BRPI0509129A - métodos para gerar um conjunto de redes neurais e para produzir registros de furo de sondagem sintéticos de registros de nêutron pulsado reais, e, aparelho para produzir como saìdas valores sintéticos de pelo menos um parámetro geofìsico para um poço em resposta a entradas de valores reais de parámetros geofìsicos medidos no poço - Google Patents

métodos para gerar um conjunto de redes neurais e para produzir registros de furo de sondagem sintéticos de registros de nêutron pulsado reais, e, aparelho para produzir como saìdas valores sintéticos de pelo menos um parámetro geofìsico para um poço em resposta a entradas de valores reais de parámetros geofìsicos medidos no poço

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Publication number
BRPI0509129A
BRPI0509129A BRPI0509129-2A BRPI0509129A BRPI0509129A BR PI0509129 A BRPI0509129 A BR PI0509129A BR PI0509129 A BRPI0509129 A BR PI0509129A BR PI0509129 A BRPI0509129 A BR PI0509129A
Authority
BR
Brazil
Prior art keywords
records
well
synthetic
producing
drillhole
Prior art date
Application number
BRPI0509129-2A
Other languages
English (en)
Inventor
Dingding Chen
Syed Hamid
Harry D Smith Jr
Original Assignee
Halliburton Energy Serv Inc
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 Halliburton Energy Serv Inc filed Critical Halliburton Energy Serv Inc
Publication of BRPI0509129A publication Critical patent/BRPI0509129A/pt

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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • 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
    • G06N3/086—Learning methods using evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • 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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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Software Systems (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • General Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Mathematical Physics (AREA)
  • General Physics & Mathematics (AREA)
  • Computing Systems (AREA)
  • Computational Linguistics (AREA)
  • Molecular Biology (AREA)
  • General Health & Medical Sciences (AREA)
  • Biophysics (AREA)
  • Biomedical Technology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Physiology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Medical Informatics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Geophysics And Detection Of Objects (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
  • Feedback Control In General (AREA)
  • Debugging And Monitoring (AREA)
  • Medical Treatment And Welfare Office Work (AREA)

Abstract

MéTODOS PARA GERAR UM CONJUNTO DE REDES NEURAIS E PARA PRODUZIR REGISTROS DE FURO DE SONDAGEM SINTéTICOS DE REGISTROS DE NêUTRON PULSADO REAIS, E, APARELHO PARA PRODUZIR COMO SAìDAS VALORES SINTéTICOS DE PELO MENOS UM PARãMETRO GEOFìSICO PARA UM POçO EM RESPOSTA A ENTRADAS DE VALORES REAIS DE PARáMETROS GEOFìSICOS MEDIDOS NO POçO Um sistema e método para gerar um conjunto de redes neurais, Algoritmos convencionais são usados para treinar várias redes neurais tendo diversidade de erro, por exemplo tendo um número diferente de nós ocultos em cada rede. Um algoritmo genético tendo um função de aptidão de múltiplos objetivos é usado para selecionar um ou mais conjuntos. A função de aptidão inclui um objetivo de correlação de erro negativo para assegurar diversidade entre os membros de conjunto. Um algoritmo genético pode ser usado para selecionar fatores de ponderação para a função de múltiplos objetivos. Em uma aplicação, um modelo treinado pode ser usado para produzir registros de furo de sondagem sintéticos em resposta a entradas de dados de registro de furo encamisado.
BRPI0509129-2A 2004-03-26 2005-03-22 métodos para gerar um conjunto de redes neurais e para produzir registros de furo de sondagem sintéticos de registros de nêutron pulsado reais, e, aparelho para produzir como saìdas valores sintéticos de pelo menos um parámetro geofìsico para um poço em resposta a entradas de valores reais de parámetros geofìsicos medidos no poço BRPI0509129A (pt)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US10/811,403 US7280987B2 (en) 2004-03-26 2004-03-26 Genetic algorithm based selection of neural network ensemble for processing well logging data
PCT/US2005/009494 WO2005098647A2 (en) 2004-03-26 2005-03-22 Genetic algorithm based selection of neural network ensemble for processing well logging data

Publications (1)

Publication Number Publication Date
BRPI0509129A true BRPI0509129A (pt) 2007-08-28

Family

ID=35125728

Family Applications (1)

Application Number Title Priority Date Filing Date
BRPI0509129-2A BRPI0509129A (pt) 2004-03-26 2005-03-22 métodos para gerar um conjunto de redes neurais e para produzir registros de furo de sondagem sintéticos de registros de nêutron pulsado reais, e, aparelho para produzir como saìdas valores sintéticos de pelo menos um parámetro geofìsico para um poço em resposta a entradas de valores reais de parámetros geofìsicos medidos no poço

Country Status (7)

Country Link
US (1) US7280987B2 (pt)
BR (1) BRPI0509129A (pt)
CA (1) CA2559860C (pt)
EG (1) EG25977A (pt)
GB (1) GB2427946B (pt)
MX (1) MXPA06011012A (pt)
WO (1) WO2005098647A2 (pt)

Families Citing this family (61)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1205863A1 (en) * 2000-11-14 2002-05-15 Honda R&D Europe (Deutschland) GmbH Multi-objective optimization
EP1235180A1 (en) * 2001-02-26 2002-08-28 Honda R&D Europe (Deutschland) GmbH Parameter adaptation in evolution strategies
US8374974B2 (en) * 2003-01-06 2013-02-12 Halliburton Energy Services, Inc. Neural network training data selection using memory reduced cluster analysis for field model development
EP1598751B1 (en) * 2004-01-12 2014-06-25 Honda Research Institute Europe GmbH Estimation of distribution algorithm (EDA)
EP1557788B1 (en) * 2004-01-26 2008-04-16 Honda Research Institute Europe GmbH Reduction of fitness evaluations using clustering technique and neural network ensembles
US7526461B2 (en) * 2004-11-17 2009-04-28 Gm Global Technology Operations, Inc. System and method for temporal data mining
US7613665B2 (en) * 2005-06-24 2009-11-03 Halliburton Energy Services, Inc. Ensembles of neural networks with different input sets
US7587373B2 (en) * 2005-06-24 2009-09-08 Halliburton Energy Services, Inc. Neural network based well log synthesis with reduced usage of radioisotopic sources
EP1768053A1 (en) * 2005-09-12 2007-03-28 Honda Research Institute Europe GmbH Evolutionary search for robust solutions
US7505947B2 (en) * 2005-10-20 2009-03-17 International Business Machines Corporation Computer controlled method using genetic algorithms to provide non-deterministic solutions to problems involving physical restraints
US20070288410A1 (en) * 2006-06-12 2007-12-13 Benjamin Tomkins System and method of using genetic programming and neural network technologies to enhance spectral data
WO2008006883A2 (en) * 2006-07-13 2008-01-17 4Sc Ag Benzopyranopyrazoles
US8065244B2 (en) * 2007-03-14 2011-11-22 Halliburton Energy Services, Inc. Neural-network based surrogate model construction methods and applications thereof
JP2010537192A (ja) 2007-08-17 2010-12-02 インスティトゥット・フォア・エネルギテクニック ガス排出量の経験的アンサンブルに基づく仮想センシングのためのシステム及び方法
US20090182693A1 (en) * 2008-01-14 2009-07-16 Halliburton Energy Services, Inc. Determining stimulation design parameters using artificial neural networks optimized with a genetic algorithm
US8364528B2 (en) * 2008-05-06 2013-01-29 Richrelevance, Inc. System and process for improving product recommendations for use in providing personalized advertisements to retail customers
US9514388B2 (en) * 2008-08-12 2016-12-06 Halliburton Energy Services, Inc. Systems and methods employing cooperative optimization-based dimensionality reduction
WO2010024802A1 (en) * 2008-08-26 2010-03-04 Halliburton Energy Services, Inc. Method and system of processing gamma count rate decay curves using neural networks
NO329798B1 (no) * 2009-02-16 2010-12-20 Inst Energiteknik System og fremgangsmate for empirisk ensemblebasert virtuell sensing av svevestovpartikler
US8898045B2 (en) 2009-04-21 2014-11-25 Halliburton Energy Services, Inc. System and method of predicting gas saturation of a formation using neural networks
US9092737B2 (en) * 2009-07-30 2015-07-28 Northwestern University Systems, methods, and apparatus for 3-D surface mapping, compliance mapping, and spatial registration with an array of cantilevered tactile hair or whisker sensors
JP2011107648A (ja) * 2009-11-20 2011-06-02 Fujifilm Corp レンズユニット
US9104978B2 (en) 2010-10-07 2015-08-11 Microsoft Technology Licensing, Llc System and method for parameter evaluation
US9015093B1 (en) 2010-10-26 2015-04-21 Michael Lamport Commons Intelligent control with hierarchical stacked neural networks
US8775341B1 (en) 2010-10-26 2014-07-08 Michael Lamport Commons Intelligent control with hierarchical stacked neural networks
CN102681011B (zh) * 2011-03-15 2014-08-13 无锡物联网产业研究院 一种地震数据的挖掘方法和系统
US8700549B2 (en) 2012-05-23 2014-04-15 King Fahd University Of Petroleum And Minerals Method of predicting gas composition
US9022140B2 (en) 2012-10-31 2015-05-05 Resource Energy Solutions Inc. Methods and systems for improved drilling operations using real-time and historical drilling data
CN105308640A (zh) * 2013-01-31 2016-02-03 泽斯特财务公司 用于自动生成高质量不良行为通知的方法和系统
WO2014160741A1 (en) * 2013-03-27 2014-10-02 Schlumberger Canada Limited Automatic geosteering and evolutionary algorithm for use with same
EP2924560A1 (en) 2014-03-28 2015-09-30 ForecasstThis Ltd Apparatus and process for automating discovery of effective algorithm configurations for data processing using evolutionary graphical search
US9501740B2 (en) 2014-06-03 2016-11-22 Saudi Arabian Oil Company Predicting well markers from artificial neural-network-predicted lithostratigraphic facies
JP6612855B2 (ja) 2014-09-12 2019-11-27 マイクロソフト テクノロジー ライセンシング，エルエルシー 出力分布による生徒ｄｎｎの学習
US10410118B2 (en) 2015-03-13 2019-09-10 Deep Genomics Incorporated System and method for training neural networks
US11062228B2 (en) 2015-07-06 2021-07-13 Microsoft Technoiogy Licensing, LLC Transfer learning techniques for disparate label sets
EP3428856A4 (en) * 2016-03-09 2019-04-10 Sony Corporation INFORMATION PROCESSING METHOD AND INFORMATION PROCESSING DEVICE
TWI612433B (zh) * 2016-11-17 2018-01-21 財團法人工業技術研究院 整體式學習預測裝置與方法、以及非暫存電腦可讀的儲存媒介
US10474925B2 (en) 2017-07-31 2019-11-12 Industrial Technology Research Institute Deep neural network with side branches for recognizing and classifying media data and method for using the same
US11060214B2 (en) 2017-08-11 2021-07-13 Northwestern University Processing methods and apparatus to manufacture a functional, multi-scale, tapered fiber from polymer filament
US10885900B2 (en) 2017-08-11 2021-01-05 Microsoft Technology Licensing, Llc Domain adaptation in speech recognition via teacher-student learning
US11074503B2 (en) 2017-09-06 2021-07-27 SparkCognition, Inc. Execution of a genetic algorithm having variable epoch size with selective execution of a training algorithm
US11106978B2 (en) 2017-09-08 2021-08-31 SparkCognition, Inc. Execution of a genetic algorithm with variable evolutionary weights of topological parameters for neural network generation and training
US10635978B2 (en) 2017-10-26 2020-04-28 SparkCognition, Inc. Ensembling of neural network models
US11010670B2 (en) 2018-08-27 2021-05-18 D5Ai Llc Building a deep neural network with diverse strata
US11443244B2 (en) * 2019-06-05 2022-09-13 International Business Machines Corportation Parallel ensemble of machine learning algorithms
US11634980B2 (en) 2019-06-19 2023-04-25 OspreyData, Inc. Downhole and near wellbore reservoir state inference through automated inverse wellbore flow modeling
US11741370B2 (en) * 2019-08-28 2023-08-29 International Business Machines Corporation Transfer learning based on cross-domain homophily influences
US11475297B2 (en) 2019-08-28 2022-10-18 International Business Machines Corporation Cross-domain homophily quantification for transfer learning
CN111027782B (zh) * 2019-12-25 2020-11-06 安徽建筑大学 基于无中心遗传算法的建筑二次供水优化调度方法及装置
CN111414977B (zh) * 2020-03-09 2023-06-20 西南交通大学 机床主轴热误差建模的加权集成温度敏感点组合选取方法
CN111767674B (zh) * 2020-05-29 2024-03-29 中国科学技术大学 一种基于主动域适应的测井岩性识别方法
CN111915097B (zh) * 2020-08-14 2022-11-11 南通大学 基于改进遗传算法优化lstm神经网络的水质预测方法
KR102428572B1 (ko) * 2020-12-10 2022-08-03 한국전자기술연구원 유전자 알고리즘을 이용한 경량 인공지능 알고리즘의 하이퍼 파라미터 최적화 방법
US12504556B2 (en) * 2021-02-11 2025-12-23 China Petroleum & Chemical Corporation Method and apparatus for obtaining formation density
CN116522125B (zh) * 2022-01-21 2025-12-09 中国石油化工股份有限公司 测井数据异常检测方法
CN114996796B (zh) * 2022-04-20 2024-07-23 中国石油天然气集团有限公司 一种井眼轨迹预测方法及装置和系统
CN115729097B (zh) * 2022-11-18 2025-09-30 中国科学院合肥物质科学研究院 一种shmff泵送系统并联水泵节能控制方法
CN116012688B (zh) * 2023-03-27 2023-06-09 成都神鸟数据咨询有限公司 一种用于城市管理评估系统的图像增强方法
CN116644662B (zh) * 2023-05-19 2024-03-29 之江实验室 一种基于知识嵌入神经网络代理模型的布井优化方法
CN117519054B (zh) * 2023-12-11 2024-06-11 广州智业节能科技有限公司 一种高效冷站控制系统
CN118014009B (zh) * 2024-02-26 2024-09-03 广芯微电子（广州）股份有限公司 一种基于遗传算法的神经网络初值寻优方法

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5251286A (en) * 1992-03-16 1993-10-05 Texaco, Inc. Method for estimating formation permeability from wireline logs using neural networks
US5444619A (en) * 1993-09-27 1995-08-22 Schlumberger Technology Corporation System and method of predicting reservoir properties
US5828981A (en) * 1995-05-11 1998-10-27 Texaco Inc. Generating pore types and synthetic capillary pressure curves from wireline logs using neural networks
US5862513A (en) * 1996-11-01 1999-01-19 Western Atlas International, Inc. Systems and methods for forward modeling of well logging tool responses
US6789620B2 (en) * 2001-02-16 2004-09-14 Halliburton Energy Services, Inc. Downhole sensing and flow control utilizing neural networks

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Publication number Publication date
US7280987B2 (en) 2007-10-09
WO2005098647A2 (en) 2005-10-20
GB2427946A (en) 2007-01-10
CA2559860A1 (en) 2005-10-20
MXPA06011012A (es) 2007-01-25
GB0620649D0 (en) 2006-12-06
US20050246297A1 (en) 2005-11-03
EG25977A (en) 2012-11-18
WO2005098647A3 (en) 2009-04-16
CA2559860C (en) 2011-06-07
GB2427946B (en) 2008-03-19

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B07A Application suspended after technical examination (opinion) [chapter 7.1 patent gazette]
B09B Patent application refused [chapter 9.2 patent gazette]
B09B Patent application refused [chapter 9.2 patent gazette]