Machine Learning Involvement in Reservoir Simulation by Optimizing Algorithms in SAGD and SA-SAGD Processes

atmire.migration.oldid5534
dc.contributor.advisorChen, Zhangxing (John)
dc.contributor.authorZhang, Yu
dc.contributor.committeememberWang, Yingxu
dc.contributor.committeememberRangelova, Elena Veselinova
dc.date.accessioned2017-05-01T15:12:04Z
dc.date.available2017-05-01T15:12:04Z
dc.date.issued2017
dc.date.submitted2017en
dc.description.abstractMachine learning, as a subset of Artificial Intelligence, has invaded many industries in recent years, thanks to the advancement of the computing power. Over the past decade, the use of machine learning, predictive analytics, and other artificial intelligence-based technologies in the oil and gas industry has grown immensely. Global optimization techniques are useful tools for process optimization and design in various petroleum-engineering disciplines. In this research, the genetic algorithm, one of the global optimization branches, acts as the optimizer for the Steam-Assisted Gravity Drainage (SAGD) process and for the Solvent-Assisted Steam-Assisted Gravity Drainage (SA-SAGD) process. Both binary and continuous encoding techniques that are embedded in a Computer Modeling Group (CMG) simulator STARS perform the primary role to optimize the simulation results. In the end, the comparison with a gradient-based optimization algorithm is studied. The genetic algorithm optimizer coupled with a reservoir simulator is employed to optimize the steam injection rates over the life of a steam-assisted gravity drainage process in a particular reservoir. A concise comparison between the genetic algorithm and a back propagation method discloses the advantages of the genetic algorithm. In the end, the simulation results for various scenarios illustrate the impacts brought by the optimization tool that is coded in programming language Python. The conclusion may be effective in the specific reservoir condition only, though it indicates a worth-trying approach to optimize the SAGD and SA-SAGD operations for various reservoirs.en_US
dc.identifier.citationZhang, Y. (2017). Machine Learning Involvement in Reservoir Simulation by Optimizing Algorithms in SAGD and SA-SAGD Processes (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/26806en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/26806
dc.identifier.urihttp://hdl.handle.net/11023/3757
dc.language.isoeng
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectEngineering--Petroleum
dc.titleMachine Learning Involvement in Reservoir Simulation by Optimizing Algorithms in SAGD and SA-SAGD Processes
dc.typemaster thesis
thesis.degree.disciplineChemical and Petroleum Engineering
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Engineering (MEng)
ucalgary.item.requestcopytrue
Files