Joint Inversion and Unsupervised Learning Applied to NMR Data Processing that Eliminates the Need for Regularization

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dc.contributor.advisorMyers, Michael T.
dc.contributor.committeeMemberHathon, Lori A.
dc.contributor.committeeMemberSoliman, Mohamed Y.
dc.contributor.committeeMemberQin, Guan
dc.contributor.committeeMemberArad, Alon
dc.contributor.committeeMemberGe, Xinmin
dc.creatorKrishnaraj, Naveen
dc.date.accessioned2022-06-18T22:34:49Z
dc.date.createdAugust 2021
dc.date.issued2021-08
dc.date.submittedAugust 2021
dc.date.updated2022-06-18T22:34:50Z
dc.description.abstractAn accurate data processing algorithm is at the heart of a successful Nuclear Magnetic Resonance (NMR) log interpretation. The first step in the traditional inversion algorithm is inverting for T2 distribution from the magnetization data at a single depth. A recent innovation involves finding volume fractions of the different components (capillary bound water, clay bound water, free water, organic matter, oil, etc.) using data from multiple depths/measurements and adopting Blind Source Separation (BSS) techniques. NMR data inversion and Blind Source Separation are both ill-posed problems. These algorithms are strongly influenced by noise and have significant error bars, especially for low values of T2. This research develops an algorithm that utilizes a joint inversion and blind source separation approach using a new technique, "Kernel Incorporated Non-Negative Matrix Factorization" (KINMF). This algorithm outputs T2 distributions and the volume fractions of different components from magnetization data by incorporating multiple measurements. This single-step, hybrid approach has a de-noising effect and generates accurate results without regularization. It significantly reduces the smearing effect that arises from standard regularization techniques and leads to one-to-two orders of magnitude improvement in processing speeds (e.g., compute time for the conventional method in a clastic system is higher than 120 sec; for the KINMF it is less than 15 sec). The algorithm was validated using forward modeling and comparison with experimental datasets. We conclude that the major impact of applying KINMF is for T2 relaxation times less than 100 ms and significantly improved computational times (enhanced real-time data processing). This should lead to broader applicability and improved physical interpretation of the data.
dc.description.departmentPetroleum Engineering, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10657/9367
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectMachine Learning, Data Science, Inverse Problems, Unsupervised Learning, Blind Source Separation, NMR and Petrophysics
dc.titleJoint Inversion and Unsupervised Learning Applied to NMR Data Processing that Eliminates the Need for Regularization
dc.type.dcmiText
dc.type.genreThesis
dcterms.accessRightsThe full text of this item is not available at this time because the student has placed this item under an embargo for a period of time. The Libraries are not authorized to provide a copy of this work during the embargo period.
local.embargo.lift2023-08-01
local.embargo.terms2023-08-01
thesis.degree.collegeCullen College of Engineering
thesis.degree.departmentPetroleum Engineering, Department of
thesis.degree.disciplinePetroleum Engineering
thesis.degree.grantorUniversity of Houston
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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