CA3076887A1 - Method for cost effective thermo-dynamic fluid property predictions using machine-learning based models - Google Patents
Method for cost effective thermo-dynamic fluid property predictions using machine-learning based models Download PDFInfo
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- CA3076887A1 CA3076887A1 CA3076887A CA3076887A CA3076887A1 CA 3076887 A1 CA3076887 A1 CA 3076887A1 CA 3076887 A CA3076887 A CA 3076887A CA 3076887 A CA3076887 A CA 3076887A CA 3076887 A1 CA3076887 A1 CA 3076887A1
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
- G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
- G06N20/20—Ensemble learning
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/0499—Feedforward networks
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/09—Supervised learning
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computing arrangements based on specific mathematical models
- G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/30—Prediction of properties of chemical compounds, compositions or mixtures
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/70—Machine learning, data mining or chemometrics
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
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- Bioinformatics & Cheminformatics (AREA)
- Crystallography & Structural Chemistry (AREA)
- Chemical & Material Sciences (AREA)
- Computational Linguistics (AREA)
- Biomedical Technology (AREA)
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- Databases & Information Systems (AREA)
- Algebra (AREA)
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Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US201762563460P | 2017-09-26 | 2017-09-26 | |
| US62/563,460 | 2017-09-26 | ||
| PCT/US2018/051684 WO2019067282A1 (en) | 2017-09-26 | 2018-09-19 | METHOD FOR PREDICTING ECONOMIC FLUID THERMODYNAMIC PROPERTIES USING MODELS BASED ON AUTOMATIC LEARNING |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CA3076887A1 true CA3076887A1 (en) | 2019-04-04 |
Family
ID=63794679
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA3076887A Pending CA3076887A1 (en) | 2017-09-26 | 2018-09-19 | Method for cost effective thermo-dynamic fluid property predictions using machine-learning based models |
Country Status (6)
| Country | Link |
|---|---|
| US (1) | US11449747B2 (https=) |
| EP (1) | EP3688683A1 (https=) |
| JP (1) | JP7229233B2 (https=) |
| CN (1) | CN111344710A (https=) |
| CA (1) | CA3076887A1 (https=) |
| WO (1) | WO2019067282A1 (https=) |
Families Citing this family (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110096785B (zh) * | 2019-04-25 | 2020-09-01 | 华北电力大学 | 一种应用于超超临界机组的堆叠自编码器建模方法 |
| EP3872038A1 (en) * | 2020-02-27 | 2021-09-01 | Grundfos Holding A/S | Reinforcement learning for h2s abatement |
| CA3175274A1 (en) * | 2020-03-09 | 2021-09-16 | Schlumberger Canada Limited | Fast front tracking in eor flooding simulation on coarse grids |
| CN115087995A (zh) * | 2020-03-31 | 2022-09-20 | Abb瑞士股份有限公司 | 用于工业车间的特定生产过程的迁移学习方法 |
| CN111546035B (zh) * | 2020-04-07 | 2021-07-02 | 大连理工大学 | 一种基于学习与预测的齿轮在线快速装配方法 |
| US11815650B2 (en) | 2020-04-09 | 2023-11-14 | Saudi Arabian Oil Company | Optimization of well-planning process for identifying hydrocarbon reserves using an integrated multi-dimensional geological model |
| US11486230B2 (en) | 2020-04-09 | 2022-11-01 | Saudi Arabian Oil Company | Allocating resources for implementing a well-planning process |
| US11693140B2 (en) | 2020-04-09 | 2023-07-04 | Saudi Arabian Oil Company | Identifying hydrocarbon reserves of a subterranean region using a reservoir earth model that models characteristics of the region |
| AU2021255730B2 (en) * | 2020-04-17 | 2024-08-08 | Chevron U.S.A. Inc. | Compositional reservoir simulation |
| US20210390424A1 (en) * | 2020-06-10 | 2021-12-16 | At&T Intellectual Property I, L.P. | Categorical inference for training a machine learning model |
| CN114139432B (zh) * | 2020-09-04 | 2025-06-27 | 中国石油化工股份有限公司 | 利用神经网络技术的裂缝性油藏co2驱流动模拟方法 |
| WO2022061331A1 (en) * | 2020-09-18 | 2022-03-24 | Schlumberger Technology Corporation | Generalizable machine learning algorithms for flash calculations |
| US12019059B2 (en) * | 2020-10-16 | 2024-06-25 | Saudi Arabian Oil Company | Detecting equipment defects using lubricant analysis |
| CN112668607B (zh) * | 2020-12-04 | 2024-09-13 | 深圳先进技术研究院 | 一种用于目标物体触觉属性识别的多标签学习方法 |
| CN112632787B (zh) * | 2020-12-25 | 2023-11-28 | 浙江中控技术股份有限公司 | 多解闪蒸优化策略的仿真测试方法 |
| CN113539387A (zh) * | 2021-07-09 | 2021-10-22 | 西南石油大学 | 一种基于CPA状态方程预测NaCl水溶液中CO2溶解度的方法 |
| US12271445B2 (en) * | 2022-10-28 | 2025-04-08 | Yahoo Assets Llc | Electronic information extraction using a machine-learned model architecture method and apparatus |
| CN115688592B (zh) * | 2022-11-09 | 2023-05-09 | 福建德尔科技股份有限公司 | 用于电子级四氟化碳制备的精馏控制系统及其方法 |
| CN116992296A (zh) * | 2023-09-27 | 2023-11-03 | 广东电网有限责任公司珠海供电局 | 电子敏感设备发生暂降的中断概率评估方法、装置和设备 |
| CN119849340B (zh) * | 2025-03-20 | 2025-07-15 | 中国石油大学(华东) | 一种基于机器学习相识别模型的三相闪蒸计算方法 |
Family Cites Families (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6633857B1 (en) * | 1999-09-04 | 2003-10-14 | Microsoft Corporation | Relevance vector machine |
| FR2848320B1 (fr) * | 2002-12-10 | 2005-01-28 | Inst Francais Du Petrole | Methode pour modeliser des caracteristiques hydrodynamiques d'ecoulements polyphasiques par reseaux de neurones |
| US7490071B2 (en) * | 2003-08-29 | 2009-02-10 | Oracle Corporation | Support vector machines processing system |
| JP2008221146A (ja) * | 2007-03-13 | 2008-09-25 | Sumitomo Chemical Co Ltd | 計算装置、計算方法、計算装置制御プログラム、及び該計算装置制御プログラムを記録したコンピュータ読み取り可能な記録媒体 |
| CA2805446C (en) | 2010-07-29 | 2016-08-16 | Exxonmobil Upstream Research Company | Methods and systems for machine-learning based simulation of flow |
| CN102147807A (zh) * | 2011-03-10 | 2011-08-10 | 南京信息工程大学 | 基于gis的海量雷电数据时空分析方法 |
| EP2729051B1 (en) * | 2011-07-05 | 2018-06-06 | Saudi Arabian Oil Company | Systems, computer medium and computer-implemented methods for coaching employees based upon monitored health conditions using an avatar |
| US10119374B2 (en) | 2012-03-12 | 2018-11-06 | Total Sa | Method for simulating fluid flows, a computer program and a computer readable medium |
| US10289962B2 (en) * | 2014-06-06 | 2019-05-14 | Google Llc | Training distilled machine learning models |
| SG10201403287VA (en) * | 2014-06-16 | 2016-01-28 | Ats Group Ip Holdings Ltd | Flash flooding detection system |
| US20150377667A1 (en) * | 2014-06-30 | 2015-12-31 | Saudi Arabian Oil Company | Virtual multiphase flow metering and sand detection |
| CN106526708B (zh) * | 2016-09-21 | 2018-10-30 | 广东奥博信息产业股份有限公司 | 一种基于机器学习的气象强对流天气的智能预警分析方法 |
| WO2018117890A1 (en) | 2016-12-21 | 2018-06-28 | Schlumberger Technology Corporation | A method and a cognitive system for predicting a hydraulic fracture performance |
| CN106920544A (zh) * | 2017-03-17 | 2017-07-04 | 深圳市唯特视科技有限公司 | 一种基于深度神经网络特征训练的语音识别方法 |
-
2018
- 2018-09-19 CA CA3076887A patent/CA3076887A1/en active Pending
- 2018-09-19 EP EP18783247.2A patent/EP3688683A1/en not_active Withdrawn
- 2018-09-19 JP JP2020517468A patent/JP7229233B2/ja active Active
- 2018-09-19 CN CN201880073266.5A patent/CN111344710A/zh active Pending
- 2018-09-19 WO PCT/US2018/051684 patent/WO2019067282A1/en not_active Ceased
- 2018-09-26 US US16/142,757 patent/US11449747B2/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| US20190095792A1 (en) | 2019-03-28 |
| US11449747B2 (en) | 2022-09-20 |
| CN111344710A (zh) | 2020-06-26 |
| WO2019067282A1 (en) | 2019-04-04 |
| EP3688683A1 (en) | 2020-08-05 |
| JP7229233B2 (ja) | 2023-02-27 |
| JP2020537221A (ja) | 2020-12-17 |
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