Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.

Thesis (M. Sc.)--University of Alberta, 2009. / Title from pdf file main screen (viewed on July 27, 2009). "A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science in Petroleum Engineering, Department of Civil and Environmental Engineering, University of Alberta." Includes bibliographical references.

Thesis (M.Sc.)--University of Alberta, 2010. / Title from PDF file main screen (viewed on Apr. 1, 2010). A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science in Petroleum Engineering, Department of Civil and Environmental Engineering, University of Alberta. Includes bibliographical references.

No description available.

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

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

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