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S e i s m i c i n v e r s i o n f o r r e s e r v o i r p r o p e r t i e s

556 The Leading Edge May 2012

SPECIAL SECTION: Seismic inversion for reservoir properties

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The variation in well performance observed between various shale gas plays, and indeed within individual

basins and on individual pads, has gone some way to dispelling myths regarding the perceived homogeneity of “shale gas” targets. With increased quantities of data and more determined analysis, we show that understanding the micro- and mesoscale heterogeneity can be advanced through interdisciplinary studies that incorporate traditional and advanced geophysical data and methods with geological understanding and engineering measurements. This under-standing is critical in optimizing well placement, the spacing and length of horizontal wells, and hydraulic fracturing effort to maximize recovery. Specifically, we illustrate that in the Muskwa Formation and the Otter Park, Klua and Evie members of the Horn River Formation, reservoir quality can be predicted using lambda-rho and mu-rho data extracted from AVO inversion studies. From log data, we show that the most prospective reservoir intervals are characterized by decreasing lambda, increasing mu and/or a lambda:mu ratio less than one.

The Horn River BasinThe Horn River Basin (HRB) in northeast British Columbia (BC), Canada, covers more than 1.3 million hectares (Figure 1a). A number of geological formations within the basin are prospective for oil and/or gas. Since 2003, however, the pri-mary focus for exploration and development has been organ-ic-rich Devonian shales of the Muskwa Formation, and of the Horn River Formation (specifically the Otter Park Member and Evie Member) (Figure 1b). The BC Ministry of Energy, Mines and Petroleum Resources estimates original gas in place (OGIP) within the basin at approximately 500 TCF.

Similar to other shale gas plays in North America, HRB is largely an engineering-driven play. The combination of long-reach lateral wells from single pads and large hydraulic fracture treatments (fracks) underpin the economic produc-tion of gas. Economies of scale are reached with reduction in geological risk, associated with the continuous nature of the reservoir, making the large-scale investment required econom-ic. However, with this approach and the effective removal of “casing point decisions”, there are even greater penalties for poor well placement as it is likely not apparent until after an expensive fracture treatment. Almost without fail, once a play has reached operational maturity, the largest single expense for a shale gas well is the completion and stimulation (before that point drilling typically still comprises the single biggest spend).

Apache Canada has amassed a large number and variety of data types in the Two Island Lake region of HRB since 2007, while drilling more than 50 wells. These include 3D and 4D

D. CLOSE, Origin EnergyM. PEREZ, B. GOODWAY, and G. PURDUE, Apache Canada

seismic reflection data, microseismic data from more than 100 frack stages, full core, sidewall core, chemostrat data from drill cuttings, full log suites (in pilot wells), and gamma-ray (GR) logs in horizontal wells, in addition to pertinent completions data (i.e., total proppant, total fluids, shut-in pressure, etc.) for hundreds of frack stages. Each data set provides important information about the variability and heterogeneity of the res-ervoir zone. In isolation, the data can and have been used to alter drilling patterns and completion strategy. However, by integrating drilling and completions operations data with geo-logical interpretations and geophysically derived rock property volumes, we show an understanding, at least in part, of the variability associated with changes in rock properties and stress variations.

Calibrating AVO inversion data in this way provides great-er confidence in using 3D seismic data to plan the location of new drilling pads, the optimal length of individual wells from the pad, the lateral well spacing and even the frack stage length and locations. Perez et al. (2011) develop templates for under-standing the seismic inversion data in a rock physics context, with consideration of the interdependencies of lithology, po-rosity, pore pressure, and stress. By providing predictive tools, it is possible for geophysical data to have impact on what is largely an engineering-driven play.

Geophysics and shale gasStandard or conventional geophysical workflows continue to be important in unconventional plays for mapping geological surfaces and seismic stratigraphic relationships, and identify-ing major faults and more subtle structural trends. Poststack attributes, such as coherence and curvature, are being suc-cessfully utilized to map discontinuities and structural trends that may impact drilling, completion, and/or production. However, the role of seismic in shale gas, and unconventional plays more generally, has evolved to be far more than simply a tool for mapping structure. As the only remotely sensed, pre-drill data available, 3D seismic can be used predictively to en-sure that wells are planned to intersect zones or “sweet spots” that are likely to have good gas storage (i.e., higher relative porosity and gas saturation) and allow optimal stimulation (i.e., the “right” combination of lithological compressibility and rigidity, and an absence of major barriers to stimulated fracture propagation).

Imaging sweet spots using seismic data requires AVO in-version, which allows the physical properties of the reservoir and bounding formations to be predicted. The properties that are typically directly inverted for are acoustic impedance (Ip), shear impedance (Is), and density (�) which can be trans-formed into elastic properties including Young’s modulus, Poisson’s ratio, bulk modulus, shear modulus and Lamé’s con-

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May 2012 The Leading Edge 557


stants lambda (�) and mu (μ). These elastic properties, which govern the seismic response, also largely control the response, and therefore the efficacy, of stimulation efforts. The estima-tion of density can be challenging in land environments where often there is insufficient offset or angle coverage to confident-ly invert for density.

Log data are essential in the AVO inversion workflow for constructing an appropriate low-frequency or background model and for wavelet extraction. In addition, log data are critical for providing an expectation of what combination of properties that can be extracted from AVO inversion is appro-priate for mapping sweet spots. The lambda-rho (LR)—mu-rho (MR) or LMR crossplot space has been adapted by Good-way et al. (2006, 2010) and Perez et al. (2011) to provide a template for interpreting shale gas AVO inversion data. Using log data and knowledge of a play or basin, the expected LMR response can be calibrated. This knowledge is then exploited in interpretation and reservoir characterization studies.

There is also the potential for further layers of information to be added from seismic via: (1) azimuthal studies (i.e., am-plitude variation with azimuth or AVAZ and velocity variation with azimuth or VVAZ); (2) azimuthal and offset amplitude studies (i.e,. azimuthal AVO); (3) converted-wave data pro-cessing for fast and slow shear-wave propagation orientation; and (4) 4D or time-lapse surveying to determine the extent of unstimulated reservoir following hydraulic fracturing.

However, these methods are beyond the scope of this article.

Expanded quantitative interpretation workflow for shale gas geophysics, illustrated for the Horn River BasinWorkflow summary. The interpretation workflow being uti-lized by Apache Canada in the HRB is illustrated in Figure 2. This workflow captures the main elements that can be con-sidered standard operating procedure—that is a process/data type that is likely to be executed/acquired on an ongoing basis once a play is in development phase. In this article we focus on steps 3–10.

Within this workflow are a number of points where mea-sured data provide a calibration point that could require a step back in the workflow to a previous step. For instance, during the drilling process of new development wells, it is likely that geological markers will be updated and provide a means of real-time updating of the velocity model, which could sub-stantially change geosteering prognoses based on seismic data.

Inversion and interpretation. Inversion of prestack seismic data in the Two Island Lake area of the Horn River Basin has been undertaken as part of an integrated workflow to improve understanding of acreage currently under development. Seis-mic gathers were partially stacked into four equal angle stacks from 0–32° (i.e., 8° angle bands) and simultaneously inverted using wavelets extracted from each angle stack. A low-frequen-cy model was constructed using average well-log properties from the two wells available over the zone of interest. This standard AVO inversion workflow inverts directly for acous-tic impedance (Ip), shear impedance (Is), and density (�). For interpretation purposes these properties are recast in terms of

Figure 1. (a) The Horn River Basin in northeast British Columbia covers 1.3 million Ha (from BC Ministry of Energy, Mines and Petroleum Resources). (b) Indicative stratigraphic column for the Horn River Basin area. Modified from Gal and Jones (2003) and Ross and Bustin (2008).




LMR using the following definitions:

�� = Ip2 − 2 Is2 (LR)

μ�= Is2 (MR)

In physical terms, lambda is a measure of incompressibility and mu is a measure of shear rigidity. Crossplots of LR ver-sus MR are, therefore, not fundamentally different from other conventional interpretation templates such as Ip versus VP/VS or Ip versus Poisson’s ratio. The advantages of LMR analysis are that LR and MR are fundamental properties and MR is independent from LR in a way that clearly VP/VS and Poisson’s ratio are not independent of Ip. Goodway et al. (2010) and Perez et al. (2011) developed LMR interpretation templates for shale gas using rock physics trends (Figure 3). These tem-plates when combined with log and core data from different parts of a basin allow seismic inversion data to be used predic-tively to predict changes in reservoir properties such as poros-ity, lithology, and stiffness (or “frackability”). Ultimately, being able to predict the reservoir response to fracking is of critical importance as this is the primary control on the initial produc-tion (IP) rates and on the estimated ultimate recovery (EUR).

Successful fracking requires that a complex fracture net-work is induced in the reservoir by pumping a slurry of fluids

and proppant through the well into the formation. The mini-mum horizontal closure stress, defined as the minimum pres-sure required to open a pre-existing fracture or plane of weak-ness, must be exceeded by the slurry to continue to propogate fractures away from the wellbore after a fracture is initiated (typically at some higher pressure—the breakdown pressure) (McLennan and Roegiers, 1982). Accordingly, the minimum closure stress impacts frack efficiency and efficacy and is of substantial importance from an operational and economic standpoint. Several methods for estimating closure stress have been proposed by different workers; these include methods based on frictional equilibrium (Moos and Zoback, 1990), rock strength criteria (e.g., Jaeger et al., 2007) and/or Hooke’s law (Sayers, 2010). The closure-stress equation shown by Say-ers comprises a number of distinct terms that relate minimum horizontal closure stress, pore pressure, tectonic strain, and elastic properties of the material. These terms can be consid-ered in isolation and by examining the limits of these terms it can be shown that properties derived from AVO inversion are critical in controlling this important parameter. The closure stress equation given by Sayers was recast by Goodway et al. (2006, 2010) in terms of lambda and mu:

(1)

Figure 2. The key steps in an integrated quantitative interpretation (QI) workflow for shale gas. The workflow is differentiated from a standard QI workflow by the integration of data from each stage of the hydraulic fracturing completion strategy employed in shale gas plays.




Figure 4. Three wells from the south, central, and northeastern (left to right, respectively) HRB in well-section view and their associated LMR crossplots. Crossover between neutron porosity and density is closely correlated visually with crossover between LR and MR. The “E-Zone” is used here simply to characterize potential frack barriers, although it is clear from the crossplots that the nature of this zone changes markedly from clay-rich in the south (low MR values) to carbonate-rich in the north (high MR and LR). Each specific mineralogy trend line increases in porosity from 0% to critical porosity toward the origin.

Figure 3. An LMR crossplot template showing rock physics trend lines for different minerals from the zero-porosity unit (PU) end member toward an approximate critical porosity of 40 PU (large dots mark 5 PU intervals) approaching the origin. Quartz-rich lithologies plot counter-clockwise of clay-rich lithologies and the closure stress scalar (CSS) isovalue lines show an associated reduction in a counter-clockwise direction.




where �xx – p is the effective minimum horizontal closures stress, �zz – p is the effective vertical stress (overburden stress), and �xx and��yy are the strains in the minimum and maximum horizontal stress directions, respectively.

In the isotropic case, where the tectonic strain energy vec-tors (�xx and �yy) are equal, this equation reduces to simply:

(2)

From Equation 2, it follows that the minimum amount of pressure that must be applied to open a fracture (�xx – p) and overburden-pore pressure differential (�zz – p) are related by:

(3)

Goodway et al. (2010) label this the closure stress scalar (CSS) or bound Poisson’s ratio. This term is effectively a rock quality factor. The consequences of changes in the CSS can be examined by considering the limits of Equation 3:

For a given MR

For a given MR

For a given LR

For a given overburden and pore pressure, an increase in the CSS results in an increase in the amount of pressure re-quired to initiate and sustain a fracture, which in general is considered suboptimal from a completions standpoint. From Equation 3, it is clear that a coupled decrease in LR and in-crease in MR leads to lower CSS values; it is also clear that the reasons for lower CSS values are ambiguous as they could result from an independent increase in MR or decrease in LR (this ambiguity is inherent for all ratios including Poisson’s ra-tio). CSS isovalue lines plot linearly in LMR crossplot space and decreasing CSS isovalue lines plot counter-clockwise (Fig-ure 3); that is, as we move toward higher quartz content in a simple two-mineral system (quartz and illite), there is a de-crease in CSS values (Figure 3).

Qualitatively, we can describe high clay-content rocks as

Figure 5. (a) Closure-stress scalar map extracted over the Muskwa/Otter Park zones compared to instantaneous shut-in pressure (ISIP) per frack stage (black circles where size is proportional to ISIP) and initial production per frack (purple circles at well toes where size is proportional to initial production). (b) Maximum curvature projected onto a horizon at the reservoir level showing trend of faults intersected by the northern half of the pad, other data as per (a).

Figure 6. A perspective view looking north of the Keg River horizon in the Two Island Lake area with three full pads and one half pad shown (black well traces). The only penetrations of the Keg River and the only sonic logs available occur in the wells highlighted as thick blue traces.




having higher ductility and are, therefore, less able to resist the vertical stress applied by the overburden with intrinsic stiffness or “strength.” As a consequence, a substantial amount of this vertical stress is transferred into a higher confining horizontal stress that must be overcome to initiate and sustain a fracture. In summary, we propose that sweet spots within the reservoir zone are characterized by low LR and higher MR values or, alternatively, low CSS values. Where shear sonic data are avail-able, this can be tested against traditional petrophysical evalu-ations of shale gas quality indicators.

Comparisons of LR and MR logs (calculated from mea-sured compressional and shear sonic logs) with neutron and density porosity illustrate a close visual correlation between these geophysical parameters and more traditional petrophysi-cal properties (Figure 4). Indeed, the crossover observed be-tween LR and MR (where LR drops to be less than MR) in most cases (at least within the HRB) closely correlates to where the traditional gas indicator of neutron-density crossover oc-curs. Cutoffs of the CSS or LR:MR ratio can also be selected to correlate closely to porosity cutoffs used to define sweet spots from log data. The crossplots of LR versus MR in Figure 4 also show how properties can change regionally. The major frack barrier between the upper targets of the Muskwa/Otter Park zones and lower targets of the Evie zone changes from being clay-rich in the south to carbonate-rich in the northeast. This change is evidenced by the movement of points along and diagonally across the 90% calcite rock physics trend line in the crossplots, and can also be interpreted from the logs based on the gamma-ray and porosity logs. Thus, both theoretically and empirically, we have established that the LMR volumes de-rived from AVO inversion can be utilized to map sweet spots.

Myriad other parameters beyond variations in mineralogy control the LMR properties of rocks, including porosity, pore fluid(s), anisotropy associated with tectonic stress and/or the presence of fractures, and pore pressure. The effect of porosity in particular is worth noting where typically saturation risk is low (relative to conventional plays). Increases in MR associ-

ated with more quartz-rich lithologies (note that going from 50% Qz to 75% Qz manifests as a counter-clockwise shift in LMR crossplot space, which entails an increase in MR and a decrease in LR) may be offset by the commonly associated in-crease in porosity, such that the net effect is more noticeable as a decrease in LR. This effect was described by Goodway et al. (2010), where production data were used to support the asser-tion that decreases in LR provide a first-order estimate of sweet spot locations within the reservoir by capturing the porosity-density effect that is not possible with CSS or Poisson’s ratio.

Mapping areas of low LR or low CSS values within differ-ent reservoir zones defined by standard seismic stratigraphic interpretation provides a tool for qualitatively comparing pro-spectivity within different parts of a basin. Such maps can be combined with edge-detection attributes indicative of major

Figure 7. (a) Gridded sonic-log velocities, averaged over the Muskwa-to-Keg River interval, projected onto the Keg River surface. (b) Interval velocities based on a velocity model created using laterally varying interval velocities that best fit interpreted seismic horizons and geological tops available from well control. (Note that there are many penetrations through the top of the reservoir but only three through the base.)

Figure 8. CSS data extracted from the lateral sections of a full pad with what was deemed a reasonable initial velocity model (VM Initial) plotted against the same extractions using the final velocity model (VM Final) where data from the laterals were incorporated.




structures to provide additional detail regarding potential de-velopment locations. Also, in look-back analyses, it may be possible to broadly correlate production to sweet-spot maps and structural trends (Figure 5).

Qualitative correlations between the CSS estimated from AVO inversion and initial production are evident in Figure 5. In relative terms, production per frack from the north-western half of the pad is reduced relative to the southeastern half of the pad. Broadly, this correlates to higher average CSS values through the reservoir in the northwestern part of the pad. Maps such as this are sensitive to extraction intervals and methodologies (i.e., whether mean values, minimum values, maximum values, or mean above/below a given threshold, etc. are mapped). A meaningful interval that also captures the tra-jectories of all well bores can often be difficult to define unless the wells have been actively and successfully geosteered. These are some of the primary limitations of qualitative map analysis in shale gas plays.

Although, in the example illustrated in Figure 5a, we in-terpret a correlation between higher CSS values from AVO inversion and decreased initial production, this particular ex-ample is complicated by the intersection of a major structure

that crosses the northwestern half of the pad, striking approxi-mately north-south, and a secondary structure striking north-west-southeast. These structures can be mapped using reflec-tor terminations in full-stack data and are also evident in 3D curvature volumes (Figure 5b). Completions and production data suggest that, in addition to wells in the northwestern part of the pad intersecting reservoir rock of lesser quality, changes in the tectonic stress regime (and potentially natural fractures) also occur. Microseismic data illustrate that frack stages proxi-mal to the fault intersection behaved unexpectedly, refrack-ing previous stages before fracking virgin reservoir and leaving an unstimulated wellbore section of approximately 400 m. In addition to the microseismic evidence, breakdown pressures and instantaneous shut-in pressures (ISIP) also increased at these stages and several stages failed to initiate fractures at first attempt. In this example, it is clear that being proximal and intersecting a major fault have reduced the potential produc-tivity of the northwestern half of the pad. This result is in-teresting and is of course of interest for future development planning; however, it is not taken as evidence that faults and major structures will always decrease productivity.

Velocity modelingAs the native domain of the seismic reflection and AVO inver-sion data is two-way time (TWT), it is necessary to construct an accurate time-depth relationship to ensure that data are compared and correlated accurately. With the total number of wells in the Two Island Lake area, this would typically be a relatively trivial problem. Due to the nature of pad drill-ing, however, and the limited number of pilot wells and sonic logs (and the complete absence of check-shot or VSP data), it is anything but trivial. Pad drilling results in many penetra-tions of geological horizons above the zone of interest in a relatively confined area below the heel of the pad, but very few penetrations along the horizontal length and close to the toe of the wells which are up to 2 km from the surface pad location (Figure 6). Also, there are relatively few penetrations of the underlying horizons, the Keg River in this instance, as it is not encountered by any of the horizontal legs, only by the vertical and deviated pilot or stratigraphic test wells.

Analysis of slowness logs over the Muskwa to Keg River interval from the three pilot wells in the Two Island Lake area illustrates that the average sonic velocity over this interval var-ies by approximately 4%. Clearly, applying a single interval velocity to the reservoir zone in depth conversion would create

Figure 9. LR log data overlaid on an inverted LR section in the Two Island Lake area (red = low LR, gray = high LR). A good correlation is seen between the log data and the AVO inversion data.

Figure 10. A simplified schematic of pressure as a function of time for a single pressurization cycle or frack stage. The wellbore profile schematic is a map view of a vertical well being fracked where the fracture tip propagates in the direction of the maximum horizontal stress and fracture dilates in the direction of the minimum horizontal closure stress.




substantial errors. The highest average velocity of 4200 m/s is observed in the most southern of the pilot wells (Figure 7a). A simple calculation of interval velocity based on interpreted tops (isopachs) and their corresponding seismic horizons (iso-chrones) shows a similar trend to that observed in data from sonic logs (Figure 7b). Specific differences in the interval ve-locity maps illustrated in Figure 7, however, demonstrate the substantial uncertainty in a velocity model with relatively few constraints. A simple initial velocity model was constructed from the available observed data that were deemed “reason-able.” However, on detailed analysis, this initial velocity model suggested that several wells on the half pad in the southwest of the area were drilled partially out of zone (i.e., down into the Keg River or up into the Fort Simpson) whereas geosteering results and rock chip analysis precluded the possibility that these out-of-zone excursions had actually occurred.

To increase the number of constraints on the velocity model, geological tops interpreted in the laterals were also in-corporated into the velocity model. As it is difficult to make confident interpretations in lateral sections, particularly where wells are designed to parallel bedding, Apache Canada de-veloped a software package (PetroSleek) to assist in real-time geosteering and in the interpretation of geological tops in lat-eral sections. PetroSleek primarily uses gamma-ray data from offset vertical wells as the basis for the interpretation, but is able to incorporate other corroborating data that stratigraphic boundaries have been intersected, such as rate-of-penetration logs and gas shows. PetroSleek was critical in constraining the velocity model and improving its accuracy, and also in inter-preting sub-seismic discontinuities in the strata (structural faults and/or karst/buildup related differential compaction induced faults). Figure 8 illustrates seismic-derived CSS val-ues extracted along the laterals of a full pad, using the ini-tial and final velocity models, where the final model includes constraints based on PetroSleek interpretations of geological tops in lateral sections. The scatter evident in Figure 8 demon-strates that it is imperative to have an accurate velocity model for any realistic attempt at quantitative correlation between log- or engineering-based data and seismic property extrac-tions; if not, then any relationship that may exist is masked by the poor colocation of data.

Inversion calibration and log processingThe primary data available for calibration of AVO inversion results are typically log data. Ideally, there are sufficient wells with shear sonic logs such that blind wells (i.e., wells that are not used in either the low-frequency model, LFM, con-structed for the inversion or in the wavelet-extraction step) are available to test the results of the inversion. As there are only three wells in the Two Island Lake area with shear data over the zone of interest, there are limited opportunities to use log data to independently verify AVO inversion results. A visual comparison of LR log data, filtered to the seismic bandwidth, and inverted LR data (Figure 9) demonstrates that the char-acter of the seismic inversion and log data are similar (using a simple LFM).

The scope for this method of comparison is limited by

the number of vertical wells drilled and logged. It is rare for horizontal wells to be compared, given how few of these wells are logged. In areas where there is good well control but rela-tively few wells logged with dipole-sonic, it is necessary to use standard geological or petrophysical indicators of reservoir quality for calibrating AVO inversion data. This may require petrophysical processing specific to the reservoir interval as standard methods of estimating lithology and porosity from natural gamma-ray, density, neutron and resistivity log data (i.e., triple-combo data) can lead to large errors in many or-ganic-rich shales.

Critical to calibrating wireline log data are core mea-surements and advanced logs in at least some key wells over the reservoir interval. Advanced logs typically include natu-ral gamma-ray spectroscopy, elemental spectroscopy, dipole sonic, and image logs. Close et al. (2010) utilized a core and advanced log-calibrated petrophysical model in the Montney shale gas play in the Western Canadian Sedimentary Basin to predict porosity-height (Phi-H) through the reservoir zone. These point data were correlated qualitatively to maps of VP/VS extracted through the zone of interest. Close et al. also utilized crossplots of VP/VS against Phi-H to demonstrate the strength of the relationship between reservoir quality trends from well control and AVO inversion data. Using a map-based seismic extraction negates the necessity for an accurate velocity model (assuming the correct seismic horizons and intervals are being mapped); however, there are limits to the extent of the quan-titative comparisons that can be performed in this manner.

Another traditional method of testing inversion results is to compare them with production results using bubble maps and inversion property maps (e.g., Figure 5). Production bubble maps in shale gas plays drilled with long-reach hori-zontals, however, are difficult to interpret as the large number of frack stages along a well make attributing production to a specific lateral or vertical interval within the reservoir difficult. Attempting to define the relative contribution of individual stages is also difficult in long-reach horizontals as obtaining a reliable production log is difficult, and as such reliable stage-by-stage estimates of production are rarely available. Accord-ingly, it is necessary to investigate novel methods of calibrating AVO inversion data.

Hydraulic fracture-stimulation response, production monitoring, and inversion calibrationThe fracking process results in failure deformation of the reservoir over a specific interval, typically controlled using packers and frack ports or perforation clusters, whereby the rock is put under increased strain by the increasing volume of slurry (primarily proppant and slickwater) in the vicinity of the well until it fractures. The incipient fracture then allows the slurry to penetrate the rock and propagate the fracture away from the well. Volume rates of material pumped into the wellbore (typically on the order of 2–5 bbls/minute) and well-head pressure are recorded during the process and provide estimates of the breakdown pressure (the pressure at which the formation first fractures), the average treating or propaga-tion pressure (the pressure maintained during the hours long




process while anywhere from 20 to 300 tons of proppant and several thousand cubic meters of fluid are injected into the formation), and ISIP—the pressure measured immediately after shut-in and often assumed to be equal to the final propa-gation pressure minus the friction pressure (McLennan and Roegiers, 1982) (Figure 10).

Measurements made during and after fracking are typi-cally available for all stages of all wells in a development. Many variables associated with a frack job (such as total volume of proppant, number of perforation clusters, total length of frack stage, etc.) are controlled by operational decisions and empirical analysis. However, other measurements, such as breakdown pressure and ISIP, are controlled largely by exter-nal factors such as reservoir properties and the relative stress environment. Although ISIP is impacted by other factors, under appropriate circumstances it is generally accepted that ISIP is indicative of the in-situ stress regime (McLennan and Roegiers).

Measurements of breakdown pressure generally show much greater scatter than measurements of ISIP. This is in-terpreted to be a function of the scale of the “experiments” by which each property is measured. When fracturing is first initiated and an estimate of breakdown pressure is made, the volume of rock with which the slurry is interacting is limited; whereas when the ISIP is estimated, the fracture network that has been established over the preceding hours has penetrated a far greater volume of reservoir rock. The effect of this on the individual measurements is that the ISIP is indicative of far-field stresses and bulk reservoir properties throughout the stimulated rock volume (SRV); whereas the breakdown pres-sure may be biased upward or downward depending on near wellbore effects.

In general, we would expect a relationship between ISIP and CSS estimates from seismic. Although CSS is by defini-tion a scalar, if the tectonic stress vector is not spatially vari-ant in a development area and wells are drilled with identical

trajectories in the direction of the minimum horizontal stress (such that fractures also dilate in the minimum horizontal stress direction), the CSS can be expected to correlate to ISIP (assuming other variables are held as constant as operationally possible). This hypothesis can be tested using the data avail-able in the HRB.

To facilitate a comparison of measurements of ISIP spe-cific to any given frack stage, a single CSS estimate from AVO inversion data must be extracted. In addition to requiring an accurate velocity model, this requires a methodology to ex-tract points along a well path, or around perforation clusters, that likely intersect multiple seismic traces for any given frack stage. As the frack propagates both vertically and horizontally away from the well, the number of traces and samples of seis-mic to investigate are also not immediately intuitive. If the stimulated rock volume (SRV) and the geometry of that vol-ume are known, then this could be used as a constraint in the seismic rock property extraction. Typically, however, SRV is an unknown. To investigate the impact of varying the volume of

Figure 11. LR versus MR crossplots for data extracted at the same frack stages from the same AVO inversion volumes utilizing different extraction volumes. The results show that, as expected, there is greater scatter in the data extracted using the smaller volume (i.e., fewer data points being averaged resulting in less smoothing), but that overall the pattern of the data distribution is the same.

Figure 12. CSS estimates extracted at individual frack stages for two different horizontal wells in the Two Island Lake area plotted as a function of ISIP estimated by reservoir engineers. The correlation coefficient (CC) varies for different wells, which is partly a function of operational variables and the inability to quantitatively account for anisotropy in the CSS estimate, as well as the inherent uncertainty in correlating a parameter estimated from surface seismic against a parameter interpreted from pressure-time plots during and after fracking.




data extracted, LR and MR data were extracted using cylinders of different dimensions around the central perforation cluster of a set of fracks from a single pad in the Two Island Lake area of the HRB. Figure 11 illustrates the comparison of the LR versus MR crossplots for two extraction volumes that differ by a factor of approximately 30, and demonstrates that the results are relatively robust to variations in extraction intervals (and indeed to methodology).

Plotting ISIP as a function of estimated CSS supports our interpretation that there is, in general, a positive correlation. The correlation between these two estimates, which are based on an inelastic process (ISIP from fracking) and an elastic pro-cess (CSS from surface seismic) of different scales, is variably robust (Figure 12). The stage-by-stage estimation of the CSS is sensitive to any errors in velocity modeling and the ISIP mea-surement is also somewhat interpretative; despite these limita-tions, ISIP can be correlated to the CSS estimate in the major-ity of wells where this analysis can be completed. Additional scatter in our CSS estimates likely results from our inability at this point to incorporate anisotropic effects in the AVO in-version; this is particularly true where, based on independent data, we suspect changes in tectonic stress orientation. Meth-ods to complete azimuthal AVO inversion are being actively researched and tested in the HRB (e.g., Downton and Roure, 2010; Downton, 2011).

A qualitative correlation between ISIP and production is also evident in available data (the number of development wells where sufficient production data is available for decline analysis remains relatively limited). The metrics used to com-pare productivity, such as EUR/frack, are made for entire wells rather than for individual stages; accordingly, to compare seismically estimated rock properties against production, the values extracted from AVO inversion data for all stages must be averaged. Figure 13 shows an LMR crossplot where each data point is plotted as a function of the average LR and MR

along the length of the wellbore; these points are then colored by EUR/frack. As expected, based on our interpretation tem-plates, the higher EUR/frack wells are characterized by lower LR values. This independent corroboration of the rock physics interpretation template and the resulting AVO inversion is an important achievement for shale gas geophysics.

The positive correlation between low CSS values from seis-mic inversion, lower ISIP, and improved initial production is becoming more firmly established. Data from HRB support the conclusions of Goodway et al. (2010) that lower LR, as-sociated with increased porosity and typically a more quartz-rich lithology, correlates to improved production. LR varia-tions seem to be of greater importance than MR in terms of CSS variations and correlation to production, which suggests that above some ductility threshold increased porosity is opti-mal even if the trade-off is decreased shear rigidity, but further work and greater volumes of production data are required to fully understand this. This positive correlation, between ISIP and the seismically derived CSS and EUR is important as it establishes a basis for using quantitative geophysical data in drilling and completions planning.

ConclusionsShale gas plays present some unique challenges to geophysical interpretation. Impedance contrasts are often small relative to conventional reservoirs and structural height is not a primary control on well productivity. Additionally, the conventional means by which AVO inversions are quality controlled, us-ing blind wells or production results that can be associated to a specific reservoir interval, are less effective in these plays where long-reach horizontal wells are rarely logged and pro-duction is comingled from kilometers’ worth of reservoir. For these reasons, it is necessary to attempt to use novel means of determining the validity and utility of AVO inversion data. AVO inversion results can be indirectly calibrated by using data from independent sources, such as completion engineer-ing data and average production data.

Even without independent calibration, AVO inversion data illuminate heterogeneity and can be used to map sweet spots, laterally and vertically, using the relative differences in inferred rock properties. Templates based on rock physics models help understand how such sweet spots manifest in AVO inversion data. Templates developed for the HRB in LMR space are an important interpretative tool that links a rock physics model to the measured surface seismic and important completion and production attributes. The templates and analysis of data from the HRB suggest that in general lower LR, higher MR and correspondingly lower CSS values are indicative of better reservoir—although it should be noted that, for a quartz-rich lithology, an increase in MR is largely indicative of decreased porosity.

The workflow presented attempts to capture the critical steps in an integrated reservoir study of shale gas plays that includes prestack seismic data. The workflow takes advantage of rock physics templates developed for the HRB and provides a basis for iterating the templates as necessary when more data are available. The look-back analysis and calibration are critical

Figure 13. An LMR crossplot populated with points located by their average LR and MR extractions along the well; the points are colored by EUR/frack and support the interpretation that higher EUR/frack wells will be predicted by lower LR values.




in this workflow. The integrated approach illustrated by this workflow and case study results allows geology, petrophysics, geophysics, geosteering/drilling, and engineering data to be used as necessary in an integrated fashion to optimize devel-opment of shale gas assets.

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Acknowledgments: The authors thank Howard Pitts, Ryan Birken-feld, Chris Spies, Andrew Iverson, Doug Nimchuk, and Audrey Rasmussen for technical discussions and inputs. Thanks are also due to all the Horn River Business Unit members who have worked dil-igently for a number of years to compile the large amounts of raw data. Finally, the authors thank Apache Canada Ltd. and EnCana for permission to publish.

Corresponding author: [email protected]


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