High-precision geosteering via reinforcement learning and particle filters.
Saved in:
| Title: | High-precision geosteering via reinforcement learning and particle filters. |
|---|---|
| Authors: | Muhammad, Ressi Bonti1 (AUTHOR) ressi.b.muhammad@uis.no, Srivastava, Apoorv2 (AUTHOR) apoorv1@stanford.edu, Alyaev, Sergey3 (AUTHOR), Bratvold, Reidar B.1 (AUTHOR), Tartakovsky, Daniel M.2 (AUTHOR) |
| Source: | Computational Geosciences. Apr2025, Vol. 29 Issue 2, p1-26. 26p. |
| Abstract: | Geosteering, a key component of drilling operations, traditionally involves manual interpretation of various data sources such as well-log data. This introduces subjective biases and inconsistent procedures. Academic attempts to solve geosteering decision optimization with greedy optimization and approximate dynamic programming (ADP) showed promise but lacked adaptivity to realistic diverse scenarios. Reinforcement learning (RL) offers a solution to these challenges, facilitating optimal decision-making through reward-based iterative learning. State estimation methods, e.g., particle filter (PF), provide a complementary strategy for geosteering decision-making based on online information. We introduce RL-Estimation, a method that integrates an RL-based geosteering framework with PF to address realistic geosteering scenarios. RL-Estimation deploys PF to process real-time well-log data to estimate the location of the well relative to the stratigraphic layers, which then informs the RL-based decision-making process. Our findings indicate that RL-Estimation achieves at least 20% higher reservoir contact compared to using RL or PF alone. Additionally, RL-Estimation performs within 2% of the theoretically optimal benchmark, which assumes access to the true state instead of relying on estimates from PF. These results demonstrate the synergy between RL and PF, highlighting the method’s effectiveness and near-optimal performance in realistic geosteering scenarios. [ABSTRACT FROM AUTHOR] |
| Copyright of Computational Geosciences is the property of Springer Nature 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 | Links: – Type: pdflink Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 184546218 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: High-precision geosteering via reinforcement learning and particle filters. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Muhammad%2C+Ressi+Bonti%22">Muhammad, Ressi Bonti</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ressi.b.muhammad@uis.no</i><br /><searchLink fieldCode="AR" term="%22Srivastava%2C+Apoorv%22">Srivastava, Apoorv</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> apoorv1@stanford.edu</i><br /><searchLink fieldCode="AR" term="%22Alyaev%2C+Sergey%22">Alyaev, Sergey</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bratvold%2C+Reidar+B%2E%22">Bratvold, Reidar B.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tartakovsky%2C+Daniel+M%2E%22">Tartakovsky, Daniel M.</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computational+Geosciences%22">Computational Geosciences</searchLink>. Apr2025, Vol. 29 Issue 2, p1-26. 26p. – Name: Abstract Label: Abstract Group: Ab Data: Geosteering, a key component of drilling operations, traditionally involves manual interpretation of various data sources such as well-log data. This introduces subjective biases and inconsistent procedures. Academic attempts to solve geosteering decision optimization with greedy optimization and approximate dynamic programming (ADP) showed promise but lacked adaptivity to realistic diverse scenarios. Reinforcement learning (RL) offers a solution to these challenges, facilitating optimal decision-making through reward-based iterative learning. State estimation methods, e.g., particle filter (PF), provide a complementary strategy for geosteering decision-making based on online information. We introduce RL-Estimation, a method that integrates an RL-based geosteering framework with PF to address realistic geosteering scenarios. RL-Estimation deploys PF to process real-time well-log data to estimate the location of the well relative to the stratigraphic layers, which then informs the RL-based decision-making process. Our findings indicate that RL-Estimation achieves at least 20% higher reservoir contact compared to using RL or PF alone. Additionally, RL-Estimation performs within 2% of the theoretically optimal benchmark, which assumes access to the true state instead of relying on estimates from PF. These results demonstrate the synergy between RL and PF, highlighting the method’s effectiveness and near-optimal performance in realistic geosteering scenarios. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computational Geosciences is the property of Springer Nature 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=184546218 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10596-025-10352-y Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 1 Titles: – TitleFull: High-precision geosteering via reinforcement learning and particle filters. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Muhammad, Ressi Bonti – PersonEntity: Name: NameFull: Srivastava, Apoorv – PersonEntity: Name: NameFull: Alyaev, Sergey – PersonEntity: Name: NameFull: Bratvold, Reidar B. – PersonEntity: Name: NameFull: Tartakovsky, Daniel M. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 14200597 Numbering: – Type: volume Value: 29 – Type: issue Value: 2 Titles: – TitleFull: Computational Geosciences Type: main |
| ResultId | 1 |
