Prediction of Waterflooding Performance with a New Machine Learning Method by Combining Linear Dynamical Systems with Neural Networks.
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| Title: | Prediction of Waterflooding Performance with a New Machine Learning Method by Combining Linear Dynamical Systems with Neural Networks. |
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| Authors: | Bai, Jingjin1 (AUTHOR), Cai, Jiujie2 (AUTHOR), Liu, Jiazheng3 (AUTHOR), Teng, Bailu1 (AUTHOR) bailu@cugb.edu.cn |
| Source: | Energies (19961073). Apr2026, Vol. 19 Issue 8, p1885. 18p. |
| Subject Terms: | *Linear dynamical systems, *Artificial neural networks, *Petroleum production rates, *Machine learning, *Forecasting, *Ensemble learning, *Oil field flooding, *Oil reservoir engineering |
| Abstract: | Machine learning methods have gained significant attention in forecasting waterflooding performance in recent years, but their accuracy often remains insufficient for practical field applications. This study proposes a hybrid framework that integrates a linear dynamical system (LDS) with a neural network (NN). The framework improves oil-rate prediction by decomposing the injection–production relationship into linear and nonlinear components. Specifically, the aggregate injection rate is approximately linearly related to total liquid production, which is effectively captured by the LDS model, based on reservoir material balance principles. In contrast, the oil fraction of the produced liquid, defined as the ratio of oil rate to liquid rate, is bounded between 0 and 1 and typically decreases over time. This nonlinear trend is accurately modeled using a neural network (NN). The parameters of the LDS–NN framework are learned from historical injection and production data via a supervised training process. Furthermore, key hyperparameters within the model can be adjusted to optimize the performance for different reservoir characteristics. The proposed hybrid method is evaluated using both simulated reservoir cases and real field data, and compared against the performance of LDS-only and NN-only models. The results demonstrate that the LDS–NN framework consistently provides more accurate oil-rate predictions than either standalone LDS or NN approaches, across both synthetic and real-world waterflooding scenarios. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 193438225 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Prediction of Waterflooding Performance with a New Machine Learning Method by Combining Linear Dynamical Systems with Neural Networks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bai%2C+Jingjin%22">Bai, Jingjin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cai%2C+Jiujie%22">Cai, Jiujie</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Jiazheng%22">Liu, Jiazheng</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Teng%2C+Bailu%22">Teng, Bailu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> bailu@cugb.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Apr2026, Vol. 19 Issue 8, p1885. 18p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Linear+dynamical+systems%22">Linear dynamical systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Petroleum+production+rates%22">Petroleum production rates</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Oil+field+flooding%22">Oil field flooding</searchLink><br />*<searchLink fieldCode="DE" term="%22Oil+reservoir+engineering%22">Oil reservoir engineering</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Machine learning methods have gained significant attention in forecasting waterflooding performance in recent years, but their accuracy often remains insufficient for practical field applications. This study proposes a hybrid framework that integrates a linear dynamical system (LDS) with a neural network (NN). The framework improves oil-rate prediction by decomposing the injection–production relationship into linear and nonlinear components. Specifically, the aggregate injection rate is approximately linearly related to total liquid production, which is effectively captured by the LDS model, based on reservoir material balance principles. In contrast, the oil fraction of the produced liquid, defined as the ratio of oil rate to liquid rate, is bounded between 0 and 1 and typically decreases over time. This nonlinear trend is accurately modeled using a neural network (NN). The parameters of the LDS–NN framework are learned from historical injection and production data via a supervised training process. Furthermore, key hyperparameters within the model can be adjusted to optimize the performance for different reservoir characteristics. The proposed hybrid method is evaluated using both simulated reservoir cases and real field data, and compared against the performance of LDS-only and NN-only models. The results demonstrate that the LDS–NN framework consistently provides more accurate oil-rate predictions than either standalone LDS or NN approaches, across both synthetic and real-world waterflooding scenarios. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=193438225 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en19081885 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 1885 Subjects: – SubjectFull: Linear dynamical systems Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Petroleum production rates Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Forecasting Type: general – SubjectFull: Ensemble learning Type: general – SubjectFull: Oil field flooding Type: general – SubjectFull: Oil reservoir engineering Type: general Titles: – TitleFull: Prediction of Waterflooding Performance with a New Machine Learning Method by Combining Linear Dynamical Systems with Neural Networks. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bai, Jingjin – PersonEntity: Name: NameFull: Cai, Jiujie – PersonEntity: Name: NameFull: Liu, Jiazheng – PersonEntity: Name: NameFull: Teng, Bailu IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 19 – Type: issue Value: 8 Titles: – TitleFull: Energies (19961073) Type: main |
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