Thesis (M.A.)--University of Central Florida, 2009. / Adviser: Arlen Chase. Includes bibliographical references (p. 60-68).

Thesis (Ph. D.)--University of Texas at Austin, 2003. / Vita. Includes bibliographical references. Available also from UMI Company.

Thesis (M.S. in E.A.S.)--School of Earth and Atmospheric Sciences, Georgia Institute of Technology, 2004. Directed by Daniel Lizarralde. / Includes bibliographical references (leaves 69-73).

One of the important goals in petroleum exploration and production is to make quantitative estimates of a reservoir’s properties from all available but indirectly related surface data, which constitutes an inverse problem. Due to the inherent non-uniqueness of most inverse procedures, a deterministic solution may be impossible, and it makes more sense to formulate the inverse problem in a statistical Bayesian framework and to fully solve it by constructing the Posterior Probability Density (PPD) function using Markov Chain Monte Carlo (MCMC) algorithms. The derived PPD is the complete solution of an inverse problem and describes all the consistent models for the given data. Therefore, the estimated PPD not only leads to the most likely model or solution but also provides a theoretically correct way to quantify corresponding uncertainty. However, for many realistic applications, MCMC can be computationally expensive due to the strong nonlinearity and high dimensionality of the problem. In this research, to address the fundamental issues of efficiency and accuracy in parameter estimation and uncertainty quantification, I have incorporated some new developments and designed a new multiscale MCMC algorithm. The new algorithm is justified using an analytical example, and its performance is evaluated using a nonlinear pre-stack seismic waveform inversion application. I also find that the new technique of multi-scaling is particularly attractive in addressing model parameterization issues especially for the seismic waveform inversion. To derive an accurate reservoir model and therefore to obtain a reliable reservoir performance prediction with as little uncertainty as possible, I propose a workflow to integrate 4D seismic and well production data in a Bayesian framework. This challenging 4D seismic history matching problem is solved using the new multi-scale MCMC algorithm for reasonably accurate reservoir characterization and uncertainty analysis within an acceptable time period. To take advantage of the benefits from both the fine scale and the coarse scale, a 3D reservoir model is parameterized into two different scales. It is demonstrated that the coarse-scale model works like a regularization operator to make the derived fine-scale reservoir model smooth and more realistic. The derived best-fitting static petrophysical model is further used to image the evolution of a reservoir’s dynamic features such as pore pressure and fluid saturation, which provide a direct indication of the internal dynamic fluid flow. / text

Dispersivity data compiled over many lengths show that values at typical interwell distances are about two to four factors of ten larger than those measured on cores. Such large dispersivities may represent large mixing zones in the reservoir or they may be a result of convective spreading driven by permeability heterogeneity. This dissertation uses the idea of flow reversal (echo tests) to distinguish between convective spreading and dispersive mixing. Spreading is reversible, mixing is not. A zero or small value of echo dispersivity (estimated after flow reversal) implies little or no mixing and convection dominated transport. An echo dispersivity value equal to the transmission value (estimated after forward flow) would imply well mixed transport. A particle tracking code is developed to simulate echo tests for tracer transport in single phase, incompressible flow through three-dimensional, heterogeneous permeable media. Echo dispersivities are estimated for typical heterogeneity realizations and compared with corresponding transmission values at the field scale. The most important observation is that echo dispersivities are significantly larger than core scale values. They also lie on the overall trend of measured dispersivities and corroborate the large echo dispersivities previously inferred from single well tracer test data. This implies that significant mixing occurs in field scale transport. Echo dispersivities increase with permeability heterogeneity (variance and autocorrelation lengths). This is the effect of local (point or pore scale) mixing in the transverse direction, integrated over long and tortuous flow paths. Transport in typical reservoir formations, with significant autocorrelation in permeabilities, is most likely to be in a pre-asymptotic regime and cannot be described by a unique dispersivity value. This is because the Fickian model for dispersion fails to capture the mixing zone growth correctly in this regime. These results highlight the need to develop representative models for dispersion and improve upscaling methodologies. / text

Many of the world's oil fields and aquifers are found in carbonate strata. Some of these formations contain vugs or cavities several centimeters in size. Flow of fluids through such rocks depends strongly upon the spatial distribution and connectivity of the vugs. Enhanced oil recovery processes such as enriched gas drives and groundwater remediation efforts like soil venting operations depend on the amount of hydrodynamic dispersion of such rocks. Selecting a representative scale to measure permeability and dispersivity in such rocks can be crucial because the connected vug lengths can be longer than typical core diameters. Large touching vug (centimeter-scale), Cretaceous carbonate rocks from an exposed rudist (caprinid) reef buildup at the Pipe Creek Outcrop in Central Texas were studied at three different scales. Single-phase airflow and gas-tracer experiments were conducted on 2.5 in. diameter by 5 in. long cores (core-scale) and 5- to 10-ft-radius well tests (field-scale). Zhang et al. (2005) studied a 10 in. diameter by 14 in. high sample (bench-scale). Vertical permeability in the bench-scale varied from 100 darcies to 10 md and in the core-scale averaged 2.5 darcies. The field-scale permeability was estimated to be 500 md from steady state airflow and pressure transient tests. In the bench and core scales a connected path of vugs dominates flow and tracer concentration breakthrough profile. Tracer transport showed immediate breakthrough times and a long tail in the tracer concentrations characterized by multiple plateaus in concentrations. Neither flow nor tracer transport can be explained at these scales by the standard continuum equations (Darcy’s law or 1D convection dispersion equation). However, interpreting field-scale measurements with standard continuum equations suggested that a strongly connected path of vugs did not extend past a few feet. In particular, the tracer experiment in the field scale can be modeled accurately using an equivalent homogeneous porous medium with a dispersivity of 0.5 ft. In our measurements, permeability decreased with scale, while vug connectivity and multi-scale effects associated with vug connectivity decreased with increasing scale. We concluded that approximately 5 ft could be considered the representative scale for the large-touching-vug carbonate rocks at the Pipe Creek Outcrop. The major contribution of this research is the introduction of an integrated, multi-scale, experimental approach to understanding fluid flow in carbonate rocks with interconnected networks of vugs too large to be adequately characterized in core samples alone. / text

Many gas wells suffer a loss in productivity due to liquid accumulation in the near wellbore region. This problem starts as the flowing bottom hole pressure drops below the dew point in wells producing from gas condensate reservoirs. Chemical stimulation may be used as a remedy, by altering the wettability to non-liquid wetting. Successful treatments decrease liquid trapping, increase fluids mobility, and improve the well’s deliverability. The main focus in this research was to develop an effective chemical treatment to mitigate liquid blocking in gas wells producing from carbonate reservoirs. In the initial stages, screening tests were developed to quickly and effectively identify suitable chemicals from a large pool of compounds. X-ray Photoelectron Spectroscopy (XPS) measurements, drop imbibition tests, and contact angle measurements with water and n-decane were found to be necessary but not sufficient indicators of the effectiveness of the chemicals and were used as screening tools. An integral part of the development of the treatment solution was the selection of a solvent mixture capable of delivering the fluorinated chemical to the rock surface. The treatment solution, mixture of chemical dissolved in solvent, must be stable in the presence of both brine and condensate so that it will not precipitate and will not reduce permeability of the rock. Through phase behavior studies the compatibility of the treatment solution and in-situ brines were investigated to reduce the risk of failure in the coreflood experiments. The measured relative permeability values in Texas Cream Limestone and Silurian Dolomite cores are demonstrate from high-pressure, high-temperature coreflood experiments before and after treatment. Measurements were made using a pseudo-steady-state method with synthetic gas-condensate mixtures. To enhance the durability of the treatment a special amine primer is introduced. / text

Paleokarst reservoirs consist of complex cave networks, which are formed by various mechanisms and associated collapsed cave facies. Traditionally, cave structures are defined using variogram-based methods in flow models and this description does not precisely represent the reservoir geology. Algorithms based on multiple-point statistics (MPS) are widely used in modeling complex geologic structures. Statistics required for these algorithms are inferred from gridded training images. However, structures like modern cave networks are represented by point data sets. Thus, it is not practical to apply rigid and gridded templates and training images for the simulation of such features. Therefore, a quantitative algorithm to characterize and model paleokarst reservoirs based on physical and geological attributes is needed. In this study, a unique non-gridded MPS analysis and pattern simulation algorithms are developed to infer statistics from modern cave networks and simulate distribution of cave structures in paleokarst reservoirs. Non-gridded MPS technique is practical by eliminating use of grids and gridding procedure, which is challenging to apply on cave network due to its complex structure. Statistics are calculated using commonly available cave networks, which are only represented by central line coordinates sampled along the accessible cave passages. Once the statistics are calibrated, a cave network is simulated by using a pattern simulation algorithm in which the simulation is conditioned to sparse data in the form of locations with cave facies or coordinates of cave structures. To get an accurate model for the spatial extent of the cave facies, an algorithm is also developed to simulate cave zone thickness while simulating the network. The proposed techniques are first implemented to represent connectivity statistics for synthetic data sets, which are used as point-set training images and are analogous to the data typically available for a cave network. Once the applicability of the algorithms is verified, non-gridded MPS analysis and pattern simulation are conducted for the Wind Cave located in South Dakota. The developed algorithms successfully characterize and model cave networks that can only be described by point sets. Subsequently, a cave network system is simulated for the Yates Field in West Texas which is a paleokarst reservoir. Well locations with cave facies and identified cave zone thickness values are used for conditioning the pattern simulation that utilizes the MP-histograms calibrated for Wind Cave. Then, the simulated cave network is implemented into flow simulation models to understand the effects of cave structures on fluid flow. Calibration of flow model against the primary production data is attempted to demonstrate that the pattern simulation algorithm yields detailed description of spatial distribution of cave facies. Moreover, impact of accurately representing network connectivity on flow responses is explored by a water injection case. Fluid flow responses are compared for models with cave networks that are constructed by non-gridded MPS and a traditional modeling workflow using sequential indicator simulation. Applications on the Yates Field show that the cave network and corresponding cave facies are successfully modeled by using the non-gridded MPS. Detailed description of cave facies in the reservoir yields accurate flow simulation results and better future predictions. / text

published_or_final_version / Environmental Management / Master / Master of Science in Environmental Management

Not available / text