Time-Lapse Seismic Imaging, Full-waveform Inversion, and Uncertainty Quantification

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dc.contributor.advisorInnanen, Kristopher A.H.
dc.contributor.authorFu, Xin
dc.contributor.committeememberInnanen, Kristopher A.H.
dc.contributor.committeememberDettmer, Jan
dc.contributor.committeememberTrad, Daniel Osvaldo
dc.contributor.committeememberJames, Brandon Anthony
dc.contributor.committeememberLiao, Wenyuan
dc.contributor.committeememberMalcolm, Alison E.
dc.date2023-11
dc.date.accessioned2023-10-10T22:35:08Z
dc.date.available2023-10-10T22:35:08Z
dc.date.issued2023-09-15
dc.description.abstractTime-lapse seismic, also known as 4D seismic, is a powerful tool for monitoring subsurface changes over time. By comparing seismic data acquired at different intervals, it enables the detection and characterization of dynamic reservoir processes, aiding in reservoir management, production optimization, and enhanced oil recovery. It has applications in geothermal energy, CO2 storage monitoring, and environmental impact assessment. However, accurate analysis of time-lapse seismic data remains a challenging task. It requires well-repeated time-lapse seismic surveys, including well-repeated acquisition geometry and equipment as well as well-repeated ambient noise. This thesis is to alleviate the non-repeatability issues in time-lapse seismic imaging and full-waveform inversion (FWI), and to realize the uncertain quantification for time-lapse seismic waveform inversion. A time-lapse imaging approach that involves two new frequency-domain matching filters is developed. The first filter requires source wavelet estimates from both baseline and monitoring data, while the second filter is source-independent but more sensitive to data noise. By applying these filters, we successfully reduce source wavelet non-repeatability, and the new approach improves the accuracy of time-lapse imaging. Furthermore, a stepsize-sharing time-lapse FWI strategy is designed to reduce artifacts caused by the variability of convergence in conventional strategies. The strategy demonstrates good adaptivity in different tested realistic scenarios using synthetic data. It is stable for scenarios using biased starting models, while the conventional strategies fail in this regard. Moreover, to realize the uncertain quantification, a Bayesian time-lapse FWI procedure, based on a Markov chain Monte Carlo (MCMC) algorithm, is formulated. The formulation employs several existing strategies, including the use of a double-difference time-lapse FWI, incorporation of time-domain multi-source data, and application of a local-updating target-oriented inversion. It incorporates these within a stochastic framework, involving the computation of model covariance with an adaptive Metropolis algorithm, and a method to estimate data error statistics based on the features of time-lapse difference data is incorporated. A random walk Metropolis-Hastings MCMC is adopted for optimization.
dc.identifier.citationFu, X. (2023). Time-lapse seismic imaging, full-waveform inversion, and uncertainty quantification (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.urihttps://hdl.handle.net/1880/117369
dc.identifier.urihttps://doi.org/10.11575/PRISM/42212
dc.language.isoen
dc.publisher.facultyScience
dc.publisher.institutionUniversity of Calgary
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectSeismic imaging
dc.subjectTime-lapse seismic
dc.subjectFull-waveform inversion
dc.subjectUncertainty Quantification
dc.subjectCO2 sequestration monitoring
dc.subject4D seismic
dc.subject.classificationGeophysics
dc.subject.classificationEducation--Sciences
dc.subject.classificationEngineering--Petroleum
dc.titleTime-Lapse Seismic Imaging, Full-waveform Inversion, and Uncertainty Quantification
dc.typedoctoral thesis
thesis.degree.disciplineGeoscience
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameDoctor of Philosophy (PhD)
ucalgary.thesis.accesssetbystudentI do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.
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