Dissertations / Theses on the topic 'Geophysics'

The first part of my thesis is mainly focused on the effect of grain size distribution on compaction localization in porous sandstone. To identify the microstructural parameters that influence compaction band formation, I conducted a systematic study of mechanical deformation, failure mode and microstructural evolution in Bleurswiller and Boise sandstones, of similar porosity (∼25%) and mineralogy but different sorting. Discrete compaction bands were observed to develop over a wide range of pressure in the Bleurswiller sandstone that has a relatively uniform grain size distribution. In contrast, compaction localization was not observed in the poorly sorted Boise sandstone. My results demonstrate that grain size distribution exerts important influence on compaction band development, in agreement with recently published data from Valley of Fire and Buckskin Gulch, as well as numerical studies.

The second part aimed to improve current knowledge on inelastic behavior, failure mode and brittle-ductile transition in another sedimentary rock, porous carbonates. A micritic Tavel (porosity of ∼13%) and an allochemical Indiana (∼18%) limestones were deformed under compaction in wet and dry conditions. At lower confining pressures, shear localization occurred in brittle faulting regime. Through transitional regime, the deformation switched to cataclastic flow regime at higher confining pressure. Specifically in the cataclastic regime, the (dry and wet) Tavel and dry Indiana failed by distributed cataclastic flow, while in contrast, wet Indiana failed as compaction localization. My results demonstrate that different failure modes and mechanical behaviors under different deformation regimes and water saturation are fundamental prior to any geophysical application in porous carbonates.

The third part aimed to focus on investigating compaction on quartz aggregate starting at low (MPa) using X-ray diffraction. We report the diffraction peak evolution of quartz with increasing pressures. Through evaluating the unit cell lattice parameters and the volume of the quartz sample, macroscopic stress and strain were resolved. Moreover, we observed quartz peak broadened asymmetrically at low pressure, such extent is more prominent in axial than in radial direction. Our evaluation on peak [101] (highest intensity among peaks) demonstrated that full width at half maximum can be a good proxy for microscopic stress distribution. We observed deviations in the pressure-volume curves at P = ∼0.4 GPa and speculated that it was the point of which onset of grain crushing and pore collapse occur in quartz. This is on the same order of which onset of grain crushing (commonly known as P*) is observed in sandstones in the rock mechanics literature. This demonstrated that there is potential in estimating grain crushing and pore collapse pressure with our technique.

Brittleness is a key characteristic for effective reservoir stimulation and is mainly controlled by mineralogy in unconventional reservoirs. Unfortunately, there is no universally accepted means of predicting brittleness from measures made in wells or from surface seismic data. Brittleness indices (BI) are based on mineralogy, while brittleness average estimations are based on Young's modulus and Poisson's ratio. I evaluate two of the more popular brittleness estimation techniques and apply them to a Barnett Shale seismic survey in order to estimate its geomechanical properties. Using specialized logging tools such as elemental capture tool, density, and P- and S wave sonic logs calibrated to previous core descriptions and laboratory measurements, I create a survey-specific BI template in Young's modulus versus Poisson's ratio or alternatively λρ versus μρ space. I use this template to predict BI from elastic parameters computed from surface seismic data, providing a continuous estimate of BI estimate in the Barnett Shale survey. Extracting λρ-μρ values from microseismic event locations, I compute brittleness index from the template and find that most microsemic events occur in the more brittle part of the reservoir. My template is validated through a suite of microseismic experiments that shows most events occurring in brittle zones, fewer events in the ductile shale, and fewer events still in the limestone fracture barriers.

Estimated ultimate recovery (EUR) is an estimate of the expected total production of oil and/or gas for the economic life of a well and is widely used in the evaluation of resource play reserves. In the literature it is possible to find several approaches for forecasting purposes and economic analyses. However, the extension to newer infill wells is somewhat challenging because production forecasts in unconventional reservoirs are a function of both completion effectiveness and reservoir quality. For shale gas reservoirs, completion effectiveness is a function not only of the length of the horizontal wells, but also of the number and size of the hydraulic fracture treatments in a multistage completion. These considerations also include the volume of proppant placed, proppant concentration, total perforation length, and number of clusters, while reservoir quality is dependent on properties such as the spatial variations in permeability, porosity, stress, and mechanical properties. I evaluate parametric methods such as multi-linear regression, and compare it to a non-parameteric ACE to better correlate production to engineering attributes for two datasets in the Haynesville Shale play and the Barnett Shale. I find that the parametric methods are useful for an exploratory analysis of the relationship among several variables and are useful to guide the selection of a more sophisticated parametric functional form, when the underlying functional relationship is unknown. Non-parametric regression, on the other hand, is entirely data-driven and does not rely on a pre-specified functional forms. The transformations generated by the ACE algorithm facilitate the identification of appropriate, and possibly meaningful, functional forms.

Reservoir properties are mainly determined based on well log information. However, wells in most reservoirs are sparse and widely spread compared to the size of the reservoir. Seismic data is thus one of the most important complementary sources of information used to build 3D models of hydrocarbon reservoirs. The need for a high quality reservoir description starts as soon as a discovery is made. In the appraisal phase, hydrocarbons in place and the amount of recoverable reserves are estimated based on the reservoir model. Improved structural models are also needed in optimal well placement during the production and development phase of a reservoir. Knowledge about saturation and pressure distributions in a reservoir are valuable both in the exploration and development phase of a reservoir. This knowledge is used to evaluate the size of a field, determine an optimal drainage pattern, and decide on optimal well design to reduce risks for blow-outs and damage on production equipment. Reducing uncertainty in reservoir property estimates from seismic data have large economic impact on the development of a hydrocarbon reservoir.

Quantitative reservoir property information can be obtained either through direct estimates of reservoir properties from seismic data or through estimates of elastic properties (velocities and densities) that are related to reservoir properties. The relationship between physical properties of rocks and fluids and P-wave seismic data are often empirical and non-unique. This leads to large uncertainties in reservoir models derived from pressure wave seismic data alone. Since shear waves do not propagate through fluids, combined use of pressure wave seismic data and shear wave seismic data might increase our ability to derive fluid and lithology properties from seismic data. One way to obtain information about shear wave velocities over a large area is to acquire multicomponent seismic data (for instance x, y, and z component geophone data). Parts of this thesis focus on methods to combine the information from multicomponent seismic data with pressure wave (hydrophone) seismic data. In this way we improve the accuracy in the estimates of pressure wave velocity, shear wave velocity and density in the subsurface.

To obtain information about changes in reservoir parameters like fluid saturation and pore pressure during production, comparisons between different vintages of seismic data acquired over the field can be performed. Differences in the seismic signal from the same area over a time period (time-lapse seismic data) can be interpreted as changes in reservoir properties. Benefits of improved reservoir characterization include ability to locate bypassed oil and mapping of fluid fronts. This leads to saved costs due to reduced number of misplaced wells, and increased production because of optimized well placement. In the early days of seismic reservoir monitoring, the analyses were qualitative, e.g. to identify undrained areas, analyzing the sealing capacity of faults, and detect drainage patterns. Today, time-lapse seismic analysis is still mainly qualitative. To be able to obtain more quantitative estimates of changes in reservoir properties from the time-lapse seismic data, we need to establish links between the rock parameters and the seismic data. I have used both time-lapse surface seismic data and time-lapse multicomponent seismic data to estimate production related changes in fluid saturation and pressure.

Finally, to be able to utilize rock physical information obtained from seismic reservoir characterization in reservoir modelling, information about uncertainties in the estimates are essential. One way to do this is to use deterministic models (rock physics models) that relates reservoir properties to seismic data, and assume that the model parameters are independent. However, the variables in these estimations are inherently dependent and should be treated as such. By formulating the problem in a Bayesian framework, dependencies between the different variables and spatial dependencies can easily be included. I have used both deterministic uncertainty analysis and Bayesian estimation methods to quantify uncertainties in the estimates.

Thermophysical properties (dissociation enthalpy, heat capacity, metastability) and compositional properties (hydrate number, free water and fractionation) of natural gas hydrate were studied experimentally on samples that contained large amounts of ice. Methods for continuous hydrate production and sampling, and for quantification of the properties were developed. Hydrate was produced from a natural gas of ethane (5 %mol) and propane (3 %mol) in methane.

A low temperature scanning calorimetry method was developed to measure dissociation enthalpy, heat capacity, hydrate number and free water (ice). During the analysis, the hydrate samples were pressurized to 1.7 MPa with methane and the system operated between the hydrate equilibrium curves of methane and the hydrate forming natural gas. A sample conditioning procedure eliminated thermal effects of desorption as the ice melted. Desorption occurred since the samples were produced and refrigerated to 255 K under a natural gas pressure of 6-10 MPa, but were analyzed and melted under a methane pressure of 1.7 MPa.

A low temperature isothermal calorimetry method was developed to quantify the metastability properties. Metastability was confirmed for temperatures up to 268 K and quantified in terms of the low dissociation rate.

Fractionation data were obtained in the range 3.0 to 7.5 MPa and for subcoolings between 2 and 16 K. High pressure and large subcooling is desirable to suppress fractionation. A fractionation model was proposed. The model coincides with the van der Waals-Platteeuw model for zero subcooling. No fractionation is assumed for hypothetical hydrate formation at infinite driving force (subcooling). Between these two extremes an exponential term was used to describe the fractionation. The model predicted fractionation with an accuracy of about 1%abs corresponding to 1-10%rel.

The DC resistivity method has been an important tool for mineral exploration for the direct detection of conductive bodies with economic value. It has also been used for the structural mapping of lithology and alteration where boundaries are zones of economic interest and the detection of edges is the primary goal of surveying. Edge preserving inversion has been explored extensively within the context of potential field methods but has seen relatively little attention for the DC resistivity method. The focus of this thesis is to develop and implement methods which employ specific advantages of the DC method to aid the recovery of edges in the earth’s resistivity distribution.

I begin by utilizing sparse a priori geologic knowledge to create a geologic concept of pervasive blocky resistivity. l₁ and l₀ approximating measures of model values and model gradients are used as a vehicle to inject the a priori knowledge into a regularized inversion. An iterative method is used to solve for the model that minimizes a total objective function using these general measures.

A series of synthetic modelling and inversion scenarios demonstrate the effectiveness of l₁ and minimum gradient support regularization to recover boundaries when compared to traditional sum-of-squares regularization. These blocky inversion schemes also exhibit an improved recovery of the resistivity value of distinct bodies. Additionally, I recognize that the various regularization types have different strengths and weaknesses. I exploit this property to create a new composite regularization that combines smooth model and blocky model regularization. This composite regularization exhibits the strengths of both regularization styles and less of the weaknesses.

A case study on field data from the Sabajo gold deposit was performed utilizing this methodology. Sharp lithologic contacts from drillholes informed the creation of a sharp resistivity concept. A blocky inversion was performed to recover a blocky model that was consistent with this concept and compared to the results of a smooth model inversion. Important differences were noted with their economic implications. I observed that the blocky regularized inversion may have recovered better estimates of the conductivity of features and this can greatly aid prioritization of targets for drilling. Finally, the differences between the inversions utilizing diverse regularization styles provided a proxy for model uncertainty.