Prediction of steam-assisted gravity drainage steam to oil ratio from reservoir characteristics

Oguz Akbilgic, Da Zhu, Ian D. Gates, Joule A. Bergerson

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Forecasts suggest that production of bitumen from oil sands reservoirs will increase by a factor of at least 2.5 times over roughly the next 15 years. Although a significant economic benefactor to the Canadian economy, there are challenges faced by oil sands operators with respect to greenhouse gas emissions and water consumption. For the Athabasca deposit, the current oil recovery process of choice is the SAGD (steam-assisted gravity drainage) method where high temperature and high pressure steam is injected into the oil sands formation. At present, there are more than ten SAGD operators in Alberta, Canada and results to date reveal that the geology of the reservoir impacts SAGD performance. Given the growth of the SAGD industry in Alberta, forecasting tools are required that can predict performance versus reservoir characteristics. Here, we present a neural network-based model to predict the SOR (steam-to-oil ratio) in oil sands reservoirs by using log and core data to characterize the reservoir porosity, permeability, oil saturation, depth and thickness. Our analysis confirms that the lower the porosity, permeability, and oil saturation of the reservoir, the worse the performance of the operation. In other words, the lower the quality of the reservoir, the lower the oil rate, and the higher the SOR. Our analysis also shows that well performance (i.e., SOR), is predictable with a relatively high degree of accuracy (R2~0.80) using log and core data via a neural network model. These results imply that the depth of the reservoir, gamma ray readings, and permeability are the most important determinants of the variation in SOR.

Original languageEnglish (US)
Pages (from-to)1663-1670
Number of pages8
JournalEnergy
Volume93
DOIs
StatePublished - Jan 1 2015

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Drainage
Gravitation
Steam
Oil sands
Petroleum reservoirs
Porosity
Neural networks
Oils
Geology
Gas emissions
Greenhouse gases
Gamma rays
Deposits
Recovery
Economics
Water
Industry

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Building and Construction
  • Pollution
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

Cite this

Prediction of steam-assisted gravity drainage steam to oil ratio from reservoir characteristics. / Akbilgic, Oguz; Zhu, Da; Gates, Ian D.; Bergerson, Joule A.

In: Energy, Vol. 93, 01.01.2015, p. 1663-1670.

Research output: Contribution to journalArticle

Akbilgic, Oguz ; Zhu, Da ; Gates, Ian D. ; Bergerson, Joule A. / Prediction of steam-assisted gravity drainage steam to oil ratio from reservoir characteristics. In: Energy. 2015 ; Vol. 93. pp. 1663-1670.
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