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MX2015005627A - Sistemas y metodos para conversion de profundidad de datos sismicos 3d utlizando redes neuronales artificiales. - Google Patents

Sistemas y metodos para conversion de profundidad de datos sismicos 3d utlizando redes neuronales artificiales.

Info

Publication number
MX2015005627A
MX2015005627A MX2015005627A MX2015005627A MX2015005627A MX 2015005627 A MX2015005627 A MX 2015005627A MX 2015005627 A MX2015005627 A MX 2015005627A MX 2015005627 A MX2015005627 A MX 2015005627A MX 2015005627 A MX2015005627 A MX 2015005627A
Authority
MX
Mexico
Prior art keywords
seismic data
methods
systems
neural networks
artificial neural
Prior art date
Application number
MX2015005627A
Other languages
English (en)
Inventor
Stewart Arthur Levin
Jacky M Wiener
Michael John Eberhard
Original Assignee
Landmark Graphics Corp
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Landmark Graphics Corp filed Critical Landmark Graphics Corp
Publication of MX2015005627A publication Critical patent/MX2015005627A/es

Links

Classifications

    • 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
    • G01V1/32—Transforming one recording into another or one representation into another
    • 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
    • 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
    • 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
    • G01V1/301—Analysis for determining seismic cross-sections or geostructures
    • G01V1/302—Analysis for determining seismic cross-sections or geostructures in 3D data cubes
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/40—Transforming data representation
    • G01V2210/48—Other transforms
    • 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
    • G01V2210/64—Geostructures, e.g. in 3D data cubes
    • 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
    • G01V2210/66—Subsurface modeling

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)
  • Computational Linguistics (AREA)
  • General Engineering & Computer Science (AREA)
  • Biophysics (AREA)
  • Artificial Intelligence (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Biomedical Technology (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Health & Medical Sciences (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Geophysics And Detection Of Objects (AREA)
  • Processing Or Creating Images (AREA)
  • Image Processing (AREA)

Abstract

La presente invención cumple con las necesidades anteriores y supera una o más deficiencias en el arte actual al proporcionar sistemas y métodos para la conversión de datos sísmicos 3D agrupados, o preferiblemente, migrados en tiempo y atributos sísmicos asociados de un dominio del tiempo a un dominio de la profundidad. En una modalidad, la presente invención incluye un método para convocar datos sísmicos tridimensionales de un dominio del tiempo a un dominio de la profundidad.
MX2015005627A 2012-12-05 2012-12-05 Sistemas y metodos para conversion de profundidad de datos sismicos 3d utlizando redes neuronales artificiales. MX2015005627A (es)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/US2012/067989 WO2014088571A1 (en) 2012-12-05 2012-12-05 Systems and methods for 3d seismic data depth conversion utilizing artificial neural networks

Publications (1)

Publication Number Publication Date
MX2015005627A true MX2015005627A (es) 2016-02-03

Family

ID=50883829

Family Applications (1)

Application Number Title Priority Date Filing Date
MX2015005627A MX2015005627A (es) 2012-12-05 2012-12-05 Sistemas y metodos para conversion de profundidad de datos sismicos 3d utlizando redes neuronales artificiales.

Country Status (9)

Country Link
US (1) US20150316673A1 (es)
EP (1) EP2888606B1 (es)
CN (1) CN104884974A (es)
AU (1) AU2012396293B2 (es)
CA (1) CA2890187C (es)
MX (1) MX2015005627A (es)
RU (1) RU2015110010A (es)
SG (1) SG11201501141UA (es)
WO (1) WO2014088571A1 (es)

Cited By (1)

* Cited by examiner, † Cited by third party
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CN106094028A (zh) * 2016-06-22 2016-11-09 中国石油化工股份有限公司 基于位移场的深度域地震属性提取方法

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CN105247546A (zh) 2013-06-10 2016-01-13 埃克森美孚上游研究公司 确定用于井动态优化的井参数
CA2960134A1 (en) * 2014-10-14 2016-04-21 Landmark Graphics Corporation Using representative elemental volume to determine subset volume in an area of interest earth model
CN105974475B (zh) * 2016-06-24 2018-02-06 中国石油集团川庆钻探工程有限公司地球物理勘探公司 一种时深转换方法
EP3507771B1 (en) * 2016-09-15 2020-06-24 Google LLC Image depth prediction neural networks
CN106873033B (zh) * 2017-03-13 2019-03-22 中国海洋石油集团有限公司 一种基于神经网络的多属性地震信息融合裂缝预测的方法
US11231513B2 (en) * 2017-12-06 2022-01-25 Chevron U.S.A. Inc. Systems and methods for refining estimated parameter values in seismic imaging
CN108490491A (zh) * 2018-03-06 2018-09-04 中国石油集团东方地球物理勘探有限责任公司 一种基于波形指示反演的滩体预测方法
CN110837111B (zh) * 2018-08-15 2021-08-24 中国石油化工股份有限公司 地震数据插值方法及系统
US20220206176A1 (en) * 2019-05-10 2022-06-30 Schlumberger Technology Corporation System and method for identifying subsurface structures
CN112346117B (zh) * 2019-08-09 2023-06-30 中国石油天然气集团有限公司 一种基于地震属性融合的储层特征预测方法和装置
US11169287B2 (en) 2020-03-27 2021-11-09 Saudi Arabian Oil Company Method and system for automated velocity model updating using machine learning
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
CN113589363B (zh) * 2020-04-30 2024-03-19 中国石油化工股份有限公司 融合人工神经网络与地质统计学的油气预测新方法
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
EP4348523A1 (en) * 2021-05-27 2024-04-10 Services Pétroliers Schlumberger Seismic well tie based on machine learning
US11668848B2 (en) 2021-06-24 2023-06-06 Saudi Arabian Oil Company Method and system for seismic imaging using S-wave velocity models and machine learning
US11874419B2 (en) * 2021-06-30 2024-01-16 Saudi Arabian Oil Company System and method for automated domain conversion for seismic well ties
CN113671568B (zh) * 2021-08-20 2022-07-15 中国科学院武汉岩土力学研究所 一种扰动应力-微震同时空集成传感系统及方法
US12196903B2 (en) * 2021-12-15 2025-01-14 Saudi Arabian Oil Company Method and system for determining seismic velocities using global path tracing

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Publication number Priority date Publication date Assignee Title
US20060047429A1 (en) * 2004-08-24 2006-03-02 Adams Steven L Method of estimating geological formation depths by converting interpreted seismic horizons from the time domain to the depth domain
US7613665B2 (en) * 2005-06-24 2009-11-03 Halliburton Energy Services, Inc. Ensembles of neural networks with different input sets
US7525874B2 (en) * 2006-04-21 2009-04-28 Prism Seismic Inc. Method for converting seismic data from the time domain to the depth domain
CN104614766B (zh) * 2008-05-22 2017-10-13 埃克森美孚上游研究公司 地震层位骨架化
GB0820377D0 (en) * 2008-11-07 2008-12-17 Isis Innovation Mapping and characterization of cavitation activity
CN102147479B (zh) * 2011-01-11 2013-05-29 中国海洋石油总公司 一种储层空间物性参数的建模方法
US9026420B2 (en) * 2011-01-13 2015-05-05 Landmark Graphics Corporation Method and system of updating a geocellular model
US10055684B2 (en) * 2011-01-31 2018-08-21 Landmark Graphics Corporation System and method for using an artificial neural network to simulate pipe hydraulics in a reservoir simulator
CN102565853A (zh) * 2011-12-20 2012-07-11 中国石油集团川庆钻探工程有限公司地球物理勘探公司 对地质构造的速度模型进行建模的方法

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106094028A (zh) * 2016-06-22 2016-11-09 中国石油化工股份有限公司 基于位移场的深度域地震属性提取方法

Also Published As

Publication number Publication date
SG11201501141UA (en) 2015-03-30
AU2012396293B2 (en) 2016-04-21
AU2012396293A1 (en) 2015-03-19
RU2015110010A (ru) 2017-01-11
CA2890187C (en) 2018-11-06
CA2890187A1 (en) 2014-06-12
EP2888606B1 (en) 2019-11-13
US20150316673A1 (en) 2015-11-05
CN104884974A (zh) 2015-09-02
WO2014088571A1 (en) 2014-06-12
EP2888606A4 (en) 2016-07-27
EP2888606A1 (en) 2015-07-01

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