Date of Award

2020

Document Type

Open Access Master's Thesis

Degree Name

Master of Science in Geophysics (MS)

Administrative Home Department

Department of Geological and Mining Engineering and Sciences

Advisor 1

Roohollah Askari

Advisor 2

Snehamoy Chatterjee

Committee Member 1

Aleksey Smirnov

Abstract

We proposed a novel seismic inversion approach that integrates the physical properties of litho-facies, and geophysical data, within the multiple-point geostatistical frameworks to reduce the uncertainty in predictions of litho-facies spatial arrangement away from wells or control points. The litho-facies groups (rock-type) in the well locations are defined and conditioned to the distribution of elastic properties, including P-wave velocity (Vp) and facies density (ρ) in the well locations. A conceptual geological model (training image) is utilized within a wavelet-based multiple-point geostatistical simulation (WAVESIM) algorithm to generate litho-facies realizations. In our inversion algorithm, the forward model is created by implementing the bivariate Kernel density estimation technique of the litho-facies properties (Vp and ρ) that are distributed in the well locations. The inversion approach is an iterative process, where a particular number of elastic properties (Vp and ρ) for each WAVESIM realization are drawn, and then the forward model was utilized to create synthetic seismograms. For each generated set of the WAVESIM realizations, a series of synthetic seismograms are produced, and one realization is selected that provides the best-match synthetic seismogram compared to the input seismic data using crosscorrelation function. Our inversion technique was successfully applied to synthetic and field datasets. The results demonstrate the efficiency of our inversion approach to characterize highly heterogeneous reservoirs.

Available for download on Sunday, August 08, 2021

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