{"id":{"repo_id":"regina","oai_identifier":"oai:uregina.scholaris.ca:10294/16431"},"canonical_url":"https://search.dev.ndltd.org/etd/regina/oai:uregina.scholaris.ca:10294/16431","repository":{"repo_id":"regina","name":"University of Regina","base_url":"https://uregina.scholaris.ca/server/oai/request"},"display":{"title":"Integrated optimization of hybrid steam-solvent injection in post-CHOPS reservoirs with wormhole networks and foamy oil behaviour under uncertainty","abstract":"As for the enormous heavy oil resources deposited in Western Canada, cold heavy oil production with sand (CHOPS) has achieved a low recovery about 5-15% of the originaloil- in-place (OOIP), though both its in-situ generated open channels (i.e., wormholes) and induced foamy oil lead to a higher oil recovery than expected. Hybrid steam-solvent injection has been considered as the most effective recovery method for heavy oil reservoirs; however, the existence of wormhole networks makes it technically and operationally challenging and foamy oil may be artificially induced. Physically, geological uncertainty needs to be considered appropriately with respect to wormhole networks and foamy oil flow since model-based optimization methods find their limits. In this study, integrated techniques have been developed to optimize performance of the hybrid steam-solvent injection processes in a depleted post-CHOPS reservoir with consideration of wormhole networks and foamy oil flow. After a reservoir geological model has been built and calibrated with the measured production profiles, its wormhole network is inversely determined with the newly developed pressure-gradient-based (PGB) sand failure criterion. Such a calibrated reservoir geological model is then used to optimize the net present value (NPV) of a hybrid steam-solvent injection process by selecting injection time, soaking time, production time, injection rate, steam temperature, and steam quality as the controlling variables. A hybrid optimization technique based on the genetic algorithm (GA) is then integrated with the orthogonal array (OA) and Tabu search (TS) to maximize the objective function by rationalizing the injection and production parameters and simultaneously retarding the displacement front to extend the reservoir life under uncertainty. In a given CHOPS well, not only can the proposed method be used to determine the overall morphology of the wormhole network, including intensity and coverage, but also design, evaluate, and optimize performance of any potential enhanced oil recovery (EOR) processes in a post-CHOPS reservoir within a unified, efficient, and accurate framework. In the optimized scenarios, the NPV of the optimized scenarios are significantly higher than that of the unoptimized scenario (i.e., the Reference case). In particular, the NPV of Scenario #3-2 (i.e., the hybrid GA-OA-TS optimization algorithm) is significantly higher than that of the Reference case and is 56.01% higher than that of Scenario #3-1 (i.e., only the GA optimization algorithm). The hybrid optimization algorithm can well balance the relationship between recovery factor and operational costs to maximize the NPV within a unified, consistent, and efficient framework. Then, such a hybrid algorithm has been extended for a CHOPS reservoir containing either dendritic (i.e., fractal) or regional wormhole under uncertainty with relatively accurate and consistent results. The pressure distribution of the dendritic wormhole network is more uniform, while the pressure drop range of the regional wormhole network is mainly concentrated in a high permeability area around the wellbore. The pressure distribution of the dendritic wormhole model is more realistic; however, its computational costs are much higher due to the need for refining local grids.","abstract_html":"As for the enormous heavy oil resources deposited in Western Canada, cold heavy oil production with sand (CHOPS) has achieved a low recovery about 5-15% of the originaloil- in-place (OOIP), though both its in-situ generated open channels (i.e., wormholes) and induced foamy oil lead to a higher oil recovery than expected. Hybrid steam-solvent injection has been considered as the most effective recovery method for heavy oil reservoirs; however, the existence of wormhole networks makes it technically and operationally challenging and foamy oil may be artificially induced. Physically, geological uncertainty needs to be considered appropriately with respect to wormhole networks and foamy oil flow since model-based optimization methods find their limits. In this study, integrated techniques have been developed to optimize performance of the hybrid steam-solvent injection processes in a depleted post-CHOPS reservoir with consideration of wormhole networks and foamy oil flow. After a reservoir geological model has been built and calibrated with the measured production profiles, its wormhole network is inversely determined with the newly developed pressure-gradient-based (PGB) sand failure criterion. Such a calibrated reservoir geological model is then used to optimize the net present value (NPV) of a hybrid steam-solvent injection process by selecting injection time, soaking time, production time, injection rate, steam temperature, and steam quality as the controlling variables. A hybrid optimization technique based on the genetic algorithm (GA) is then integrated with the orthogonal array (OA) and Tabu search (TS) to maximize the objective function by rationalizing the injection and production parameters and simultaneously retarding the displacement front to extend the reservoir life under uncertainty. In a given CHOPS well, not only can the proposed method be used to determine the overall morphology of the wormhole network, including intensity and coverage, but also design, evaluate, and optimize performance of any potential enhanced oil recovery (EOR) processes in a post-CHOPS reservoir within a unified, efficient, and accurate framework. In the optimized scenarios, the NPV of the optimized scenarios are significantly higher than that of the unoptimized scenario (i.e., the Reference case). In particular, the NPV of Scenario #3-2 (i.e., the hybrid GA-OA-TS optimization algorithm) is significantly higher than that of the Reference case and is 56.01% higher than that of Scenario #3-1 (i.e., only the GA optimization algorithm). The hybrid optimization algorithm can well balance the relationship between recovery factor and operational costs to maximize the NPV within a unified, consistent, and efficient framework. Then, such a hybrid algorithm has been extended for a CHOPS reservoir containing either dendritic (i.e., fractal) or regional wormhole under uncertainty with relatively accurate and consistent results. The pressure distribution of the dendritic wormhole network is more uniform, while the pressure drop range of the regional wormhole network is mainly concentrated in a high permeability area around the wellbore. The pressure distribution of the dendritic wormhole model is more realistic; however, its computational costs are much higher due to the need for refining local grids.","abstract_has_math":false,"creators":["Hou, Senhan"],"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":["Yang, Daoyong (Tony)"],"committee_chairs":[],"committee_members":["Shirif, Ezeddin"],"year":2023,"date_issued":"2023-12","date_published":"2023-12","updated_at":"2026-07-24T04:03:39Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.82465/4542"],"render_values":[{"text":"https://doi.org/10.82465/4542","href":"https://doi.org/10.82465/4542","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10294/16431","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Yang, Daoyong (Tony)"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Shirif, Ezeddin"]},{"key":"dc:creator","label":"Author","values":["Hou, Senhan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2024-10-11T17:38:52Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-10-11T17:38:52Z"]},{"key":"dc:date.issued","label":"Date","values":["2023-12"]},{"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/10.82465/4542"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10294/16431"]}]},{"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 of Applied Science in Petroleum Systems Engineering, University of Regina. xiii, 134 p."]},{"key":"dc:description.abstract","label":"Abstract","values":["As for the enormous heavy oil resources deposited in Western Canada, cold heavy oil production with sand (CHOPS) has achieved a low recovery about 5-15% of the originaloil- in-place (OOIP), though both its in-situ generated open channels (i.e., wormholes) and induced foamy oil lead to a higher oil recovery than expected. Hybrid steam-solvent injection has been considered as the most effective recovery method for heavy oil reservoirs; however, the existence of wormhole networks makes it technically and operationally challenging and foamy oil may be artificially induced. Physically, geological uncertainty needs to be considered appropriately with respect to wormhole networks and foamy oil flow since model-based optimization methods find their limits. In this study, integrated techniques have been developed to optimize performance of the hybrid steam-solvent injection processes in a depleted post-CHOPS reservoir with consideration of wormhole networks and foamy oil flow. After a reservoir geological model has been built and calibrated with the measured production profiles, its wormhole network is inversely determined with the newly developed pressure-gradient-based (PGB) sand failure criterion. Such a calibrated reservoir geological model is then used to optimize the net present value (NPV) of a hybrid steam-solvent injection process by selecting injection time, soaking time, production time, injection rate, steam temperature, and steam quality as the controlling variables. A hybrid optimization technique based on the genetic algorithm (GA) is then integrated with the orthogonal array (OA) and Tabu search (TS) to maximize the objective function by rationalizing the injection and production parameters and simultaneously retarding the displacement front to extend the reservoir life under uncertainty. In a given CHOPS well, not only can the proposed method be used to determine the overall morphology of the wormhole network, including intensity and coverage, but also design, evaluate, and optimize performance of any potential enhanced oil recovery (EOR) processes in a post-CHOPS reservoir within a unified, efficient, and accurate framework. In the optimized scenarios, the NPV of the optimized scenarios are significantly higher than that of the unoptimized scenario (i.e., the Reference case). In particular, the NPV of Scenario #3-2 (i.e., the hybrid GA-OA-TS optimization algorithm) is significantly higher than that of the Reference case and is 56.01% higher than that of Scenario #3-1 (i.e., only the GA optimization algorithm). The hybrid optimization algorithm can well balance the relationship between recovery factor and operational costs to maximize the NPV within a unified, consistent, and efficient framework. Then, such a hybrid algorithm has been extended for a CHOPS reservoir containing either dendritic (i.e., fractal) or regional wormhole under uncertainty with relatively accurate and consistent results. The pressure distribution of the dendritic wormhole network is more uniform, while the pressure drop range of the regional wormhole network is mainly concentrated in a high permeability area around the wellbore. The pressure distribution of the dendritic wormhole model is more realistic; however, its computational costs are much higher due to the need for refining local grids."]},{"key":"dc:title","label":"Title","values":["Integrated optimization of hybrid steam-solvent injection in post-CHOPS reservoirs with wormhole networks and foamy oil behaviour under uncertainty"]}]}],"canonical_facts":{"dc:contributor.advisor":["Yang, Daoyong (Tony)"],"dc:contributor.committeemember":["Shirif, Ezeddin"],"dc:creator":["Hou, Senhan"],"dc:date.accessioned":["2024-10-11T17:38:52Z"],"dc:date.available":["2024-10-11T17:38:52Z"],"dc:date.issued":["2023-12"],"dc:description":["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. xiii, 134 p."],"dc:description.abstract":["As for the enormous heavy oil resources deposited in Western Canada, cold heavy oil production with sand (CHOPS) has achieved a low recovery about 5-15% of the originaloil- in-place (OOIP), though both its in-situ generated open channels (i.e., wormholes) and induced foamy oil lead to a higher oil recovery than expected. Hybrid steam-solvent injection has been considered as the most effective recovery method for heavy oil reservoirs; however, the existence of wormhole networks makes it technically and operationally challenging and foamy oil may be artificially induced. Physically, geological uncertainty needs to be considered appropriately with respect to wormhole networks and foamy oil flow since model-based optimization methods find their limits. In this study, integrated techniques have been developed to optimize performance of the hybrid steam-solvent injection processes in a depleted post-CHOPS reservoir with consideration of wormhole networks and foamy oil flow. After a reservoir geological model has been built and calibrated with the measured production profiles, its wormhole network is inversely determined with the newly developed pressure-gradient-based (PGB) sand failure criterion. Such a calibrated reservoir geological model is then used to optimize the net present value (NPV) of a hybrid steam-solvent injection process by selecting injection time, soaking time, production time, injection rate, steam temperature, and steam quality as the controlling variables. A hybrid optimization technique based on the genetic algorithm (GA) is then integrated with the orthogonal array (OA) and Tabu search (TS) to maximize the objective function by rationalizing the injection and production parameters and simultaneously retarding the displacement front to extend the reservoir life under uncertainty. In a given CHOPS well, not only can the proposed method be used to determine the overall morphology of the wormhole network, including intensity and coverage, but also design, evaluate, and optimize performance of any potential enhanced oil recovery (EOR) processes in a post-CHOPS reservoir within a unified, efficient, and accurate framework. In the optimized scenarios, the NPV of the optimized scenarios are significantly higher than that of the unoptimized scenario (i.e., the Reference case). In particular, the NPV of Scenario #3-2 (i.e., the hybrid GA-OA-TS optimization algorithm) is significantly higher than that of the Reference case and is 56.01% higher than that of Scenario #3-1 (i.e., only the GA optimization algorithm). The hybrid optimization algorithm can well balance the relationship between recovery factor and operational costs to maximize the NPV within a unified, consistent, and efficient framework. Then, such a hybrid algorithm has been extended for a CHOPS reservoir containing either dendritic (i.e., fractal) or regional wormhole under uncertainty with relatively accurate and consistent results. The pressure distribution of the dendritic wormhole network is more uniform, while the pressure drop range of the regional wormhole network is mainly concentrated in a high permeability area around the wellbore. The pressure distribution of the dendritic wormhole model is more realistic; however, its computational costs are much higher due to the need for refining local grids."],"dc:identifier.doi":["https://doi.org/10.82465/4542"],"dc:identifier.uri":["https://hdl.handle.net/10294/16431"],"dc:language.iso":["en"],"dc:publisher":["Faculty of Graduate Studies and Research, University of Regina"],"dc:title":["Integrated optimization of hybrid steam-solvent injection in post-CHOPS reservoirs with wormhole networks and foamy oil behaviour under uncertainty"],"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":"2026-07-24T04:03:39Z"}