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Title: A Feature-Based Stochastic Permeability of Shale: Part 2–Predicting Field-Scale Permeability

Abstract

We report that in a recent numerical study, it was demonstrated that characterizing reservoir permeability in terms of rock’s quality, as observed in lab and field, is the most important step before implementing an enhanced oil recovery operation or drilling a new well in a tight formation. In that study, it was shown that permeable features in shale-like organic matter (OM) and fractures were the only regions that allowed some reasonable movement of fluid, whereas inorganic matter (iOM) that occupies larger pore volume with significant saturation of hydrocarbons has extremely low permeability that did not allow any reasonable fluid movement to affect production. That study demonstrated the importance of characterizing reservoir heterogeneity in shale in order to economically exploit the shale resource. This study proposes a method to predict spatially heterogeneous field-scale permeability of shale in terms of natural fractures, and matrix (iOM and OM). The method developed in Part 1 is combined with a history-matching process that uses only readily available information from lab-scale and outcrop (information from geologists) to predict field-scale permeability. The method also ensures consistency between the underlying fracture distribution and optimally matched fracture lengths and their apertures, in addition to accounting for random distribution ofmore » fractures and their abundance. Optimized parameters of fracture distribution are used to generate multiple realizations of geological model, and the “best-fitting” (most-likely) permeability scenario is chosen by generating production response of each realization of the geological model and comparing them against the observed field production history. Finally, the novelty of the proposed to predict field-scale permeability is that it uses only readily available information while also ensuring consistency between the underlying fracture distribution and optimally matched fracture lengths and their apertures, in addition to accounting for random distribution of fractures and their abundance.« less

Authors:
ORCiD logo [1];  [2]
  1. National Energy Technology Lab. (NETL), Morgantown, WV (United States)
  2. China Univ. of Geosciences, Wuhan (China)
Publication Date:
Research Org.:
National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV, and Albany, OR (United States)
Sponsoring Org.:
USDOE Office of Fossil Energy (FE)
OSTI Identifier:
1532659
Resource Type:
Accepted Manuscript
Journal Name:
Transport in Porous Media
Additional Journal Information:
Journal Volume: 126; Journal Issue: 3; Journal ID: ISSN 0169-3913
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; Fractures; Tight oil; Field scale; Optimization; Organic/inorganic

Citation Formats

Singh, Harpreet, and Cai, Jianchao. A Feature-Based Stochastic Permeability of Shale: Part 2–Predicting Field-Scale Permeability. United States: N. p., 2018. Web. doi:10.1007/s11242-018-1076-4.
Singh, Harpreet, & Cai, Jianchao. A Feature-Based Stochastic Permeability of Shale: Part 2–Predicting Field-Scale Permeability. United States. doi:10.1007/s11242-018-1076-4.
Singh, Harpreet, and Cai, Jianchao. Mon . "A Feature-Based Stochastic Permeability of Shale: Part 2–Predicting Field-Scale Permeability". United States. doi:10.1007/s11242-018-1076-4. https://www.osti.gov/servlets/purl/1532659.
@article{osti_1532659,
title = {A Feature-Based Stochastic Permeability of Shale: Part 2–Predicting Field-Scale Permeability},
author = {Singh, Harpreet and Cai, Jianchao},
abstractNote = {We report that in a recent numerical study, it was demonstrated that characterizing reservoir permeability in terms of rock’s quality, as observed in lab and field, is the most important step before implementing an enhanced oil recovery operation or drilling a new well in a tight formation. In that study, it was shown that permeable features in shale-like organic matter (OM) and fractures were the only regions that allowed some reasonable movement of fluid, whereas inorganic matter (iOM) that occupies larger pore volume with significant saturation of hydrocarbons has extremely low permeability that did not allow any reasonable fluid movement to affect production. That study demonstrated the importance of characterizing reservoir heterogeneity in shale in order to economically exploit the shale resource. This study proposes a method to predict spatially heterogeneous field-scale permeability of shale in terms of natural fractures, and matrix (iOM and OM). The method developed in Part 1 is combined with a history-matching process that uses only readily available information from lab-scale and outcrop (information from geologists) to predict field-scale permeability. The method also ensures consistency between the underlying fracture distribution and optimally matched fracture lengths and their apertures, in addition to accounting for random distribution of fractures and their abundance. Optimized parameters of fracture distribution are used to generate multiple realizations of geological model, and the “best-fitting” (most-likely) permeability scenario is chosen by generating production response of each realization of the geological model and comparing them against the observed field production history. Finally, the novelty of the proposed to predict field-scale permeability is that it uses only readily available information while also ensuring consistency between the underlying fracture distribution and optimally matched fracture lengths and their apertures, in addition to accounting for random distribution of fractures and their abundance.},
doi = {10.1007/s11242-018-1076-4},
journal = {Transport in Porous Media},
number = 3,
volume = 126,
place = {United States},
year = {2018},
month = {5}
}

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