Reservoir Characterization for Shales: a Barnett Shale Case Study

GUSS14-24 Reservoir Characterization for Shales: a Barnett Shale Case Study JOHN PENDREL CGG GeoSoftware ALFONSO IUNIO MARINI Eni E&P This paper has been selected for presentation for the 2014 Gussow Geosciences Conference. The authors of this material have been cleared by all interested companies/employers/clients to authorize the Canadian Society of Petroleum Geologists (CSPG), to make this material available to the attendees of Gussow 2014 and online. ABSTRACT This paper describes the integration of disparate reservoir measures and analyses toward an improved reservoir description and the facilitation of more efficient exploration and production programs. We use the elastic inversion of pre-stack seismic reflection data to compute key reservoir properties such as volume of quartz and brittleness. Bayesian inference is then used to define and map relevant litho-facies. Geometric analysis of the seismic stack data is completed to identify lineaments describing faults and collapse events. All these are then combined with information form logs to design optimum well paths and fracking programs. This paper is a continuation of our previous work (Varga et al., 2012). INTRODUCTION Designing optimum drilling programs for shale plays requires a multi-faceted reservoir characterization scheme which can simultaneously address several issues. These include: Drilling through rocks with optimum brittleness Drilling normal to the direction of maximum present local stress Determining the proximity of natural fractures, their nature and chances of reactivation Drilling to avoid water infiltration, i.e. avoiding collapse features (examples from Barnett) Drilling through regions of enhanced quartz content Drilling through regions of enhanced porosity Drilling through regions of optimal TOC The key tools of the explorationist are elastic reservoir property estimates from seismic inversions, geometric attributes and discrete fracture networks (DFNs) from seismic and relative inversions. We follow others in using an indexed scaled average of Poisson s Ratio and Young s Modulus as a proxy for brittleness. We use observed relations in logs to estimate Vquartz and porosity from the native outcomes of inversions: P Impedance, Vp/Vs, and Density. From these we also use Bayesian analysis to identify and map key facies 1

defined in Vquartz Brittleness TOC space. We estimate the direction of maximum stress from geometric attributes and DFN analysis of both seismic and relative inversions. These allow us to also map pre-existing fractures. Geometric attributes can also be used to highlight Barnett collapse features, and correlate water encroachment events to this paleomorphological evidence. Optimizing recoverable reserves from shales requires proper alignment of horizontal well paths and placement of the lateral portions within the optimum layers and optimal sweet facies. According to published statistics, the well mis-positioning account for a 50% failure ratio in a development campaign. The effect of adding also EOR (fracking) failures generates a failure number close to 70%. If causes are examined, the scarcity of subsurface data collected in order to describe and model the key reservoir properties is the major problem. A method is presented here to identify these optimum regions in a petro-elastic space, characterizing shales in a way useful to help building static models for simulation. The first stage of the process uses well data. Generally, logs from a very dedicated petrophysical model integrated with core data analyses are needed to understand the relationships between petrophysical vs. mechanical (elastic) rock properties. These latter are then used to reach a segmentation of the involved lithologies via the important petrophysical and mechanical shale reservoirs properties. As well, properties will allow us to predict how the rocks will respond to hydraulic fracturing. Next, a simultaneous AVO seismic inversion is performed to determine primary elastic properties from seismic (moduli) indicative of the rock stress state, and also derivative descriptive attributes. For instance, rock frackability is estimated from Poisson s Ratio and Young s Modulus, from a weighted average relationship. The final stage is an integration of the inverted properties, possibly interpreted in terms of a number of significant and reservoir quality-related facies, particularly designed to recognize the most suitable one for fracturing and the optimization of production. This is often obtained through a Bayesian probabilistic procedure of classification, using well data-based facies petro-elastic model, describing the a priori information concerning the facies probability density functions (pdf s) distributions in the petro-elastic space. These models are typically used in well planning to identify optimum area, proper lateral directions and vertical depth variances required to drill and complete successful production wells. The Barnett Shale is one of the most important hydrocarbon-bearing geologic formations in North America. (Loucks and Ruppel, 2007). The structurally deepest part of the Fort Worth Basin lies to the northeast where the Barnett Shale formation is more than 1000 ft. thick and interbedded with limestone units. Multiple tectonic events have structurally deformed the Barnett so that it is a naturally fractured heterogeneous reservoir. The collapse of the Barnett Shale into karst features in the underlying Ellenburger Group carbonates is a common feature, easily revealed by seismic geomorphological analyses. Yucatan Cenotes -like, circular and depressed structures are generated by the corrosive actions of the flushing water in the karst reservoir resulting in massive collapses of significant carbonatic rock volumes underlying the Barnett formation. The collapsed Barnett retains some of the structure induced by the underlying rock collapse, resulting in a connection to the aquifer zone. Therefore, the presence of sub-circular rim faults in the Barnett is strongly suggestive of water encroachment. The Barnett is an organically rich, thermally mature, and vertically variable formation, in its lithological and geomechanical properties. The formation permeability is in the micro to nano-darcy range and porosity varies between 0.5 and 10%. From the reconstructed subsurface images it is clear that the range of the optimum reservoir has a maximum elongated axis of 1-2 km. Within this environment, we have very discontinuous spatial variability of the optimal sweet spots geo-bodies. Shale play sweet spots are typically characterized by mid to high kerogen content (not very high, since high kerogen values induce an excess of ductility), lower clay volumes, higher effective porosity and low water saturation. From a petro-elastic point of view, this corresponds to high Young s Modulus and low Poisson s Ratio. The mineralogy of the Barnett Shale is comprehensive of clays, quartz, calcite, kerogen and other minerals. Shales may also be described in terms of mechanical properties, using measures such as silica content, fracture content, brittleness and pressure gradient. Natural fractures are present and re-activated during stimulation and increase production efficiency by widening the treatment zone. Our test area is in Figure 1 showing the 2

time structure of the Barnett along with the location of the seven vertical wells. Figure 1. The study area showing the TWT structure of the Barnett and the location of the seven vertical wells. RESULTS The native properties derivable from AVO inversions of pre-stack seismic data are P Impedance, Vp/Vs and Density (Figure 2). An important quality control of the reservoir properties from inversion is a check for bias. For accurate QI work, we require that the outcomes of inversion be unbiased. This is determined by cross-plots of high-cut filtered logs vs inversion properties at the log locations. This check is done not only for the native results of inversion but also for all of the derivative properties. These derivatives are other properties of interest, such as brittleness, Vquartz and porosity. Density is usually not fully determined for conventional seismic data due to the limited extent of the incident angle range and the small number of partial stacks available. Anyway, we used incident angle components higher than 50, in order to obtain an independent contribution to the results. Other information comes from the observed relations between density, P Impedance and Vp/Vs observed in the recorded logs. We quantified the Barnett Shale s brittleness factor by averaging Young s Modulus and Poisson s Ratio. Averaging these two disparate properties was made possible by first indexing them on a scale of 1-100. The volume of quartz (Vqtz) was determined by levering on relations between Vqtz and Vp/Vs, LambdaRho and Density, again observed in the log data via cross-plot and discriminant analyses. A neural net application was then used to compute the final Vqtz volume. Next a set of lithological/geomechanical facies were defined based on Vqtz and brittleness. The design template is shown in Figure 3 where the concentric ellipses represent pdf s corresponding to the facies. It is believed that the best reservoir is the high quartz zone with medium brittleness, so avoiding the poorly-fracable ductile high TOC shale, and the too brittle low-toc more-quartzy layers. The design template facilitates the computation of the probability of occurrence of each of the facies In addition, a most-probable facies is determined. Applying the same analysis to high-cut filtered logs and overlaying them is a powerful QC. The results are shown in Figure 4. Overlain are high-cut filtered logs. The matches to the logs are good but not perfect since the high frequency information of the logs was not exposed to the inversion algorithm and the relation between Vqtz and other combinations of logs is itself probabilistic and uncertain within a tolerable error level. Ductile shales at the Barnett Fm. base (blue in Figure 4, lower panel) act as seals at the base of the reservoir and can isolate the productive sequence above from the waterbearing Ellenburger formation below. The type 3 (high quartz - medium brittleness) facies is colored in orange and represents the zone of greatest exploration potential as the kerogen content is relatively high. Figure 2. The native outcomes of elastic inversion are shown along an arbitrary line through the seven vertical wells. The flattened top of Barnett is indicated by the white arrow on the left hand sides of each panel. In each panel, high-cut filtered logs are overlain. The reservoir extends to the black area (Viola) in each panel. 3

Figure 3. Facies template for Bayesian analysis. The facies are described by Vqtz and Brittleness Index. The ellipses are 2D PDFs for each facies (two standard deviations shown). This is illustrated in the log cross-plot in Figure 5 where Kerogen content is plotted vs. brittleness and coloured by facies type. Figure 6 is a time slice through the mostprobable facies volume shown in 3D perspective. The facies volumes were next transformed from time to depth, enabling the construction of probablistic net pay maps. Also estimated, were effective porosity and kerogen content. Porosity was computed from the observed linear relation to P Impedance. Kerogen was found to be related to density although different linear relations were observed and utilized for each facies type. Major natural fractures in the eastern portion of the survey area were identified from seismic structural attribute analyses. Figure 7 shows the discrete fracture network (DFN) which was created from one such analysis. We experimented with different inputs to the DFN process and found that the Figure 4. Quartz % (Vqtz in the upper panel) and Brittleness Index (middle) derived from AVO Inversion. High-cut logs are overlain. The lowest panel shows the most-probable facies from Bayesian analysis. Green represents the most brittle rocks with high quartz content. Medium brittleness and high Vqtz are in orange. The lower blue facies are compliant, shale-rich rocks. Figure 6. Time slice through the most-probable facies volume Figure 5. The Type 3 facies with high Vqtz and medium brittleness exhibit the best kerogen content in the reservoir interval. Figure 7. Discrete fracture network from FractureSpark TM 4

outputs from relative inversions were particularly useful. Coherency and most-negative or most-positive curvatures were also found to be useful. The goal was to fully characterize structural and stratigraphic complexities in the sequence of interest, to identify intervals with favorable characteristics for drilling horizontal wells, possible fracture barriers, and potential hazards such as water conduits. This is illustrated in Figure 8 where a brittleness section, well trajectory, curvature depth slice and structural map of the carbonate substratum, revealing highs and lows, are corendered. Plots such as these, facilitate the optimum design of fracking stages. Another such example is shown in Figure 9. The termination point of the well occurs where a fracture event connects the reservoir to the water-bearing Viola below. In addition, the fault is coincident with a karst collapse feature. The combination of these provided easy access for the water, which made further drilling impossible. Figure 9. The well was terminated when significant amounts of water were encountered. This location is coincident with both an Ellenburger-penetrating fault and a collapse feature. A structural sag in the sequence of interest already revealed a possible lowered area. The colours in the superimposed section indicate the best (green) and poorer reservoir facies (red). The B&W background section is a Continuity display showing the main and detailed structural lineaments. The facies quality is also poor at the end of the well path, a fact which is confirmed by the gamma ray log (high values are red along the well track). CONCLUSION Figure 8. A brittleness section, well trajectory, curvature slice and structural maps of the carbonate substratum highs are co-rendered in depth. The red lines highlight the main fault systems affecting the well bore. Fracking programs can be optimally designed when these disparate pieces of information are brought together and integrated in a model frame. In this paper we have discussed a reservoir characterization workflow for the Barnett shale which has proved to result in drilling successes and better understanding of several geological observations. Data from a few vertical pilot wells (seven), seismic inversion and its derivatives and geometric attributes were integrated to create a 3-D solid model allowing better delineation and management of subsurface heterogeneities for the drilling of and production from, some 200 horizontal wells. The generated products were checked along hundreds of horizontal production wells, to verify matches, identify reasons for failures and to indicate the best productive areas, their associated local risks and benefits. The result was a greater understanding of gas vs water production and their relations to aquifer encroachment. Given this very positive experience, the generated datasets were included in the reservoir model for use in simulation experiments. The discussed workflow offers economic potential for both this and other types of unconventional reservoirs, LTO first of all. 5

ACKNOWLEDGMENT The authors gratefully acknowledge Eni E&P for permission to show the results of this study. Assistance and advice from our CGG colleagues at Jason, TerraSpark and Hampson- Russell was very much appreciated. REFERENCES Loucks, R.G., and Ruppel, S.C., 2007, Mississippian Barnett Shale: Lithofacies and depositional setting of a deepwater shale-gas succession in the Fort Worth Basin, Texas, American Association of Petroleum Geologists Bulletin, v. 91, #4, p. 579-601. Varga, R., Lotti, R., Pachos, A., Holden, T., Marini, I. Spadafora, E., Pendrel, J., 2012, Seismic inversion in the Barnett Shale successfully pinpoints sweet spots to optimize well-bore placement and reduce drilling risks, Society of Exploration Geophysicists International Annual Meeting, Technical Program Expanded Abstracts 2012: 1-5. 6