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

• Author / Creator

  Wang, Dashun

• 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.

• Subjects / Keywords

  • Petroleum Engineering
  • Statistical Learning

• Graduation date

  Fall 2016

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R3NP1WQ06

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. 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.