely to have failed and the second

group, of event trends less likely to have failed. The rose diagram of interpreted microseismic

event trends (Figure 2B) illustrates these two modes and their relative abundance. In the

absence of nearby estimates of SHmax we assume that the observed microseismic event trends

are produced by shear rupture along pre-existing small faults and fracture zones and then ask

which direction of SHmax will produce failure along these trends. Mohr-Coulomb failure criteria

is used to answer this question (See Wilson and Sullivan, 2016, submitted).

The Mohr circle (Figure 3) illustrates the location of interpreted microseismic event trends

relative to the failure envelope. The vertical stress (Sv) is assumed to be the maximum

compressive principle stress and was computed at the approximate completion depth in the

Marcellus using the integrated density log. Shmin was estimated using the instantaneous shut-

in pressures. We use a coefficient of friction () =0.6. The pore pressure is based on the pore

pressure gradient reported for the Whipkey well in Greene Co. to the northeast of the area. The

small faults and fractures along which rupture occurs are assumed to be critically stressed and

have nearly zero cohesive strength (zero intercept in Figure 4).

An iterative analysis suggests that the orientation of SHmax that minimizes the number of

interpreted events falling below the failure envelope is N84oE. The analysis also revealed that

a coefficient of friction of 0.75 would not facilitate failure, while as low as 0.25 would be

required for failure to occur along all event trends. The of 0.6 results in failure for the

majority of event trends. Events falling below the failure envelope may occur in response to

local stress perturbations associated with the hydraulic treatment, may simply be

misinterpreted or, a mixture of both. equal to 0.6 is often assumed (Byerlee, 1978).

We next ask the question whether the seismic discontinuities extracted from the 3D seismic

data could accommodate failure in response to hydraulic fracture stimulation in an area where

SHmax is oriented approximately N84oE. To answer this question, interpreted seismic

discontinuity trends (Figure 2A) were also evaluated in the context of the Mohr-Coulomb

failure criterion; however, in this case the trend of SHmax is fixed at N84oE based on the analysis

of microseismic event trends.

Figure 3: A) Mohr circle shows locations of interpreted microseismic event trends for an

orientation of SHmax equal to N84oE: the orientation that minimizes the number of events

falling below the failure envelope. The larger red dots represent locations of events with

northeast trend; the black dots represent events with northwest trend. B) Interpreted

microseismic event trends shown in Figure 2 are further subdivided into events likely to fail

(red) and unlikely to fail (black) based on the Mohr Coulomb failure criterion.

Figure 4: A) Mohr circle shows locations of interpreted discontinuity trends relative to the

failure envelope for an orientation of SHmax equal to N84oE. The larger red dots represent

locations of events with northeast trend; the smaller black dots represent events with northwest

trend. B) Interpreted microseismic event trends shown in Figure 2 are further subdivided into

events likely to fail (red) and unlikely to fail (black) based on the Mohr Coulomb failure

criterion.

The results of the analysis reveal that the majority of the seismic discontinuities have

orientations that will accommodate failure observed in the microseismic event trends (Figure

4). The analysis also indicates that the post-stack processing workflow used to extract seismic

discontinuities yields trends that are consistent with those inferred from microseismic events

produced during hydraulic fracture stimulation. Thus, in the absence of any direct evidence

about the nature of the fracture network in the reservoir, the discontinuities extracted using this

workflow serve as a reliable proxy for the reservoir fracture system likely to be encountered

during development and stimulation.

Developing the model discrete fracture network

Reservoir fracture sets

The foregoing analysis provides the basis for defining the dominant reservoir fracture sets that

interact with the hydraulic fracture treatment of wells in the study area. We see that the network

is likely composed of two sets: one, the dominant set with N51oE trend, and the second, the

less common, N65oW set.

Relative fracture intensity

One approach for estimating fracture intensity in stimulated regions would be to create a

geocellular model of the subsurface and count the number of events observed within individual

grid cells. The gridded stimulated volume consists of the volume of occupied cells weighted

by the number of events in each cell (Figure 5A). A model of the hydraulic fracture network

produced during stimulation can use this simple event-density grid to control variations of

fracture intensity in the stimulated region.

As an alternative, radiated microseismic energy provides a direct measure of fracture surface

area (e.g. Kanamori and Anderson, 1975, and Hanks and Bakun, 2008). Radiated energy within

each grid cell provides a direct measure of treatment-induced rupture area in each cell (Figure

5B) and of induced fracture intensity. Generating the energy-based stimulated volume requires

replacement of individual events with a number of events corresponding to a multiple of the

minimum energy event. The variability of density within individual cells (Figure 5B) reflects

the variation in magnitude and radiated energy of enclosed events. The energy-weighted grid

was developed in this study to control the variations of fracture intensity in the model DFN.

Magnitudes of events observed in the treatment of all three wells were converted into their

energy equivalent using the relationship log 1.5 4.8E M (e.g., Kanamori, 1982) where

1.563096*10 ME in Joules. Since radiated energy is proportional to rupture area (e.g.

Kanamori and Anderson, 1975), the energy contained within a grid cell provides a measure of

fracture rupture area and fracture intensity (fracture area per unit volume) within those cells.

The energy weighted grid cells (Figure 5B) are used in the fracture model development

workflow to control the intensity. The energy-weighted grid is scaled to provide intensities for

the two fracture sets interpreted from the event trend analysis. Model fractures with northwest

trend are assigned intensities equal to 20% of intensities used to control the distribution of the

northeast trending set.

Figure 5: Well 3. A) Event density within grid cells. Cells containing only one event are

colored light blue. Cells with 5 events are colored red. B) Energy density grid colored by

number of common magnitude events with number increasing from purple to red.

The resulting discrete fracture network (Figure 6) consists of the two fracture sets inferred from

the microseismic event trends and Mohr-Coulomb failure criterion. Fractures are distributed

through the area using the energy weighted event density driver (Figure 7).

Figure 6: Close-up view of the model DFN showing the two fracture sets inferred from the

microseismic data and Mohr-Coulomb failure criterion. Microseismic event trends are shown

in the inset for reference.

Figure 7: The energy weighted event density grid.

The energy weighted grid is used to distribute fractures throughout the stimulated region

(Figure 8).

Figure 8: Stimulated fracture distribution for all three laterals using an energy weighted

density grid to control fracture density.

The properties of the fracture network, including fracture porosity and permeability, are up-

scaled or distributed into the grid block subdivisions. These porosity and permeability grids

can be dissected to reveal possible variations in the properties of the stimulated reservoir.

As might be expected from the energy density grid, considerable variation in reservoir

properties can be expected. This heterogeneity occurs both laterally and vertically through

the reservoir. The permeability map within the reservoir (Figure 9). Reveals high

permeability regions in the heel half of laterals 1 and 2. (Figure 9).

Figure 9: Relative variations in fracture permeability within the reservoir.

The results of production analysis presented by Wilson et al. (2016) indicated high

correlation between production and in-zone radiated microseismic energy (Figure 10). The

number 1 well has the largest 2-year cumulative production and in cross section view

Figure 10: Two-year cumulative production versus total radiated microseismic energy for 5

wells in the study area including the three wells discussed in this current report.

(Figure 11) generally intersects higher permeability zones within the reservoir.

Figure 11: Relative variations in fracture permeability viewed along a north-south slice

through the permeability grid.

Conclusions

3D seismic processing workflows developed in this study reveal the presence of subtle

discontinuities in the 3D seismic reflection response that are interpreted to arise from zones of

increased fracture intensity in the lower Marcellus Shale in the southwestern PA study area.

Extracted discontinuities form two distinct modes: one with ~N51E trend and a second with

~N45W trend. The trends of reservoir discontinuities do not coincide with the ~N25E trend of

local Alleghenian folds and faults. Microseismicity induced during hydraulic fracture

treatments of wells in the area also forms two distinct trends: a dominant N51E set and a much

less pronounced N56W trend. These observations suggest that the reservoir drainage

architecture will be dominated primarily by northeast-southwest oriented fractures in the

reservoir fracture network.

The distribution of microseismic event trends is used to estimate the orientation of SHmax.

Mohr-Coulomb failure criteria was applied to the microseismic event trends. The orientation

that minimized the number of interpreted trends unlikely to fail (to fall below the failure

envelope) is N84E. Excluding interpreted events that were unlikely to represent actual failure,

the orientations of two main fracture sets were inferred to be the dominant N51E set and a

minor N65W set. The same orientation of SHmax was then used to evaluate the interpreted

seismic discontinuities. That analysis reveal that the majority of interpreted seismic

discontinuities could accommodate failure observed along microseismic event trends. The

average trends of discontinuities most likely to fail N54E and N67W.

Development of a model discrete fracture network incorporated the two fracture sets inferred

from the microseismic. The radiated microseismic energy was averaged into grid cells. This

energy-density grid was then used to control the intensity of fractures in the grid. Following

development of the model DFN, the properties of the model consisting of fracture porosity and

the tensor components of fracture permeability were up-scaled into the model grid. The results

revealed a reservoir with complex porosity and permeability structure. The distribution of

fracture permeability from well to well in this study is consistent with cumulative production

from these wells in that more productive wells intersect larger high permeability volumes along

the length of the lateral.

Overall, the work conducted under this contract provides approaches for improved

reservoir characterization. The results of this work outline methods used to better define

the state of stress in the Marcellus, highlight the use of energy density as a better measure

of stimulated reservoir volume, reveal insights into the heterogeneity of fracture systems

generated in response to hydraulic fracture treatment and their relationship to cumulative

well production.

Acknowledgements

This research is undertaken through the Houston Advanced Research Center’s

Environmentally Friendly Drilling Program funded through the Research Partnership to

Secure Energy for America (RPSEA). A special thanks to Douglas Patchen of the

Environmentally Friendly Drilling Technology East Center for his support of this undertaking.

Schlumberger Petrel and IHS Kingdom Suite software were used to undertake much of the

analysis. Appreciation is extended to the Energy Corporation of America for providing 3D

seismic, microseismic and well data evaluated in this research effort.

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Engelder, T, G. Lash, R. Uzcategui, 2009, Joint sets that enhance production from Middle

and Upper Devonian gas shales of the Appalachian Basin: American Association of

Petroleum Geologists Bulletin, 93, 7, 857-889.

Harris, L. D., 1978, The eastern interior aulacogen and its relation to Devonian shale gas

production: Second Eastern Gas Shales Symposium, U.S. Department of Energy,

Morgantown Energy Technology Center, DOE/METC/SP-78/6, 2, 55–72.

Nickelsen, R. P., and V. N. D. Hough, 1967, Jointing in the Appalachian Plateau of

Pennsylvania: Geological Society of America Bulletin, 78, 609–629.

Shumaker, R. C., 1986, Structural development of Paleozoic continental basins of eastern

North America, in M. J. Aldrich, Jr., and A. W. Laughlin, eds., Proceedings of the 6th

International Conference on Basement Tectonics: Salt Lake City, Basement Tectonics

Association, 82–95.

Steidl, P.F., 1977. Geology and methane content of the Upper Freeport coalbed in Fayette

County, PA. Bureau of Mines Report of Investigations 8226r. 21 pp.

Thomas, W. A., 1993, Low-angle detachment geometry of the late Precambrian–Cambrian

Appalachian-Ouachita rifted margin of southeastern North America: Geology, 21, 921–924.

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interior basin: Bulletin of the American Association of Petroleum Geologists, 84, 3, 376-398.

Additional references to papers resulting from this contract are noted on pages 2 and 3.