A shift in drilling economics has made it increasingly attractive for operators to explore and produce unconventional plays. By using new techniques in horizontal drilling and hydraulic fracturing, operators now access resources that were never before considered viable. Rising commodity prices and worldwide demand reward these operators for their efforts to free tight oil and shale gas.
Unlike conventional plays, shale plays have very low permeability and are both the trap and seal. These resources, until recently considered only the source rock for hydrocarbon reservoirs, are now recognized in their own right for their huge potential for both oil and gas production.
As with conventional plays, the economic case for developing and producing a field is based on how
much hydrocarbon resource exists, whether it is primarily a gas or oil opportunity, and how much can be extracted at what cost. The answers in shale plays lie in the volume and maturity of the total organic carbon (TOC) and the ability to create an effective fracture network that will conduct the hydrocarbons to each borehole. This in turn requires an understanding of mineralogy, lithology, relative rock brittleness, natural fracturing and the directionality of in situ rock stresses.
This paper provides a rock properties-based workflow for shale plays and discusses the influence of local variation on the specific analysis performed. The goal of such an analysis is to gather enough intelligence to define drilling locations, well bore placement and orientation, plus provide valuable input for developing the completion and stimulation program.
Rock Properties for Success in ShalesTed Holden, John Pendrel, Fred Jenson, and Peter Mesdag
• Gamma ray logs: indicate overall clay and uranium content, which has a known association with organic richness and is useful in differentiating shales from other lithologies.
• Resistivity logs: record high readings for hydrocarbon fluids and lower readings for high clay or pyrite presence.2 Highly mature reservoirs can have a resistivity significantly lower than the same formation at lower thermal maturities.
• Density logs: used to build proxies for TOC when there are no large local variations in other parameters that would affect bulk density. These can be very useful when combined with high resolution resistivity logs to differentiate subtle and closely spaced vertical variation in TOC.3
• Compressional and shear sonic logs: calibrated to TOC content due to low p-wave velocity or organic matter when there is no significant local variation in parameters such as porosity and mineralogy.
• Borehole image logs: useful for identifying closely spaced vertical variation in resistivity and detecting both open and healed fractures and fracture orientation.
• Core data: provides matrix permeability, bulk mineral density, kerogen, grain density, total porosity, and gas-filled porosity (both free and adsorbed gas). Cores provide ground truthing for well log and seismic data.4
• 3D Seismic data: adds valuable perspective on the areas beyond well control. Seismic data enables better characterization of structural and stratigraphic complexities, reveals fracture orientation and shows preferential stress direction based on azimuthal anisotropy.
Shale Play Workflow
Shale play sweet spots are typically characterized by mid to high kerogen content, lower clay volumes, higher effective porosity, low water saturation, high Young’s Modulus and low Poisson’s Ratio. Using these properties as a guide, reservoir engineers can define a drilling program that focuses on the best targets in the field and optimizes the recovery from each well.
Petrophysical analysis is the starting point, combining laboratory measurements, core data and well logs. Rock physics then establishes the relationship between petrophysical and elastic properties of the formation and enables the creation of synthetics for missing and bad log data from drilling and invasion effects. Seismic data analysis moves the analysis beyond well control to the whole field.
High level workflow steps are:
1. Determine TOC and mineralogy including porosity and water saturation, using petrophysical and rock properties analysis. Determine bulk density for each mineral, calculate TOC weight percentage, and convert this measure to bulk volume kerogen.1
2. Extend analysis beyond well control to visualize the entire area of interest by combining well log and seismic data. Characterize structural and stratigraphic complexities to identify high value intervals and potential hazards like water conduits.
3. Evaluate relative brittleness and ductility from well logs and seismic inversion to identify areas prone to fracturing.
4. Analyze rock stresses, natural fracture networks, and fracture directionality by examining image logs, directional borehole acoustics and azimuthal seismic inversion data to determine optimal horizontal well direction and fracturing strategy.
5. Plan the well bore trajectory.
At the conclusion of the workflow there should be sufficient information about the reservoir character to select optimal drilling locations, as well as orientation and placement of horizontal wells for the most effective production program.
Determining total organic volume and mineral composition within the zone of interest is a critical first step in unconventional formation evaluation (Figure 1). The relative quantity and distribution of minerals and TOC are key to understanding the formation and optimizing production from it.5 For example, certain minerals such as quartz are more prone to fracture, while clay tends to fill and close fractures when they occur (Figure 2). Pyrite is commonly present and decreases measured resistivity if volume is sufficient. Kerogen type and maturity determine the oil/gas ratio, and volume establishes whether there is sufficient economic potential to continue the analysis.
The highly laminated nature of most shales presents a challenge for traditional analysis. These fine grain sand formations harbor consolidated and compacted parasequences of shallow marine sediment, clay, quartz, feldspar, and heavy minerals.6 They exhibit ultra-to-low inter-particle permeability, low-to-moderate porosity, and complex pore connectivity.7
A stochastic or statistical model is used to estimate relative volume and distribution of TOC and minerals. First, the presence and volume of some constituents are determined directly from core and well log measurements, such as shale volume from gamma ray or natural gamma ray logs and dry clay bulk density from crossplots of porosity and resistivity. Then these constituents are used as input to the model to estimate relative volume and distribution of TOC and minerals. If mineral composition is well understood, a deterministic approach can be taken instead.
Core data is the optimum control mechanism to validate the model. Uranium can also be a quality check, as its presence is a strong indicator of TOC. Passey and Modified Passey methods can also be used as a quality control check on the volume of total organic carbon. The methods work best in shale sections where there is high clay content and no permeability. If the reservoir is self-sourcing and self-sealing, TOC is directly proportional to the kerogen volume, which can be determined in an area by calibrating log responses to core data for at least one well in that area.8
Figure 1: Petrophysical analysis using PowerLog® yields initial estimates of clay volume, kerogen volume and porosity. These values can be used as input to stochastic modeling in Statmin™ to estimate TOC and mineral volume and distribution.
Figure 2: Log plot displaying a quartz-rich zone bounded by two clay-rich zones identified in FaciesID™. The quartz-rich sweet spot in this log plot is characterized by relatively higher porosity and higher brittleness.
Density plays an important role in the analysis, given the disparity between various constituents (e.g., pyrite is high density and has a smaller volume percent; kerogen has a larger volume percent than indicated by weight percent).9 Core-XRD mineralogy provides bulk-rock mineral weight percent, but excludes porosity and kerogen, whereas volume percent includes all minerals plus kerogen.
After the model is built and validated against well and core data, it can be applied to other wells in the field within the same general lithology. Geoscientists can then compare water saturation, porosity, and mineralogy with confidence.10
Field Level Lithology
Once well and core data are interpreted they are combined with seismic data to extend the understanding of rock properties to the space between wells (Figure 3). This allows a better understanding of lithological detail across the field and leads to identification of the most attractive facies.
Shales present several challenges to seismic interpretation:
• Laminations cause polar anisotropy that distorts seismic data and therefore must be corrected during seismic processing or inversion.
• Laminations are below seismic data resolution, so special averaging must be performed to accurately reflect the composition of the formation.
• A tie must be interpolated between well and seismic data, such that data at any wellbore can be recreated by the seismic. This well tie is what enables characterization and modeling of the field away from well control (Figure 4).
Simultaneous AVO inversion produces a deterministic set of rock properties that can be QC’d against core and well log data (Figure 5). The inversion process accounts for AVO anomalies and reduces tuning and interference effects that can be problematic in simple seismic data analysis. Because laminations are below the seismic data resolution, Backus averaging is employed to transform laminations to the seismic scale. Detail is added through a low frequency model generated as part of the inversion workflow.
Geostatistical inversion provides additional layer detail necessary to simulate flow. It simultaneously inverts impedance and lithology, producing more objective and geologically plausible models than obtained with other methods. The models are accurate both near and away from wells and have realistic detail, often beyond the seismic band. They also include uncertainty estimates (Figure 6).
Integrating 3D seismic into geostatistical modeling can be challenging. The physical relationship between petrophysical properties and seismic measurement must be specified directly or by analyzing well log data in conjunction with rock physics modeling. This software-based analysis establishes a proper multivariate statistical relationship between elastic and petrophysical properties of interest (e.g., impedance and porosity) that accounts for uncertainty.
Petrophysical properties of interest are simulated by constraining them to the relationship (specified or
Figure 3: Cross plot of Young’s Modulus vs Poisson’s Ratio, colored by Sw. Cross plots such as this are used to define key identifiable reservoir facies. The data points within the polygon are highlighted (white) in the log plots.
statistical) and inverted together with the elastic properties. This method simultaneously produces detailed volumes of petrophysical properties, elastic properties and lithology. Alternatively, combined with the volumes of elastic parameters and lithology from geostatistical inversion, cosimulation yields highly detailed models of lithology-dependent petrophysical properties.
Following seismic inversion and analysis, there should be sufficient detail about the distribution of TOC and minerals across the field to make a preliminary assessment of the distribution of the reservoir facies for production. Potential well bore trajectories can be defined and refined with brittleness, rock stress and directionality information.
Brittleness and Ductility
Once TOC, mineralogy and lithology are understood, the formation can be evaluated for relative fracability. Brittleness is a key factor, indicating the likeliness to fracture under stress. Ductile shale naturally heals, while brittle silty shale with a quartz fraction is more likely to fracture and remain open.11 Geomechanical properties aid in determining relative brittleness or ductility of rock, providing valuable input into completion and fracture stimulation design.
A combination of static and dynamic testing—triaxial compression for the former and ultrasonic velocity for the latter—establish a relative brittleness measure that is generally accepted in the industry.12 Zones with high Young’s Modulus (ability to maintain a fracture) and low Poisson’s Ratio (propensity to resist failure under stress)13 will be more brittle and have higher reservoir quality (TOC and porosity are both higher) (Figure 7). High Poisson’s Ratio and low Young’s Modulus rock is ductile.
Calculating Poisson’s Ratio from seismic data is straightforward given that it depends strictly on P-impedance and S-impedance. Young’s Modulus requires a measure of density, which is usually unavailable due to the limited range of angles in the seismic data. In this case, it is necessary to evaluate several different potential proxies for density to determine the best one
Figure 4: Well log and seismic data are tied by identifying a matching wavelet using Well Tie. Once the well tie is made, simultaneous seismic data inversion is performed using RockTrace® to obtain an initial field wide estimate of lithology.
Figure 5: Poisson’s Ratio was computed from P Impedance and S Impedance using RockTrace deterministic simultaneous AVO inversion. The plot is overlain with Poisson’s Ratio from logs. The white arrow indicates the reservoir level in the lower Barnett. Low Poisson’s Ratio rocks are more brittle.
Figure 6: The volume of quartz obtained from the mean of ten realizations using cosimulation using RockMod® geostatistical inversion. Smoothed Vquartz logs are overlaid. The interval shown is from the Top Barnett to the Top Viola.
for the particular geology. The starting point is P-impedance, although this is rarely sufficient. Other potential proxies involve S-impedance plus Poisson’s Ratio, or Poisson’s Ratio plus S-impedance and a regression of P-impedance. The method chosen for inferring density depends on the specific lithology. Core analysis can then be used as a real world confirmation at each well location.
Note that brittleness is a relative not an absolute measure. It is estimated based on a combination of core and sonic data (well log and/or seismic) and assumes that fractures open and remain open better in brittle rock. Shale formations are quite distinct from each other and vary in quality internally.14
Facies can be identified from deterministic inversions following a Bayesian scheme (Figure 8). The inputs to the process are the inversion outcomes and PDFs representing the facies to be determined. These can be estimated from log data or analogues. The outputs of the process are probability volumes for each facies and a most-probable facies volume. An example of the most probable facies is shown in Figure 9.
Fracture Directionality
Following brittleness analysis, the areas most prone to fracturing should be well understood. The next step is to determine the best well bore direction for optimized conductivity and production.
Productivity is a function of fracture direction, induced fracture extent, network intensity, propensity to sustain fractures15, effective conductivity and matrix permeability.16 These properties are governed by mineralogy—discussed earlier—and rock stresses, which can be evaluated from seismic. Determining these properties improves sweet spot identification, reserve estimation, well placement, completion design, stimulation effectiveness, and production enhancement.17
Fractures occur when the rock is stressed naturally or with stimulation. Induced fractures run perpendicular to the direction of minimum rock stress, and open fractures created perpendicular to the well bore provide the best opportunity to drain the area around the well bore. These fractures are typically vertical. If the formation is incorrectly fraced, the fractures may close again, extend into water areas, or be ineffective in conducting hydrocarbons to the wellbore.
The three principal components of rock stress allow estimation of how rocks are likely to fracture under stress during fracture stimulation.18 The vertical stress component is the overburden pressure of the rock on top of the reservoir. Differential horizontal stress components (minimum and maximum) are consequences of tectonics.
The effects of rock stress can be seen on borehole images (Figure 10), where natural fractures are quite apparent. Differential effective stress squeezes the borehole causing breakouts in the direction of the
Figure 7: Log crossplots of Young’s Modulus vs. Poisson’s Ratio colored by Brittleness from Logs (upper) and Brittleness from Inversion (lower). The arrow shows the direction of increase in Brittleness and also Vquartz.
Figure 8: Pdfs (Probability Density Functions) of predicted volume of quartz vs. predicted brittleness displayed in Facies and Fluids Probabilities™. The five numbered zones each enclose similarly colored clusters of data points that indicate the different lithotypes.
Figure 9: Cross section of the most likely lithology correlated across all of the wells from the Top Barnett to the Top Viola computed using Facies and Fluids Probabilities.
minimum stress effect. The structural model from seismic also shows how the rock is stressed, and seismic structural attributes can be used in some situations to indicate fractures below accepted seismic resolution. The coherence attribute, used to detect faults or discontinuous features, can pick up swarms of parallel fractures.
Directional stress is determined using azimuthal anisotropy analysis.19 The distinct layers of organic material in laminated shales exhibit electrical anisotropy—electrical conductivity in one direction that is different from another. Sand-bearing hydrocarbon assets have high resistivity (low conductivity) whereas shales have lower resistivity. Anisotropy has a first-order influence on shear and mode-converted PS-waves, which split into fast and slow modes with orthogonal polarizations.20 Because fractures and faults are mostly in the vertical direction and aligned along the direction of maximum horizontal stress, the result is azimuthal anisotropy (HTI).
An azimuthal map can show the direction of the fast component, its magnitude and a measure of the difference between the maximum and minimum velocity (Figure 11). Together, these data help determine the drilling direction, well positioning and fracturing strategy.
The differential horizontal stress ratio can be calculated from seismic parameters without any knowledge of the stress state of the reservoir.21 Wide angle, wide azimuth 3D seismic is best suited for this. Greater differential stress and/or higher fracture density results in greater anisotropy. By mapping the anisotropy at the reservoir level geoscientists can see the direction, magnitude and difference between the maximum and minimum. A combination of Young’s Modulus and differential horizontal stress indicates high
potential areas for creating fracture networks, optimal drilling locations and best well bore orientation.22
Azimuthal anisotropy is typically caused by near-vertical systems of aligned fractures and microcracks23, pinpointing higher potential producing areas. Anisotropic analysis identifies both higher differential stress and natural fracturing, but the difference between them is dependent on the play and cannot be separated mathematically.
Through these analyses, geoscientists can find natural fractures and areas with low anisotropy that are prone to fracturing. Where there are many faults, many fracs may be required. Where anisotropy is high, fracture networks may already exist and fewer
Figure 11: Map of Interval Velocity Anisotropy from the Fayetteville shale. Color indicates magnitude of anisotropy; arrow length indicates magnitude of fast velocity. Arrow directions represent azimuths. Initial production from Well Y was three times that from Well X (Courtesy Southwestern Energy).
Figure 10: Borehole image log showing faults and fractures.
fracs are needed. Effective fracturing determines the production rate and drainage area recovery.24 Complex fractures appear to be preferable to long, planar fractures25, and drainage is very efficient when a high-relative-conductivity primary fracture is present compared to a uniform-conductivity network.26
Planning the Well Trajectory
By this final step in the workflow, there should be sufficient information to determine the details of well placement and fracture stimulation. Planning each well trajectory is an important element of success, given the heterogeneity common in shales.
Target facies are identified through petrophysical and lithological analyses and refined with brittleness analysis. These targets should consist of mid to high kerogen content, low clay and brittle rock (e.g., quartz, carbonate). Because of the laminated nature of shale, these targets may vary in height of pay interval and proximity to each other (laterally and/or vertically), requiring adjustments to the well bore trajectory to optimize contact.
Results from rock stress analysis identify the optimal well bore direction for fractures that will remain open and provide effective conductivity to the well bore, with the assistance of proppants close to the bore hole and perhaps beyond. Placement of the vertical segment of each well can then be guided by the optimal horizontals in combination with surface considerations.
Each well can be placed to remain in the optimum stratigraphy throughout its entire length and simultaneously avoid water and ductile zones. Known fracture conductivity barriers can be used to separate the well bore from water zones. Stimulation can be managed to keep fractures small and avoid communications with adjacent water bearing zones. High clay zones can be mapped so that frac jobs are not wasted, with oil and gas trapped in ‘permeability jail’27 because the fractures do not remain open.
Fractures must remain open to be conductive, which may require propping or partial propping. To maximize fracture complexity, operators may utilize closer spacing of perforation clusters with
more fracture treatments, small proppants at higher injection rates, closer spacing between laterals, and simultaneously alternate fracture treatments in offsetting wells to focus stimulation energy. Success of these strategies depends on a strong understanding of the rock properties and rock stresses unique to each field and well.
Conclusion
Shale plays require special analysis to consistently obtain optimum results from each well drilled. By combining all the well and field data—including cores, well logs and pre-stack 3D seismic data—geoscientists can understand key characteristics that enable them to estimate reserves, place well bores in the most appropriate trajectory and define the overall drilling completion and fracture stimulation program.
Combining well log and core analysis, seismic attribute analysis, and seismic inversion is the best practice for success in shales. Petrophysical analysis, rock physics and stochastic modeling determine the distribution of TOC and minerals. Seismic data extends this understanding from individual well bores to the field level, creating a 3D lithological model. Analysis of the formation propensity to fracture and ability to remain open requires an understanding of formation density. Given that density generally cannot be extracted directly from seismic, a method of inferring density must be carefully evaluated and employed specifically for each field’s geology.
With a proxy for density, brittleness and ductility can be evaluated and combined with previous TOC and mineral distribution data to determine the sweet spots for both hydrocarbon content and fracability. Finally, individual bore hole trajectories can be plotted based on azimuthal data.
All of this analysis is enhanced and accelerated by specialized reservoir characterization software for methodical analysis of total organic carbon, minerals, natural fractures, rock stresses, fracture orientation, brittleness and other aspects of the play. Using these tools and methods, geoscientists can make better decisions about where to drill and how to frac, and can better predict economic outcomes.
Jason (jason.cgg.com) delivers innovative software products and services to help clients identify and produce hydrocarbon deposits by integrating information from the various geoscience disciplines. Jason software applications make it possible to integrate geological, geophysical, geostatistical, petrophysical and rock physics information into a single consistent model of the earth.
Applying Jason’s technology through its software and consulting services substantially improves E&P investment return by adding invaluable reservoir model information to reduce the risks, costs and cycle-times associated with exploration, appraisal and field development and production. Jason is a CGG company.
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R. Varga, R. Lotti, A. Pachos, T. Holden, I. Marini, E. Spadaford, and J. Pendrel. “Seismic Inversion in the Barnett Shale Successfully Pinpoints Sweet Spots to Optimize Well-bore Placement and Reduce Drilling Risks.” SEG 2012.
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End Notes
1 S. Chopra, R.K. Sharma, J. Keay and K.J. Marfurt. “Shale Gas Reservoir Characterization Workflows.” SEG 2012 Abrstract.
2 Q.R. Passey, K.M. Bohacs, W.L. Esch, R. Klimentidis, and S. Sinha. “From Oil-Prone Source Rock to Gas-Producing Shale Reservoir—Geologic and Petrophysical Characterization of Unconventional Shale-Gas Reservoirs.” SPE 131350. Society of Petroleum Engineers, 2010.
3 Passey, ibid.
4 Themig. “New Technologies Enhance Efficiency of Horizontal, Multistage Fracturing.” Journal of Petroleum Technology, April 2011.
5 Haege, S. Maxwell,L. Sonneland, and M. Norton. “Integration of Passive Seismic and 3D Reflection Seismic in an Unconventional Shale Gas Play: Relationship Between Rock Fabric and Seismic Moment of Microseismic Events.” SEG 2012 Abstract.
6 Haege, ibid.
7 Themig. op. cit.
8 F. Jenson, H. Rael. “Stochastic Modeling & Petrophysical Analysis of Unconventional Shales: Spraberry-Wolfcamp Example.” 2012.
9 Haege, op. cit.
10 A.N. Tutuncu. “Incorporating Stress Anisotropy in Determining Time-Dependent Rock Properties During Production Optimization and Environmental Monitoring in Shale Reservoirs.” SEG 2012 Abstract.
11 K. Bandyopadhyay, R. Sain, E. Liu, C. Harris, A. Martinez, M. Payne, and Y. Zhu. “Rock Property Inversion in Organic-Rich Shale: Uncertainties, Ambiguities, and Pitfalls.” SEG 2012 Abstract.
12 L.K. Britt and J. Schoeffler. “The Geomechanics of a Shale Play: What Makes a Shale Prospective!” SPE Paper 125525, 2009.
13 R. Varga, R. Lotti, A. Pachos, T. Holden, I. Marini, E. Spadaford, and J. Pendrel. “Seismic inversion in the Barnett Shale successfully pinpoints sweet spots to optimize well-bore placement and reduce drilling risks.” SEG 2012.
14 L.K. Britt, op. cit.
15 K.Bandyopadhyay, op. cit.
16 D. Denney. “Highlights from Whole Core vs. Plugs: Integrating Log and Core Data to Decrease Uncertainty in Petrophysical Interpretation and Oil-In-Place Calculations.” Journal of Petroleum Technology, August 2011.
17 Passey, op. cit.
18 R. Varga, R. Lotti, A. Pachos, T. Holden, I. Marini, E. Spadaford, and J. Pendrel. “Seismic Inversion in the Barnett Shale Successfully Pinpoints Sweet Spots to Optimize Well-bore Placement and Reduce Drilling Risks.” SEG 2012.
19 C.L. Cipolla. “Modeling Production and Evaluating Fracture Performance in Unconventional Gas Reservoirs.” Journal of Petroleum Technology, September 2009.
20 I. Tsvankin, J. Gaiser, V. Grechka, M. van der Baan, and L. Thomsen. “Seismic Anisotropy in Exploration and Reservoir Characterization: An Overview.” Geophysics, Vol 75, No 5, September-October 2010.
21 Varga, op. cit.
22 Cipolla, op. cit.
23 I. Tsvankin, J. Gaiser, V. Grechka, M. van der Baan, and L. Thomsen. “Seismic Anisotropy in Exploration and Reservoir Characterization: An Overview.” Geophysics, Vol 75, No 5, September-October 2010.
24 R. Kennedy. “Shale Gas Challenges/Technologies Over the Asset Lifecycle”. US-China Oil and Gas Forum Presentation. Septemer 2010.
25 A. Kalantari-Dahaghi, S.D. Mohaghegh. “Top-Down Intelligent Reservoir Modeling of New Albany Shale.” SEP 125859. Society of Petroleum Engineers, 2009.
