{"id":{"repo_id":"regina","oai_identifier":"oai:uregina.scholaris.ca:10294/6822"},"canonical_url":"https://search.dev.ndltd.org/etd/regina/oai:uregina.scholaris.ca:10294/6822","repository":{"repo_id":"regina","name":"University of Regina","base_url":"https://uregina.scholaris.ca/server/oai/request"},"display":{"title":"Permeability Distribution Estimation Based on Semi-Analytical Reservoir Simulator","abstract":"Estimation of rock permeability distribution can be realized by automatic history matching production data. The process of automatic history matching involves the minimization of an objective function which usually includes the sum of data mismatch part as well as the sum of prior model mismatch part. For our semi-analytical simulator, the choice of optimization algorithms to minimize the objective function becomes of vital importance. For our simulator, we considered both gradient based methods and non-gradient based methods. Gradient based algorithms have the big advantage of much faster convergence rate over non-gradient based algorithms. It is illustrated in this study that application of gradient based methods is not practical for this simulator. Among the non-gradient based methods, we choose particle swarm optimization. For this algorithm, reservoir simulator can be treated as a black box. It is easy to implement and be transferred to another simulator within just a few hours. However, for gradient based algorithms, a set of completely different gradient equations are required, whereas greatest advantage of particle swarm optimization is its fast convergence rate at the beginning stage. Three examples are presented to show application of particle swarm optimization in automatic history matching based on synthetic production data. The first example simulates a high permeability channel existing in a relatively lower permeability formation. The second example simulates a formation with gradual changing permeability distribution. The third example represents a formation with sweet spot. The permeability of central part is higher compared to neighbouring area. Estimated permeability distributions have shown that the general permeability trends of these three examples can be caught. At the same time, predictions using estimated permeability field have a very good match with synthetic production history. As such, particle swarm optimization has been introduced and successfully applied to do automatic history matching in our semi-analytical simulator. Uncertainties associated with permeability distribution estimations are reduced significantly.","abstract_html":"Estimation of rock permeability distribution can be realized by automatic history matching production data. The process of automatic history matching involves the minimization of an objective function which usually includes the sum of data mismatch part as well as the sum of prior model mismatch part. For our semi-analytical simulator, the choice of optimization algorithms to minimize the objective function becomes of vital importance. For our simulator, we considered both gradient based methods and non-gradient based methods. Gradient based algorithms have the big advantage of much faster convergence rate over non-gradient based algorithms. It is illustrated in this study that application of gradient based methods is not practical for this simulator. Among the non-gradient based methods, we choose particle swarm optimization. For this algorithm, reservoir simulator can be treated as a black box. It is easy to implement and be transferred to another simulator within just a few hours. However, for gradient based algorithms, a set of completely different gradient equations are required, whereas greatest advantage of particle swarm optimization is its fast convergence rate at the beginning stage. Three examples are presented to show application of particle swarm optimization in automatic history matching based on synthetic production data. The first example simulates a high permeability channel existing in a relatively lower permeability formation. The second example simulates a formation with gradual changing permeability distribution. The third example represents a formation with sweet spot. The permeability of central part is higher compared to neighbouring area. Estimated permeability distributions have shown that the general permeability trends of these three examples can be caught. At the same time, predictions using estimated permeability field have a very good match with synthetic production history. As such, particle swarm optimization has been introduced and successfully applied to do automatic history matching in our semi-analytical simulator. Uncertainties associated with permeability distribution estimations are reduced significantly.","abstract_has_math":false,"creators":["Chen, Shuai"],"institution":"Faculty of Graduate Studies and Research, University of Regina","degree_name":"Master of Applied Science (MASc)","degree_level":"Master's","degree_discipline":"Engineering - Petroleum Systems","degree_department":null,"school":null,"contributors":[],"advisors":["Zhao, Gang"],"committee_chairs":[],"committee_members":["Jin, Yee-Chung","Zeng, Fanhua (Bill)"],"year":2015,"date_issued":"2015-10","date_published":"2015-10","updated_at":"2026-07-24T04:03:29Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.82465/3961"],"render_values":[{"text":"https://doi.org/10.82465/3961","href":"https://doi.org/10.82465/3961","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10294/6822","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Zhao, Gang"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Jin, Yee-Chung","Zeng, Fanhua (Bill)"]},{"key":"dc:creator","label":"Author","values":["Chen, Shuai"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2016-07-27T17:43:00Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2016-07-27T17:43:00Z"]},{"key":"dc:date.issued","label":"Date","values":["2015-10"]},{"key":"dc:publisher","label":"Institution","values":["Faculty of Graduate Studies and Research, University of Regina"]},{"key":"dc:type","label":"Dc Type","values":["master thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Engineering - Petroleum Systems"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Master's"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Applied Science (MASc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Faculty of Graduate Studies and Research, University of Regina"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.82465/3961"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10294/6822"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master's of *, University of Regina. *, * p.","A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Applied Science in Petroleum Systems Engineering, University of Regina. x, 102 p."]},{"key":"dc:description.abstract","label":"Abstract","values":["Estimation of rock permeability distribution can be realized by automatic history matching production data. The process of automatic history matching involves the minimization of an objective function which usually includes the sum of data mismatch part as well as the sum of prior model mismatch part. For our semi-analytical simulator, the choice of optimization algorithms to minimize the objective function becomes of vital importance. For our simulator, we considered both gradient based methods and non-gradient based methods. Gradient based algorithms have the big advantage of much faster convergence rate over non-gradient based algorithms. It is illustrated in this study that application of gradient based methods is not practical for this simulator. Among the non-gradient based methods, we choose particle swarm optimization. For this algorithm, reservoir simulator can be treated as a black box. It is easy to implement and be transferred to another simulator within just a few hours. However, for gradient based algorithms, a set of completely different gradient equations are required, whereas greatest advantage of particle swarm optimization is its fast convergence rate at the beginning stage. Three examples are presented to show application of particle swarm optimization in automatic history matching based on synthetic production data. The first example simulates a high permeability channel existing in a relatively lower permeability formation. The second example simulates a formation with gradual changing permeability distribution. The third example represents a formation with sweet spot. The permeability of central part is higher compared to neighbouring area. Estimated permeability distributions have shown that the general permeability trends of these three examples can be caught. At the same time, predictions using estimated permeability field have a very good match with synthetic production history. As such, particle swarm optimization has been introduced and successfully applied to do automatic history matching in our semi-analytical simulator. Uncertainties associated with permeability distribution estimations are reduced significantly."]},{"key":"dc:title","label":"Title","values":["Permeability Distribution Estimation Based on Semi-Analytical Reservoir Simulator"]}]}],"canonical_facts":{"dc:contributor.advisor":["Zhao, Gang"],"dc:contributor.committeemember":["Jin, Yee-Chung","Zeng, Fanhua (Bill)"],"dc:creator":["Chen, Shuai"],"dc:date.accessioned":["2016-07-27T17:43:00Z"],"dc:date.available":["2016-07-27T17:43:00Z"],"dc:date.issued":["2015-10"],"dc:description":["A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master's of *, University of Regina. *, * p.","A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Applied Science in Petroleum Systems Engineering, University of Regina. x, 102 p."],"dc:description.abstract":["Estimation of rock permeability distribution can be realized by automatic history matching production data. The process of automatic history matching involves the minimization of an objective function which usually includes the sum of data mismatch part as well as the sum of prior model mismatch part. For our semi-analytical simulator, the choice of optimization algorithms to minimize the objective function becomes of vital importance. For our simulator, we considered both gradient based methods and non-gradient based methods. Gradient based algorithms have the big advantage of much faster convergence rate over non-gradient based algorithms. It is illustrated in this study that application of gradient based methods is not practical for this simulator. Among the non-gradient based methods, we choose particle swarm optimization. For this algorithm, reservoir simulator can be treated as a black box. It is easy to implement and be transferred to another simulator within just a few hours. However, for gradient based algorithms, a set of completely different gradient equations are required, whereas greatest advantage of particle swarm optimization is its fast convergence rate at the beginning stage. Three examples are presented to show application of particle swarm optimization in automatic history matching based on synthetic production data. The first example simulates a high permeability channel existing in a relatively lower permeability formation. The second example simulates a formation with gradual changing permeability distribution. The third example represents a formation with sweet spot. The permeability of central part is higher compared to neighbouring area. Estimated permeability distributions have shown that the general permeability trends of these three examples can be caught. At the same time, predictions using estimated permeability field have a very good match with synthetic production history. As such, particle swarm optimization has been introduced and successfully applied to do automatic history matching in our semi-analytical simulator. Uncertainties associated with permeability distribution estimations are reduced significantly."],"dc:identifier.doi":["https://doi.org/10.82465/3961"],"dc:identifier.uri":["https://hdl.handle.net/10294/6822"],"dc:language.iso":["en"],"dc:publisher":["Faculty of Graduate Studies and Research, University of Regina"],"dc:title":["Permeability Distribution Estimation Based on Semi-Analytical Reservoir Simulator"],"dc:type":["master thesis"],"thesis:degree_discipline":["Engineering - Petroleum Systems"],"thesis:degree_level":["Master's"],"thesis:degree_name":["Master of Applied Science (MASc)"],"thesis:institution_name":["Faculty of Graduate Studies and Research, University of Regina"]},"updated_at":"2026-07-24T04:03:29Z"}