MX2021002871A - Analisis de atributos sismicos basado en el aprendizaje automatico. - Google Patents
Analisis de atributos sismicos basado en el aprendizaje automatico.Info
- Publication number
- MX2021002871A MX2021002871A MX2021002871A MX2021002871A MX2021002871A MX 2021002871 A MX2021002871 A MX 2021002871A MX 2021002871 A MX2021002871 A MX 2021002871A MX 2021002871 A MX2021002871 A MX 2021002871A MX 2021002871 A MX2021002871 A MX 2021002871A
- Authority
- MX
- Mexico
- Prior art keywords
- machine learning
- reservoir
- seismic attributes
- based analysis
- learning model
- Prior art date
Links
- 238000010801 machine learning Methods 0.000 title abstract 3
- 238000009825 accumulation Methods 0.000 abstract 1
- 229930195733 hydrocarbon Natural products 0.000 abstract 1
- 150000002430 hydrocarbons Chemical class 0.000 abstract 1
- 238000000034 method Methods 0.000 abstract 1
- 238000013508 migration Methods 0.000 abstract 1
- 230000005012 migration Effects 0.000 abstract 1
Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/30—Analysis
- G01V1/303—Analysis for determining velocity profiles or travel times
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/282—Application of seismic models, synthetic seismograms
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/30—Analysis
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/30—Analysis
- G01V1/306—Analysis for determining physical properties of the subsurface, e.g. impedance, porosity or attenuation profiles
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/40—Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging
- G01V1/44—Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging using generators and receivers in the same well
- G01V1/48—Processing data
- G01V1/50—Analysing data
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/61—Analysis by combining or comparing a seismic data set with other data
- G01V2210/614—Synthetically generated data
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/61—Analysis by combining or comparing a seismic data set with other data
- G01V2210/616—Data from specific type of measurement
- G01V2210/6169—Data from specific type of measurement using well-logging
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/62—Physical property of subsurface
- G01V2210/622—Velocity, density or impedance
- G01V2210/6222—Velocity; travel time
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/62—Physical property of subsurface
- G01V2210/622—Velocity, density or impedance
- G01V2210/6224—Density
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/63—Seismic attributes, e.g. amplitude, polarity, instant phase
- G01V2210/632—Amplitude variation versus offset or angle of incidence [AVA, AVO, AVI]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
- G06N3/044—Recurrent networks, e.g. Hopfield networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
- G06N3/045—Combinations of networks
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Remote Sensing (AREA)
- General Physics & Mathematics (AREA)
- Acoustics & Sound (AREA)
- Environmental & Geological Engineering (AREA)
- Geology (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Geophysics (AREA)
- Theoretical Computer Science (AREA)
- Software Systems (AREA)
- Data Mining & Analysis (AREA)
- Artificial Intelligence (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Mathematical Physics (AREA)
- Computing Systems (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- Biomedical Technology (AREA)
- Molecular Biology (AREA)
- General Health & Medical Sciences (AREA)
- Computational Linguistics (AREA)
- Biophysics (AREA)
- Health & Medical Sciences (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
Se describen sistemas y métodos que incluyen la generación de perfiles de propiedades del yacimiento correspondientes a las propiedades del yacimiento para pseudopozos basados en datos del yacimiento, la generación de atributos sísmicos para los pseudopozos y el entrenamiento de un modelo de aprendizaje automático comparando los perfiles de propiedades del yacimiento con los atributos sísmicos. De esta manera, el modelo de aprendizaje automático puede usarse para predecir las propiedades del yacimiento para su uso con la exploración sísmica sobre una región de un subsuelo que contiene características estructurales o estratigráficas que conducen a la presencia, migración o acumulación de hidrocarburos.
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US201862731411P | 2018-09-14 | 2018-09-14 | |
PCT/US2019/050720 WO2020056073A1 (en) | 2018-09-14 | 2019-09-12 | Machine learning-based analysis of seismic attributes |
Publications (1)
Publication Number | Publication Date |
---|---|
MX2021002871A true MX2021002871A (es) | 2021-06-04 |
Family
ID=67957466
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
MX2021002871A MX2021002871A (es) | 2018-09-14 | 2019-09-12 | Analisis de atributos sismicos basado en el aprendizaje automatico. |
Country Status (9)
Country | Link |
---|---|
US (1) | US11262468B2 (es) |
EP (1) | EP3850401B1 (es) |
CN (1) | CN112703429B (es) |
AU (1) | AU2019338412B2 (es) |
BR (1) | BR112021004693B1 (es) |
CA (1) | CA3112197C (es) |
EA (1) | EA202190728A1 (es) |
MX (1) | MX2021002871A (es) |
WO (1) | WO2020056073A1 (es) |
Families Citing this family (33)
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GB2565526A (en) * | 2017-06-12 | 2019-02-20 | Foster Findlay Ass Ltd | A method for validating geological model data over corresponding original seismic data |
US11073637B2 (en) * | 2018-10-04 | 2021-07-27 | Saudi Arabian Oil Company | Data structure for fast invasion percolation modeling software |
US11105944B2 (en) * | 2019-04-30 | 2021-08-31 | Chevron U.S.A. Inc. | System and method for lateral statistical estimation of rock and fluid properties in a subsurface formation |
US11409011B2 (en) * | 2019-08-29 | 2022-08-09 | Advanced Geophysical Technology Inc. | Methods and systems for obtaining reconstructed low-frequency seismic data for determining a subsurface feature |
WO2021075994A1 (en) * | 2019-10-16 | 2021-04-22 | Saudi Arabian Oil Company | Determination of elastic properties of a geological formation using machine learning applied to data acquired while drilling |
US11428835B2 (en) | 2020-03-23 | 2022-08-30 | Landmark Graphics Corporation | Facilitating hydrocarbon exploration and extraction by applying a machine-learning model to seismic data |
NO20210264A1 (en) * | 2020-04-07 | 2021-02-26 | Landmark Graphics Corp | Data-driven domain conversion using machine learning techniques |
US11614557B2 (en) * | 2020-04-07 | 2023-03-28 | Landmark Graphics Corporation | Data-driven domain conversion using machine learning techniques |
CN111487679B (zh) * | 2020-04-22 | 2023-04-07 | 中国石油天然气集团有限公司 | 横波速度预测方法、装置和设备 |
CN111551985A (zh) * | 2020-05-15 | 2020-08-18 | 广州市高速公路有限公司 | 一种基于桩锤激震和k近邻算法的地下溶洞探测方法 |
CN111751878B (zh) * | 2020-05-21 | 2023-05-30 | 中国石油天然气股份有限公司 | 横波速度的预测方法和装置 |
CN111736217B (zh) * | 2020-05-27 | 2023-12-26 | 中国石油天然气集团有限公司 | 地震属性融合方法及装置 |
KR20210150916A (ko) * | 2020-06-04 | 2021-12-13 | 에스케이이노베이션 주식회사 | 웰 로그를 학습하여 s파 속도를 추정하는 방법 및 장치 |
CN111983691B (zh) * | 2020-08-18 | 2023-10-13 | 郑州市混沌信息技术有限公司 | 一种多模型融合的储层预测方法及软件系统 |
CN112017289B (zh) * | 2020-08-31 | 2023-03-24 | 电子科技大学 | 一种基于深度学习的井震联合初始岩性模型构建方法 |
CN114198096B (zh) * | 2020-09-01 | 2023-09-26 | 中国石油天然气股份有限公司 | 钻井误差预测方法及装置 |
CN114152978B (zh) * | 2020-09-07 | 2023-06-06 | 中国石油化工股份有限公司 | 储层参数预测方法、装置、存储介质及电子设备 |
CN114185087B (zh) * | 2020-09-14 | 2024-08-02 | 中国石油化工股份有限公司 | 叠前频变avo反演方法、装置、电子设备及介质 |
CN112987091B (zh) * | 2020-12-09 | 2024-02-02 | 中国石油天然气股份有限公司 | 储层检测方法、装置、电子设备和存储介质 |
US11796714B2 (en) | 2020-12-10 | 2023-10-24 | Saudi Arabian Oil Company | Determination of mechanical properties of a geological formation using deep learning applied to data acquired while drilling |
US11630224B2 (en) * | 2020-12-11 | 2023-04-18 | Landmark Graphics Corporation | Subsurface fluid-type likelihood using explainable machine learning |
US11703608B2 (en) | 2020-12-29 | 2023-07-18 | Landmark Graphics Corporation | Reservoir characterization using machine-learning techniques |
CN112686315A (zh) * | 2020-12-31 | 2021-04-20 | 山西三友和智慧信息技术股份有限公司 | 一种基于深度学习的非自然地震分类方法 |
CN112782690B (zh) * | 2021-01-14 | 2023-10-13 | 中国科学院国家空间科学中心 | 星载雷达高度计的近岸非海洋波形检测分类方法及系统 |
WO2022153984A1 (ja) * | 2021-01-15 | 2022-07-21 | 株式会社Preferred Networks | 学習データ生成方法、モデル生成方法および学習データ生成装置 |
CN115327643B (zh) * | 2021-05-11 | 2024-09-10 | 中国石油化工股份有限公司 | 用于油气智能检测的机器学习训练样本扩充及评价方法 |
CN113514875B (zh) * | 2021-06-03 | 2024-04-02 | 德仕能源科技集团股份有限公司 | 一种基于大数据的岩性油气藏的勘测和描述方法及系统 |
US11828168B2 (en) | 2021-06-30 | 2023-11-28 | Saudi Arabian Oil Company | Method and system for correcting and predicting sonic well logs using physics-constrained machine learning |
CN113705644B (zh) * | 2021-08-17 | 2023-09-12 | 西安交通大学 | 一种物理规律和数据双驱动的地震成像方法、系统、设备及存储介质 |
US11852768B2 (en) | 2021-11-19 | 2023-12-26 | Saudi Arabian Oil Company | Multimodal approach to target stratigraphic plays through seismic sequence stratigraphy, rock physics, seismic inversion and machine learning |
CN114895359B (zh) * | 2022-07-13 | 2022-09-13 | 中国科学院地质与地球物理研究所 | 一种das同井监测实时微地震有效事件去噪方法及系统 |
CN115877464B (zh) * | 2022-12-30 | 2024-02-13 | 中海石油(中国)有限公司深圳分公司 | 一种岩性识别方法、装置、计算机设备及存储介质 |
CN116609852B (zh) * | 2023-07-06 | 2024-01-23 | 中国石油大学(华东) | 一种井震融合的地下介质参数高精度建模方法以及设备 |
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US6374185B1 (en) * | 2000-02-18 | 2002-04-16 | Rdsp I, L.P. | Method for generating an estimate of lithological characteristics of a region of the earth's subsurface |
US7869955B2 (en) * | 2008-01-30 | 2011-01-11 | Chevron U.S.A. Inc. | Subsurface prediction method and system |
CN107436452A (zh) * | 2016-05-27 | 2017-12-05 | 中国石油化工股份有限公司 | 基于概率神经网络算法的烃源岩预测方法及装置 |
CN110462445B (zh) | 2017-02-09 | 2022-07-26 | 地质探索系统公司 | 地球物理深度学习 |
US11360233B2 (en) * | 2017-09-12 | 2022-06-14 | Schlumberger Technology Corporation | Seismic image data interpretation system |
KR101867475B1 (ko) * | 2017-11-03 | 2018-06-14 | 한국지질자원연구원 | 딥러닝을 이용한 저류층 불확실성 평가 방법 |
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2019
- 2019-09-12 EA EA202190728A patent/EA202190728A1/ru unknown
- 2019-09-12 EP EP19779241.9A patent/EP3850401B1/en active Active
- 2019-09-12 CA CA3112197A patent/CA3112197C/en active Active
- 2019-09-12 MX MX2021002871A patent/MX2021002871A/es unknown
- 2019-09-12 BR BR112021004693-3A patent/BR112021004693B1/pt active IP Right Grant
- 2019-09-12 AU AU2019338412A patent/AU2019338412B2/en active Active
- 2019-09-12 WO PCT/US2019/050720 patent/WO2020056073A1/en unknown
- 2019-09-12 US US16/568,352 patent/US11262468B2/en active Active
- 2019-09-12 CN CN201980059951.7A patent/CN112703429B/zh active Active
Also Published As
Publication number | Publication date |
---|---|
CN112703429B (zh) | 2024-02-13 |
EP3850401B1 (en) | 2023-09-06 |
AU2019338412A1 (en) | 2021-04-08 |
CN112703429A (zh) | 2021-04-23 |
US20200088897A1 (en) | 2020-03-19 |
EP3850401A1 (en) | 2021-07-21 |
EA202190728A1 (ru) | 2021-07-27 |
AU2019338412B2 (en) | 2024-01-04 |
BR112021004693B1 (pt) | 2023-01-31 |
US11262468B2 (en) | 2022-03-01 |
BR112021004693A2 (pt) | 2021-06-22 |
CA3112197A1 (en) | 2020-03-19 |
WO2020056073A1 (en) | 2020-03-19 |
CA3112197C (en) | 2023-08-29 |
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