Masters Theses

Author

Yandong Zhang

Keywords and Phrases

Polymer flooding; Screening criteria

Abstract

"Enhanced oil recovery (EOR) processes are regarded as important methods to recovery remaining oil after primary and secondary recovery. It is significant to select the most appropriate EOR process among the possible techniques for a candidate reservoir. EOR screening criteria has been created using available EOR datasets and served as the first step to compare the suitability of each EOR method for a particular reservoir. Most of these datasets are collected from EOR surveys published by Oil & Gas Journal. This study proposes a comprehensive study of a dataset including 55 pilot and field polymer flooding applications in China. Statistical analysis has been used to analyze the data collected. Histograms and box plots combined with violin plots are used to show the distribution of each parameter and present the range of the data. Scatter plots are constructed to compare relationships between different polymer properties and reservoir properties. Screening criteria for polymer flooding has been updated by real pilot and field polymer flooding data. Multiple imputation method is also proposed and implemented on the original dataset and a predicting model to predict incremental oil recovery using reservoir and polymer properties is constructed in steps"--Abstract, page iii.

Advisor(s)

Wei, Mingzhen
Bai, Baoju

Committee Member(s)

Flori, Ralph E.

Department(s)

Geosciences and Geological and Petroleum Engineering

Degree Name

M.S. in Petroleum Engineering

Publisher

Missouri University of Science and Technology

Publication Date

Fall 2015

Pagination

xiii, 110 pages

Note about bibliography

Includes bibliographic references (pages 105-109).

Rights

© 2015 Yandong Zhang, All rights reserved.

Document Type

Thesis - Open Access

File Type

text

Language

English

Library of Congress Subject Headings

Enhanced oil recovery -- China -- Statistical methods
Oil field flooding
Multiple imputation (Statistics)

Thesis Number

T 10809

Electronic OCLC #

936209650

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