An integrated seismic reservoir characterization workflow to predicthydrocarbon production capacity in unconventional plays

Gorka Garcia Leiceaga1, Mark Norton2, and Joël Le Calvez1

Abstract

Seismic-derived elastic properties may be used to help evaluate hydrocarbon production capacity in uncon-ventional plays such as tight or shale formations. By combining prestack seismic and well log data, inversion-based volumes of elastic properties may be produced. Moreover, a petrophysical evaluation and rock physicsanalysis may be carried out, thus leading to a spatial distribution of hydrocarbon production capacity. The resultobtained is corroborated with the available well information, confirming our ability to accurately predict hydro-carbon production capacity in unconventional plays.

IntroductionWithin the last decade, new technologies such as hori-

zontal drilling and hydraulic fracturing have transformedthe potential of unconventional plays to amultibillion dol-lar industry. In addition, extracting hydrocarbons fromtight oil/gas formations is accompanied by geoscientificand engineering challenges which are significantly differ-ent from the ones associated with conventional resourceexploration and production. The use of seismic-derivedrock properties to evaluate reservoir quality in uncon-ventional plays has recently become a topic of interest,which stems from the need to use more sophisticatedmethods to identify high-producing resource plays. Inthis paper, a seismic reservoir characterization (SRC)workflow is presented where inversion-derived elasticproperties have been used to determine hydrocarbon(tight-gas) production capacity in the Montney forma-tion, northeast British Columbia, Canada. The Montneyformation is a stratigraphic unit of Lower Triassic ageand is mostly composed of low-permeability, highlylaminated organic clay, silt, and fine sand.

The workflow begins with seismic data condition-ing, then prestack, simultaneous inversion for elasticproperties, followed by a petrophysical evaluation androck physics analysis to determine a spatial distribu-tion of hydrocarbon production capacity. The resultobtained is corroborated with the available wellsdrilled within the survey area. In addition, a volume ofseismic discontinuities representing natural fracturesand faults is used along with microseismic data to helpimprove our understanding of the relationship betweenreservoir production capacity and seismic-derived elas-tic properties.

MethologyThe available data set (Figure 1a) used in our study

includes four conditioned seismic partial stacks (8°–20°, 20°–28°, 28°–35°, and 35°–45°), horizontal and ver-tical wells (some with microseismic), and a volume ofnatural fractures and faults (Pedersen et al., 2002) de-rived from poststack seismic data. A gas productionevaluation for the horizontal wells discussed in thisstudy is split into three zones of interest as shown inFigure 1b. Zone 1 contains two medium-producing hori-zontal wells, Zone 2 includes two low-producing wells,and Zone 3 encompasses three horizontal wells in closeproximity; one low, onemedium, and one high-producer.In addition, Zone 3 contains a vertical well with two per-forated intervals with the shallow interval providing highproduction, while the bottom interval produces muchless.

The work carried out in this SRC study has beendivided into four main components (Figure 2).

• The seismic data set is conditioned to preservethe amplitude versus offset (AVO) response whileflattening the events to adequately attenuate noisewithout generating artifacts.

• Prestack inversion for rock properties is carriedout using simulated annealing and composed ofa globally optimized algorithm which simultane-ously inverts the angle stacks and their corre-sponding wavelets. The model parameterizationis based on the Aki and Richards (1980) approxi-mation of the Zoeppritz equations.

• A petrophysical evaluation on three available ver-tical wells is initially carried out to predict mineral

1Schlumberger, PTS, Houston, Texas, USA. E-mail: ggarcia23@slb.com; jcalvez2@slb.com.2Progress Energy Corporation, Calgary, Canada. E-mail: mnorton@progressenergy.com.Manuscript received by the Editor 13 February 2013; revised manuscript received 22 April 2013; published online 24 October 2013. This paper

appears in Interpretation, Vol. 1, No. 2 (November 2013); p. SB15–SB25, 10 FIGS., 1 TABLE.http://dx.doi.org/10.1190/INT-2013-0007.1. © 2013 Society of Exploration Geophysicists and American Association of Petroleum Geologists. All rights reserved.

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Special section: Interpretation for unconventional resources

Interpretation / November 2013 SB15Interpretation / November 2013 SB15

fractions, porosity, total organic carbon (TOC),and hydrocarbon saturation. Next, all wells aresplit into three categories (high, medium, andlow) with respect to gas production. The obtainedpetrophysical logs are used to build a relationshipbetween the elastic response of the subsurfaceand reservoir production. The relationship is thenused to predict a spatial distribution of hydrocar-bon production capacity.

• The interpretation of results consists of compar-ing the obtained volume of hydrocarbon produc-tion capacity with the production of the existingwells. The placement of these wells to date hasbeen primarily driven by targeting zones with rel-atively low Poisson’s ratio (PR), so this strategy isexamined in our analysis. Moreover, a volume ofseismic discontinuities representing natural frac-tures and faults is used along with microseismicdata to help improve our understanding of the re-lationship between reservoir production capacityand seismic-derived elastic properties.

Seismic data conditioningSeismic data conditioning is vital to properly invert

for rock properties, although it does not replace high-quality seismic data acquisition and processing. The ob-jective for conditioning seismic before inversion is toensure amplitude preservation while eliminating thepostprocessing residual effects such as anisotropyand noise, which should make the seismic data moreAVO- and inversion-friendly.

The procedure must begin with an analysis of thecommon midpoint (CMP) gathers for determininghow the data can be improved. The potential issuesmay be residual multiples, linear noise, random noise,gather flatness, and wavelet stretch. Only after deter-mining which issues are present in the data can an ad-equate workflow be designed.

Figure 3 shows various types of conditioning en-hancements which can be applied to improve the qual-ity of seismic data. These conditioning processes arehere subdivided into two segments: noise attenuationand gather flattening. A preliminary step may be theanalysis of S/N to identify whether the data have noise

Figure 3. Diagram showing the main types of improvementswhich may be applied to CMP gathers for optimizing data qual-ity in AVO inversion for rock properties.

Figure 1. (a) Three-dimensional view of the project area dis-playing the drilled vertical and horizontal wells with associ-ated mapped microseismic events, a time slice highlightingthe fracture and fault networks in the lower Montney forma-tion as well as seismic and Poisson’s ratio cross sections.(b) Seismic depth slice with fractures and faults showingthe horizontal wells discussed in this study along with mappedmicroseismic hypocenters.

Figure 2. Main components of the SRC workflow.

SB16 Interpretation / November 2013

problems which must be addressed. If noise is in fact anissue, determining whether the noise is linear, random,or comes from multiples may be achieved by analyzingthe gathers. For example, if the data exhibit residualmultiples, a weighted least squares radon transformis effective in modeling the coherent residual multiples.The application of such processes to model seismicevents by moveout discrimination will aid in reducinginterference from multiples with primary energy. Cau-tion is required in this process given the risk of remov-ing usable signal, especially at near offsets where themoveout differentiation between primaries and multi-ples is difficult to achieve.

For interbed multiples, regular gap deconvolutionwith zero phase correction is an option which can beapplied with a frequency bandwidth constraint. Forrandom noise issues, it is assumed that the stackingprocess will reduce or suppress noncoherent noise,although in specific environments such as carbonates,there is low fold and low transmission which has theeffect of significantly lowering the S/N ratio. In thissituation, a random noise attenuation technique mayprove worthy for increasing the continuity of events.

Accurately predicting reservoir properties using si-multaneous prestack inversion can be highly dependenton the degree of “flatness” in seismic gather events. Theprimary cause for the need to apply gather flatteningtechniques to seismic data is residual moveout due toanisotropy, where its effects typically appear as “hockeysticks” in the farther offsets. If the data are stacked with-out correcting for residual moveout, output will besmeared and lower in frequency content.

Another issue which may also pose a threat to attain-ing success in AVO inversion studies consists ofresidual time shifts caused by unaccounted time shiftsamong the traces. Trim statics was used in our study,which is a commonly used technique that uses maxi-mum crosscorrelation values with a model trace fromdifferent computation windows. This process can dy-namically shift data samples in time and preserve wave-let character across the entire offset range.

The removal of residual anisotropy and time shifts isvital, especially in PP, PS, and time-lapse inversion,where multiple independently acquired data sets (i.e.,baseline and monitor surveys) will need to be aligned.Seismic-driven characterization studies will usuallybenefit from seismic data conditioning work. Thereare instances where the job performed during the dataprocessing sequence is sufficient and no postprocessingconditioning is required. This depends highly on thequality of the processing algorithms available and theexperience level of the seismic processor.

Prestack seismic inversionThe use of seismic inversion is central in obtaining a

3D distribution of rock properties. By correlating theseismic and well domains, sparse 1D borehole measure-ments are extrapolated into 3D space, thereby providinga powerful tool to characterize reservoirs. Various inver-

sion methodologies exist to predict subsurface rockproperties, which can be applied to various types of seis-mic data including conventional, multicomponent, time-lapse, crosswell, and azimuthal. The available outputsfrom the inversion process depend on the log data typeavailable as well as whether there is prestack or post-stack seismic to be used. The workflow discussed in thispaper focuses on deterministic, simultaneous AVO inver-sion with key inputs of prestack seismic data, extractedwavelets, and three a priori models.

The primary inputs into the inversion kernel includethe seismic partial stacks, extracted wavelets and a pri-ori models. In the model-based inversion approach, inde-pendent wavelets are extracted for each partial anglestack and estimated from the correlation between thewell reflectivity and the seismic trace at the well loca-tion. Variations in amplitude, frequency, and phase be-tween the different seismic input stacks are capturedby the wavelets so there is no need for scaling, phaserotation, or frequency balancing of the seismic data.The parameters which have the most impact on waveletquality include the log calibration into the time domain,the method of extraction (e.g., amplitude and phase as-sumptions), the analysis window, and to some degree thelength of the wavelets. If multiple wells exist within theseismic coverage area, a multiwell estimation approachshould be used to better capture the lateral variations inacoustic and elastic properties of the subsurface. More-over, time-varying changes in wavelet frequency due toattenuation must also be taken into account. This attenu-ation factor may be determined by building an attenua-tion model derived from the seismic data.

The a priori or low-frequency model (LFM) inputdata include the calibrated wells, seismic horizons,and dip information obtained from the seismic data(Rasmussen, 1999). The LFM is an integral part ofthe inversion iteration process and although its contri-bution is a small part of a full-bandwidth inversion, itsrole is crucial. Without a correct background trend, ac-curate rock property values cannot be obtained in theinversion no matter how precisely the mid-to-high fre-quency information is predicted. The structural com-plexity of the study area and the number of availablewells dictate how the LFM is generated, because vari-ous methodologies exist for generating such models. Ifenough spatially properly distributed well data areavailable, geostatistical methods such as kriging, cok-riging, sequential Gaussian simulation, and movingaverage may be appropriate to extrapolate the lowfrequencies from the wells using the stacking velocitiesas a guide. In many reservoir characterization studieswith limited well information, an extrapolation algo-rithmwhere the weight of the extrapolated well log datais inversely proportional to the square of the offset dis-tance from the well is a common practice.

The main inversion outputs in the deterministic ap-proach include volumes of acoustic impedance, VP/VS,and density. Through algebraic manipulation, otherelastic properties (Table 1) may be generated from

Interpretation / November 2013 SB17

Table 1. Various types of inversion processes along with their respective methodologies, well and seismic datarequirements, and possible volume outputs. For time lapse inversion, n-vintages refer to the unlimited number ofmonitor surveys which may be used to determine the ratio change with respect to the baseline survey.

Seismic inversion processes and applications

Inversion processes Method Requiredseismic data

Required well data Output volumes

Acoustic inversion Deterministic Fullstack VP, RHOB Al

AVO inversion Deterministic Prestack VP,VS, RHOB Al, SI, VP/VS, PR, RHOB, LR,MR, LM, FF, K, E, M, G, lambda

Acoustic inversion Stochastic Fullstack VP, RHOB Al

AVO inversion Stochastic Prestack VP,VS, RHOB Al, SI, VP/VS, PR, RHOB, LR,MR, LM, FF, K, E, M, G, lambda

Acoustic inversion Discrete spike Poststack VP, RHOB Al

AVO inversion Discrete spike Prestack VP.VS, RHOB Al, SI, VP/VS, PR, RHOB, LR,MR, LM, FF, K, E, M, G, lambda

Poststack multi-component inversion

Deterministic PP Fullstack, PSFullstack

VP, RHOB Al, SI

MulticomponentAVO inversion

Deterministic PP Prestack, PSPrestack

VP.VS, RHOB Al, SI, VP/VS, PR, RHOB, FF, K,E, M, G, lambda

Poststack time-lapseinversion

Deterministic BaselineFullstack,n-vintagesmonitorfullstack

VP, RHOB Baseline Al, n-vintages ratiochange Al

Time-lapse AVOinversion

Deterministic Baselineprestack,n-vintagesmonitorprestack

VP.VS, RHOB Baseline At, n-vlntages ratiochange Al, SI, VP/VS, PR,RHOB, LR, MR, LM, FF, K, E,M,G, lambda

AVO azimuthalinversion

Deterministic Prestackazimuthal stacks

VP, VS Isotropic, VS

Fast VS Slow, VS FastAzimuth, RHOB

Al, RHOB [vertical isotropic],SI fast,SI slow, SI fast azimuth, SIslow/SI fast ratio

Crosswell inversion Deterministic Fullstack VP, RHOB Al, PHIT, VP

AVO azimuthalinversion

Deterministic Prestack VP.VS, RHOB Al, SI, VP/VS, PR, RHOB, LR,MR, LM, FF, K, E, M, G, lambda

Inversionapplications

Method Seismic data Wall data Output volumes

Lithology prediction Rock physics Prestack VP, VS, RHOB, Vd,Sw,PHIT

User defined lithology andassociated probabilities

Porosity Crossplotanalysis

Poststack,prestack

VP, RHOB, PHIT (VS

for Prestack)Total porosity

Joint porosity andsaturation

Rock physics Prestack VP, VS, RHOB, PHIT.Sw

Total porosity, watersaturation and associatedprobabilities

Water saturation,resistivity

Neuialnetworks

Prestack VP,VS,RHOB,Sw Water saturation, resistivity

Volume of shaleVolume of clay

Rock physics,neural

Prestack VP, VS.RHOB.Vsh, Vd Volume of shale, volume ofclay

Pore pressure (highresolution vels)

Geomechanics Prestack VP, VS.RHOB.Vsh, Vd Pore presssure

Mechanicalearth modeling

Geomechanics Prestack VP, VS,RHOB, PHIT,PHIE, GR, Vd

Stress

Wellborestability

Geomechanics Prestack VP, VS,RHOB, PHIT,PHIE, GR, Vsh, Vd

Shear failure gradient, fracturegradient, breakout andbreakdown failures

Note: AI = acoustic impedance, SI = shear impedance, PR = Poisson’s ratio, LR = lambda rho, MR = mu rho, LM = lambda mu, FF = fluid factor, K = bulk modulus, E =Young’s modulus, M = P-wave modulus, G = shear modulus, lambda = Lamé’s first parameter.

SB18 Interpretation / November 2013

the main inversion outputs for consideration in the seis-mic petrophysics part of this SRC workflow. As the in-version estimates the physical properties, the reflectioncoefficients for each partial stack are calculated andconvolved with the appropriate wavelet to comparethe modeled seismic with the measured seismic data.The mid-to-far stack strongly contributes to the estima-tion of VP/VS ratio and density. The high-resolutioncontribution from the near- and midstacks is used with-out contradicting the low-frequency information in thefar-stack. For all input stacks, the application of a sep-arate wavelet ensures that the synthetic seismic foreach partial stack has frequency content and phasecomparable to that of the measured seismic data.

The objective function Z of the inversion algorithmfor each layer property p may be written as

Zp ¼ arg min Eseismic þ Eprior þ Ehorizontal þ Evertical;

where:

• Eseismic–Penalty for the differences between theseismic data and the synthetic seismic is accumu-lated over all partial stacks; related to the S/N ofeach PP seismic angle stack.

• Eprior–Penalty for deviation of the estimated layerproperties from the LFMs are accumulated overall layer properties; guides the solution toward/against the a priori model.

• Ehorizontal–Penalty for horizontal changes are ac-cumulated over all layer properties; controls thehorizontal continuity of the results.

• Evertical–Penalty for vertical changes are accumu-lated over all layer properties; controls the thresh-old for reflection coefficients.

The results obtained from the inversion carried outin our study match nicely with the measured logs(Figure 4). The same goes for all wells used in the in-version process. Such achievement gives a high level ofconfidence in the postinversion work subsequentlycarried out.

Seismic petrophysicsTight gas reservoirs are associated with a high de-

gree of lateral and vertical heterogeneity, low matrixporosity, and low permeability (Boyer et al., 2006).To determine a proper understanding of the reservoir,a simultaneous global error minimization solver is usedto perform the petrophysical analysis. Using standardtriple combo logs (gamma ray, resistivity, and neu-tron-density), core data, and available cuttings descrip-tions, we determine mineralogy, TOC, porosity, andhydrocarbon saturation over the Montney formation.The mineralogy (Figure 5) consists of a mix of clay, silt,quartz, carbonates, coal, anhydrite, kerogen, and heavyminerals. Quartz volume shows a high correlation withporosity and is likely to be a key driver in determiningreservoir quality in the Montney formation.

Subsequently, a production classification is carriedout where all wells are split into three categories (high,medium, and low) with respect to gas production. Theclassification is based on a 30-day average, initial pro-duction (IP) rate, where a medium producer falls within3–5 MMCF∕day. A good correlation is observed whencomparing 30-day IP averages versus the long term pro-duction of the well. Moreover, production rates are nor-malized to the lateral length of the horizontal wells.

A comparison between petrophysical properties andproduction reveals that effective porosity, effectivewater saturation, and clay volume best correlate withproduction. In an attempt to represent the three produc-tion classifications in terms of petrophysical properties,cutoffs are applied to these petrophysical logs. Althoughall three affect reservoir quality, clay volume also in-fluences the ability to effectively induce fractures inthe formation.

Next, an n-dimensional probability density function(PDF) is established from a cluster analysis on thelog data as a representation of the variability in the for-mation properties. Bayesian decision theory is thenused to establish probability density functions forsolving rock physics classification problems (Dudaet al., 2000). The theorem allows for expressing the

Figure 4. Comparison of well logs (red curves), low-frequency model (green curve), and prestack inversion traces (blue curves)at a vertical well for acoustic impedance (a), lambda/mu (b), and density (c). The Montney formation is highlighted in yellow. Thecorrelation between inversion results and well logs is high, giving confidence in the postinversion work carried out.

Interpretation / November 2013 SB19

probability of a particular class given an observed x us-ing Bayes’ theorem as

PðCjjxÞ ¼Pðx; CjÞPðxÞ ¼ PðxjCjÞPðCjÞ

PðxÞ ;

where Pðx; CjÞ is the joint probability of x andCj; PðxjCjÞ denotes the conditional probability of xgiven Cj (Avseth et al., 2005). To establish a class x(i.e., high producer, medium producer, low producer),a rock diagnostic is performed where, using theavailable well log data, a set of crossplots is generatedusing the inversion output attributes (i.e., acousticimpedance, PR, density, etc.) and petrophysical proper-ties. The objective is to determine which seismic-de-rived elastic properties best separate out the desiredclasses in the crossplot analysis.

In this analysis, a 2D PDF using Lambda/Mu anddensity is used to predict the reservoir’s hydrocarbonproduction capacity. Lambda/Mu (λ∕μ) may be derivedfrom PR (σ) using the equation

2σ1 − 2σ

¼ λ

μ;

where

σ ¼ λ

2λþ 2μ:

The incompressibility term Lambda (λ) representsLamé’s coefficient and Mu (μ) is the ratio of shear stressto shear strain, also termed rigidity or shear modulus.Lambda/Mu in rocks behaves similarly to PR, althoughfor a change in PR, the numerator increases as twice theσ change while the denominator is reduced by 1‐2σ,thereby enhancing the λ∕μ change to a maximal asymp-totic approach for σ ¼ 0.5, where λ∕μ is infinite (Good-way, 2001).

Last, the generated probability density function is ap-plied to the selected seismic-derived elastic propertyoutput cubes to produce a spatial distribution of the res-ervoir’s capacity to produce hydrocarbons. At eachsample, there is an associated probability which repre-sents the likelihood of each defined class based on thecrossplot analysis explained above. Several examples,as well as a more comprehensive explanation of Baye-sian statistics, are given in Bachrach et al. (2004), Av-seth et al. (2005), and Sengupta and Bachrach (2007).

InterpretationAlthough every unconventional play is unique, the

key properties governing production potential generallyinclude porosity, TOC, stiffness, natural fractures, andclosure stress (Ouenes, 2012). Moreover, current drill-ing decisions in this field have been highly influenced bytargeting zones of relatively low PR with the belief thatthese zones are easier to fracture, and therefore pro-duce more gas. This strategy has proved useful in many

Figure 5. Montney formation depth plot of mineralogy, compressional (DTP) and shear (DTS) slowness, water saturation (SW),effective porosity (PHIE), and TOC for one of the vertical wells used in the seismic petrophysics analysis. The correspondingmineralogy is labeled on the left-most track, where VCL is volume of clay, VSIL is volume of silt, VSAN is volume of sand, VCLCis volume of calcite, VDOL is volume of dolomite, volume of coal is solid black, VANH is volume of anhydrite, VSM1 is kerogen,VSM2 is heavy minerals, and VXBW is bound water. The perforated interval depths are indicated in the depth reference track(upper perf at ∼1915 m, lower perf at ∼2050 m) as solid horizontal lines.

SB20 Interpretation / November 2013

cases, but not always. We hypothesize that seismic-derived elastic properties, together with microseismicdata and a volume of seismic discontinuities represent-ing natural faults and fractures can provide an under-standing to hydrocarbon production capacity, leadingto more successful drilling decisions. Microseismicmonitoring of hydraulic fracturing treatments in tightoil and gas reservoirs can provide useful informationabout the results and the level of success of the rockvolume stimulated. For example, the length of the gen-erated fracture systems and the geometry of the in-duced fracture system may be determined usingmapped hypocentral locations. In a naturally fracturedreservoir, hydraulic treatments may reactivate naturalfracture systems and therefore locally enhance per-meability.

The color scheme used in the resulting hydrocarbonproduction capacity (HPC) volume follows a traffic-lightapproach where green corresponds to expected highproduction, yellow to medium, and red to low; nonclas-sified samples are indicated in black. In Figure 6, a depthslice comparison between the HPC volume and PR isshown where two medium-producing horizontal wellsare located (cf. Figure 1b, Zone 1). The zone is domi-nated by relatively low PR. The HPC volume is primarilyyellow, indicating the expected production from a welldrilled in this zone is medium. Figure 7 documents themapped microseismic events as well as an estimatedstimulation volume (ESV). Most of the microseismsare clustered within a zone where 3–5 MMCF∕day pro-duction is expected. The ESV uses microseismic event

Figure 6. Depth slice comparison (924 m TVD) between the HPC volume (a) and PR (b), where two medium-producing horizontalwells are located (cf. Figure 1b Zone 1). The zone is dominated by relatively low PR. This observation is currently driving drillingdecisions in this asset play.

Figure 7. HPC volume depth slice (a) and vertical intersec-tion (b) through a medium-producing well showing microseis-mic events and an ESV where most of microseismic events areclustered within a zone where medium production is ex-pected.

Interpretation / November 2013 SB21

density to compute the volume where the data arenormalized by magnitude to compensate for distance ef-fects. This is a qualitative indicator of reservoir contact,possibly correlating to the distribution of fracturing flu-ids within the fractures. Moreover, it gives an indicationof fluid-affected volumes compared to fracture extrac-tion using planar geometry.

In Figure 8 (cf. Figure 1b, Zone 2), a comparison be-tween HPC and PR is shown along with seismic-derivedfractures and faults where two neighboring, low-producing horizontal wells exist. Both wells were tar-geted based on PR. The HPC cube accurately predictslow production from these wells. The available micro-seismic data event size is proportional to S/N.

Figure 9 shows a series of depth slices for the wellslocated in Zone 3 having different production ratings —

a high producer, a medium producer, and a low pro-ducer — which are within close proximity. In addition,a blind vertical well is displayed. The result from theHPC cube (Figure 9a) shows that the high (green trajec-

tory) and medium (yellow trajectory) producing wells(∼200 m apart) fall within a zone of 3–5 MMCF∕day ex-pected production, although the high producer pene-trates a small segment where high production isexpected. The corresponding PR result is also displayedwhere both wells are in a zone of relatively low PR. Thehigh-producing well was drilled along a preexistingnatural fault or fracture, which potentially contributedto higher production in comparison to the adjacentmedium producer. There was no significant drop in pro-duction when the medium producer was hydraulicallyfractured (neighboring high producer was drilled first),although the presence of small amounts of sand andwater were observed, which means there was someconnection between the two wells. Figure 9b showsa depth slice corresponding to the low producer (redtrajectory) in Zone 3, where the HPC volume predictionmatches nicely with the observed well production ofless than 3.0 MMCF∕day. The PR result shows the wellto be in a relatively high PR zone.

Figure 8. Depth slice comparison of the hy-drocarbon production capacity volume (a)and PR (b), both with seismic-derived frac-tures and faults, where two low-producinghorizontal wells exist (cf. Figure 1b, Zone2). Both wells were targeted based on PR.The HPC cube accurately predicts the ob-served low production from these wells.The available microseismic data event sizeis proportional to S/N. The perforation loca-tions are indicated by black disks.

SB22 Interpretation / November 2013

Figure 10. Hydrocarbon production capacitysection at a blind well where the upper perfo-ration set gave high production (green) whilethe lower perforation set is low (red). TheHPC volume shows a good correlation withthe observed production in both perforated in-tervals.

Figure 9. Depth slice comparison betweenthe reservoir’s capacity to produce versusPR with seismic-derived fractures and faults,for the depths of (a) 931 m–compare withgreen and yellow trajectories, (b) 951 m–com-pare with red trajectory, (c) 998 m–comparewith gray trajectory (upper perforation), and(d) 1,126 m–compare with gray trajectory(lower perforation). The vertical well hastwo perforation sets with different well pro-duction: upper perforated interval has highproduction while the lower perforated inter-val produces poorly.

Interpretation / November 2013 SB23

Figure 9c and 9d shows the results at the verticalwell used as a blind test where the upper perforated in-terval produces more than 5 MMCF∕day while thelower perforated interval produces very poorly. Hydro-carbon production capacity correlates nicely with theobserved production values at the perforated intervals.Moreover, low PR at this blind test well is indicative ofhigh production. In Figure 10, the vertical well HPC re-sult is shown in section view.

ConclusionsIn this paper, we propose that seismic-derived elastic

properties be used to predict an unconventional reser-voir’s capacity to produce hydrocarbons. In addition,horizontal drilling, hydraulic fracture monitoring, anda volume of seismic discontinuities representing naturalfractures and faults can assist in the interpretation byimproving the understanding of the result obtained.In our study, a predictability of ∼70% is observed whenvalidating the HPC volume with 27 vertical and horizon-tal wells. The workflow presented in this paper illus-trates how many pieces of the geoscience puzzle mayfit together to predict hydrocarbon production capac-ity, and therefore enhance recovery rates from opti-mized well placement.

AcknowledgmentsOur gratitude goes to Progress Energy Corporation

for permission to publish the paper. Also, we thankJoe Leonard andWayne Hovdebo from Progress Energyfor their contribution to the project. From Schlum-berger, we thank Innocent Kalu and James Johnsonfor their respective roles in the work carried out.

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Avseth, P., T. Mukerji, and G. Mavko, 2005, Quantitativeseismic interpretation: Applying rock physics tools toreduce interpretation risk: Cambridge University Press.

Bachrach, R., M. Beller, L. Chu Ching, J. Perdomo, D. She-lander, N. Dutta, and M. Benabentos, 2004, Combiningrock physics analysis, full waveform prestack inversionand high-resolution seismic interpretation to map lithol-ogy units in deep water: A Gulf of Mexico case study:The Leading Edge, 23, 378–383, doi: 10.1190/1.1729224.

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Duda, R. O., P. E. Hart, and D. G. Stork, 2000, Pattern clas-sification: John Wiley and Sons.

Goodway, B., 2001, AVO and Lamé constants for rock pa-rameterization and fluid detection: CSEG Recorder, 26,39–60.

Ouenes, A., 2012, Seismically driven characterization ofunconventional shale plays: CSEG Recorder, 37, 22–28.

Pedersen, S. I., T. Randen, L. Sønneland, and Ø. Steen,2002, Automatic fault extraction using artificial ants:72nd Annual International Meeting, SEG, Expanded Ab-stracts, 512–515.

Rasmussen, K. B., 1999, Use of dip in seismic inversion:61st Annual International Conference and Exhibition,EAGE, Expanded Abstracts, 4–9.

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Gorka Garcia Leiceaga received aB.S. (2001) in geophysics and a minorin mathematics and an M.S. (2005) ingeophysics, with a focus in controlled-source electromagnetics, from theUniversity of Houston. In 2006, hejoined Schlumberger and specializedin seismic inversion for rock proper-ties and reservoir characterization

studies. Since joining Schlumberger, he has been expatri-ated in Mumbai, India; Villahermosa, Mexico; Rio deJaneiro, Brazil; and Calgary, Canada. Prior to Schlum-berger, he worked for Odegaard as an inversion geo-physicist and as an adjunct mathematics instructorat the University of Houston. He is currently based inHouston, holding a senior geophysicist position in the Mi-croseismic Services group where his focus is on the inte-gration of surface seismic and microseismic.

Mark Norton received a B. S. (Hon-ors) in geophysics from the Universityof Manitoba, where he specialized inelectromagnetics, completing hisundergraduate thesis on magnetotellu-rics. He joined ExxonMobil in 2000 asa staff geophysicist in their Calgary of-fice. In 2003, he worked for ExxonMo-bil out of their Houston office in the

deepwater Gulf of Mexico. Since leaving ExxonMobil in2005, he has worked for Husky Energy and Real Resources,and now works at Progress Energy as a senior geophysicist.He is a member of SEG, CSEG, CSPG, and PESGB.

Joël Le Calvez received a B.Sc. inphysics, an M.Sc. in geosciences,and a Ph.D. in salt tectonics. He isthe North America microseismic do-main expert for Schlumberger andmanages the processing groups inU.S. Land while providing trainingand support to the internationalprocessing and interpretation groups.

He actively participates to the development of the pro-cessing, visualization, and interpretation software Schlum-berger currently uses in relation to the monitoring of

SB24 Interpretation / November 2013

induced microseismicity coupled to hydraulic fracture treat-ment and other applications (e.g., CO2 sequestration,geothermal injection, etc.) using downhole, shallow well-bores, and surface arrays. Prior to joining Schlumberger,

he worked for the Bureau of Economic Geology at theApplied Geodynamics Laboratory and Etudes et Re-cherches Géotechniques.

Interpretation / November 2013 SB25