A sequential decision and data analytics framework for maximizing value and reliability of CO2 storage monitoring.
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| Title: | A sequential decision and data analytics framework for maximizing value and reliability of CO2 storage monitoring. |
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| Authors: | Tadjer, Amine1 (AUTHOR) amine.tadjer@uis.no, Hong, Aojie1 (AUTHOR), Bratvold, Reidar B.1 (AUTHOR) |
| Source: | Journal of Natural Gas Science & Engineering. Dec2021, Vol. 96, pN.PAG-N.PAG. 1p. |
| Subjects: | Geological carbon sequestration, Contamination of drinking water, Water salinization, Carbon sequestration, Carbon emissions, Carbon dioxide, Dynamic programming |
| Geographic Terms: | Norway |
| Abstract: | Carbon capture and sequestration (carbon capture and storage or CCS) represents a unique potential strategy that can minimize CO2 emissions in the atmosphere, and it creates a pathway toward a neutral carbon balance, which cannot be solely achieved by combining energy efficiency and other forms of low carbon energy. To contribute to the decision-making process and ensure that CCS is successful and safe, an adequate monitoring program must be implemented to prevent storage reservoir leakage and contamination of drinking water in groundwater aquifers. In this paper, we propose an approach to perform value of information (VOI) analyses to address sequential decision problems in reservoir management in the context of monitoring the geological storage of CO2 operations. These sequential decision problems are often solved and modeled by approximate dynamic programming (ADP), which is a powerful technique for handling complex large-scale problems and finding a near-optimal solution for intractable sequential decision-making. In this study, we tested machine learning techniques that fall within ADP to estimate the VOI and determine the optimal time to stop CO2 injections into the reservoir based on information from seismic surveys. This ADP approach accounts for both the effect of the information obtained before a decision and the effect of the information that might be obtained to support future decisions while significantly improving the timing, value of the decision, and uncertainty of the CO2 plume behavior, thereby significantly increasing economic performance. The Utsira saline aquifer west of Norway was used to exemplify ADP's ability to improve decision support regarding CO2 storage projects. • The only way to create value is through good decision making. • A good decision does not always imply good outcomes unless if it is consistent with alternatives, uncertainty, and information. • We provide a framework to quantify the VOI of 4D seismic data in a CCS project. • Approximate dynamic programming is used to provide better reservoir management decisions. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Natural Gas Science & Engineering is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 153848336 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A sequential decision and data analytics framework for maximizing value and reliability of CO2 storage monitoring. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Tadjer%2C+Amine%22">Tadjer, Amine</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> amine.tadjer@uis.no</i><br /><searchLink fieldCode="AR" term="%22Hong%2C+Aojie%22">Hong, Aojie</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bratvold%2C+Reidar+B%2E%22">Bratvold, Reidar B.</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Natural+Gas+Science+%26+Engineering%22">Journal of Natural Gas Science & Engineering</searchLink>. Dec2021, Vol. 96, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Geological+carbon+sequestration%22">Geological carbon sequestration</searchLink><br /><searchLink fieldCode="DE" term="%22Contamination+of+drinking+water%22">Contamination of drinking water</searchLink><br /><searchLink fieldCode="DE" term="%22Water+salinization%22">Water salinization</searchLink><br /><searchLink fieldCode="DE" term="%22Carbon+sequestration%22">Carbon sequestration</searchLink><br /><searchLink fieldCode="DE" term="%22Carbon+emissions%22">Carbon emissions</searchLink><br /><searchLink fieldCode="DE" term="%22Carbon+dioxide%22">Carbon dioxide</searchLink><br /><searchLink fieldCode="DE" term="%22Dynamic+programming%22">Dynamic programming</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Norway%22">Norway</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Carbon capture and sequestration (carbon capture and storage or CCS) represents a unique potential strategy that can minimize CO2 emissions in the atmosphere, and it creates a pathway toward a neutral carbon balance, which cannot be solely achieved by combining energy efficiency and other forms of low carbon energy. To contribute to the decision-making process and ensure that CCS is successful and safe, an adequate monitoring program must be implemented to prevent storage reservoir leakage and contamination of drinking water in groundwater aquifers. In this paper, we propose an approach to perform value of information (VOI) analyses to address sequential decision problems in reservoir management in the context of monitoring the geological storage of CO2 operations. These sequential decision problems are often solved and modeled by approximate dynamic programming (ADP), which is a powerful technique for handling complex large-scale problems and finding a near-optimal solution for intractable sequential decision-making. In this study, we tested machine learning techniques that fall within ADP to estimate the VOI and determine the optimal time to stop CO2 injections into the reservoir based on information from seismic surveys. This ADP approach accounts for both the effect of the information obtained before a decision and the effect of the information that might be obtained to support future decisions while significantly improving the timing, value of the decision, and uncertainty of the CO2 plume behavior, thereby significantly increasing economic performance. The Utsira saline aquifer west of Norway was used to exemplify ADP's ability to improve decision support regarding CO2 storage projects. • The only way to create value is through good decision making. • A good decision does not always imply good outcomes unless if it is consistent with alternatives, uncertainty, and information. • We provide a framework to quantify the VOI of 4D seismic data in a CCS project. • Approximate dynamic programming is used to provide better reservoir management decisions. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Natural Gas Science & Engineering is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.jngse.2021.104298 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Geological carbon sequestration Type: general – SubjectFull: Contamination of drinking water Type: general – SubjectFull: Water salinization Type: general – SubjectFull: Carbon sequestration Type: general – SubjectFull: Carbon emissions Type: general – SubjectFull: Carbon dioxide Type: general – SubjectFull: Dynamic programming Type: general – SubjectFull: Norway Type: general Titles: – TitleFull: A sequential decision and data analytics framework for maximizing value and reliability of CO2 storage monitoring. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Tadjer, Amine – PersonEntity: Name: NameFull: Hong, Aojie – PersonEntity: Name: NameFull: Bratvold, Reidar B. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 18755100 Numbering: – Type: volume Value: 96 Titles: – TitleFull: Journal of Natural Gas Science & Engineering Type: main |
| ResultId | 1 |
