NO20052731L - Fremgangsmate for modellering av hydrodynamiske karakteristikker til flerfasestrommer ved bruk av neurale nettverk - Google Patents

Fremgangsmate for modellering av hydrodynamiske karakteristikker til flerfasestrommer ved bruk av neurale nettverk

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Publication number
NO20052731L
NO20052731L NO20052731A NO20052731A NO20052731L NO 20052731 L NO20052731 L NO 20052731L NO 20052731 A NO20052731 A NO 20052731A NO 20052731 A NO20052731 A NO 20052731A NO 20052731 L NO20052731 L NO 20052731L
Authority
NO
Norway
Prior art keywords
flows
modeling
regimes
pipelines
neural
Prior art date
Application number
NO20052731A
Other languages
English (en)
Other versions
NO20052731D0 (no
Inventor
Isabelle Rey-Fabret
Quang-Huy Tran
Veronique Henriot
Original Assignee
Inst Francais Du Petrole
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 Inst Francais Du Petrole filed Critical Inst Francais Du Petrole
Publication of NO20052731D0 publication Critical patent/NO20052731D0/no
Publication of NO20052731L publication Critical patent/NO20052731L/no

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Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B17/00—Systems involving the use of models or simulators of said systems
    • G05B17/02—Systems involving the use of models or simulators of said systems electric
    • 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
    • G06N3/045—Combinations of 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
    • G06N3/04—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

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • General Engineering & Computer Science (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Biomedical Technology (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Health & Medical Sciences (AREA)
  • Automation & Control Theory (AREA)
  • Measuring Volume Flow (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Feedback Control In General (AREA)

Abstract

Fremgangsmåte beregnet for modellering ved hjelp av neurale nettverk i sanntid av hydrodynamiske særtrekk ved flerfasestrømninger i transient fase i rørledninger. For det formål å kunne spesifikt ta med i beregningen de mulige strømningsregimer for fluider i rørledninger, vil forskjellige neurale eller "sakkyndighets" -modeller utformet for flere strømningsregimer (eller underregimer) innenfor hele variasjonsområdet for de hydrodynamiske særtrekk for strømningene (fortrinnsvis for hver og en av disse), så vel som en neuralmodell for å anslå sannsynligheten for at vedkommende strømninger tilhører ett av disse strømningsregimer eller underregimer, og da med kjennskap til noen særtrekk ved disse. De sannsynligheter som utledes blir brukt for avveining av de estimeringer som avgis av hver og en av neural-modellene, og resultatet av den avveide sum blir da den estimering som til slutt bibeholdes. Anvendelser i forskjellige industrier og særlig ved modellering av hydrokarbonstrømninger i rørledninger.
NO20052731A 2002-12-10 2005-06-07 Fremgangsmate for modellering av hydrodynamiske karakteristikker til flerfasestrommer ved bruk av neurale nettverk NO20052731L (no)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
FR0215570A FR2848320B1 (fr) 2002-12-10 2002-12-10 Methode pour modeliser des caracteristiques hydrodynamiques d'ecoulements polyphasiques par reseaux de neurones
PCT/FR2003/003583 WO2004063983A2 (fr) 2002-12-10 2003-12-03 Methode pour modeliser des caracteristiques hydrodynamiques d'ecoulements polyphasiques par reseaux de neurones

Publications (2)

Publication Number Publication Date
NO20052731D0 NO20052731D0 (no) 2005-06-07
NO20052731L true NO20052731L (no) 2005-07-05

Family

ID=32320135

Family Applications (1)

Application Number Title Priority Date Filing Date
NO20052731A NO20052731L (no) 2002-12-10 2005-06-07 Fremgangsmate for modellering av hydrodynamiske karakteristikker til flerfasestrommer ved bruk av neurale nettverk

Country Status (6)

Country Link
US (1) US7177787B2 (no)
BR (1) BR0317152A (no)
FR (1) FR2848320B1 (no)
GB (1) GB2412466B (no)
NO (1) NO20052731L (no)
WO (1) WO2004063983A2 (no)

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US8197700B2 (en) * 2005-12-30 2012-06-12 Saudi Arabian Oil Company Computational method for sizing three-phase separators
US20080270328A1 (en) * 2006-10-18 2008-10-30 Chad Lafferty Building and Using Intelligent Software Agents For Optimizing Oil And Gas Wells
US20080202763A1 (en) * 2007-02-23 2008-08-28 Intelligent Agent Corporation Method to Optimize Production from a Gas-lifted Oil Well
US8386221B2 (en) * 2009-12-07 2013-02-26 Nuovo Pignone S.P.A. Method for subsea equipment subject to hydrogen induced stress cracking
US9134454B2 (en) 2010-04-30 2015-09-15 Exxonmobil Upstream Research Company Method and system for finite volume simulation of flow
AU2011283196B2 (en) 2010-07-29 2014-07-31 Exxonmobil Upstream Research Company Method and system for reservoir modeling
CA2805446C (en) 2010-07-29 2016-08-16 Exxonmobil Upstream Research Company Methods and systems for machine-learning based simulation of flow
US10087721B2 (en) 2010-07-29 2018-10-02 Exxonmobil Upstream Research Company Methods and systems for machine—learning based simulation of flow
BR112013002114A2 (pt) 2010-09-20 2016-05-17 Exxonmobil Upstream Res Co formulações flexíveis e adaptáveis para simulações de reservatório complexas
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
BR112014005794A2 (pt) 2011-09-15 2017-03-28 Exxonmobil Upstream Res Co operações matriciais e vetoriais otimizadas em algoritmos de instrução limitada que executam cálculos de equação de estado
EP2901363A4 (en) 2012-09-28 2016-06-01 Exxonmobil Upstream Res Co ERROR REMOVAL IN GEOLOGICAL MODELS
EP3008282A2 (en) 2013-06-10 2016-04-20 Exxonmobil Upstream Research Company Determining well parameters for optimization of well performance
CA2948667A1 (en) 2014-07-30 2016-02-04 Exxonmobil Upstream Research Company Method for volumetric grid generation in a domain with heterogeneous material properties
US10803534B2 (en) 2014-10-31 2020-10-13 Exxonmobil Upstream Research Company Handling domain discontinuity with the help of grid optimization techniques
CA2963092C (en) 2014-10-31 2021-07-06 Exxonmobil Upstream Research Company Methods to handle discontinuity in constructing design space for faulted subsurface model using moving least squares
WO2018118374A1 (en) 2016-12-23 2018-06-28 Exxonmobil Upstream Research Company Method and system for stable and efficient reservoir simulation using stability proxies
CA3076887A1 (en) 2017-09-26 2019-04-04 Saudi Arabian Oil Company Method for cost effective thermo-dynamic fluid property predictions using machine-learning based models
WO2019132864A1 (en) * 2017-12-26 2019-07-04 Landmark Graphics Corporation Effective representation of complex three-dimensional simulation results for real-time operations

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5550761A (en) * 1994-02-08 1996-08-27 Institut Francais Du Petrole Method for modelling multiphase flows in pipelines
FR2756044B1 (fr) * 1996-11-18 1998-12-24 Inst Francais Du Petrole Methode pour constituer un modele representatif d'ecoulements polyphasiques dans des conduites de production petroliere
US6208983B1 (en) * 1998-01-30 2001-03-27 Sarnoff Corporation Method and apparatus for training and operating a neural network for detecting breast cancer
FR2812389B1 (fr) * 2000-07-27 2002-09-13 Inst Francais Du Petrole Methode et systeme pour estimer en temps reel le mode d'ecoulement d'une veine fluide polyphasique, en tous points d'une conduite
FR2818742B1 (fr) * 2000-12-22 2003-02-14 Inst Francais Du Petrole Methode pour former un module a reseaux neuronaux optimise, destine a simuler le mode d'ecoulement d'une veine de fluides polyphasiques

Also Published As

Publication number Publication date
FR2848320A1 (fr) 2004-06-11
BR0317152A (pt) 2005-11-01
WO2004063983A2 (fr) 2004-07-29
FR2848320B1 (fr) 2005-01-28
US7177787B2 (en) 2007-02-13
GB2412466B (en) 2006-12-20
WO2004063983A3 (fr) 2005-05-12
US20060025975A1 (en) 2006-02-02
NO20052731D0 (no) 2005-06-07
GB2412466A (en) 2005-09-28
GB0513744D0 (en) 2005-08-10

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