Integrated AVO Analysis, Seismic Inversion and Machine Learning for De-risking New Prospects in The Hutton Sandstone Formation, Onshore Australia
| dc.contributor.advisor | Castagna, John P. | |
| dc.contributor.committeeMember | Chesnokov, Evgeni M. | |
| dc.contributor.committeeMember | Metwally, Yasser M. | |
| dc.creator | Gad, Mohamed Ahmed Mahmoud 1991- | |
| dc.date.accessioned | 2019-12-17T03:47:27Z | |
| dc.date.created | December 2019 | |
| dc.date.issued | 2019-12 | |
| dc.date.submitted | December 2019 | |
| dc.date.updated | 2019-12-17T03:47:29Z | |
| dc.description.abstract | Identifying lithofacies and pore fluids is still a problematic issue in the Hutton Formation, Queensland field, onshore Australia. The target reservoir is usually a one-well prospect since it is of limited size and most wells drilled on basement influenced highs are dry amidst similar structures that are hydrocarbon charged. On the same anticlinal closure, two wells encountered different pore fluids, oil and brine, though both were high on structure, suggesting stratigraphic complexity. Because of ambiguous facies distribution, quantitative seismic analysis is badly needed to predict facies changes between wells. In this research study, different quantitative analysis methods and datasets were used for facies prediction. These included: AVO analysis, post-stack inversion, pre-stack simultaneous inversion, sparse-layer inversion, probabilistic facies prediction by Bayes classification, supervised machine learning using neural network and unsupervised machine learning using Self-Organizing Maps (SOM). To address methods and data performance for lithology and pore-fluids prediction, blind validation wells were used. In addition, a confusion matrix was constructed to compare methods. Post-stack inversion on bandwidth-extended seismic data, accomplished with sparse-layer inversion, has the highest pore-fluid prediction accuracy (94.5%). Although supervised and unsupervised machine learning shows good lateral facies distribution along wells, insufficient validation wells prevented statistically meaningful evaluation. High acoustic impedance and compressional-to-shear-wave velocity ratio correlate with meandering stratigraphic features identified from curvedeness and dip of maximum similarity seismic attributes. After co-rendering these attributes with facies distribution horizon slices, shale and brine-sand facies are distributed along these meandering features. These facies are probably the low stand systems tract of the overlaying Birkhead Formation deposited in incised paleo valley system formed after falling base level. This incision removed the whole, or the upper part of the, Hutton Formation at some locations. In addition, an observed braided channel has anomalous Class 4 AVO response and is characterized by low acoustic impedance. After using a rock-physics template and Bayesian classification, high-probability oil sand facies with high porosity are distributed along the channel feature. | |
| dc.description.department | Earth and Atmospheric Sciences, Department of | |
| dc.format.digitalOrigin | born digital | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | https://hdl.handle.net/10657/5563 | |
| dc.language.iso | eng | |
| dc.rights | The 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.subject | Sparse-layer inversion | |
| dc.subject | Bandwidth extension | |
| dc.subject | Machine learning | |
| dc.subject | Bayesian Classification | |
| dc.subject | Seismic attributes | |
| dc.subject | Seismic inversion | |
| dc.subject | AVO Attributes | |
| dc.subject | Quantitative Seismic Interpretation | |
| dc.subject | Data Integration | |
| dc.title | Integrated AVO Analysis, Seismic Inversion and Machine Learning for De-risking New Prospects in The Hutton Sandstone Formation, Onshore Australia | |
| dc.type.dcmi | Text | |
| dc.type.genre | Thesis | |
| local.embargo.lift | 2021-12-01 | |
| local.embargo.terms | 2021-12-01 | |
| thesis.degree.college | College of Natural Sciences and Mathematics | |
| thesis.degree.department | Earth and Atmospheric Sciences, Department of | |
| thesis.degree.discipline | Geophysics | |
| thesis.degree.grantor | University of Houston | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science |
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