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Confirmation of Data-Driven Reservoir Modeling Using Numerical Reservoir Simulation.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Confirmation of Data-Driven Reservoir Modeling Using Numerical Reservoir Simulation./
作者:	Al Haifi, Al Hasan Mohamed Mohamed.
面頁冊數:	1 online resource (50 pages)
附註:	Source: Masters Abstracts International, Volume: 81-11.
Contained By:	Masters Abstracts International81-11.
標題:	Petroleum engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27787618click for full text (PQDT)
ISBN:	9781392637906

Confirmation of Data-Driven Reservoir Modeling Using Numerical Reservoir Simulation.
Al Haifi, Al Hasan Mohamed Mohamed.

Confirmation of Data-Driven Reservoir Modeling Using Numerical Reservoir Simulation. - 1 online resource (50 pages)

Source: Masters Abstracts International, Volume: 81-11.

Thesis (M.S.)--West Virginia University, 2019.

Includes bibliographical references

Data driven reservoir modeling, also known as Top-Down Model (TDM), is an alternative to the traditional numerical reservoir simulation technique. Data driven reservoir modeling is a new technology that uses artificial intelligence and machine learning to build full-field reservoir models using field measurements (data - facts) instead of mathematical formulations that represent our current understanding of the physics of the fluid flow through porous media. TDM combines all field measurements into a comprehensive reservoir model to predict the production from each well in a field with multiple wells. There are many opinions, speculations and criticism about not using the physics-based approach. Therefore, in this thesis, to confirm the capabilities of TDM, synthetic data generated from a numerical reservoir simulation model will be used for the development of a Data Driven Reservoir Model. That means, the physics of the fluid flow through porous media will be modeled using the generated data from the numerical reservoir simulation model which we know everything about. In order to accomplish the objectives of this thesis a software application will be used for the development of the Top-Down Model. TDM will be developed (trained, calibrated and validated) and history matched using the data generated by a complex numerical reservoir simulation model in order to confirm the capabilities of the TDM in forecasting existing well behavior. Upon Completion of the TDM, predictions will be made using the developed TDM and are tested against the data that will be generated by the numerical reservoir simulation.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9781392637906Subjects--Topical Terms:

566616
Petroleum engineering.
Subjects--Index Terms:

Data-Driven Reservoir Modeling,Index Terms--Genre/Form:

542853
Electronic books.

Confirmation of Data-Driven Reservoir Modeling Using Numerical Reservoir Simulation.
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502 $a Thesis (M.S.)--West Virginia University, 2019.
504 $a Includes bibliographical references
520 $a Data driven reservoir modeling, also known as Top-Down Model (TDM), is an alternative to the traditional numerical reservoir simulation technique. Data driven reservoir modeling is a new technology that uses artificial intelligence and machine learning to build full-field reservoir models using field measurements (data - facts) instead of mathematical formulations that represent our current understanding of the physics of the fluid flow through porous media. TDM combines all field measurements into a comprehensive reservoir model to predict the production from each well in a field with multiple wells. There are many opinions, speculations and criticism about not using the physics-based approach. Therefore, in this thesis, to confirm the capabilities of TDM, synthetic data generated from a numerical reservoir simulation model will be used for the development of a Data Driven Reservoir Model. That means, the physics of the fluid flow through porous media will be modeled using the generated data from the numerical reservoir simulation model which we know everything about. In order to accomplish the objectives of this thesis a software application will be used for the development of the Top-Down Model. TDM will be developed (trained, calibrated and validated) and history matched using the data generated by a complex numerical reservoir simulation model in order to confirm the capabilities of the TDM in forecasting existing well behavior. Upon Completion of the TDM, predictions will be made using the developed TDM and are tested against the data that will be generated by the numerical reservoir simulation.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
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650 4 $a Artificial intelligence. $3 516317
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653 $a Data-Driven Reservoir Modeling, Artificial Intelligence
653 $a Data-Driven Reservoir Modeling, Artificial Top-Down-Model
653 $a Data Mining
655 7 $a Electronic books. $2 lcsh $3 542853
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