By Scott Singleton and Mark Suda

HOUSTON–Proper assessment of un-conventional prospects requires integratinggeology, geophysics, geomechanics, petro-physics, and engineering. Geology pro-

vides a regional stratigraphic and structuralframework, and petrophysics provide thebase-line rock property data from bothlog and core. Geophysics provide a meansto extend the petrophysical rock propertydata away from the well bore while geo-mechanics describe the stress state both

locally and regionally. Engineering data follow these other

data and delineate the results of drilling,completion, and production. All of thesedata types are essential to piecing togethera complete reservoir assessment.Much attention has been focused on

modifying the traditional conventionalgeophysical reservoir characterizationworkflow in order to provide outputs thatare of use to integrated asset teams inunconventional resource plays. Theseasset teams typically are led by, andsometimes consist mostly of, reservoirand drilling engineers. With this in mind, geophysicists are

consolidating their efforts in four primaryareas: predicting anisotropy from full-azimuth data, predicting rock propertiesalong the Vfast azimuth (which are the‘true’ rock properties, having minimaldistortion because of vertical fractures),predicting principal stresses, and fracturecharacterization.

Integrated Workflow

We have adopted this philosophy inour unconventional reservoir characteri-zation workflow (Figure 1). This integratedworkflow is an acknowledgment that geo-physics alone are not sufficient to providea comprehensive solution in resource plays.Instead, we bring together petrophysics,rock physics, geophysics and engineeringto offer a comprehensive solution.The workflow is initiated with petro-

physics and rock physics (left column,Figure 1). Logs are first edited for envi-ronmental corrections, bore hole rugosity,mud filtrate invasion, tool sticks or cycleskipping, and missing curve data. Thepetrophysics step is concluded by calcu-lating porosity, fluid saturation, andmineral volume. Then, using rock physics

Geophysicists Boost Workflow’s Value

FIGURE 1Workflow for Reservoir Characterization of Unconventional Resources

.LAS Data

Volumetric AnalysisMineralogy, TOC,f, etc.

GWLA® GeophysicalWell Log Analysis& Rock Physics

Delta WellPerturbational

Modeling

AnisotropoicRock Physics

Modeling

SeismicPost-Stack, Pre-Stack & DepthStacks | Angle Stacks | Gathers

AVATAR Data ConditioningConditioned Amplitudes & Attributes

Fracture AttributesFault Mapping

Neural NetworkFracture Facies

Fracture AnalysisSRV Analysis

Elliptical Velocity InversionAnisotropy (f, Î)

AVO InversionRock Properties

Engineering DataTreatment, Production &

Microseismic Data

Elliptical RotationThomsen Param. (d, Î, f)

Stress OrientationSv, Shmin, Shmax

MultivariateStatistics

Production Prediction

FIGURE 2Fracture Characterization Shows Vertical Tectonic Faults

And Localized Fractures

Tully LS

Marcellus Shale

Onondaga LS

The “Better Business” Publication Serving the Exploration / Drilling / Production Industry

NOVEMBER 2012

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relationships, the logs are modeled usingan appropriate effective media model thattakes into account the elastic moduli andvolumetric fractions of the constituentminerals and pore fluids. This processenables us to understand the behavior ofthe reservoir and nonreservoir zones, andto correct for some of the problems en-countered in well log data. Once an effective medium model is

generated, rock properties can be perturbedto simulate variations expected away fromthe well bore. In most unconventionalreservoirs (including the Marcellus Shale),key properties to be modeled are thickness,porosity, mineralogy, total organic contentand fractures. Finally, synthetics are cal-culated from the in-situ effective mediummodel as well as perturbations to thismodel, and these synthetics can be cor-related with seismic data at the well boreas well as elsewhere in the prospect.The central portion of the workflow in

Figure 1 is devoted to geophysical methods.It is divided into three main portions: gatherconditioning, anisotropy calculation andinversion, and fracture characterization. The purpose of gather conditioning is

to optimize the quality of pre- and poststackseismic data prior to use in AVO, impedanceinversion, and post-stack attribute calcula-tion. In general, the post-migration workflowfor conditioning gathers should be kept assimple as possible. This avoids unnecessarytime and effort spent on conditioning andeliminates the possibility of damaging orotherwise altering the rock property infor-mation carried within the seismic data. The workflow typically consists of mul-

tiple removal, NMO stretch removal,signal/noise enhancement, and reflectoralignment. This is followed by creatingangle stacks in preparation for inversion.After the seismic is conditioned,

anisotropy magnitude and fracture orien-tation are calculated by using the differencein moveout velocities from gathers orientedin different azimuth directions. These prod-ucts are used to calculate fracture densityand orientation within the reservoir zone. Fracture indications derived from

anisotropy are combined with post-stackfracture-sensitive attribute data (coherencyand related attributes, dip and dip azimuthattributes, and the curvature family of at-tributes–blue boxes in Figure 1 to the rightof the green data conditioning box). Thisyields a more detailed view of seismicallydetectible faults and fracture swarms. Finally, seismic fracture data are com-

bined with geologic data on the basin’s

tectonics and structural history to determinethe structural framework of the prospect.Drilling and completions data are pairedwith this fracture information to determine

if the faults are open or closed. The last step in the geophysical work-

flow is the inversion of seismic anglestacks along the Vfast direction, which is

FIGURE 4Automatically Mapped Faults Show Seismic

And Subseismic Fault Planes

J1 Orientation Splay

Small faultor fracturecorridor

Sphere diameter indicates event magnitude

FIGURE 3Geophysical Data Integrated with Engineering Data

Sphere diameter indicates event magnitude

J1 Orientation

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parallel to the fracture strike. This is thebest indication of the true rock propertybecause it has the least interference withnatural fractures. The objective is to derive an estimate

of reservoir rock properties that are usedto calculate quantities needed by com-pletion engineers, such as Poisson’s ratio,Young’s modulus, and rock brittleness.All of these geophysical quantities arecombined with engineering data to predictfavorable reservoir properties away fromthe well bore.All of these outputs are integrated

with engineering data (right column inFigure 1). The key workflow step here isthe multivariate statistical analysis. Thisis a technique that predicts the relationshipbetween a dependent variable and one ormore independent variables. Completion and production data are

considered the dependent variable to bepredicted. This is correlated with rock andfracture property data calculated in previoussteps to derive relationships among wellparameters, engineering, geology, seismicattributes, and production performancedata. The goal is to determine which com-bination of input parameters can be statis-tically correlated to enhanced production.

Marcellus Case Study

In this article, we illustrate a simplifiedversion of this workflow consisting of a

2009 prestack seismic data set from theMarcellus Shale in Pennsylvania that wasisotropically processed, meaning thatanisotropy azimuth and magnitude werenot available. Although this means thatthe true rock property signature likelyhas been smeared, this nonetheless is thetype of data available to many operatorsusing vintage data sets. An additionalcritical component not present becauseof isotropic processing is principal stresscalculations. This is a serious deficiencybecause it prevents an assessment of localvariations in differential horizontal stress,which is a key indicator of how the rockwill break when high pressure fluids areinjected into it.Full log conditioning, core calibration,

and rock physics diagnostics were per-formed on 12 wells, which were in turnused to calibrate the inversion volumes.Static to dynamic transforms were cal-culated from oriented core analyses. Theprestack seismic data were demultipled,anisotropically migrated (VTI only), andbandwidth extended by the processingcontractor. We followed this with a prestack con-

ditioning workflow to improve S/N, re-move NMO stretch, and align reflectorswithin the gathers. Finally, the data wereinverted with a simultaneous impedanceinversion algorithm to acoustic impedance,shear impedance, and density. These

output volumes were converted to rockmechanics volumes (dynamic Young’smodulus and Poisson’s ratio), which thenwere transformed to their static equivalents.

Using Engineering Data

Fracture characterization was performedusing post-stack fracture-sensitive attributes,in this case coherence and maximum cur-vature. The coherence data revealed clearvertical tectonic faults as well as localizedfractures in the more brittle units, such asthe carbonates (Figure 2). Of particularinterest is the mid-Marcellus, which revealsfractures that are isolated within that unitbecause of the ductile upper and lowerMarcellus, which lies both above andbelow the rigid mid-Marcellus (PurcellLimestone). The geophysical data were integrated

with engineering data at the productionpad. Microseismic data show areas of con-centrated seismic activity as well as areasof little seismic activity, indicating thatcompletions have not uniformly brokenup the formation rock (Figure 3). Largemagnitude microseismic events are locatedpreferentially in the center of the eastern-most laterals, possibly suggesting thatthese areas may contain pre-existing faults.This hypothesis can be tested by combiningother data types with the microseismicand bore hole location data. The first data type to integrate is au-

tomatically mapped faults (Figure 4).These faults are created by a programthat searches for laterally and verticallycontinuous discoherent events in the co-herence volume. These events may beseismically visible or not, and if not, theevent would be considered subseismic.The addition of seismic and subseismicfault planes to the microseismic dataclearly shows that a fault/fracture corridorthrough the central portion of these lateralswas especially prone to being openedand exploited by high-pressure injectionof fluids during completion activities. Another observation is that faults ori-

ented in the J1 direction (the primarypre-existing fracture set, which in theAppalachian foreland basin is orientedalong Shmax) are especially prone toopening if subjected to fluid injection.Other fault orientations (such as the J2direction, which is about normal to J1)were less prone to opening.Additional confirmation of the structural

weakness along this fracture zone can befound by looking at the rock property data.The two rock strength attributes–static

FIGURE 5Rock Property Data Showing Static Young’s Modulus

MODERATELYDUCTILE

RIGID

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Young’s modulus (Figure 5) and Poisson’sratio–show a zone of effective ductilityalong this fault zone. In this instance, theterm “effective” is a reference to the factthat the apparent ductility seen in thePurcell LS is a result of this thin unit beingbroken up by a series of faults and (pre-sumably) associated fractures in this faultzone. Thus, the interpretation is that thePurcell LS in this area is brittle, but it isbroken into small enough pieces that theconvolved acoustic response to seismic isthat of a moderately ductile unit.

Multivariate Statistical Prediction

Our end goal was to determine whichof these attributes, or combination of at-tributes, could be used to predict productionaway from the well bore. However, to dothat we first needed to find a proxy forproduction because production (PVT) logswere not available. We chose stimulatedreservoir volume (Figure 6) as a proxyfor production because it had a very goodcorrelation with estimated ultimate recoveryon each lateral (i.e., large SRV was asso-ciated with large EUR; small SRV wasassociated with small EUR).So, with SRV chosen as the dependent

variable, we needed to choose a varietyof seismic attributes to function as thepredictive, or independent, variables. Wechose two rock property volumes: me-chanical rigidity and vertical stratigraphicposition. Vertical stratigraphic positionwas chosen as a variable because theSRV of certain stages might be small ifthe lateral deviated down into the veryductile lower Marcellus, and so we wantedto account for this as a potential factor.We also chose two structural attributes

as independent variables–coherence andmaximum curvature. These were chosenbecause of their ability to predict faultsand fracture zones. However, note thatthese two attributes only predict the pres-ence of breaks in the rock fabric; they donot tell us anything about whether thefaults are open or sealed, or whether theyare critically stressed so as to break openif injected with fluid. For that kind of in-formation, we would need to incorporateanisotropy and principal stress into thestatistical prediction.These four volumes were input into

the nonlinear multivariate statistical pre-diction. The predictive variable (comprisingall four input seismic attributes) had acorrelation to the dependent variable of71 percent. While this is a decent correla-tion, it could be made better with a more

comprehensive suite of attribute volumesthat were not available for this study.Finally, the statistical prediction of SRV

was extrapolated laterally away from thewell pad, and the SRV values were convertedto EUR (Figure 7). In calculating this esti-mated production, several key assumptionswere made. First, the calculation assumesproduction from an entire 4,000-foot lateralcontaining 10 stages (because that was theinput data used for the calibration). Second, large seismic-scale faults were

presumed to represent hazards to be avoided,and thus these values were nulled in theresulting map (shown in black). Third, be-cause neither anisotropy nor closure stress

prediction were available, no informationabout the condition of the smaller faultsand fracture zones was available. Thus, all these features were treated

alike, meaning that they were assumed tocontribute to production in proportion totheir magnitude. This of course is an incorrectassumption, but in this case was unavoidablebecause of the limited input data available. Predicting production drivers clearly

identifies where large pre-existing faultand fracture zones are present (black areasin Figure 7). In general, these zones shouldbe avoided if they are in the J1 orientationbecause of a high risk of reopening sealedfaults, which in turn likely will result in

FIGURE 6Stimulated Reservoir Volume Used as a Proxy for Production

ALL Well 1H Well 3H

Well 6H Well 7HWell 5H

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Statistical Prediction of Stimulated Reservoir VolumeFIGURE 7

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the loss of fracture fluid. Smaller faults and fracture zones are

identified clearly by bright colors, indicatinga large EUR prediction is associated withnatural fracture networks. Away from nat-ural fracture zones, areas of favorable rockproperties (i.e. brittle rock) are shown inblue (rather than magenta in the less brittleareas) because in this area, brittle rockcorrelates with higher SRV, which in turncorrelates with enhanced production.These results demonstrate that in this

area the presence of brittle landing zonerock as well as pre-existing fracture net-works can enhance well production sig-nificantly. In fact, production estimatedfrom a lateral well bore could vary by afactor of two, depending on the presenceor absence of these two factors. The results also show that a compre-

hensive suite of fracture characterizationmethods (e.g., anisotropy and principalstresses, including closure stress) are need-ed to properly determine whether a pre-existing fracture zone will reopen orremain closed when hydraulically fractured. This case study highlights the need to

integrate data from a wide variety of dis-ciplines in order to achieve a comprehensivereservoir assessment in unconventionalplays. The study also shows that there areseveral additional pieces of data that couldimprove significantly the accuracy of pro-duction driver prediction in unconventionalplays. Specifically, this shows the need toincorporate reservoir quality data, such asTOC volume, gas in place, porosity, andthickness into the prediction so that bothcompletion quality and reservoir qualityare treated as equal and important com-ponents of the prospect evaluation. r

SCOTTSINGLETON

Scott Singleton is technical managerfor the ResSCAN™ multiclient uncon-ventional program at ION Geophysical.Before joining ION earlier this year, hewas seismic technology adviser at RockSolid Images. He joined Rock SolidImages in 2004, becoming vice presidentfor global processing and interpretationin 2008 and seismic technology adviserin 2011. Singleton began his career inmarine acquisition, processing and in-terpretation at Western Geophysical, Seis-com Delta, Digicon and Fugro-McClel-land Marine Geosciences. In 1993, hebegan consulting in quality control meth-ods for seismic acquisition and processingwith Energy Innovations Inc. Singletonthen moved to Jason Geosystems, wherehe was involved in full-bandwidth im-pedance inversion. He joined Core Lab-oratories in 2003, working in rock propertyand seismic inversion technology. Sin-gleton holds a bachelor’s in geophysicsfrom New Mexico Institute of Mining &Technology, and a master’s in geophysicsfrom Texas A&M University.

MARKSUDA

Mark Suda is senior geophysicist atRock Solid Images, with 32 years of in-dustry experience. He has been engagedin integrated reservoir characterizationby simultaneous inversion of 3-D seismicfrom deepwater West Africa, CentralAmerica, and the Far East. Previously,Suda held positions within BP-Amoco,including its Upstream TechnologyGroup. Prior to that, he served as groupleader of a seismic processing team atWestern Geophysical. His research in-terests include seismic characterizationof unconventional gas reservoirs. ForRock Solid Images, he has given morethan 80 technical presentations on seis-mic data conditioning, AVO, elastic in-versions, and rock property modelingand calibration. Suda holds a bachelor’sin mathematics from the University ofNorth Dakota, and is a member of theSociety of Exploration Geophysicistsand Geophysical Society of Houston.