Title: Analyzing the impact of reaction models on the production of hydrocarbons from thermally upgraded oil shales

Journal Article · · Journal of Petroleum Science and Engineering
 [1];  [2];  [3]
  1. University of Houston, TX (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
  2. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Finsterle GeoConsulting, Kensington, CA (United States)
  3. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Texas A & M University, College Station, TX (United States)
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Reaction parameters significantly affect oil production from shales by means of heating and in-situ upgrading. In this work, we perform numerical simulations of two chemical reaction models, which are mainly used in the research of kerogen pyrolysis and subsequent hydrocarbon decomposition in organic-rich porous media. They are the Braun and Burnham model and Wellington model. In these forward numerical simulations, we present the influence of the two reaction models on hydrocarbon production. The Braun and Burnham reaction model shows more vigorous kerogen and subsequent decomposition reactions and more hydrocarbon production than the Wellington model. A local sensitivity analysis identifies the reaction parameters with the highest influence on productivity, and the most sensitive outputs. A data-worth analysis identifies the most valuable observation data to be measured for the best prediction of total hydrocarbon production. We find that the most valuable observation data is the cumulative production of heavy oil in the Braun and Burnham model and of light oil in the Wellington model, respectively. Finally, once we determine the maximum allowable prediction uncertainty and the expected measurement uncertainty, the observation data to be measured for the minimization of prediction uncertainty can be obtained.

Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
Texas A&M University; USDOE; USDOE Office of Energy Efficiency and Renewable Energy (EERE); USDOE Office of Fossil Energy (FE)
Grant/Contract Number:
AC02-05CH11231
OSTI ID:
1479440
Journal Information:
Journal of Petroleum Science and Engineering, Journal Name: Journal of Petroleum Science and Engineering Journal Issue: C Vol. 168; ISSN 0920-4105
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

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04 OIL SHALES AND TAR SANDS
In-situ upgrading
data-worth analysis
numerical simulation
oil shale
sensitivity analysis
uncertainty prediction