Geophysical and geomechanical rock property templates for source rocks Malleswar Yenugu, Ikon Science Americas, USA Summary Sweet spot identification for source rocks involve detection of organic rich, high porous facies combined with brittleness, which is prone for hydraulic fracturing for optimized production. The current study focuses on identifying the best shale out of three shale plays (Barnett, Marcellus and Eagle Ford). The aim of the study is to understand how the source rock properties such as organic richness and porosity will affect elastic and geomechanical properties. We found from the study that Barnett shale is the best shale out of the three shale plays, because this play not only has better organic richness and porosity development, it also has better brittle nature and these properties can be characterized from seismic data. Introduction The major difference between conventional and unconventional reservoirs is the presence of organic matter (TOC Total Organic Carbon) in the unconventional reservoirs. All the sedimentary rocks do contain some fractional amount of TOC. A source rock is the sedimentary rock which contains minimum 1% of TOC, which means 1 gram of organic carbon in 100 grams of rock (Mc Carthy et al., 2011). The current study focuses on how the presence of TOC affects the rock properties (density and porosity), elastic properties (Vp and Vs), impedances (AI: Acoustic Impedance; GI: Gradient Impedance) and geomechanical properties (E: Young s modulus; ϑ: Poisson s ratio). The dataset include one well each from Barnett shale, Marcellus shale and Eagle Ford shale plays. All the three plays are in matured window (%Ro ~ 1-1.2). The dominant mineralogy of Barnett and Marcellus shale intervals is clastic (quartz+clay > 65%) and Eagle Ford is dominated by calcite and dolomite (> 65%). TOC volume fraction variation across three plays is, Eagle Ford (0-0.1), Barnett (0.05-0.1) and Marcellus (0.1-0.35). The porosity variations across the three shales are, Eagle Ford (5-8%), Barnett (16-20%) and Marcellus (10-15%). Figure 1 shows well log panel for Barnett shale well. Core measurements (Vp, Vs and TOC) also shown as closed circle symbols. The grey shaded region is the Barnett shale interval. The blue colored circles are vertical and red colored symbols are horizontal dynamic velocity measurements from the core plugs. We observe good correlation between the Vp and Vs logs and core measurements in the bedding normal/vertical (0 0 ) direction from the core samples. Figure 1: Well log section of Barnett shale well. Relationship between TOC and bulk density Figure 2 shows the relationship between TOC and bulk density for three shales. Each shale is represented by a symbol (Barnett: circle; Marcellus: square; Eagle Ford: triangle). It is evident that the bulk density of the shale decreases with increase of TOC. But the variation in bulk density (2.5-2.8 g/cc) can be caused by changes in the porosity (Vernik and Milovac, 2011). Marcellus shale, which is the most organic rich (~35%) out of the three shales, shows a bulk density of 2.28 g/cc. The porosity increases from right to left as indicated by an arrow in the Figure 2. SEG New Orleans Annual Meeting Page 3176
Figure 2: Relationship between bulk density and TOC volume for three shale plays. Velocity and rock property relationships It is useful to understand how the velocities are affected by source rock properties such as bulk density, porosity and TOC. Figure 3 shows the relationship between Vp and bulk density, color coded by volume of TOC. It is observed from that both bulk density and Vp decrease as the amount of TOC increases. The presence of TOC has a dominant effect on P velocities. The organic rich shale facies (Marcellus) are falling close to the Hashin-Shtrikman lower bound as shown in the Figure 4. Figure 5 shows the relationship between Vp and Vs colored by TOC for three shale plays. Both the velocities decrease as the amount of TOC increases. We define a source rock line, which separates organic rich and lean rocks. The equation for the source rock line is given below: Vs (m/s) = 0.488*Vp + 283.673 (m/s) (1) Figure 3: Relationship between Vp and bulk density, colored by TOC for three shales. Figure 4: Relationship between Vp and porosity, colored by TOC for three shales. SEG New Orleans Annual Meeting Page 3177
Figure 5: Relationship between Vp and Vs, colored by TOC for three shale plays. Impedance and rock property relationships Figure 6 shows the relationship between acoustic impedance (AI) and shear impedance (SI) for the three shale plays. We also plotted conventional shale (non-source rock) and gas sand lines of Vernik-Kachanov models (Vernik and Kachanov, 2010). We observe that both AI and SI are decreasing with increasing kerogen. Other factors such as effective stress also play an important role in placing the each shale play in AI-SI domain (Khadeeva and Vernik, 2014). We also calculated gradient impedance (GI) for three shale plays. In AI-GI cross plot space (Figure 7), we can linearly combine these two impedances, known as extended elastic impedance (EEI) (Whitcombe et al., 2002). We observe the three shale plays are aligning in a lithological projection from clastics (Barnett and Marcellus) to carbonates (Eagle Ford shale). The χ angle derived from this projection is useful for EEI calculations. Figure 6: Relationship between AI and SI, colored by TOC for three shales. Figure 7: Relationship between AI and GI, colored by TOC for three shales. SEG New Orleans Annual Meeting Page 3178
Geomechanical rock property relationships Brittleness is used as a measure of rock s ability to respond to hydraulic fracturing (Guo et al., 2012). Rocks with lower Poisson ratio (ϑ) likely to fracture easily, and rocks with higher Young s modulus (E) likely to remain open from the fracturing (Rickman et al., 2008). Rocks with higher brittleness index show low Poisson ratio and high Young s modulus (Figure 8). According to Goodway et al., (2010), the most frack able zones show low λρ and midrange µρ (Figure 8). Figure 8: Relationship between Young s modulus and Poisson ratio, colored by TOC for three shales. From Figures 8 and 9, it is clear that the encircled zone (Barnett shale) is the most prone zone for hydraulic fracturing in both E-ϑ and LMR domain. Figure 9: Relationship between the three shales in LMR domain, colored by TOC. Conclusions Our study shows that source rock properties (TOC, porosity, and bulk density) affect the elastic and geomechanical properties. Bulk density of source rocks decrease with increase of TOC. Elastic wave speeds (Vp and Vs) also decrease with the increase of TOC. A source rock line is defined, which separates source and non-source rocks. Acoustic and shear impedances (AI and SI) are also affected by the presence of TOC. Source rocks are projected according to dominant mineralogy in gradient impedance (GI) domain. Geomechanical rock property templates show that source rocks with high E, low ϑ, low λρ with midrange µρ are suitable for fracture initiation and maintain the fractures. From the geophysical and geomechanical rock property analysis, it is concluded that Barnett shale is not only organic-rich and has porosity, it is more prone for hydraulic fracture stimulation. SEG New Orleans Annual Meeting Page 3179
EDITED REFERENCES Note: This reference list is a copyedited version of the reference list submitted by the author. Reference lists for the 2015 SEG Technical Program Expanded Abstracts have been copyedited so that references provided with the online metadata for each paper will achieve a high degree of linking to cited sources that appear on the Web. REFERENCES Goodway, B., M. Perez, J. Varsek, and C. Abaco, 2010, Seismic petrophysics and isotropicanisotropic AVO methods for unconventional gas exploration: The Leading Edge, 29, 1500 1508, http://dx.doi.org/10.1190/1.3525367. Guo, Z., M. Chapman, and X. Li, 2012, A shale rock physics model and its application in the prediction of brittleness index, mineralogy, and porosity of the Barnett Shale: 82nd Annual International Meeting, SEG, Expanded Abstracts, doi:10.1190/segam2012-0777.1. Khadeeva, Y., and L. Vernik, 2014, Rock-physics model for unconventional shales: The Leading Edge, 33, 318 322, http://dx.doi.org/10.1190/tle33030318.1. McCarthy, K., K. Rojas, M. Niemann, D. Palmowski, K. Peters, and A. Stankiewicz, 2011, Basic petroleum geochemistry for source rock evaluation: Oilfield Review, 23, no. 2, 32 43. Rickman, R., M. Mullen, E. Petre, B. Grieser, and D. Kundert, 2008, A practical use of shale petrophysics for simulation design optimization: All shale plays are not clones of the Barnett Shale: Presented at the Annual Technical Conference & Exhibition, Society of Petroleum Engineers, http://dx.doi.org/10.2118/115258-ms. Vernik, L., and M. Kachanov, 2010, Modeling elastic properties of siliciclastic rocks: Geophysics, 75, no. 6, E171 E182, http://dx.doi.org/10.1190/1.3494031. Vernik, L., and J. Milovac, 2011, Rock physics of organic shales: The Leading Edge, 30, 318 323, http://dx.doi.org/10.1190/1.3567263. Whitcombe, D. N., P. A. Connolly, R. L. Reagan, and T. C. Redshaw, 2002, Extended elastic impedance for fluid and lithology prediction: Geophysics, 67, 63 67, http://dx.doi.org/10.1190/1.1451337. SEG New Orleans Annual Meeting Page 3180