Optimization of Oil Well Production Prediction Model Based on Inter-Attention and BiLSTM
Accurate prediction of future oil production is critical for decision-making in oil well operations. However, existing prediction models often lack precision due to the vast and complex nature of oil well data. This study proposes an oil well production prediction model based on the Inter-Attention...
Uloženo v:
| Vydáno v: | Electronics (Basel) Ročník 14; číslo 5; s. 1004 |
|---|---|
| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Basel
MDPI AG
01.03.2025
|
| Témata: | |
| ISSN: | 2079-9292, 2079-9292 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Accurate prediction of future oil production is critical for decision-making in oil well operations. However, existing prediction models often lack precision due to the vast and complex nature of oil well data. This study proposes an oil well production prediction model based on the Inter-Attention Mechanism (IAM) and Bidirectional Long Short-Term Memory Network (BiLSTM), optimized using a Comprehensive Search Algorithm (CSA). By incorporating the Inter-Attention Mechanism, the model enhances its capacity to model complex time-series data. The CSA, combined with Sequential Quadratic Programming (SQP) and Monotone Basin Hopping (MBH) algorithms, ensures both global and local parameter optimization. Using historical data from an oil well in Sichuan, the feasibility of the proposed model was validated, demonstrating superior accuracy and robustness compared to other prediction models and optimization algorithms. |
|---|---|
| AbstractList | Accurate prediction of future oil production is critical for decision-making in oil well operations. However, existing prediction models often lack precision due to the vast and complex nature of oil well data. This study proposes an oil well production prediction model based on the Inter-Attention Mechanism (IAM) and Bidirectional Long Short-Term Memory Network (BiLSTM), optimized using a Comprehensive Search Algorithm (CSA). By incorporating the Inter-Attention Mechanism, the model enhances its capacity to model complex time-series data. The CSA, combined with Sequential Quadratic Programming (SQP) and Monotone Basin Hopping (MBH) algorithms, ensures both global and local parameter optimization. Using historical data from an oil well in Sichuan, the feasibility of the proposed model was validated, demonstrating superior accuracy and robustness compared to other prediction models and optimization algorithms. |
| Audience | Academic |
| Author | Meng, Xin Duan, Hancong Liu, Xingyu Wang, Min Hu, Ze |
| Author_xml | – sequence: 1 givenname: Xin orcidid: 0009-0000-0623-1350 surname: Meng fullname: Meng, Xin – sequence: 2 givenname: Xingyu surname: Liu fullname: Liu, Xingyu – sequence: 3 givenname: Hancong surname: Duan fullname: Duan, Hancong – sequence: 4 givenname: Ze surname: Hu fullname: Hu, Ze – sequence: 5 givenname: Min surname: Wang fullname: Wang, Min |
| BookMark | eNptUEtPAjEQbgwmIvILvGziebEvdtsjEB8kEEjE6G1T-jAlS4ttOeivt7AePDhzmJlvvnnkuwY9550G4BbBESEc3utWyxS8szIiCscIQnoB-hjWvOSY496f_AoMY9zBbBwRRmAfvK8Oye7tt0jWu8KbYmXb4k23bbEOXh3lGV4HrWyXLr3SbTEVUasil3OXdCgnKWl3bguniqldvGyWN-DSiDbq4W8cgNfHh83suVysnuazyaKUBKFUktPDBtNKcoQrBhXZCqmrsTLGYFJngGHGZG0g02NUSyjUlo65IgpXgnJMBuCu23sI_vOoY2p2_hhcPtkQVFekrhnlmTXqWB-i1Y11xqcgZHal91ZmPY3N-IQRxBHllOQB0g3I4GMM2jSHYPcifDUINifZm39kJz8g43kS |
| Cites_doi | 10.1007/s10589-007-9127-8 10.1109/LSP.2021.3079850 10.1016/j.neucom.2021.03.091 10.3390/ma17143521 10.1016/j.engappai.2004.11.010 10.1109/TNNLS.2020.2979670 10.1007/s10994-023-06467-x 10.1371/journal.pone.0288044 10.1109/ICSESS47205.2019.9040779 10.1016/S1876-3804(21)60001-0 10.2118/176750-MS 10.1007/s00521-020-04849-z 10.3233/IDA-2004-8206 10.1007/s00500-016-2474-6 10.2118/229-G 10.1007/s11600-024-01388-2 10.1016/j.atech.2023.100230 10.1016/j.neunet.2021.08.030 10.1016/j.physd.2019.132306 10.3390/electronics10101163 10.1109/ACCESS.2023.3349216 10.1016/j.eswa.2022.117670 10.3390/min14070686 10.1002/anie.202218565 10.1016/j.energy.2021.121503 10.3390/electronics11121906 10.3389/feart.2022.1106622 10.1016/j.petrol.2020.107013 10.1007/s10898-024-01373-5 10.3390/s22145326 10.1109/ACCESS.2024.3468470 |
| ContentType | Journal Article |
| Copyright | COPYRIGHT 2025 MDPI AG 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: COPYRIGHT 2025 MDPI AG – notice: 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | AAYXX CITATION 7SP 8FD 8FE 8FG ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU DWQXO HCIFZ L7M P5Z P62 PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS |
| DOI | 10.3390/electronics14051004 |
| DatabaseName | CrossRef Electronics & Communications Abstracts Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central Technology Collection ProQuest One Community College ProQuest Central ProQuest SciTech Premium Collection Advanced Technologies Database with Aerospace Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China |
| DatabaseTitle | CrossRef Publicly Available Content Database Advanced Technologies & Aerospace Collection Technology Collection Technology Research Database ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest One Academic Eastern Edition Electronics & Communications Abstracts ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central China ProQuest Central Advanced Technologies & Aerospace Database ProQuest One Applied & Life Sciences ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest Central (New) ProQuest One Academic Advanced Technologies Database with Aerospace ProQuest One Academic (New) |
| DatabaseTitleList | CrossRef Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 2079-9292 |
| ExternalDocumentID | A831914943 10_3390_electronics14051004 |
| GeographicLocations | China |
| GeographicLocations_xml | – name: China |
| GroupedDBID | 5VS 8FE 8FG AAYXX ADMLS AFFHD AFKRA ALMA_UNASSIGNED_HOLDINGS ARAPS BENPR BGLVJ CCPQU CITATION HCIFZ IAO ITC KQ8 MODMG M~E OK1 P62 PHGZM PHGZT PIMPY PQGLB PROAC 7SP 8FD ABUWG AZQEC DWQXO L7M PKEHL PQEST PQQKQ PQUKI PRINS |
| ID | FETCH-LOGICAL-c311t-30510f246c912680d3bace65dfff23780d8288c7f08e517c0adb459d3d26a4923 |
| IEDL.DBID | PIMPY |
| ISICitedReferencesCount | 1 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001444142800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2079-9292 |
| IngestDate | Fri Jul 25 21:26:44 EDT 2025 Tue Nov 04 18:14:53 EST 2025 Sat Nov 29 07:14:06 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 5 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c311t-30510f246c912680d3bace65dfff23780d8288c7f08e517c0adb459d3d26a4923 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0009-0000-0623-1350 |
| OpenAccessLink | https://www.proquest.com/publiccontent/docview/3176377849?pq-origsite=%requestingapplication% |
| PQID | 3176377849 |
| PQPubID | 2032404 |
| ParticipantIDs | proquest_journals_3176377849 gale_infotracacademiconefile_A831914943 crossref_primary_10_3390_electronics14051004 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-03-01 |
| PublicationDateYYYYMMDD | 2025-03-01 |
| PublicationDate_xml | – month: 03 year: 2025 text: 2025-03-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Basel |
| PublicationPlace_xml | – name: Basel |
| PublicationTitle | Electronics (Basel) |
| PublicationYear | 2025 |
| Publisher | MDPI AG |
| Publisher_xml | – name: MDPI AG |
| References | Otter (ref_18) 2020; 32 Sabah (ref_24) 2024; 12 Niu (ref_27) 2021; 452 Azevedo (ref_13) 2024; 113 ref_14 ref_35 Sherstinsky (ref_22) 2020; 404 ref_10 Yadav (ref_34) 2023; 4 Nguyen (ref_12) 2004; 8 ref_19 ref_16 ref_15 Baioletti (ref_30) 2024; 89 Kuang (ref_3) 2021; 48 Landi (ref_25) 2021; 144 Luo (ref_21) 2021; 28 Grosso (ref_31) 2009; 43 Jalilinasrabady (ref_6) 2021; 236 Rana (ref_33) 2020; 32 Lawal (ref_2) 2024; 12 Panja (ref_17) 2022; 205 ref_23 Wang (ref_32) 2018; 22 Timmerman (ref_8) 1953; 5 ref_20 Seumer (ref_29) 2023; 62 Liu (ref_1) 2020; 189 Nguyen (ref_5) 2005; 18 ref_28 ref_26 ref_9 ref_4 ref_7 Liu (ref_11) 2024; 73 |
| References_xml | – volume: 43 start-page: 23 year: 2009 ident: ref_31 article-title: Solving molecular distance geometry problems by global optimization algorithms publication-title: Comput. Optim. Appl. doi: 10.1007/s10589-007-9127-8 – volume: 28 start-page: 1060 year: 2021 ident: ref_21 article-title: A deep feature fusion network based on multiple attention mechanisms for joint iris-periocular biometric recognition publication-title: IEEE Signal Process. Lett. doi: 10.1109/LSP.2021.3079850 – volume: 452 start-page: 48 year: 2021 ident: ref_27 article-title: A review on the attention mechanism of deep learning publication-title: Neurocomputing doi: 10.1016/j.neucom.2021.03.091 – ident: ref_26 doi: 10.3390/ma17143521 – volume: 18 start-page: 549 year: 2005 ident: ref_5 article-title: Applications of data analysis techniques for oil production prediction publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2004.11.010 – volume: 32 start-page: 604 year: 2020 ident: ref_18 article-title: A survey of the usages of deep learning for natural language processing publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2020.2979670 – volume: 113 start-page: 4055 year: 2024 ident: ref_13 article-title: Hybrid approaches to optimization and machine learning methods: A systematic literature review publication-title: Mach. Learn. doi: 10.1007/s10994-023-06467-x – ident: ref_28 doi: 10.1371/journal.pone.0288044 – ident: ref_15 doi: 10.1109/ICSESS47205.2019.9040779 – ident: ref_16 – volume: 48 start-page: 1 year: 2021 ident: ref_3 article-title: Application and development trend of artificial intelligence in petroleum exploration and development publication-title: Pet. Explor. Dev. doi: 10.1016/S1876-3804(21)60001-0 – ident: ref_14 – ident: ref_4 doi: 10.2118/176750-MS – volume: 32 start-page: 16245 year: 2020 ident: ref_33 article-title: Whale optimization algorithm: A systematic review of contemporary applications, modifications and developments publication-title: Neural Comput. Appl. doi: 10.1007/s00521-020-04849-z – ident: ref_35 – volume: 8 start-page: 183 year: 2004 ident: ref_12 article-title: Prediction of oil well production: A multiple-neural-network approach publication-title: Intell. Data Anal. doi: 10.3233/IDA-2004-8206 – volume: 22 start-page: 387 year: 2018 ident: ref_32 article-title: Particle swarm optimization algorithm: An overview publication-title: Soft Comput. doi: 10.1007/s00500-016-2474-6 – volume: 5 start-page: 51 year: 1953 ident: ref_8 article-title: Application of the Material Balance Equation to a Partial Water-Drive Reservoir publication-title: J. Pet. Technol. doi: 10.2118/229-G – volume: 73 start-page: 295 year: 2024 ident: ref_11 article-title: Reservoir production capacity prediction of Zananor field based on LSTM neural network publication-title: Acta Geophys. doi: 10.1007/s11600-024-01388-2 – volume: 4 start-page: 100230 year: 2023 ident: ref_34 article-title: An artificial neural network-particle swarm optimization (ANN-PSO) approach to predict the aeration efficiency of venturi aeration system publication-title: Smart Agric. Technol. doi: 10.1016/j.atech.2023.100230 – volume: 144 start-page: 334 year: 2021 ident: ref_25 article-title: Working memory connections for LSTM publication-title: Neural Netw. doi: 10.1016/j.neunet.2021.08.030 – volume: 404 start-page: 132306 year: 2020 ident: ref_22 article-title: Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network publication-title: Phys. D Nonlinear Phenom. doi: 10.1016/j.physd.2019.132306 – ident: ref_19 doi: 10.3390/electronics10101163 – volume: 12 start-page: 19035 year: 2024 ident: ref_2 article-title: Machine Learning in Oil and Gas Exploration—A Review publication-title: IEEE Access doi: 10.1109/ACCESS.2023.3349216 – volume: 205 start-page: 117670 year: 2022 ident: ref_17 article-title: Prediction of well performance in SACROC field using stacked Long Short-Term Memory (LSTM) network publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2022.117670 – ident: ref_7 doi: 10.3390/min14070686 – volume: 62 start-page: e202218565 year: 2023 ident: ref_29 article-title: Computational evolution of new catalysts for the Morita–Baylis–Hillman reaction publication-title: Angew. Chem. Int. Ed. doi: 10.1002/anie.202218565 – volume: 236 start-page: 121503 year: 2021 ident: ref_6 article-title: Numerical simulation and production prediction assessment of Takigami geothermal reservoir publication-title: Energy doi: 10.1016/j.energy.2021.121503 – ident: ref_23 doi: 10.3390/electronics11121906 – ident: ref_9 doi: 10.3389/feart.2022.1106622 – volume: 189 start-page: 107013 year: 2020 ident: ref_1 article-title: Forecasting oil production using ensemble empirical model decomposition based Long Short-Term Memory neural network publication-title: J. Pet. Sci. Eng. doi: 10.1016/j.petrol.2020.107013 – volume: 89 start-page: 803 year: 2024 ident: ref_30 article-title: A performance analysis of Basin hopping compared to established metaheuristics for global optimization publication-title: J. Glob. Optim. doi: 10.1007/s10898-024-01373-5 – ident: ref_10 doi: 10.3390/s22145326 – ident: ref_20 – volume: 12 start-page: 142957 year: 2024 ident: ref_24 article-title: A BiLSTM-Based Feature Fusion with CNN Model: Integrating Smartphone Sensor Data for Pedestrian Activity Recognition publication-title: IEEE Access doi: 10.1109/ACCESS.2024.3468470 |
| SSID | ssj0000913830 |
| Score | 2.318477 |
| Snippet | Accurate prediction of future oil production is critical for decision-making in oil well operations. However, existing prediction models often lack precision... |
| SourceID | proquest gale crossref |
| SourceType | Aggregation Database Index Database |
| StartPage | 1004 |
| SubjectTerms | Accuracy Algorithms Analysis Artificial intelligence Computational linguistics Data processing Decision-making Deep learning Efficiency Language processing Machine learning Mathematical optimization Natural language interfaces Neural networks Oil wells Optimization Optimization algorithms Petroleum industry Petroleum mining Prediction models Quadratic programming Search algorithms Simulation |
| Title | Optimization of Oil Well Production Prediction Model Based on Inter-Attention and BiLSTM |
| URI | https://www.proquest.com/docview/3176377849 |
| Volume | 14 |
| WOSCitedRecordID | wos001444142800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2079-9292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913830 issn: 2079-9292 databaseCode: M~E dateStart: 20120101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 2079-9292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913830 issn: 2079-9292 databaseCode: P5Z dateStart: 20120301 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2079-9292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913830 issn: 2079-9292 databaseCode: BENPR dateStart: 20120301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2079-9292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913830 issn: 2079-9292 databaseCode: PIMPY dateStart: 20120301 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LT-MwEB5Bu4flwGN3EeVR-YDEZa0mdhInJ9SiIpBoiXiI7l6ixA-pUmmhDRz57XgSFxYJ7YlLlDiHRJ7xjGc8830Ah4m2msM8QxnTOQ0KY2iiAk2lp5RMrEopUVRkE2I4jEejJHXt0QtXVrm0iZWhrtGesW7bGuGOmknMmHes14u4EHGQHD88UuSQwrNWR6ixCk0E3vIa0EzPB-mft5wLYmDG3KvBh7iN9jvvXDMLG2mECJ_2wUF9bqYr33O68bV_vQnrbg9KurXSbMGKnv6AtX-QCX_C6NKaknvXo0lmhlyOJ-ROTyYkrSFicTid4ylPdYuUahPSsy5REftY5RlptyzrYkqSTxXpjS-ubwa_4Pa0f3NyRh0JA5Xc90vKcVYMCyKZ-CyKPcWLXOooVMYYxoUdsDFbLIXxYh36Qnq5KoIwUVyxKEf0t21oTGdTvQPERlYat3yFFEVguLHqI3zlRVLokCmmWvB7OfPZQ421kdkYBQWVfSKoFhyhdDJcieU8l7lrKLAfQ0yrrBtzBK9LAt6C_aV0MrdEF9m7MHb__3oPvjMk_a0Kz_ahUc6f9AF8k8_leDFvQ7PXH6ZXbVgdvPTtNQ3_tp3evQJfruh- |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Nb9NAEB2VFAk4QIFWBArdQxEXVl3v2l7voUJpoWrUJLVEEOFk7P2QIoWkJIaqf6q_kR1_tCBV3HrozV5Ltux5ntnZnXkPYFdZjxzOHOXc5jQsnKPKhJZqZoxWHlJGFpXYhByNkslEpWtw2fbCYFll6xMrR20WGtfI93yci4WUSag-nP2kqBqFu6uthEYNixN7ce5TttV-_6O371vOjz6ND49poypAtQiCkgqEoeNhrFXA44QZUeTaxpFxznEh_YBPQhItHUtsFEjNclOEkTLC8DhHOjN_33uwHnqwsw6sp_1h-u1qVQdZNhPBanojIRTbu1azWflcJkKCtn9C4M2BoIpuR0_u2nfZgMfNPJr0auA_hTU7fwaP_mJXfA6TU-8OfzR9pmThyOl0Rr7a2YykNc0tDqdL3KmqDlEWbkYOfFg3xJ9Wa6W0V5Z1QSjJ54YcTAefx8NN-HIrr7YFnflibl8A8dmhxWlroWUROuH8LyADw2ItbcQNN11439o2O6v5QjKfZyEUshug0IV3aP8MvUm5zHXeNEX4hyEvV9ZLBBLwqVB0Ybu1f9a4mVV2bfyX_7-8Aw-Ox8NBNuiPTl7BQ44ixlUh3TZ0yuUv-xru69_ldLV80yCawPfbBssfsIk0uw |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VLULlwLvqQgEfQFywNrGTOD4gtEu7omrZRlDE3kLih7TSdrfsBlD_Wn8dM3lQkCpuPXBLHClR7M8znvH4-wBeaIfIEYHnQriCR6X3XNvIcRNYazRCyqqyFptQk0k6nepsAy66szBUVtnZxNpQ26WhHPkA_VwilUojPfBtWUS2N3579o2TghTttHZyGg1EDt35Twzf1m8O9nCsXwox3j959563CgPcyDCsuCRIehElRociSQMry8K4JLbeeyEVNmBAkhrlg9TFoTJBYcso1lZakRREbYbvvQGbSmLQ04PN0f4k-_g7w0OMm6kMGqojKXUwuFS2WWNcExNZ21_u8GqnUHu68d3_uY_uwZ12fc2GzYS4Dxtu8QBu_8G6-BCmx2gmT9vzp2zp2fFszr64-ZxlDf0tNWcr2sGqL0kubs5G6O4tw9s6h8qHVdUUirJiYdlodvTp5MMj-Hwtv7YNvcVy4XaAYdToaDlbGlVGXnqcGiq0QWKUi4UVtg-vu3HOzxoekRzjL4JFfgUs-vCKsJCTlalWhSnawxL4MeLryoepJGI-Hck-7HZYyFvzs84vgfD434-fwy1ESH50MDl8AluCtI3r-rpd6FWr7-4p3DQ_qtl69awFN4Ov142VX2k2PVU |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Optimization+of+Oil+Well+Production+Prediction+Model+Based+on+Inter-Attention+and+BiLSTM&rft.jtitle=Electronics+%28Basel%29&rft.au=Meng%2C+Xin&rft.au=Liu%2C+Xingyu&rft.au=Duan%2C+Hancong&rft.au=Hu%2C+Ze&rft.date=2025-03-01&rft.issn=2079-9292&rft.eissn=2079-9292&rft.volume=14&rft.issue=5&rft.spage=1004&rft_id=info:doi/10.3390%2Felectronics14051004&rft.externalDBID=n%2Fa&rft.externalDocID=10_3390_electronics14051004 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2079-9292&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2079-9292&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2079-9292&client=summon |