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dc.contributor.advisorJin, Ge
dc.contributor.authorSchumann, Harrison H.
dc.date.accessioned2021-09-13T10:17:28Z
dc.date.accessioned2022-02-03T13:23:12Z
dc.date.available2021-09-13T10:17:28Z
dc.date.available2022-02-03T13:23:12Z
dc.date.issued2021
dc.identifierSchumann_mines_0052N_12187.pdf
dc.identifierT 9147
dc.identifier.urihttps://hdl.handle.net/11124/176462
dc.descriptionIncludes bibliographical references.
dc.description2021 Summer.
dc.description.abstractIn 2019, two horizontal wells were drilled in the Chalk Bluff field and equipped with fiber optic sensing technology to evaluate the completion effectiveness at each stage. The primary goal of these wells was to determine the optimal completion strategies in the field and the impact of nearby legacy wells. In recent years, fiber optic sensing and machine learning have shown potential to enhance completion evaluations in unconventional reservoirs. Therefore, we developed new methods using these technologies and applied them using data from the Chalk Bluff field to address the goals mentioned previously. We present a novel use of tube waves exited by perforation (or “perf”) shots and recorded on distributed acoustic sensing (DAS) to infer and compare the hydraulic connectivity of induced fractures near the wellbore on a stage-by-stage basis. We also discuss a new machine learning multivariate analysis workflow designed to identify the most correlated variables within complex, high-dimensional datasets. After validating the workflow’s effectiveness using a synthetic example, we applied it to Chalk Bluff data to identify significant correlations and provide recommendations for improving frac effectiveness and well performance while decreasing costs.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado School of Mines. Arthur Lakes Library
dc.relation.ispartof2021 - Mines Theses & Dissertations
dc.rightsCopyright of the original work is retained by the author.
dc.subjectfiber optic sensing
dc.subjectmachine learning
dc.subjecttube waves
dc.subjectfracture characterization
dc.subjectcompletion optimization
dc.subjectmultivariate analysis
dc.titleEnhanced completion evaluations in unconventional reservoirs: new applications of fiber optic sensing and machine learning
dc.typeText
dc.contributor.committeememberShragge, Jeffrey
dc.contributor.committeememberMiskimins, Jennifer L.
dc.contributor.committeememberFisher, Wendy
thesis.degree.nameMaster of Science (M.S.)
thesis.degree.levelMasters
thesis.degree.disciplineGeophysics
thesis.degree.grantorColorado School of Mines


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