JP7229233B2 - 機械学習ベースのモデルを用いる高い費用効率の熱力学的流体特性の予測の方法 - Google Patents
機械学習ベースのモデルを用いる高い費用効率の熱力学的流体特性の予測の方法 Download PDFInfo
- Publication number
- JP7229233B2 JP7229233B2 JP2020517468A JP2020517468A JP7229233B2 JP 7229233 B2 JP7229233 B2 JP 7229233B2 JP 2020517468 A JP2020517468 A JP 2020517468A JP 2020517468 A JP2020517468 A JP 2020517468A JP 7229233 B2 JP7229233 B2 JP 7229233B2
- Authority
- JP
- Japan
- Prior art keywords
- machine learning
- phase
- learning algorithm
- training
- data points
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- 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
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
- G06N20/20—Ensemble learning
-
- 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]
-
- 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
-
- 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
-
- 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
-
- 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
-
- 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
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Software Systems (AREA)
- Computing Systems (AREA)
- Artificial Intelligence (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Medical Informatics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Crystallography & Structural Chemistry (AREA)
- Chemical & Material Sciences (AREA)
- Computational Linguistics (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Molecular Biology (AREA)
- Databases & Information Systems (AREA)
- Algebra (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Pure & Applied Mathematics (AREA)
- Computational Mathematics (AREA)
- Probability & Statistics with Applications (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
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 (3)
| Publication Number | Publication Date |
|---|---|
| JP2020537221A JP2020537221A (ja) | 2020-12-17 |
| JP2020537221A5 JP2020537221A5 (https=) | 2021-11-04 |
| JP7229233B2 true JP7229233B2 (ja) | 2023-02-27 |
Family
ID=63794679
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP2020517468A Active JP7229233B2 (ja) | 2017-09-26 | 2018-09-19 | 機械学習ベースのモデルを用いる高い費用効率の熱力学的流体特性の予測の方法 |
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 |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050049990A1 (en) | 2003-08-29 | 2005-03-03 | Milenova Boriana L. | 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 |
Family Cites Families (12)
| 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 |
| 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
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050049990A1 (en) | 2003-08-29 | 2005-03-03 | Milenova Boriana L. | 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 |
Non-Patent Citations (1)
| Title |
|---|
| GAGANIS, Vassilis, et al.,AN INTEGRATED APPROACH FOR RAPID PHASE BEHAVIOR CALCULATIONS IN COMPOSITIONAL MODELING,JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING,2014年04月03日,Vol.118,pp.74-87,[online] [検索日:2022.09.27] <URL: https://www.sciencedirect.com/science/article/abs/pii/S0920410514000795> |
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 |
| JP2020537221A (ja) | 2020-12-17 |
| CA3076887A1 (en) | 2019-04-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP7229233B2 (ja) | 機械学習ベースのモデルを用いる高い費用効率の熱力学的流体特性の予測の方法 | |
| De'Ath | Boosted trees for ecological modeling and prediction | |
| US10565523B2 (en) | Security classification by machine learning | |
| US20220253725A1 (en) | Machine learning model for entity resolution | |
| Tayyebi et al. | Assessing uncertainty dimensions in land-use change models: using swap and multiplicative error models for injecting attribute and positional errors in spatial data | |
| Sundar et al. | Reliability analysis using adaptive kriging surrogates with multimodel inference | |
| US11989626B2 (en) | Generating performance predictions with uncertainty intervals | |
| Zhen et al. | An interpretable and augmented machine-learning approach for causation analysis of major accident risk indicators in the offshore petroleum industry | |
| EP3355248A2 (en) | Security classification by machine learning | |
| CN104885101A (zh) | 包括表征选择的不确定度的基于不完备描述对新总体成员的相似成员的自动选择 | |
| CN108062448A (zh) | 预测边坡稳定性的建模及分析方法、设备和存储介质 | |
| US20220078198A1 (en) | Method and system for generating investigation cases in the context of cybersecurity | |
| Asadi et al. | Development of optimal fuzzy models for predicting the strength of intact rocks | |
| Almashan et al. | Estimating PVT properties of crude oil systems based on a boosted decision tree regression modelling scheme with K-means clustering | |
| EP3828731A1 (en) | A method and analytical engine for a semantic analysis of textual data | |
| Li et al. | A surrogate-based adaptive sampling approach for history matching and uncertainty quantification | |
| Li et al. | CRNN: Integrating classification rules into neural network | |
| Perez-Valiente et al. | Identification of reservoir analogues in the presence of uncertainty | |
| Aulia et al. | A new history matching sensitivity analysis framework with random forests and Plackett-Burman design | |
| Obayemi et al. | Uncertainty quantification of multimodal models | |
| Wang et al. | Response–surface–based embankment reliability under incomplete probability information | |
| Talapatra et al. | A Data-Based Continuous and Predictive Viscosity Model for the Oil-Surfactant-Brine Microemulsion Phase | |
| EP3861381B1 (en) | Data structure for fast invasion percolation modeling software | |
| Zou et al. | Generalization bounds of ERM algorithm with Markov chain samples | |
| Voskresenskii et al. | Leveraging the power of spatial-temporal information with graph neural networks as the key to unlocking more accurate flow rate predictions |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| A521 | Request for written amendment filed |
Free format text: JAPANESE INTERMEDIATE CODE: A523 Effective date: 20210921 |
|
| A621 | Written request for application examination |
Free format text: JAPANESE INTERMEDIATE CODE: A621 Effective date: 20210921 |
|
| RD03 | Notification of appointment of power of attorney |
Free format text: JAPANESE INTERMEDIATE CODE: A7423 Effective date: 20210921 |
|
| A977 | Report on retrieval |
Free format text: JAPANESE INTERMEDIATE CODE: A971007 Effective date: 20220926 |
|
| A131 | Notification of reasons for refusal |
Free format text: JAPANESE INTERMEDIATE CODE: A131 Effective date: 20221004 |
|
| A521 | Request for written amendment filed |
Free format text: JAPANESE INTERMEDIATE CODE: A523 Effective date: 20221226 |
|
| TRDD | Decision of grant or rejection written | ||
| A01 | Written decision to grant a patent or to grant a registration (utility model) |
Free format text: JAPANESE INTERMEDIATE CODE: A01 Effective date: 20230117 |
|
| A61 | First payment of annual fees (during grant procedure) |
Free format text: JAPANESE INTERMEDIATE CODE: A61 Effective date: 20230214 |
|
| R150 | Certificate of patent or registration of utility model |
Ref document number: 7229233 Country of ref document: JP Free format text: JAPANESE INTERMEDIATE CODE: R150 |