Evaluating covariance-based geostatistical methods with bed-scale outcrop statistics conditioning for reproduction of intra-point bar facies architecture, Cretaceous Horseshoe Canyon Formation, Alberta, Canada
| dc.contributor.author | McCarthy, Andrew Louis, author | |
| dc.contributor.author | Stright, Lisa, advisor | |
| dc.contributor.author | Ronayne, Michael, committee member | |
| dc.contributor.author | Bailey, Ryan, committee member | |
| dc.date.accessioned | 2022-08-29T10:15:52Z | |
| dc.date.available | 2022-08-29T10:15:52Z | |
| dc.date.issued | 2022 | |
| dc.description | Zip file contains 2 packages of code including Power Point files describing the run procedures. | |
| dc.description.abstract | Geostatistical characterization of petroleum reservoirs typically suffers from problems of sparse data, and modelers often draw key parameters from analogous outcrop, numerical, and experimental studies to improve predictions. While quantitative information (bed-scale statistical distributions) from outcrop studies is available, translating the data from outcrop to models and generating geologically-realistic realizations with available geostatistical algorithms is often problematic. The overarching goal of this thesis is to test the capacity of covariance-based geostatistical methods to reproduce intra-point bar facies architecture while guiding those algorithms with bed-scale outcrop statistics from the Late Cretaceous Horseshoe Canyon Formation in southeastern Alberta. First, general facies architecture reproduction is tested with 2- and 3-facies synthetic and outcrop-based experiments with variable hard data, soft data weight, and soft data reliability. Next, 3-D sector models compare performance of different geostatistical simulation methods: sequential / co-sequential indicator, plurigaussian, and nested truncated gaussian. Findings show that despite integration of outcrop statistics, all conventional covariance-based geostatistical algorithms struggle to reproduce complex facies architecture that is observed in outcrop. Specifically, problems arise with: 1) low-proportion facies and 2) a weak statistical relationship between hard data (measured sections) and soft data (probability models). Nested modeling partially mitigates low-proportion issues and performs better as a result. | |
| dc.format.medium | born digital | |
| dc.format.medium | masters theses | |
| dc.format.medium | ZIP | |
| dc.format.medium | Python 2 | |
| dc.format.medium | PPTX | |
| dc.format.medium | CSV | |
| dc.identifier | McCarthy_colostate_0053N_17257.pdf | |
| dc.identifier.uri | https://hdl.handle.net/10217/235576 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2020- | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.subject | Horseshoe Canyon formation | |
| dc.subject | geostatistics | |
| dc.subject | reservoir modeling | |
| dc.title | Evaluating covariance-based geostatistical methods with bed-scale outcrop statistics conditioning for reproduction of intra-point bar facies architecture, Cretaceous Horseshoe Canyon Formation, Alberta, Canada | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Geosciences | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science (M.S.) |