Bayesian inversion methods for seismic reservoir characterization and time-lapse studies [electronic resource]

This dissertation addresses mathematical methodologies for seismic reservoir characterization and time-lapse studies. Generally the main goal of reservoir modeling is to provide 3-dimensional models of the main properties in the reservoir in order to perform fluid flow simulations. These properties generally include rock properties, such as porosity and lithology; fluid properties, such as water and hydrocarbon saturations; and dynamic properties, such as pressure and permeability. None of these properties can be directly measured in the subsurface, therefore reservoir properties must be estimated from other measurements. In petroleum geophysics we generally have two kinds of measured data: well log data and seismic data. Well log data contain high resolution information about elastic and petrophysical properties, but they can only sample few locations of the reservoir. On the other side, seismic data cover the whole reservoir but the resolution is lower than well log data. Electromagnetic data are sometimes acquired in addition to seismic data to improve the reservoir description but the resolution is still limited. In order to obtain suitable models of the reservoir, we have to combine these two sources of information, wells and seismic, and integrate physical relations (rock physics and seismic modeling) with mathematical methodologies (inverse theory and probability and statistics). In particular by using a Bayesian approach to seismic and rock physics inversion we aim to obtain reservoir models of rock and fluid properties and the associated uncertainty. Since the resolution and the quality of seismic data are generally not ideal, uncertainty quantification plays a key role in reservoir modeling. This thesis includes three innovative methodologies for seismic reservoir characterization: the first method is a Bayesian inversion methodology suitable for reservoirs in exploration phases with a limited number of wells, the second method is a Bayesian sampling methodology that can provide multiple reservoir models honoring the given seismic dataset, the third one is a stochastic inversion methodology that provides high-detailed models suitable for reservoirs with a large number of wells. The key innovation in all these methods is the use of new statistical tools to describe the multimodal behavior of rock and properties in the reservoir and the direct integration of the rock physics model. The main principle of these methodologies is then extended to time-lapse studies to invert time-lapse seismic data and improve the reservoir description in terms of changes in rock and dynamic properties. The novelty of this method is the simultaneous inversion of the pre-production base seismic survey and repeated monitor surveys. This dissertation contributes to both deterministic and statistical seismic-based reservoir characterization. Complementary, I investigated velocity-pressure transforms to determine analytical physical models to describe the pressure effect on elastic properties and integrate these models in time-lapse reservoir studies. Finally I also developed a statistical methodology to integrate rock physics models in formation evaluation analysis and log-facies classification. All the proposed probabilistic reservoir-characterization techniques can predict reservoir models with multiple properties (static and dynamic) and the associated uncertainty. Multiple models can then be derived to run multiple scenarios and the corresponding risk analysis. All the methodologies were tested using synthetic data and applied to real case datasets. In the future, these methodologies could be integrated with history matching techniques to develop statistical methodologies for seismic history matching and improve reservoir description and monitoring by simultaneously matching seismic data and production data.

Keywords: Geophysics.
Advisor: Mavko Gary
Dvorkin Jack
Mukerji Tapan