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Summary Rock brittleness plays a significant role in effective hydraulic fracturing for shale gas production, and is often related to mineralogy, mechanical properties, and microstructure features in shales. We construct a rock physics workflow to link elastic properties of shales to complex constituents and specific microstructure attributes. Multiple compositions and various pore geometries are considered using a self-consistent approximation (SCA) method. The laminated textures due to the preferred orientations of clay particles and possible laminated distribution of kerogen are considered using Backus averaging to model the anisotropy (transverse isotropy) of shales. Results based on the analysis of the rock physics templates reveal that the degree of clay lamination significantly affects Vp/Vs of shales, whereas it has little impact on acoustic impedance of shales along the vertical direction. An increasing degree of clay lamination will increase Vp/Vs, and therefore the Poissonโs ratio. With increasing porosity, the variation of mineralogy has less impact on acoustic impedance than on Vp/Vs, which illustrates that Vp/Vs is a better indicator for lithology detection. On the other hand, acoustic impedance is a more suitable parameter to discriminate porosity compared with Vp/Vs. Our rock physics model is calibrated on the well log data from the Barnett Shale and is used to find reasonable parameters to characterize the Barnett Shale. Based on the model, we generate rock physics templates for the interpretation and prediction of shale rock brittleness, mineral constituents, and porosity from elastic properties of shales. Seismic AVO analysis based on modeling data from the top and base of the Barnett Shale illustrates that AVO intercept and gradient have predictable trends according to the variation of brittleness index, mineralogy, and porosity, which means that we can predict variations of such factors in space from seismic responses.
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- North America > United States > Texas > Haynesville Shale Formation (0.99)
- North America > United States > Texas > Fort Worth Basin > Barnett Shale Formation (0.99)
- North America > United States > Louisiana > Haynesville Shale Formation (0.99)
- North America > United States > Arkansas > Haynesville Shale Formation (0.99)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Shale gas (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
ABSTRACT The equations for fluid substitution in a sample with known porosity and the mineralโs and pore-fluidโs elastic moduli are well-documented. Discussions continue on how to conduct fluid substitution in practical situations where more than one fluid phase is present and the porosity and mineralogy are not precisely defined. We pose a different question: If we agree on a fluid substitution method, and also agree that at partial saturation the bulk modulus of the โeffectiveโ pore fluid is the harmonic average of those of the components, can we conduct fluid substitution directly on the seismic reflection amplitude? To address this question, we conducted forward modeling synthetic exercises: We systematically varied the porosity, clay content, and thickness of the reservoir and assumed that the properties of the bounding shale are fixed. Next, we used a velocity-porosity model to compute the elastic properties of the dry-rock frame and applied Gassmannโs equation to compute these properties in wet rock as well as at partial gas saturation. After that, we generated prestack synthetic seismic reflections at the top of the reservoir at full saturation and at partial saturation, and related one to the other. We found that within our assumption framework, there is an almost linear relation between the intercepts of the P-to-P reflectivity for the wet and gas reservoir. The same is true for the gradients. We have provided best-linear-fit equations that summarize these results. We applied this technique to field data and found that we can approximately predict the seismic amplitude at a gas reservoir from that measured at a wet reservoir, given that all other properties of the rock remain fixed. The solution given here should be treated as a method, meaning it should be tested and modified for various rock types and textures.
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.96)
- Geology > Geological Subdiscipline > Geomechanics (0.91)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (0.56)
Abstract Faced with increasing field maturity and production decline from conventional gas reservoirs, oil companies are shifting their focus and pursuing new alternatives; one of them being the development of shale and gas plays. To be economically viable, these low-permeability formations require fracture stimulation. Interval selection within shale reservoirs for hydraulic fracturing or horizontal laterals are based on several variables: sufficient organic matter or total organic carbon (TOC) and favorable hydraulic fracturing stimulation. The presence and extent of the natural fracture system can also influence the performance of a shale reservoir; therefore, natural fractures should be characterized within the shale formation not only from wireline or LWD borehole images logs but also from cross-dipole deep shear wave imaging which can illuminate fractures up to 60 ft away that do not intersect the well. To assess these aspects, a mineralogical, structural, and geomechanical characterization of the shale formation should be conducted. The mineralogical characterization and TOC quantifications mainly rely on a pulsed neutron spectroscopy and nuclear magnetic resonance (NMR) logs. The processing of capture and inelastic gamma ray spectra obtained from the pulsed neutron tool quantifies the formation's basic elemental composition, including silicon, calcium, aluminum, iron, sulfur, magnesium, and carbon. Geomechanical characterization is based on acoustic and density log responses. Variation in the reservoir mineralogy and TOC content affect the rock mechanics properties. Stress vs. strain curves can be derived from a micro-mechanical model of the rock which enable correlations between dynamic (obtained from acoustic logs) and static elastic properties (obtained from triaxial compression testing on core samples). Additionally, the azimuthal and transverse shear wave anisotropies are processed from cross-dipole acoustic logs to characterize the vertical and horizontal rock stiffness. This anisotropic characterization of the rock enables the evaluation of the fracture gradient and stress contrast between the target formation and the overlying and underlying formations. The paper focuses on the interaction between mineralogy, organic content and geomechanical analyses in shale gas reservoir evaluation.
- North America > United States > Texas (1.00)
- Europe (1.00)
- Asia (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Borehole Seismic Surveying (0.67)
- North America > United States > West Virginia > Appalachian Basin > Marcellus Shale Formation (0.99)
- North America > United States > Virginia > Appalachian Basin > Marcellus Shale Formation (0.99)
- North America > United States > Texas > West Gulf Coast Tertiary Basin > Eagle Ford Shale Formation (0.99)
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