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Quantitative reservoir characterization using integrated seismic data and well log data is important in sweet spot identification, well planning, and reservoir development. The process includes building up the relations between rock properties and elastic properties through rock physics modeling, inverting for elastic properties from seismic data, and inverting for rock properties from both seismic data and rock physics models. Many quantitative reservoir characterization techniques have been developed for conventional reservoirs. However, challenges remain when extending these methods to unconventional reservoirs because of their complexity, such as anisotropy, micro-scale fabric, and thin beds issues. This dissertation focuses on developing anisotropic rock physics modeling method and seismic inversion method that are appliable for unconventional reservoir characterization. The micro-scale fabric, including the complex composition, shape and alignment of clay minerals, pore space, and kerogen, significantly influences the anisotropic elastic properties. I developed a comprehensive three-step rock-physics approach to model the anisotropic elastic properties, accounting for the micro-scale fabric. In addition, my method accounts for the different pressure-dependent behaviors of P-waves and S-waves. The modeling provides anisotropic stiffnesses and pseudo logs of anisotropy parameters. The application of this method on the Upper Eagle Ford Shale shows that the clay content kerogen content and porosity decrease the rock stiffness. The anisotropy increases with kerogen content, but the influence of clay content is more complex. Comparing the anisotropy parameter pseudo logs with clay content shows that clay content increases the anisotropy at small concentrations; however, the anisotropy stays constant, or even slightly decreases, as clay content continues to increase. Thin beds and anisotropy are two important limitation of the application of seismic characterization on unconventional reservoirs. I introduced the geostatistics into stochastic seismic inversion. The geostatistical models, based on well log data, simulate small-scale vertical variations that are beyond seismic resolution. This additional information compensates the seismic data for its band-limited nature. I applied this method on the Eagle Ford Shale, using greedy annealing importance sampling as inversion algorithm. The thin Lower Eagle Ford Formation, which cannot be resolved by conventional inversion method, is clearly resolved in the inverted impedance volume using my method. In addition, because anisotropy is accounted for in the forward modeling, the accuracy of inverted S-impedance is significantly improved.
The findings of this dissertation on seismic characterization of the Eagle Ford Shale based on rock physics using actual well-log data from productive and unproductive wells in Mexico can be immediately and effectively applied to avoid future failures and can be corroborated with current and new locations for exploration and production. It was found that basic sequence stratigraphy techniques developed for unconventional reservoirs can be applied to the case of the Eagle Ford Shale in Mexico. Using well log correlation and petrophysical techniques to estimate reservoir properties, it was concluded that the zone where the horizontal well was drilled at Montanes-1 was located above the condensed sequence, bypassing the pay zone below the maximum flooding surface in the transgressive system track. It is verified that the productive well Emergente-1 was drilled in the correct zone with hydrocarbon saturation at the transgressive system track below the maximum flooding surface. It was found that using mineral assessment methods to compute brittleness, and the proper geosteering analysis is a consistent approach for placement of future horizontals. Based on that, it is concluded that any estimation of rock physics and anisotropic parameters derived from well logs at the source rock interval will be deceiving and will give a false estimation. It was concluded that the isotropic rock physic model known as friable-sand or modified friable-shale (unconsolidated sand or unconsolidated shale), or most recently called “soft-sand model”, was proved to match the data better than any other rock physic model tested to predict velocity and density. The term “non-source rock model” will be used instead for the rock physic model because it is more consistent with the Eagle Ford Shale case analyzed here. For the orientation of maximum horizontal stress, it is concluded by integrating VSP, microseismic and borehole data, that a straight north-south orientation of future horizontals is needed in order to generate the fractures in the straight east-west azimuth correlating with the maximum horizontal stress orientation.
In unconventional resources such as the Haynesville Shale, a proper understanding of natural fracture patterns is essential to enhancing the economic success of petroleum extraction. The spatial density of naturally occurring fracture sets affects drainage area and optimal drilling location(s), and the azimuth of the strike of the predominant fracture set affects the ideal orientation of wells. In the absence of data to directly determine these fracture characteristics, such as Formation Microimaging (FMI) logs, these natural fracture patterns can be analyzed by examining the seismic anisotropy present in the reservoir. Anisotropy introduced from aligned fracture sets creates predictable azimuthal variations in the seismic wavefield. This allows the reservoir anisotropy, and thus the fracturing present in the reservoir, to be studied indirectly through the azimuthal analysis of industry standard 3D seismic data. The work presented here outlines three distinct methodologies, which utilize azimuthal amplitude variations (AVAZ) present in 3D seismic data, to infer fracture characteristics without the need for substantial well log information. Two of these methods have been previously established and assume the reservoir to be characteristic of Horizontally Transverse Isotropic (HTI). The last method is novel and assumes orthorhombic anisotropy when inverting for fracture density and is able to unambiguously invert for fracture azimuth. All methodologies used in this work produced similar results, increasing confidence in the accuracy of these results through statistical repeatability. Fracture density inversion results indicate spatially varying fracture density throughout the area, with a distinct area of higher fracture density present in the Northwestern corner of the area analyzed. Spatially varying fracture density and localized pockets of fracturing is consistent with expectation from analyzing production data and FMI logs from other areas of the Haynesville. Fracture azimuth inversion results showed some variability; however, the novel method presented in this thesis indicates that the azimuth of the predominant fracture set is oriented at a compass bearing of approximately 82 degrees -- rotated slightly counterclockwise from an east-west orientation. Fracture azimuth results agree well with expectations from a regional stress analysis and from examining comparable formations with known fracture patterns in the surrounding area.
Exploration and characterization of conventional and unconventional reservoirs using seismic technologies are among the main activities of upstream technology groups and business units of oil and gas operators. However, these activities frequently encounter difficulties in quantitative seismic interpretation due to remaining confusion and new challenges in the fast developing field of seismic petrophysics. Seismic Petrophysics in Quantitative Interpretation shows how seismic interpretation can be made simple and robust by integration of the rock physics principles with seismic and petrophysical attributes bearing on the properties of both conventional (thickness, net/gross, lithology, porosity, permeability, and saturation) and unconventional (thickness, lithology, organic richness, thermal maturity) reservoirs. Practical solutions to existing interpretation problems in rock physics-based amplitude versus offset (AVO) analysis and inversion are addressed in the book to streamline the workflows in subsurface characterization. Although the book is aimed at oil and gas industry professionals and academics concerned with utilization of seismic data in petroleum exploration and production, it could also prove helpful for geotechnical and completion engineers and drillers seeking to better understand how seismic and sonic data can be more thoroughly utilized.
Accurate reservoir characterization is a key step in developing, monitoring, and managing a reservoir and optimizing production. To achieve accuracy and to ensure that all the information available at any given time is incorporated in the reservoirmodel, reservoir characterizationmust be dynamic. To achieve this goal, however, one starts with a simple model of the reservoir at a given time point (a static model). As new petrophysical, seismic, and production data become available, the reservoir model is updated to account for the changes in the reservoir. The updated model would be a better representative of the current status of the reservoir. Both static reservoir properties, such as porosity, permeability, and facies type; and dynamic reservoir properties, such as pressure, fluid saturation, and temperature, needs to be updated as more field data become available. Characterizing a reservoir by updating of both static and dynamic reservoir properties during the life of the field is referred to as dynamic reservoir characterization. Dynamic reservoir characterization is discussed in , dealing with time lapse or 4D geophysical data and reservoir monitoring. This chapter, however, focuses on static reservoir characterization.
This book is a useful guide for researchers involved in the technological innovation and production of shale gas exploration and development. It offers a thorough understanding of seismic technologies and their application in shale gas exploration and extraction.This book comprehensively and systematically presents the significance of seismic technologies in predicting shale gas sweet spots. It introduces state-of-the-art seismic-based prediction technologies as well as case studies showcasing their implementation in primary shale gas production areas in China. Innovativeness is one of the highlights of this book. Cutting-edge technologies, such as AI applied in identifying shale gas sweet spots, and achieving excellent results in shale gas production are presented.Readers will gain insights into the latest methodologies, models, and real-world examples, equipping them with the necessary tools to navigate the complex landscape of shale gas resources.