[Truncated] Reservoir modeling is the practice of generating numerical representations of reservoir conditions and properties on the basis of geological, geophysical and engineering data measured on the Earth’s surface or in depth at a limited number of borehole locations. Building an accurate reservoir model is a fundamental step of reservoir characterization and fluid flow performance forecasting, and has direct impact on reservoir management strategies, risk/uncertainty analyses and key business decisions. Seismic data, due to its high spatial resolution, plays a key role not only in defining the reservoir structure and geometry, but also in constraining the reservoir property variations. However, integration of 3D and time-lapse 4D seismic data into reservoir modeling and history matching processes poses a significant challenge due to the frequent mismatch between the initial reservoir model, the reservoir geology, and the pre-production seismic data. The key objective of this thesis is to investigate, develop and apply innovative solutions and methods to incorporate seismic data in the reservoir characterization and model building processes, and ultimately improve the consistency of the reservoir models with both geological and geophysical measurements.
In this thesis we first analyze the issues that have a significant impact on the (mis)match of the initial reservoir model with well logs and 3D seismic data. These issues include the incorporation of various seismic constraints in reservoir property modeling, the sensitivity of the results to realistic noise in seismic data, and to geostatistical modeling parameters, and the uncertainties associated with quantitative integration of seismic data in reservoir property modeling.
Inherent uncertainties and noise in real data measurements may result in conflicting geological and geophysical information for a given area; a realistic subsurface model can then only be produced by combining the datasets in some optimal manner. One approach to solving this problem is by joint inversion of the various geological and/or geophysical datasets. In this thesis we develop a new multi-objective optimization method to estimate subsurface geomodels using a stochastic search technique that allows a variety of direct and indirect measurements to simultaneously constrain the model. The main advantage of our method is its ability to define multiple objective functions for a variety of data types and constraints, and simultaneously minimize the data misfits. Using our optimization approach, the resulting models converge towards Pareto fronts (a set of best compromise model solutions). This approach is applicable in many Earth science disciplines: hydrology and ground water analyses, geothermal studies, exploration and recovery of fossil fuel energy resources, and CO2 geosequestration, among others.
|Qualification||Doctor of Philosophy|
|Publication status||Unpublished - Jun 2015|