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I address the problem of finding a background model for the estimation of resistivities in the earth from controlled-source electromagnetic (CSEM) data by using seismic data and well logs as constraints. Estimation of resistivities is normally done by trial-and-error, in a process called “inversion”, by finding a model of the earth whose responses match the data to within an acceptable error; what comes out of the inversion is what is put into the model by the geophysicist: it does not come out of the data directly. The premise underlying this thesis is that an earth model can be found that satisfies not only the CSEM data but also the seismic data and any well logs. I present a methodology to determine background resistivities from seismic velocities using rock physics, structural constraints, and depth trends. The physical parameters of the seismic wave equation are different from those in the electromagnetic diffusion equation, so there is no direct link between the governing equations. I therefore use a Bayesian framework to incorporate not only the errors in the data and our limited knowledge of the rock parameters, but also the uncertainty of our chosen and calibrated velocity-to-resistivity transform. To test the methodology I use a well log from the North Sea Harding South oil and gas field to calibrate the transform, and apply it to seismic velocities of the nearby Harding Central oil and gas field. I also use short-offset CSEM inversions to estimate the electric anisotropy and to improve the shallow part of the resistivity model, where there is no well control. Three-dimensional modelling of this resistivity model predicts the acquired CSEM data within the estimated uncertainty. This methodology makes it possible to estimate background resistivities from seismic velocities, well logs, and other available geophysical and geological data. Subsequent CSEM surveys can then focus on finding resistive anomalies relative to this background model; these are, potentially, hydrocarbon-bearing formations.

This thesis investigates the relationship between elastic properties and rock properties of the Haynesville Shale using rock physics modeling, simultaneous seismic inversion, and grid searching. A workflow is developed in which a rock physics model is built and calibrated to well data in the Haynesville Shale and then applied to 3D seismic inversion data to predict porosity and mineralogy away from the borehole locations. The rock physics model describes the relationship between porosity, mineral composition, pore shape, and elastic stiffness using the anisotropic differential effective medium model. The calibrated rock physics model is used to generate a modeling space representing a range of mineral compositions and porosities with a calibrated mean pore shape. The model space is grid searched using objective functions to select a range of models that describe the inverted P-impedance, S-impedance, and density volumes. The selected models provide a range of possible rock properties (porosity and mineral composition) and an estimate of uncertainty. The mineral properties were mapped in three dimensions within the area of interest using this modeling technique and inversion workflow. This map of mineral content and porosity can be interpreted to predict the best areas for hydraulic fracturing. / text

This dissertation focuses on interpreting the spatial variations of seismic amplitude data as a function of rock properties for the Haynesville Shale. To achieve this goal, I investigate the relationships between the rock properties and elastic properties, and calibrate rock-physics models by constraining both P- and S-wave velocities from well log data. I build a workflow to estimate the rock properties along with uncertainties from the P- and S-wave information. I correlate the estimated rock properties with the seismic amplitude data quantitatively. The rock properties, such as porosity, pore shape and composition, provide very useful information in determining locations with relatively high porosities and large fractions of brittle components favorable for hydraulic fracturing. Here the brittle components will have the fractures remain opened for longer time than the other components. Porosity helps to determine gas capacity and the estimated ultimate recovery (EUR); composition contributes to understand the brittle/ductile strength of shales, and pore shape provides additional information to determine the brittle/ductile strength of the shale. I use effective medium models to constrain P- and S-wave information. The rock-physics model includes an isotropic and an anisotropic effective medium model. The isotropic effective medium model provides a porous rock matrix with multiple mineral phases and pores with different aspect ratios. The anisotropic effective medium model provides frequency- and pore-pressure-dependent anisotropy. I estimate the rock properties with uncertainties using grid searching, conditioned by the calibrated rock-physics models. At well locations, I use the sonic log as input in the rock-physics models. At areas away from the well locations, I use the prestack seismic inverted P- and S-impedances as input in the rock-physics models. The estimated rock properties are correlated with the seismic amplitude data and help to interpret the spatial variations observed from seismic data. I check the accuracy of the estimated rock properties by comparing the elastic properties from seismic inversion and the ones derived from estimated rock properties. Furthermore, I link the estimated rock properties to the microstructure images and interpret the modeling results using observations from microstructure images. The characterization contributes to understand what causes the seismic amplitude variations for the Haynesville Shale. The same seismic reservoir characterization procedure could be applied to other unconventional gas shales. / text

Yellowstone National Park hosts over 10,000 thermal features (e.g. geysers, fumaroles, mud pots, and hot springs), yet little is known about the hydrothermally active zones hundreds of meters beneath the features. Transient electromagnetic (TEM) soundings and 2D direct current (DC) resistivity profiles show that hydrothermal alteration at active sites have a higher electrical conductivity than the surrounding hydrothermally inactive areas. For that reason, airborne TEM is an effective method to characterize large areas and identify hydrothermally active and inactive zones using electrical conductivity. Here we present results from an airborne TEM survey acquired jointly by the U.S. Geological Survey and the University of Wyoming in November, 2016. We integrate resistivity from the airborne electromagnetic (EM) survey with research drillhole data and rock physics models to investigate the controls on electrical conductivity in the upper few hundreds of meters of the Yellowstone hydrothermal system. Resistivities in Yellowstone are the product of complex variations of lithology, temperature, salinity, clay content, and hydrothermal fluids. Results show that the main drivers in lowering the high resistivitiy of volcanic rocks are water saturation and hydrothermal alteration. Salinities are not significantly elevated in Yellowstone and temperature is not a first order affect. / Master of Science / Yellowstone National Park is a popular scientific and tourist destination because of it’s vast amount of thermal features including hot springs like Grand Prismatic, geysers like Old Faithful, and many more. But what is happening beneath those features and how can we use geophysics to find out? In November 2016, the U.S. Geological Survey and University of Wyoming conducted an airborne geophysical survey that measures how conductive the rock is beneath Yellowstone. Using this data, we map fluids and hydrothermal activity, and relate them to the local geology. The goal of this thesis is to understand the geologic factors that make the rock beneath Yellowstone’s features conductive. We have shown that the main factors that contribute to the high conductivities in thermal areas of Yellowstone are hydrothermal alteration of the rocks and the high amount of fluids filling space inside the rocks.

Horizontal drilling and hydraulic stimulation make hydrocarbon production from organic-rich shales economically viable. Identification of suitable zones to drill a horizontal well and to initiate or contain hydraulic fractures requires detection and quantification of many factors, including elastic mechanical properties. Elastic behavior of rocks is affected by rock composition and fabric, pore pressure, confining stress, and other factors. Rock fabric refers to the arrangement of the rock’s solid and fluid constituents. The objective of this thesis is to quantify rock fabric properties of hydrocarbon-bearing shales affecting elastic properties, including load-bearing matrix, anisotropic cracks, and shape of rock components. Once rock fabric is validated with sonic logs, results can be used to identify suitable zones to drill a horizontal well, initiate hydraulic stimulation, and contain fracture propagation. We develop a method to estimate elastic properties based on rock composition. The differential effective medium (DEM) theory is invoked to model rock elastic properties with a load-bearing component in which remaining minerals and pores are added as spheres or ellipsoids. The method can be combined with the self-consistent approximation (SCA) to construct a load-bearing matrix made of two solid phases. Anisotropic inclusions are added via Hudson’s model. Subsequently, Gassmann’s theory is invoked to saturate the rock with fluids and determine low-frequency elastic properties for comparison to sonic logs. Rock fabric properties remain constant in a vertically homogeneous formation. In vertically heterogeneous strata, the depth interval of interest is divided into rock types, based on rock solid composition, and each rock type is associated with a specific fabric. Quantification of rock fabric properties is a non-unique process, and one should take into account as much petrophysical and geological information as possible to ensure physically viable results. Our simulation and interpretation method is implemented in two wells in both the Haynesville and Barnett shales. Averages of relative errors between estimated velocities and sonic logs are less than 4% in the four wells. Simulations in the Haynesville shale are isotropic, and therefore indicate that rock fabric may not be the main cause of mechanical anisotropy in cases where such behavior is inferred from field data. Rock fabric properties are constant with depth in both wells. Consequently, identification of suitable zones to drill a horizontal well or to contain fracture propagation is not based on rock fabric; it is deduced from Young’s modulus. Simulated Poisson’s ratio is shown to be more sensitive to errors in velocities than Young’s modulus and is therefore not used in the interpretation. Favorable depth intervals for gas production exhibit sizeable volumes of gas and organic content. In the Barnett shale, the two wells exhibit different rock fabrics. Such a behavior indicates that the formation is laterally heterogeneous. Rock physics models should therefore be extrapolated from one well to another with caution. Simulations assume anisotropic elastic behavior and suggest the presence of compliant horizontal pores in one case. Natural vertical fractures are observed on electric image logs in the remaining case and are modeled with Hudson’s theory. This behavior suggests that rock fabric causes mechanical anisotropy in the formation. Heterogeneity of the Barnett shale rock fabric is inferred from the necessary use of rock typing to adequately reproduce sonic logs in both wells. Intervals with large porosity and high gas saturation identify suitable zones to perform hydraulic stimulation. Among such zones, rock types that exhibit stiff load-bearing matrices (comprising mostly calcite, for example) indicate suitable depths to drill horizontal wells or to contain hydraulic fractures. Intervals with dense layering of different rock types are unsuitable for fracture propagation and should be avoided during hydraulic-fracturing operations. / text

This dissertation focuses on quantitatively interpreting the elastic properties of the Cranfield reservoir for CO₂ saturation. In this work, quantitative interpretation starts by examining the relationship between CO₂ saturation and the elastic properties of the reservoir. This relationship comes from a rock-physics model calibrated to measured well data. Seismic data can then be inverted using a model for CO₂ saturation and rock-property estimates. The location and saturation of injected CO₂ are important metrics for monitoring the long-term effectiveness of carbon capture utilization and storage. Non-uniform CO₂ saturation is a contributing factor to both lateral and time-lapse changes in the elastic properties of the Cranfield reservoir. In the Cranfield reservoir, CO₂ saturation and porosity can be estimated from the ratio of P-wave velocity (Vp) to S-wave velocity (Vs) and P-impedance (Ip), respectively. Lower values of Ip for a given rock matrix often correlate to higher porosity. Similarly, for a given area of the reservoir, lower Vp/Vs frequently can be associated with higher CO₂ saturation. If a constant porosity from the baseline to the time-lapse survey is assumed, changes in Ip over time can be attributed to changes in CO₂ saturation in lieu of using Vp/Vs. Decreases in Ip between the baseline and time-lapse survey can be attributed to increases in CO₂ saturation. With a rock-physics model calibrated to the reservoir, Ip and Is from a vertical seismic profile were correlated to statistical ranges of porosity and CO₂ saturations. To expand the lateral interpretation of reservoir porosity and CO₂ saturation, the time-variant changes in Ip between baseline and time-lapse surface seismic datasets were compared to changes in CO₂ saturation calculated from the rock-physics model. Characterizing the CO₂ saturation of the Tuscaloosa sandstones helped to establish a workflow for estimating reservoir properties and fluid saturation from multiple types of geophysical data. Additionally, this work helped establish an understanding for how CO₂ injected into a reservoir alters and changes the elastic properties of the reservoir and the degree to which those changes can be detected using geophysical methods. / text

Compliance with current and evolving federal and commercial regulations require the monitoring of injected carbon dioxide for geological sequestration. The goal of this project is to provide geophysicists with tools to quantitatively interpret seismic data for the amount of carbon dioxide retained in subsurface reservoirs. Rock physics can be used to predict the effects on the seismic response of injecting carbon dioxide on the reservoir. However, classical rock physics models fail when chemical reactions alter the microstructure of the host rock. These chemically induced changes can stiffen or soften the rock frame by precipitation or dissolution, respectively, of minerals in the pore space. Increasing pore pressure is another effect of sequestering carbon dioxide. The amount of change in the microstructure due to chemical reactions and pressure variations depends on the reservoir into which the fluid is injected. Therefore, measuring velocities on site-specific subsurface core samples may provide the ability to differentiate between chemical reactions and pressure variations on the elastic properties of the reservoir rock. Core samples come from the Lower Tuscaloosa Sandstone of the Cranfield study area in Mississippi. The experiments consisted of injecting core plugs with carbon dioxide rich brine and measuring compressional and shear velocities at different effective pressures. The elastic moduli of the rock frame are calculated from the measured elastic wave propagation velocities at specific injected pore volumes and effective pressures. Injecting carbon dioxide rich brine into sandstone core samples, which are composed on average of 80% quartz and 20% clay minerals, resulted in softening of the rock frame due to the dissolution of iron bearing minerals. The moduli exponentially decreased with injected pore volumes and were linearly proportional to effective pressure. The bulk modulus and rigidity of the more quartz rich sample decreased by 13% and 6.5%, respectively, due to a combined effect of changing differential pressure from 35 MPa to 27 MPa and injecting CO₂-rich brine. For the more clay rich sample, the moduli decreased by even larger percentages (39.0% and 20.1%, respectively), which could have significant implications on time-lapse seismic data and subsequent estimations of injected CO₂ volumes. / text

I assess the potential of seismic and time-domain controlled-source electromagnetic (CSEM) methods to monitor carbon dioxide (CO2) migration through the application of a monitorability workflow. The monitorability workflow describes a numerical modelling approach to model variations in the synthetic time-lapse response due to CO2 migration. The workflow consists of fluid-flow modelling, rock-physics modelling and synthetic seismic or CSEM forward modelling. I model CO2 injected into a simple, homogeneous reservoir model before applying the workflow to a heterogeneous model of the Bunter Sandstone reservoir, a potential CO2 storage reservoir in the UK sector of the North Sea. The aim of this thesis is to model the ability of seismic and time-domain CSEM methods to detect CO2 plume growth, migration and evolution within a reservoir, as well as the ability to image a migrating front of CO2. The ability to image CO2 plume growth and migration within a reservoir has not been demonstrated in the field of CSEM monitoring. To address this, I conduct a feasibility study, simulating the time-lapse CSEM time-domain response of CO2 injected into a saline reservoir following the multi-transient electromagnetic (MTEM) method. The MTEM method measures the full bandwidth response. First, I model the response to a simple homogeneous 3D CO2 body, gradually increasing the width and depth of the CO2. This is an analogue to vertical and lateral CO2 migration in a reservoir. I then assess the ability of CSEM to detect CO2 plume growth and evolution within the heterogeneous Bunter Sandstone reservoir model. I demonstrate the potential to detect stored and migrating CO2 and present the synthetic results as time-lapse common-offset time sections. The CO2 plume is imaged clearly and in the right coordinates. The ability to image seismically a migrating front of CO2 remains challenging due to uncertainties regarding the pore-scale saturation distribution of fluids within the reservoir and, in turn, the most appropriate rock-physics model to simulate this: uniform or patchy saturation. I account for this by modelling both saturation models, to calculate the possible range of expected seismic velocities prior to generating and interpreting the seismic response. I demonstrate the ability of seismic methods to image CO2 plume growth and evolution in the Bunter Sandstone saline reservoir model and highlight clear differences between the two rock-physics models. I then modify the Bunter Sandstone reservoir to depict a depleted gas field by including 20% residual gas saturation. I assess the importance and implication of patchy saturation and present results which suggest that seismic techniques may be able to detect CO2 injected into depleted hydrocarbon fields.

Petrophysical properties are fundamental to understanding fluid flow processes in hydrocarbon reservoirs. Special Core Analysis (SCAL) routinely used in industry are time-consuming, expensive, and often destructive. Alternatively, easily available thin section data is lacking the representation of pore space in 3D, which is a requisite for generating pore network models (PNM) and computing petrophysical properties. In this study, these challenges were addressed using a numerical SCAL workflow that employs pore volume reconstruction from thin section images obtained from confocal laser scanning microscopy (CLSM). A key objective is to investigate methods capable of 2D to 3D reconstruction, to obtain PNM used for the estimation of transport properties. Representative thin sections from a well-known Middle-Eastern carbonate formation were used to obtain CLSM images. The thin-sections were specially prepared by spiking the resin with UV dye, enabling high-resolution imaging. The grayscale images obtained from CLSM were preprocessed and segmented into binary images. Generative Adversarial Networks (GAN) and Two-Point Statistics (TPS) were applied, and PNM were extracted from these binary datasets. Porosity, Permeability, and Mercury Injection Porosimetry (MIP) on the corresponding core plugs were conducted and an assessment of the properties computed from the PNM obtained from the reconstructed 3D pore volume is presented. Moreover, the results from the artificial pore networks were corroborated using 3D confocal images of etched pore casts (PCE). The results showed that based on visual inspection only, GAN outperformed TPS in mimicking the 3D distribution of pore scale heterogeneity, additionally, GAN and PCE outperformed the results of MIP obtained by TPS on the Skeletal-Oolitic facies, without providing a major improvement on more heterogeneous samples. All methods captured successfully the porosity while absolute permeability was not captured. Formation resistivity factor and thermal conductivity showcased their strong correlation with porosity. The study thus provides valuable insights into the application of 2D to 3D reconstruction to obtain pore network models of heterogeneous carbonate rocks for petrophysical characterization for quick decision. The study addresses the following important questions: 1) how legacy thin sections can be leveraged to petrophysically characterize reservoir rocks 2) how reliable are 2D to 3D reconstruction methods when predicting petrophysical properties of carbonates.