東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Developing a Grid-Based Surrogate Reservoir Model Using Artificial Intelligence.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Developing a Grid-Based Surrogate Reservoir Model Using Artificial Intelligence./
作者:	Amini, Shohreh.
面頁冊數:	1 online resource (183 pages)
附註:	Source: Dissertations Abstracts International, Volume: 76-11, Section: B.
Contained By:	Dissertations Abstracts International76-11B.
標題:	Petroleum engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3701938click for full text (PQDT)
ISBN:	9781321728620

Developing a Grid-Based Surrogate Reservoir Model Using Artificial Intelligence.
Amini, Shohreh.

Developing a Grid-Based Surrogate Reservoir Model Using Artificial Intelligence. - 1 online resource (183 pages)

Source: Dissertations Abstracts International, Volume: 76-11, Section: B.

Thesis (Ph.D.)--West Virginia University, 2015.

Includes bibliographical references

Reservoir simulation models are the major tools for studying fluid flow behavior in hydrocarbon reservoirs. They are now being used extensively in performing any kind of studies related to fluid production/injection in hydrocarbon bearing formations. Reservoir simulation models are constructed based on geological models, which are developed by integrating data from geology, geophysics, and petro-physics. This data comes from observation, measurements, and interpretations. Integration of maximum data from geology, geophysics, and petro-physics, contributes to building geologically complex and more realistic models. As the complexity of a reservoir simulation model increases, so does the computation time. Therefore, to perform any comprehensive study which involves thousands of simulation runs (such as uncertainty analysis), a massive amount of time is needed to complete all the required simulation runs. On many occasions, the sheer number of required simulation runs, makes the accomplishment of a project's objectives impractical. In order to address this problem, several efforts have been made to develop proxy models which can be used as a substitute for complex reservoir simulation models. These proxy models aim to reproduce the outputs of the reservoir models in a very short amount of time. In this study, a Grid-Based Surrogate Reservoir Model (SRM) is developed to be used as a proxy model for a complex reservoir simulation model. SRM is a customized model based on Artificial Intelligent (AI) and Data Mining (DM) techniques and consists of several neural networks, which are trained, calibrated, and validated before being used online. In this research, a numerical reservoir simulation model is developed and history matched for a CO2 sequestration project, which was performed in Otway basin, Australia where CO2 is injected into a depleted gas reservoir through one injection well. In order to develop SRM, a handful of appropriate simulation scenarios for different operational constraints and/or geological realizations are designed and run. A comprehensive spatio-temporal data set is generated by integrating data from the conducted simulation runs and it is used to train, calibrate, and verify several neural networks which are further combined to make the surrogate model. This model is able to generate pressure, saturation, and CO2 mole fraction at each grid block of the reservoir with a significantly less computational effort compared to the numerical reservoir simulation model.

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

Mode of access: World Wide Web

ISBN: 9781321728620Subjects--Topical Terms:

566616
Petroleum engineering.
Subjects--Index Terms:

Artificial intelligenceIndex Terms--Genre/Form:

542853
Electronic books.

Developing a Grid-Based Surrogate Reservoir Model Using Artificial Intelligence.
LDR:03990nmm a2200397K 4500 001 2360958
005 20231015185502.5
006 m o d
007 cr mn ---uuuuu
008 241011s2015 xx obm 000 0 eng d
020 $a 9781321728620
035 $a (MiAaPQ)AAI3701938
035 $a (MiAaPQ)wvu:10768
035 $a AAI3701938
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Amini, Shohreh. $3 3701606
245 1 0 $a Developing a Grid-Based Surrogate Reservoir Model Using Artificial Intelligence.
264 0 $c 2015
300 $a 1 online resource (183 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 76-11, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Mohaghegh, Shahab D.
502 $a Thesis (Ph.D.)--West Virginia University, 2015.
504 $a Includes bibliographical references
520 $a Reservoir simulation models are the major tools for studying fluid flow behavior in hydrocarbon reservoirs. They are now being used extensively in performing any kind of studies related to fluid production/injection in hydrocarbon bearing formations. Reservoir simulation models are constructed based on geological models, which are developed by integrating data from geology, geophysics, and petro-physics. This data comes from observation, measurements, and interpretations. Integration of maximum data from geology, geophysics, and petro-physics, contributes to building geologically complex and more realistic models. As the complexity of a reservoir simulation model increases, so does the computation time. Therefore, to perform any comprehensive study which involves thousands of simulation runs (such as uncertainty analysis), a massive amount of time is needed to complete all the required simulation runs. On many occasions, the sheer number of required simulation runs, makes the accomplishment of a project's objectives impractical. In order to address this problem, several efforts have been made to develop proxy models which can be used as a substitute for complex reservoir simulation models. These proxy models aim to reproduce the outputs of the reservoir models in a very short amount of time. In this study, a Grid-Based Surrogate Reservoir Model (SRM) is developed to be used as a proxy model for a complex reservoir simulation model. SRM is a customized model based on Artificial Intelligent (AI) and Data Mining (DM) techniques and consists of several neural networks, which are trained, calibrated, and validated before being used online. In this research, a numerical reservoir simulation model is developed and history matched for a CO2 sequestration project, which was performed in Otway basin, Australia where CO2 is injected into a depleted gas reservoir through one injection well. In order to develop SRM, a handful of appropriate simulation scenarios for different operational constraints and/or geological realizations are designed and run. A comprehensive spatio-temporal data set is generated by integrating data from the conducted simulation runs and it is used to train, calibrate, and verify several neural networks which are further combined to make the surrogate model. This model is able to generate pressure, saturation, and CO2 mole fraction at each grid block of the reservoir with a significantly less computational effort compared to the numerical reservoir simulation model.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Petroleum engineering. $3 566616
653 $a Artificial intelligence
653 $a Co2 sequestration
653 $a Neural network
653 $a Proxy modeling in reservoir engineering
653 $a Surrogate reservoir modeling
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0765
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a West Virginia University. $b Statler College of Engineering and Mineral Resources. $3 2104614
773 0 $t Dissertations Abstracts International $g 76-11B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3701938 $z click for full text (PQDT)