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Production Data Analysis by Machine Learning.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Production Data Analysis by Machine Learning./
Author:	Zhou, Peng.
Description:	1 online resource (123 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 81-10, Section: B.
Contained By:	Dissertations Abstracts International81-10B.
Subject:	Petroleum engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27916786click for full text (PQDT)
ISBN:	9781658437400

Production Data Analysis by Machine Learning.
Zhou, Peng.

Production Data Analysis by Machine Learning. - 1 online resource (123 pages)

Source: Dissertations Abstracts International, Volume: 81-10, Section: B.

Thesis (Ph.D.)--Texas A&M University, 2019.

Includes bibliographical references

In this dissertation, I will present my research work on two different topics. The first topic is production data analysis of low-permeability well. The second topic is a quantitative evaluation of key completion controls on shale oil production.In Topic 1, I propose and investigate two novel methodologies that can be applied to improve the results of low-permeability well decline curve analysis. Specifically, I first proposed an iterative two-stage optimization algorithm for decline curve parameter estimation on the basis of two-segment hyperbolic model. This algorithm can be applied to find optimal parameter results from the production history data. By making use of a useful relation that exits between material balance time (MBT) and the original production profile, we propose a three-step diagnostic approach for the preliminary analysis of production history data, which can effectively assist us in identifying fluid flow regimes and increase our confidence in the estimation of decline curve parameters. The second approach is a data-driven method for primary phase production forecasting. Functional principal component analysis (fPCA) is applied to extract key features of production decline patterns on basis of multiple wells with sufficiently long production histories. A predictive model is then built using principal component functions obtained from the training production data set. Finally, we make predictions for the test wells to assess the quality of prediction with reference to true production data. Both methods are validated using field data and the accuracy of production forecasts gives us confidence in the new approaches.In Topic 2, generalized additive model (GAM) is applied to investigate possibly nonlinear associations between production and key completion parameters (e.g., completed lateral length, proppant volume per stage, fluid volume per stage) while accounting for the influence of different geological environments on hydrocarbon production. The geological cofounding effect is treated as a random clustered effect and incorporated in the GAM model by means of a state-of-the-art statistical machine learning method graphic fused LASSO. We provide several key findings on the relation between completion parameters and hydrocarbon production, which provide guidance in the development of efficient completion practices.

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

Mode of access: World Wide Web

ISBN: 9781658437400Subjects--Topical Terms:

566616
Petroleum engineering.
Subjects--Index Terms:

Production data analysisIndex Terms--Genre/Form:

542853
Electronic books.

Production Data Analysis by Machine Learning.
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100 1 $a Zhou, Peng. $3 1943260
245 1 0 $a Production Data Analysis by Machine Learning.
264 0 $c 2019
300 $a 1 online resource (123 pages)
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500 $a Source: Dissertations Abstracts International, Volume: 81-10, Section: B.
500 $a Advisor: Lee, William J.;Sang, Huiyan.
502 $a Thesis (Ph.D.)--Texas A&M University, 2019.
504 $a Includes bibliographical references
520 $a In this dissertation, I will present my research work on two different topics. The first topic is production data analysis of low-permeability well. The second topic is a quantitative evaluation of key completion controls on shale oil production.In Topic 1, I propose and investigate two novel methodologies that can be applied to improve the results of low-permeability well decline curve analysis. Specifically, I first proposed an iterative two-stage optimization algorithm for decline curve parameter estimation on the basis of two-segment hyperbolic model. This algorithm can be applied to find optimal parameter results from the production history data. By making use of a useful relation that exits between material balance time (MBT) and the original production profile, we propose a three-step diagnostic approach for the preliminary analysis of production history data, which can effectively assist us in identifying fluid flow regimes and increase our confidence in the estimation of decline curve parameters. The second approach is a data-driven method for primary phase production forecasting. Functional principal component analysis (fPCA) is applied to extract key features of production decline patterns on basis of multiple wells with sufficiently long production histories. A predictive model is then built using principal component functions obtained from the training production data set. Finally, we make predictions for the test wells to assess the quality of prediction with reference to true production data. Both methods are validated using field data and the accuracy of production forecasts gives us confidence in the new approaches.In Topic 2, generalized additive model (GAM) is applied to investigate possibly nonlinear associations between production and key completion parameters (e.g., completed lateral length, proppant volume per stage, fluid volume per stage) while accounting for the influence of different geological environments on hydrocarbon production. The geological cofounding effect is treated as a random clustered effect and incorporated in the GAM model by means of a state-of-the-art statistical machine learning method graphic fused LASSO. We provide several key findings on the relation between completion parameters and hydrocarbon production, which provide guidance in the development of efficient completion practices.
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 Production data analysis
653 $a Machine learning
653 $a Decline curve analysis
653 $a Parameter inference
653 $a Functional principal component analysis
653 $a Completion design
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 Texas A&M University. $b Petroleum Engineering. $3 3689607
773 0 $t Dissertations Abstracts International $g 81-10B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27916786 $z click for full text (PQDT)