Two unconventional approaches to electromagnetic inversion: hierarchical Bayesian inversion and inverse scattering series
AuthorKwon, Myoung Jae
AdvisorSnieder, Roel, 1958-
Keywordsinverse scattering series
Bayesian statistical decision theory
Seismic waves -- Speed
MetadataShow full item record
AbstractElectromagnetic methods are effective complementary tools, when combined with seismic exploration, for the delineation of a hydrocarbon reservoir, because electromagnetic methods provide extra information about, for example, electric conductivity, which is an important property for the economic evaluation of reservoirs. In this study, we analyze unconventional approaches of electromagnetic inversion: hierarchical Bayesian inversion and inverse scattering series. We apply the hierarchical Bayesian inversion to the uncertainty analysis for the joint inversion and utilize rock-physics models to integrate these two disparate data sets. The study shows that the uncertainties in the seismic wave velocity and electric conductivity play a more significant role in the variation of posterior uncertainty than do the seismic and CSEM data noise. The numerical simulations also show that the uncertainty in porosity is most affected by the uncertainty in seismic wave velocity and that the uncertainty in water saturation is most influenced by the uncertainty in electric conductivity. The framework of the uncertainty analysis presented in this study can be utilized to effectively reduce the uncertainty of the porosity and water saturation derived from integration of seismic and CSEM data. We also study the feasibility of the inverse scattering series, which can effectively resolve the nonlinearity of an inverse problem, for the interpretation of electromagnetic data. The application of the inverse scattering series has been limited because the series converges when the reference model sufficiently close to the true model. This study quantifies convergence conditions of the inverse scattering series and suggests a different approach of the inverse series, the modified inverse scattering series, which guarantees the convergence of the series and facilitates the choice of a reference model.
RightsCopyright of the original work is retained by the author.
Showing items related by title, author, creator and subject.
Incorporating prior information into geophysical inversion: from regularized inversion of thermal data to a framework using conditional variational autoencodersLi, Yaoguo; McAliley, W. Anderson; Nakagawa, Masami; Ganesh, Mahadevan; Irons, Trevor P.; Swidinsky, Andrei; Krahenbuhl, Richard A. (Colorado School of Mines. Arthur Lakes Library, 2021)Geophysical inversion provides physical property models which are essential to understanding and characterizing the subsurface. However, traditional inversion methods recover models with smooth features that do not resemble geologic structures. Incorporating prior information into inversion can encourage geologic realism in recovered models, but how best to do so remains an open research question. I develop methods to inject prior information into inversion to recover more geologically realistic models. First, I develop methods for generalized inversion of temperature and heat flow data. While thermal data containing information about the subsurface distribution of thermal conductivity are widely available, methods to invert thermal data are underexplored. I formulate Tikhonov inversion algorithms to recover continuously varying distributions of thermal conductivity from borehole temperature data and surface heat flow data. The naïve application of Tikhonov inversion produces unrealistically smooth models that only contain structure near data locations, so I employ sensitivity-based model weighting and an lp model norm to improve the inversion results. Next, I develop a framework that is capable of promoting complicated geologic structures in inverted models. Geological realism is challenging to quantify, but recently, generative neural networks have proven capable of capturing complex spatial and petrophysical information from a set of example models. I train a conditional variational autoencoder to incorporate learned information into geophysical inversion and generate models that resemble the example models while honoring the specific data to be inverted. I apply this framework to two different problems. First, I invert gravity data, using synthetic layered and faulted density models as a training set. I show that the method can recover models that exhibit faulting, layering, compact bodies, and sharp boundaries, all difficult characteristics to enforce using traditional inversion methods. Second, I apply the framework to magnetotelluric inversion, using publicly available borehole logs to construct a set of conductivity models for training. By training on models that exemplify how conductivity is distributed in the subsurface, I incorporate geologic information from a widely available yet vastly underutilized type of data into the inversion results.
Joint inversion of 9C 3D seismic data for reservoir characterization in the Bakken Formation, Banner field, Mountrail County, North DakotaDavis, Thomas L. (Thomas Leonard), 1947-; Kreeprasertkul, Kritti; Sonnenberg, Stephen A.; Batzle, Michael L. (Colorado School of Mines. Arthur Lakes Library, 2014)Joint PP and SS seismic inversion is a geophysical analysis method that uses the variation in reflectivity of PP and SS seismic data to derive elastic properties of the subsurface and specifically reservoir properties. The major objective of this research is to characterize the elastic properties of the Bakken petroleum system at Banner Field, Mountrail County, North Dakota using joint PP and SS seismic inversion. The combination of PP and SS data into the inversion process helped improve the accuracy of inversion results by increasing the correlation coefficient by 10 percent on both P- and S- impedance. Results from this analysis show a variation in elastic properties from the western part to the eastern portion of the study area. The western part is interpreted to be higher in shale volume, porosity and maturity. Additionally, the natural fracture network is an integral part of a tight oil reservoir. The existence and delineation of natural fractures coincides with faults that have been delineated within the reservoir interval. The elastic parameters and structural detailing from this study has been used to delineate a potential exploration opportunity in the Three Forks Formation on the west side of the survey.