US20050273296A1 - Neural network model for electric submersible pump system - Google Patents

Neural network model for electric submersible pump system Download PDF

Info

Publication number
US20050273296A1
US20050273296A1 US11/195,080 US19508005A US2005273296A1 US 20050273296 A1 US20050273296 A1 US 20050273296A1 US 19508005 A US19508005 A US 19508005A US 2005273296 A1 US2005273296 A1 US 2005273296A1
Authority
US
United States
Prior art keywords
submersible pump
pump application
electric submersible
neural network
behavior
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.)
Abandoned
Application number
US11/195,080
Inventor
Dehao Zhu
Alex Crossley
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Baker Hughes Holdings LLC
Original Assignee
Baker Hughes 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 Baker Hughes Inc filed Critical Baker Hughes Inc
Priority to US11/195,080 priority Critical patent/US20050273296A1/en
Publication of US20050273296A1 publication Critical patent/US20050273296A1/en
Abandoned legal-status Critical Current

Links

Images

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
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only

Definitions

  • the present invention relates to modeling behavior of a characteristic of real-world artificial lift technology and systems, more specifically to modeling the behavior of a characteristic of an oil and water artificial lift pump such as an electrical submersible pump (ESP) and its application.
  • ESP electrical submersible pump
  • Newer modeling techniques can often save time and cost, e.g. a model of a wind-tunnel session can often be less costly—and nearly or as accurate—as using an actual wind tunnel.
  • Modeling of behavior such as neural networks, can adapt to changes in supplied environmental variables and can further be refined using real world data, leading to decreased time to market and decreased time to test a new design.
  • Such modeling has been used and/or suggested for such applications as replacement and/or augmentation of flow performance features in a wind tunnel and other flow modeling applications.
  • FIG. 1 is a schematic diagram of an exemplary system for modeling behavior of electric submersible pump application.
  • FIG. 2 is a flowchart of an embodiment of a method of modeling behavior of electric submersible pump application.
  • FIG. 3 is a flowchart of an embodiment of a further method of modeling behavior of electric submersible pump application.
  • system 10 for modeling behavior of electric submersible pump application 1 comprises computer 12 , data store 14 operatively in communication with computer 12 , training data set 20 comprising data 22 stored in data store 14 , source 30 of measured data 23 for electric submersible pump application 1 operatively in communication with computer 12 ; and neural network model 40 of electric submersible pump application 1 .
  • Training data set 20 is of data related to one or more behaviors of electric submersible pump application 1 .
  • Training data set 20 comprises data representative of an electric submersible pump application. These data may relate to at least one predetermined characteristic of the electric submersible pump application and may further be arranged as a plurality of data sets generated from deterministic model 42 of electric submersible pump application 1 where deterministic model 42 may be obtained using at least one mathematical algorithm or at least one collection of mathematical algorithms based on engineering and physics principles that model one or more desired behaviors of electrical submersible pump application 1 .
  • Data such as data from source 30 of measured data 23 , may be stored in data store 14 which may be a persistent data store, by way of example and not limitation including a magnetic medium, an optical medium, or an electronic medium.
  • Neural network model 40 is resident in computer 12 . Neural network model is able to utilize training set 20 and measured data 23 to manipulate a model of submersible electrical pump application 1 .
  • neural network model 40 is an adaptable neural network, and, more typically, self-adaptable.
  • Neural network model 40 may comprise a weight matrix, a topology of neural network model 40 , a training algorithm, an activation function, or the like, or a combination thereof.
  • behavior of a characteristic of electric submersible pump application 1 may be predicted by generating, step 100 , a training data set 20 comprising data representative of an electric submersible pump application 1 , the data related to at least one predetermined characteristic of the electric submersible pump application 1 ; establishing, step 110 , an initial neural network model 40 model for the electric submersible pump application 1 , the neural network model 40 model related to the at least one predetermined characteristic of the electric submersible pump application 1 ; using, step 120 , the training data set 20 by the initial neural network model 40 to create a predictive model of behavior of the at least one predetermined characteristic of the electric submersible pump application 1 ; obtaining, step 130 , measured electrical submersible pump application operational data; and adapting, step 140 , the neural network model 40 using the measured electrical submersible pump application operational data 23 to create a predictive model of behavior of the at least one predetermined characteristic of the electric submersible pump application 1 .
  • training data set 20 is generated from deterministic model 42 of an electric submersible pump application 1 .
  • Training data set 20 may be obtained from data related to electric submersible pump application 1 as installed and used in a real world environment.
  • weight matrix 45 may be adjusted using training algorithm 46 (not shown in the figures) that corresponds to neural network model 40 to predict actual behavior of electric submersible pump application 1 such as by minimizing a training error.
  • a predetermined output of neural network model 40 may be used to aid with data matching of historical measured data versus the output, fault diagnosis of electric submersible pump application 1 , prediction of an operational characteristic of the electric submersible pump application 1 , or the like, or a combination thereof.
  • Adaptation of neural network model 40 may be self-adaptation.
  • modeling may occur in stages, e.g. a learning stage may be provided, step 200 , a testing stage may be provided, step 210 , and an adaptive stage may be provided, step 220 .
  • the learning stage may comprise modeling one or more desired behaviors of electric submersible pump application 1 such as by using one or more deterministic mathematical algorithms based on engineering and physics principles that model the desired behavior of electrical submersible pump application 1 .
  • Training data set 20 comprising of data related to the desired behavior of electric submersible pump application 1 , may be generated from the modeled behavior. Once generated, training data set 20 may be provided to an initial neural network model 40 and neural network model 40 created to model one or more predetermined characteristics of electric submersible pump application 1 .
  • the behavior model of electric submersible pump application 1 may be dependent on a predetermined number of inputs and outputs related to behavior of an actual electric submersible pump application 1 . Such a behavior model may be useful for a prediction of a desired behavior of an actual electric submersible pump application 1 , adaptation of a desired behavior of an actual electric submersible pump application 1 , or the like, or a combination thereof.
  • a measured data set 23 may be obtained for the electric submersible pump application 1 .
  • At least one output from neural network model 40 may be generated where the output relates to the one or more predetermined characteristics of the electric submersible pump application 1 , e.g. for validation purposes.
  • the output from neural network model 40 may comprise a simulated value for the predetermined characteristic of the electric submersible pump application 1 , a calculated value for determined characteristic of the electric submersible pump application 1 , or the like, or a combination thereof.
  • One or more predetermined outputs of the behavior model of the neural network model 40 may be compared to real world data for a desired behavior modeled by the neural network model 40 . Further, during an adaptive stage, revisions of neural network model 40 may be iterated, e.g. by self adaptation of neural network model 40 , to refine obtained predicted electric submersible pump application 1 behaviors, e.g. by the comparison process described above.
  • neural network model 40 may be adapted, either by neural network model 40 itself or by another process or by human intervention.
  • neural network model 40 may be used to provide automated interpretation of real world data related to electric submersible pump application 1 .
  • real world data related to actual behavior of electric submersible pump application 1 may be obtained, e.g. from source 30 and then provided to neural network model 40 . These real world data may be used during iterations of neural network model 40 to improve predictions of behavior.

Abstract

An apparatus and method is disclosed for modeling an electric submersible pump using a neural network, data from a deterministic model, and, optionally, data obtained from a real world electric submersible pump. It is emphasized that this abstract is provided to comply with the rules requiring an abstract which will allow a searcher or other reader to quickly ascertain the subject matter of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims.

Description

    PRIORITY INFORMATION
  • This application is a continuation of U.S. patent application Ser. No. 10/226,941, filed on Aug. 23, 2002.
    • FIELD OF INVENTION
  • The present invention relates to modeling behavior of a characteristic of real-world artificial lift technology and systems, more specifically to modeling the behavior of a characteristic of an oil and water artificial lift pump such as an electrical submersible pump (ESP) and its application.
    • BACKGROUND OF THE INVENTION
  • Prior to implementing a design, especially in a production mode, such as for machinery including motors or pumps used with artificial lift technology and systems, manufacturers often like to test assumptions and engineering decisions about that machine. For some machinery, a testing phase can become expensive and involve prototypes and project scheduling delays. Exhaustive testing, e.g. using multiple scenarios to investigate response of the machine to the scenario, can be prohibitively costly or time consuming or both.
  • Newer modeling techniques can often save time and cost, e.g. a model of a wind-tunnel session can often be less costly—and nearly or as accurate—as using an actual wind tunnel. Modeling of behavior, such as neural networks, can adapt to changes in supplied environmental variables and can further be refined using real world data, leading to decreased time to market and decreased time to test a new design. Such modeling has been used and/or suggested for such applications as replacement and/or augmentation of flow performance features in a wind tunnel and other flow modeling applications.
    • BRIEF DESCRIPTION OF THE DRAWINGS
  • FIG. 1 is a schematic diagram of an exemplary system for modeling behavior of electric submersible pump application; and
  • FIG. 2 is a flowchart of an embodiment of a method of modeling behavior of electric submersible pump application; and
  • FIG. 3 is a flowchart of an embodiment of a further method of modeling behavior of electric submersible pump application.
    • DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT
  • Referring now to FIG. 1, system 10 for modeling behavior of electric submersible pump application 1 comprises computer 12, data store 14 operatively in communication with computer 12, training data set 20 comprising data 22 stored in data store 14, source 30 of measured data 23 for electric submersible pump application 1 operatively in communication with computer 12; and neural network model 40 of electric submersible pump application 1.
  • Training data set 20 is of data related to one or more behaviors of electric submersible pump application 1. Training data set 20 comprises data representative of an electric submersible pump application. These data may relate to at least one predetermined characteristic of the electric submersible pump application and may further be arranged as a plurality of data sets generated from deterministic model 42 of electric submersible pump application 1 where deterministic model 42 may be obtained using at least one mathematical algorithm or at least one collection of mathematical algorithms based on engineering and physics principles that model one or more desired behaviors of electrical submersible pump application 1.
  • Data, such as data from source 30 of measured data 23, may be stored in data store 14 which may be a persistent data store, by way of example and not limitation including a magnetic medium, an optical medium, or an electronic medium.
  • Neural network model 40 is resident in computer 12. Neural network model is able to utilize training set 20 and measured data 23 to manipulate a model of submersible electrical pump application 1. In a preferred embodiment, neural network model 40 is an adaptable neural network, and, more typically, self-adaptable.
  • Neural network model 40 may comprise a weight matrix, a topology of neural network model 40, a training algorithm, an activation function, or the like, or a combination thereof.
  • In the operation of an exemplary embodiment, referring now to FIG. 2, behavior of a characteristic of electric submersible pump application 1 may be predicted by generating, step 100, a training data set 20 comprising data representative of an electric submersible pump application 1, the data related to at least one predetermined characteristic of the electric submersible pump application 1; establishing, step 110, an initial neural network model 40 model for the electric submersible pump application 1, the neural network model 40 model related to the at least one predetermined characteristic of the electric submersible pump application 1; using, step 120, the training data set 20 by the initial neural network model 40 to create a predictive model of behavior of the at least one predetermined characteristic of the electric submersible pump application 1; obtaining, step 130, measured electrical submersible pump application operational data; and adapting, step 140, the neural network model 40 using the measured electrical submersible pump application operational data 23 to create a predictive model of behavior of the at least one predetermined characteristic of the electric submersible pump application 1.
  • In a preferred embodiment, training data set 20 is generated from deterministic model 42 of an electric submersible pump application 1. Training data set 20 may be obtained from data related to electric submersible pump application 1 as installed and used in a real world environment.
  • As will be understood by those of ordinary skill in the neural network arts, weight matrix 45 (not shown in the figures) may be adjusted using training algorithm 46 (not shown in the figures) that corresponds to neural network model 40 to predict actual behavior of electric submersible pump application 1 such as by minimizing a training error.
  • A predetermined output of neural network model 40 may be used to aid with data matching of historical measured data versus the output, fault diagnosis of electric submersible pump application 1, prediction of an operational characteristic of the electric submersible pump application 1, or the like, or a combination thereof.
  • Adaptation of neural network model 40 may be self-adaptation.
  • In a further embodiment, referring now to FIG. 3, modeling may occur in stages, e.g. a learning stage may be provided, step 200, a testing stage may be provided, step 210, and an adaptive stage may be provided, step 220.
  • The learning stage may comprise modeling one or more desired behaviors of electric submersible pump application 1 such as by using one or more deterministic mathematical algorithms based on engineering and physics principles that model the desired behavior of electrical submersible pump application 1. Training data set 20, comprising of data related to the desired behavior of electric submersible pump application 1, may be generated from the modeled behavior. Once generated, training data set 20 may be provided to an initial neural network model 40 and neural network model 40 created to model one or more predetermined characteristics of electric submersible pump application 1.
  • The behavior model of electric submersible pump application 1 may be dependent on a predetermined number of inputs and outputs related to behavior of an actual electric submersible pump application 1. Such a behavior model may be useful for a prediction of a desired behavior of an actual electric submersible pump application 1, adaptation of a desired behavior of an actual electric submersible pump application 1, or the like, or a combination thereof.
  • During a testing stage, a measured data set 23 may be obtained for the electric submersible pump application 1. At least one output from neural network model 40 may be generated where the output relates to the one or more predetermined characteristics of the electric submersible pump application 1, e.g. for validation purposes. The output from neural network model 40 may comprise a simulated value for the predetermined characteristic of the electric submersible pump application 1, a calculated value for determined characteristic of the electric submersible pump application 1, or the like, or a combination thereof.
  • One or more predetermined outputs of the behavior model of the neural network model 40 may be compared to real world data for a desired behavior modeled by the neural network model 40. Further, during an adaptive stage, revisions of neural network model 40 may be iterated, e.g. by self adaptation of neural network model 40, to refine obtained predicted electric submersible pump application 1 behaviors, e.g. by the comparison process described above.
  • For example, once compared, further analysis may be undertaken, e.g. neural network model 40 may be adapted, either by neural network model 40 itself or by another process or by human intervention. In a further embodiment, neural network model 40 may be used to provide automated interpretation of real world data related to electric submersible pump application 1. By way of additional example, real world data related to actual behavior of electric submersible pump application 1 may be obtained, e.g. from source 30 and then provided to neural network model 40. These real world data may be used during iterations of neural network model 40 to improve predictions of behavior.
  • It will be understood that various changes in the details, materials, and arrangements of the parts which have been described and illustrated above in order to explain the nature of this invention may be made by those skilled in the art without departing from the principle and scope of the invention as recited in the appended claims.

Claims (20)

1. A method of predicting behavior of a characteristic of an electric submersible pump application, comprising:
a. generating a training data set comprising data representative of an electric submersible pump application, the data related to at least one predetermined characteristic of the electric submersible pump application;
b. establishing an initial neural network model for the electric submersible pump application, the neural network model related to the at least one predetermined characteristic of the electric submersible pump application; and
c. using the training data set by the initial neural network to create a predictive model of behavior of the at least one predetermined characteristic of the electric submersible pump application.
2. The method of claim 1, further comprising:
a. obtaining measured electrical submersible pump application operational data; and
b. adapting the neural network using the measured electrical submersible pump application operational data to create a predictive model of behavior of the at least one predetermined characteristic of the electric submersible pump application.
3. The method of claim 1, wherein:
a. the training data set is generated from a deterministic model of an electric submersible pump application.
4. The method of claim 3, wherein:
a. the deterministic model comprises mathematical algorithm based on engineering and physics principles that model the behavior of an electrical submersible pump application.
5. The method of claim 3, wherein:
a. the training data set comprises a plurality of data sets generated from the deterministic model of an electric submersible pump application obtained using at least one mathematical algorithm based on engineering and physics principles that model the behavior of an electrical submersible pump application.
6. The method of claim 3, wherein:
a. the data set is obtained from data related to the electric submersible pump application as installed and used in a real world environment.
7. The method of claim 1, wherein:
a. the neural network comprises at least one of (i) a weight matrix, (ii) a topology of neural network, (iii) a training algorithm, or (iv) an activation function.
8. The method of claim 7, further comprising:
a. adjusting the weight matrix using a training algorithm that corresponds to the neural network to predict actual behavior of the electric submersible pump application by minimizing a training error.
9. The method of claim 1, further comprising:
a. using a predetermined output of the neural network to aid with at least one of (i) data matching of historical measured data versus the output, (ii) fault diagnosis of the electric submersible pump application, or (iii) prediction of an operational characteristic of the electric submersible pump application.
10. The method of claim 2, wherein:
a. adaptation of the neural network is self-adaptation.
11. A method of predicting behavior of an electric submersible pump application, comprising:
a. providing a learning stage, further comprising:
i. modeling a behavior of an electric submersible pump application using at least one deterministic mathematical algorithm based on engineering and physics principles that model the behavior of an electrical submersible pump application;
ii. generating a training data set comprising data related to the behavior of a electric submersible pump application from the modeled behavior;
iii. providing the training data set to an initial neural network; and
iv. creating a neural network model of a predetermined characteristic of the electric submersible pump application;
b. providing a testing stage, further comprising:
i. obtaining a measured data set for the electric submersible pump application; and
ii. generating at least one output from the neural network related to the predetermined characteristic of the electric submersible pump application for a validation purpose; and
c. providing an adaptive stage, further comprising:
i. iterating the neural network model to refine a predicted electric submersible pump application behavior.
12. The method of claim 11, wherein:
a. the behavior model of the electric submersible pump application is dependent on a predetermined number of inputs and outputs related to behavior of an actual electric submersible pump application.
13. The method of claim 11, wherein:
a. the behavior model is useful for at least one of (i) a prediction of a desired behavior of an actual electric submersible pump application or (ii) adaptation of a desired behavior of an actual electric submersible pump application.
14. The method of claim 11, wherein:
a. the at least one output from the neural network related to a desired characteristic of the electric submersible pump application comprises at least one of (i) a simulated value for the predetermined characteristic of the electric submersible pump application or (ii) a calculated value for determined characteristic of the electric submersible pump application.
15. The method of claim 11, further comprising:
a. obtaining real world data related to actual behavior of the electric submersible pump application;
b. providing the real world data to the neural network; and
c. using the real world data during the iterations of the behavior model to create successive revisions of the neural network of the electric submersible pump application to refine the predicted electric submersible pump application behavior.
16. The method of claim 11, further comprising:
a. comparing a predetermined output of the behavior model of the neural network to a real world datum for a desired behavior modeled by the neural network.
17. The method of claim 11, further comprising:
a. using the neural network to provide automated interpretation of real world data related to the electric submersible pump application.
18. A system for modeling behavior of a electric submersible pump application, comprising:
a. a computer;
b. a data store operatively in communication with the computer;
C. a training data set generator adapted to generate a training data set comprising data stored in the data store, the training data set related to behavior of a electric submersible pump application;
d. a source of measured data for the electric submersible pump application operatively in communication with the computer, data from the source of measured data being storable in the data store;
e. a software modeler adapted to provide a learning stage, the learning stage comprising modeling a behavior of an electric submersible pump application using at least one deterministic mathematical algorithm based on engineering and physics principles that model the behavior of an electrical submersible pump application, providing the training data set to an initial neural network, and creating a neural network model of a predetermined characteristic of the electric submersible pump application; and
f. a neural network model of the electric submersible pump application, the neural network resident in the computer, the neural network able to utilize the training data set and measured data to manipulate a model of the submersible electrical pump application and generate at least one output related to the predetermined characteristic of the electric submersible pump application for a validation purpose.
19. The system of claim 18, wherein:
a. the neural network model is an adaptable neural network adapted to be interated to refine a predicted electric submersible pump application behavior.
20. The system of claim 19, wherein:
a. the adaptable neural network model is self-adaptable.
US11/195,080 2003-08-18 2005-08-02 Neural network model for electric submersible pump system Abandoned US20050273296A1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US11/195,080 US20050273296A1 (en) 2003-08-18 2005-08-02 Neural network model for electric submersible pump system

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US10/644,073 US6947870B2 (en) 2003-08-18 2003-08-18 Neural network model for electric submersible pump system
US11/195,080 US20050273296A1 (en) 2003-08-18 2005-08-02 Neural network model for electric submersible pump system

Related Parent Applications (1)

Application Number Title Priority Date Filing Date
US10/226,941 Continuation US7644773B2 (en) 2002-08-23 2002-08-23 Self-conforming screen

Publications (1)

Publication Number Publication Date
US20050273296A1 true US20050273296A1 (en) 2005-12-08

Family

ID=34194004

Family Applications (2)

Application Number Title Priority Date Filing Date
US10/644,073 Expired - Lifetime US6947870B2 (en) 2003-08-18 2003-08-18 Neural network model for electric submersible pump system
US11/195,080 Abandoned US20050273296A1 (en) 2003-08-18 2005-08-02 Neural network model for electric submersible pump system

Family Applications Before (1)

Application Number Title Priority Date Filing Date
US10/644,073 Expired - Lifetime US6947870B2 (en) 2003-08-18 2003-08-18 Neural network model for electric submersible pump system

Country Status (1)

Country Link
US (2) US6947870B2 (en)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070265713A1 (en) * 2006-02-03 2007-11-15 Michel Veillette Intelligent Monitoring System and Method for Building Predictive Models and Detecting Anomalies
US20130175030A1 (en) * 2012-01-10 2013-07-11 Adunola Ige Submersible Pump Control
US9645575B2 (en) 2013-11-27 2017-05-09 Adept Ai Systems Inc. Method and apparatus for artificially intelligent model-based control of dynamic processes using probabilistic agents
WO2018200269A1 (en) * 2017-04-28 2018-11-01 Schlumberger Technology Corporation Methods related to startup of an electric submersible pump
US10900489B2 (en) 2013-11-13 2021-01-26 Schlumberger Technology Corporation Automatic pumping system commissioning
WO2023009741A1 (en) * 2021-07-28 2023-02-02 Schlumberger Technology Corporation Integrating domain knowledge with machine learning to optimize electrical submersible pump performance

Families Citing this family (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8713025B2 (en) 2005-03-31 2014-04-29 Square Halt Solutions, Limited Liability Company Complete context search system
US8082217B2 (en) * 2007-06-11 2011-12-20 Baker Hughes Incorporated Multiphase flow meter for electrical submersible pumps using artificial neural networks
US9298859B2 (en) * 2012-02-13 2016-03-29 Baker Hughes Incorporated Electrical submersible pump design parameters recalibration methods, apparatus, and computer readable medium
US20130278183A1 (en) * 2012-04-19 2013-10-24 Schlumberger Technology Corporation Load filters for medium voltage variable speed drives in electrical submersible pump systems
CA2944635A1 (en) * 2014-04-03 2015-10-08 Schlumberger Canada Limited State estimation and run life prediction for pumping system
WO2016094530A1 (en) 2014-12-09 2016-06-16 Schlumberger Canada Limited Electric submersible pump event detection
DE102017216634A1 (en) * 2017-09-20 2019-03-21 Siemens Aktiengesellschaft Method and training data generator for configuring a technical system and control device for controlling the technical system
US10962968B2 (en) * 2018-04-12 2021-03-30 Saudi Arabian Oil Company Predicting failures in electrical submersible pumps using pattern recognition
WO2020117252A1 (en) 2018-12-06 2020-06-11 Halliburton Energy Services, Inc. Distributed and centralized adaptive control of electric submersible pumps
CA3140567A1 (en) * 2019-05-17 2020-11-26 Schlumberger Canada Limited System and method for managing wellsite event detection
EP3822489B8 (en) 2019-11-15 2024-03-27 Grundfos Holding A/S Method for determining a fluid flow rate through a pump

Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5225352A (en) * 1989-09-06 1993-07-06 Centre National De La Recherche Scientifique Agents for the diagnosis of demyelinating neuropathies, in particular multiple sclerosis
US5418710A (en) * 1991-10-31 1995-05-23 Kabushiki Kaisha Toshiba Simulator using a neural network
US5479571A (en) * 1991-06-14 1995-12-26 The Texas A&M University System Neural node network and model, and method of teaching same
US5704012A (en) * 1993-10-08 1997-12-30 International Business Machines Corporation Adaptive resource allocation using neural networks
US5767606A (en) * 1992-11-27 1998-06-16 Hydor S.R.L. Synchronous electric motor, particularly for submersible pumps, and pump including the motor
US5862513A (en) * 1996-11-01 1999-01-19 Western Atlas International, Inc. Systems and methods for forward modeling of well logging tool responses
US5919267A (en) * 1997-04-09 1999-07-06 Mcdonnell Douglas Corporation Neural network fault diagnostics systems and related method
US6305216B1 (en) * 1999-12-21 2001-10-23 Production Testing Services Method and apparatus for predicting the fluid characteristics in a well hole
US6353815B1 (en) * 1998-11-04 2002-03-05 The United States Of America As Represented By The United States Department Of Energy Statistically qualified neuro-analytic failure detection method and system
US6529780B1 (en) * 1997-04-14 2003-03-04 Siemens Aktiengesellschaft Method for automatic operation of industrial plants
US6704689B1 (en) * 2000-01-20 2004-03-09 Camco International, Inc. Complexity index methodology for the analysis of run life performance

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6012015A (en) 1995-02-09 2000-01-04 Baker Hughes Incorporated Control model for production wells
US5634522A (en) * 1996-05-31 1997-06-03 Hershberger; Michael D. Liquid level detection for artificial lift system control

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5225352A (en) * 1989-09-06 1993-07-06 Centre National De La Recherche Scientifique Agents for the diagnosis of demyelinating neuropathies, in particular multiple sclerosis
US5479571A (en) * 1991-06-14 1995-12-26 The Texas A&M University System Neural node network and model, and method of teaching same
US5418710A (en) * 1991-10-31 1995-05-23 Kabushiki Kaisha Toshiba Simulator using a neural network
US5767606A (en) * 1992-11-27 1998-06-16 Hydor S.R.L. Synchronous electric motor, particularly for submersible pumps, and pump including the motor
US5704012A (en) * 1993-10-08 1997-12-30 International Business Machines Corporation Adaptive resource allocation 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
US5919267A (en) * 1997-04-09 1999-07-06 Mcdonnell Douglas Corporation Neural network fault diagnostics systems and related method
US6529780B1 (en) * 1997-04-14 2003-03-04 Siemens Aktiengesellschaft Method for automatic operation of industrial plants
US6353815B1 (en) * 1998-11-04 2002-03-05 The United States Of America As Represented By The United States Department Of Energy Statistically qualified neuro-analytic failure detection method and system
US6305216B1 (en) * 1999-12-21 2001-10-23 Production Testing Services Method and apparatus for predicting the fluid characteristics in a well hole
US6704689B1 (en) * 2000-01-20 2004-03-09 Camco International, Inc. Complexity index methodology for the analysis of run life performance

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070265713A1 (en) * 2006-02-03 2007-11-15 Michel Veillette Intelligent Monitoring System and Method for Building Predictive Models and Detecting Anomalies
US7818276B2 (en) * 2006-02-03 2010-10-19 Recherche 2000 Inc. Intelligent monitoring system and method for building predictive models and detecting anomalies
US20130175030A1 (en) * 2012-01-10 2013-07-11 Adunola Ige Submersible Pump Control
US9057256B2 (en) * 2012-01-10 2015-06-16 Schlumberger Technology Corporation Submersible pump control
US10900489B2 (en) 2013-11-13 2021-01-26 Schlumberger Technology Corporation Automatic pumping system commissioning
US9645575B2 (en) 2013-11-27 2017-05-09 Adept Ai Systems Inc. Method and apparatus for artificially intelligent model-based control of dynamic processes using probabilistic agents
WO2018200269A1 (en) * 2017-04-28 2018-11-01 Schlumberger Technology Corporation Methods related to startup of an electric submersible pump
WO2023009741A1 (en) * 2021-07-28 2023-02-02 Schlumberger Technology Corporation Integrating domain knowledge with machine learning to optimize electrical submersible pump performance

Also Published As

Publication number Publication date
US20050043921A1 (en) 2005-02-24
US6947870B2 (en) 2005-09-20

Similar Documents

Publication Publication Date Title
US20050273296A1 (en) Neural network model for electric submersible pump system
KR101961421B1 (en) Method, controller, and computer program product for controlling a target system by separately training a first and a second recurrent neural network models, which are initially trained using oparational data of source systems
KR102365150B1 (en) Condition monitoring data generating apparatus and method using generative adversarial network
US20130282635A1 (en) Method For The Computer-Assisted Modeling Of A Technical System
JP2008536221A (en) Control system and method
JP2005504367A (en) Combinatorial method for monitoring neural network learning
WO2022094559A1 (en) Edge computing device with artificial intelligence model for emulating control logic of a programmable logic controller
Liu et al. Deep learning for prediction and fault detection in geothermal operations
Luo et al. An improved recursive ARIMA method with recurrent process for remaining useful life estimation of bearings
CN107061032B (en) A kind of prediction technique and forecasting system of engine operating state
JP2019220156A (en) Generating hybrid models of physical systems
US11761623B2 (en) Apparatus for combustion optimization and method therefor
KR102457539B1 (en) Structural Performance Based Inverse Material Design Method
JP7152938B2 (en) Machine learning model building device and machine learning model building method
KR20200021768A (en) An apparatus for optimizing fluid dynamics analysis and a method therefor
US11629856B2 (en) Apparatus for managing combustion optimization and method therefor
Xiao et al. Reliability analysis of aging control system via stability margins
US20240045386A1 (en) Method for reproducing noise components of lossy recorded operating signals, and control device
AU2021280953B2 (en) An industrial process model generation system
RU2816861C2 (en) Calculation and/or adaptation of models of manufacturing and/or technical processes
Gong et al. Degradation index construction and learning-based prognostics for stochastically deteriorating feedback control systems
US11182515B2 (en) Apparatus for diagnosing analysis and method therefor
EP3945375A1 (en) Machine controller and methods for configuring and using the machine controller
Buchner et al. An Artificial-Intelligence-Based Method to Automatically Create Interpretable Models from Data Targeting Embedded Control Applications
CN117157592A (en) Machine controller and method for configuring a machine controller

Legal Events

Date Code Title Description
STCB Information on status: application discontinuation

Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION