{"id":{"repo_id":"must-thes","oai_identifier":"oai:scholarsmine.mst.edu:doctoral_dissertations-3755"},"canonical_url":"https://search.dev.ndltd.org/etd/must-thes/oai:scholarsmine.mst.edu:doctoral_dissertations-3755","repository":{"repo_id":"must-thes","name":"Missouri University of Science and Technology","base_url":"https://scholarsmine.mst.edu/do/oai/"},"display":{"title":"Semantic model and updated screening criteria for polymer flooding","abstract":"\"Enhanced oil recovery (EOR) processes produce oil that conventional methods leave behind, where interfacial forces, heavy oil viscosity, and reservoir heterogeneity make it difficult to produce. Many EOR methods are available but usually cannot be used at the same time for a candidate reservoir. Therefore, it is important to select the most appropriate EOR process from among the possible techniques. EOR screening criteria have been created using EOR datasets and serve as the first step to compare the suitability of each technique for a specific reservoir. Most of these datasets are based on field data collected from EOR surveys published in the <i>Oil & Gas Journal</i> and therefore are limited because they lack data from laboratory experiments and do not represent more recent research efforts. This project proposes a comprehensive study of data related to polymer flooding technology, from laboratory, pilot, and field applications. The project starts with intensive analysis of polymer flooding as a specified knowledge domain, extracting major concepts in the domain and data that can be generated from or by laboratory experiments, pilot and field applications. One of the goals of this project is to have easy access of polymer flooding techniques such a specialized domain to the public. Therefore, semantic modeling techniques are applied to construct semantic models based on the relations among the major concepts and measuring data. The models will also be published as one part of an ambition to build a semantic knowledge repository for EOR technologies. Then, laboratory data and pilot and field application data were collected and analyzed. Methodologies that can be applied to improve data quality have been studied and investigated; and screening criteria have been updated; and potential prediction methods based on the data we have are studied and investigated\"--Abstract, page iv.","abstract_html":"&quot;Enhanced oil recovery (EOR) processes produce oil that conventional methods leave behind, where interfacial forces, heavy oil viscosity, and reservoir heterogeneity make it difficult to produce. Many EOR methods are available but usually cannot be used at the same time for a candidate reservoir. Therefore, it is important to select the most appropriate EOR process from among the possible techniques. EOR screening criteria have been created using EOR datasets and serve as the first step to compare the suitability of each technique for a specific reservoir. Most of these datasets are based on field data collected from EOR surveys published in the &lt;i&gt;Oil &amp; Gas Journal&lt;/i&gt; and therefore are limited because they lack data from laboratory experiments and do not represent more recent research efforts. This project proposes a comprehensive study of data related to polymer flooding technology, from laboratory, pilot, and field applications. The project starts with intensive analysis of polymer flooding as a specified knowledge domain, extracting major concepts in the domain and data that can be generated from or by laboratory experiments, pilot and field applications. One of the goals of this project is to have easy access of polymer flooding techniques such a specialized domain to the public. Therefore, semantic modeling techniques are applied to construct semantic models based on the relations among the major concepts and measuring data. The models will also be published as one part of an ambition to build a semantic knowledge repository for EOR technologies. Then, laboratory data and pilot and field application data were collected and analyzed. Methodologies that can be applied to improve data quality have been studied and investigated; and screening criteria have been updated; and potential prediction methods based on the data we have are studied and investigated&quot;--Abstract, page iv.","abstract_has_math":false,"creators":["Saleh, Laila Dao"],"institution":"Missouri University of Science and Technology","degree_name":"Ph. D. in Petroleum Engineering","degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":null,"date_issued":"","date_published":null,"updated_at":"2026-07-24T03:18:26Z","subjects":["Criteria","EOR","Polymer","Prediction","Screening","Semantic","Petroleum Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarsmine.mst.edu/doctoral_dissertations/2750","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Saleh, Laila Dao"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:type","label":"Dc Type","values":["Dissertation - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph. 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Therefore, it is important to select the most appropriate EOR process from among the possible techniques. EOR screening criteria have been created using EOR datasets and serve as the first step to compare the suitability of each technique for a specific reservoir. Most of these datasets are based on field data collected from EOR surveys published in the <i>Oil & Gas Journal</i> and therefore are limited because they lack data from laboratory experiments and do not represent more recent research efforts. This project proposes a comprehensive study of data related to polymer flooding technology, from laboratory, pilot, and field applications. The project starts with intensive analysis of polymer flooding as a specified knowledge domain, extracting major concepts in the domain and data that can be generated from or by laboratory experiments, pilot and field applications. One of the goals of this project is to have easy access of polymer flooding techniques such a specialized domain to the public. Therefore, semantic modeling techniques are applied to construct semantic models based on the relations among the major concepts and measuring data. The models will also be published as one part of an ambition to build a semantic knowledge repository for EOR technologies. Then, laboratory data and pilot and field application data were collected and analyzed. Methodologies that can be applied to improve data quality have been studied and investigated; and screening criteria have been updated; and potential prediction methods based on the data we have are studied and investigated\"--Abstract, page iv."]},{"key":"dc:title","label":"Title","values":["Semantic model and updated screening criteria for polymer flooding"]}]}],"canonical_facts":{"dc:creator":["Saleh, Laila Dao"],"dc:description.abstract":["\"Enhanced oil recovery (EOR) processes produce oil that conventional methods leave behind, where interfacial forces, heavy oil viscosity, and reservoir heterogeneity make it difficult to produce. Many EOR methods are available but usually cannot be used at the same time for a candidate reservoir. Therefore, it is important to select the most appropriate EOR process from among the possible techniques. EOR screening criteria have been created using EOR datasets and serve as the first step to compare the suitability of each technique for a specific reservoir. Most of these datasets are based on field data collected from EOR surveys published in the <i>Oil & Gas Journal</i> and therefore are limited because they lack data from laboratory experiments and do not represent more recent research efforts. This project proposes a comprehensive study of data related to polymer flooding technology, from laboratory, pilot, and field applications. The project starts with intensive analysis of polymer flooding as a specified knowledge domain, extracting major concepts in the domain and data that can be generated from or by laboratory experiments, pilot and field applications. One of the goals of this project is to have easy access of polymer flooding techniques such a specialized domain to the public. Therefore, semantic modeling techniques are applied to construct semantic models based on the relations among the major concepts and measuring data. The models will also be published as one part of an ambition to build a semantic knowledge repository for EOR technologies. Then, laboratory data and pilot and field application data were collected and analyzed. Methodologies that can be applied to improve data quality have been studied and investigated; and screening criteria have been updated; and potential prediction methods based on the data we have are studied and investigated\"--Abstract, page iv."],"dc:identifier":["https://scholarsmine.mst.edu/doctoral_dissertations/2750"],"dc:subject":["Criteria","EOR","Polymer","Prediction","Screening","Semantic","Petroleum Engineering"],"dc:title":["Semantic model and updated screening criteria for polymer flooding"],"dc:type":["Dissertation - Open Access"],"thesis:degree_name":["Ph. 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