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Permanent link (DOI): https://doi.org/10.7939/R3NP1WQ06

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Predicting Waterflooding Performance in Low-permeability Reservoirs with Linear Dynamical System Open Access

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Other title
Subject/Keyword
Statistical Learning
Petroleum Engineering
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Wang, Dashun
Supervisor and department
Niu, Di (Dept. of Electrical and Computer Engineering)
Li, Huazhou (Dept. of Civil and Environmental Engineering)
Examining committee member and department
Chen, Jie (Dept. of Electrical and Computer Engineering)
Jin, Zhehui (Dept. of Civil and Environmental Engineering)
Li, Huazhou (Dept. of Civil and Environmental Engineering)
Niu, Di (Dept. of Electrical and Computer Engineering)
Department
Department of Electrical and Computer Engineering
Specialization
Computer Engineering
Date accepted
2016-09-22T11:14:32Z
Graduation date
2016-06:Fall 2016
Degree
Master of Science
Degree level
Master's
Abstract
Several interwell connectivity models such as multiple linear regression (MLR) and the capacitance model (CM) have been proposed to model waterflooding performance in high-permeability reservoirs based on observed production data. However, the existing methods are not effective at characterizing the behavior of transient flows which are prevalent in low-permeability reservoirs. This thesis presents a novel dynamic waterflooding model based on linear dynamical systems (LDS) to characterize the injection-production relationships in an oilfield during both stationary and nonstationary production phases. We leverage a state space model, in which the changing rates of control volumes between injector-producer pairs in the reservoir of interest serve as time-varying hidden states depending on the reservoir condition, and can thus predict production rates more accurately for low-permeability reservoirs and many dynamic scenarios. We propose a self-learning procedure for the model to train its parameters as well as the evolution of the hidden states only based on past observations of injection and production rates. We tested LDS in comparison with the state-of-the-art CM method in a wide range of synthetic reservoir models including both high-permeability and low-permeability reservoirs, as well as various dynamic scenarios involving varying bottom-hole injection pressure (BHIP), injector shut-ins and reservoirs of a larger scale. We also tested LDS on the real production data collected from Changqing oilfield containing low-permeability formations. Testing results demonstrate that LDS significantly outperforms CM in terms of modeling and predicting waterflooding performance in low-permeability reservoirs and various dynamic scenarios.
Language
English
DOI
doi:10.7939/R3NP1WQ06
Rights
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
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