Solvent-pore interactions in the Eagle Ford shale formation

DiStefano, Victoria H.; McFarlane, Joanna; Stack, Andrew G.; Perfect, Edmund; Mildner, D. F. R.; Bleuel, Markus; Littrell, Kenneth C.; Cheshire, Michael C.; Manz, Katherine E.; Anovitz, L. M.

doi:10.1016/j.fuel.2018.10.010

Cited by 43 publications

(21 citation statements)

References 53 publications

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“…However, as stated previously, B PLS statistics for predictor variables to MINC and HI have opposite signs. These findings support the theory that organic carbon is better associated with nanopores within the kerogen matrix or in larger pores within the rock matrix rather than the minerals [34].…”

Section: Banff/exshawsupporting

confidence: 83%

“…Figure 4 shows that the oil saturate fraction does not correlate with the other response variables (mobile gas saturation, production and porosity). The inverse correlation with mobile gas saturate is as would be expected in consideration of the kerogen maturation process [34].…”

Section: Uticasupporting

confidence: 64%

“…Another explanation may be the Utica's inclination for mobile gas saturate, which constitutes 80-95% of pore volume in the samples. As suggested in recent small angle neutron scattering (SANS) research performed on shale rock, hydrocarbons related to shale gas may reside in nanopores while those related to larger carbon chains may reside in mineral interfaces [34]. In the Utica model, porosity (measured in % bulk volume) is correlated with mobile gas saturate, suggesting that nanopores make up most of the volume in samples.…”

Section: Uticamentioning

confidence: 87%

“…In the PLS models, S2 was used as a marker of the hydrocarbon potential of shale. T max , the temperature at which the maximum amount of S2 was generated, was taken as an indicator of maturity and type of hydrocarbon [19,34]. The TOC parameter represents a rock's organic loading as a fraction of mineral composition and is usually positively correlated with the hydrocarbon potential of shale [34].…”

Section: Predictor and Response Data Characterizing Shale Playsmentioning

confidence: 99%

“…T max , the temperature at which the maximum amount of S2 was generated, was taken as an indicator of maturity and type of hydrocarbon [19,34]. The TOC parameter represents a rock's organic loading as a fraction of mineral composition and is usually positively correlated with the hydrocarbon potential of shale [34]. S1, S2, S3, and TOC were used to calculate the Hydrogen Index (HI), Oxygen Index (OI) and Production Index (PI), which provide information about the shale's production generating potential, kerogen maturation and type, and the evolution of organic matter respectively [27].…”

Section: Predictor and Response Data Characterizing Shale Playsmentioning

confidence: 99%

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Using PCA and PLS on publicly available data to predict the extractability of hydrocarbons from shales

Gallmeier

Zhang

McFarlane

2017

Journal of Natural Gas Science and Engineering

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Prediction of hydrocarbon extraction from shale requires specialized knowledge of shale play characteristics and analysis to assess effective, economical, and sustainable implementation of oil and natural gas production. In this work, we present a statistical approach that can be used as a preliminary investigation into the hydrocarbon resource potential of a shale play based on limited data. Statistical algorithms for Principal Component Analysis (PCA) and Partial Least Squares Regression (PLS) were used to determine if depositional environments and lithographic boundary characteristics of different plays allowed prediction of specific production parameters. This project characterizes Eagle Ford and Utica formations-two high-producing shale plays in the United States-and Banff/Exshaw and Colorado formations-two recently assessed shale plays in Alberta, Canada. Partial Least Squares Regression models were unable to model gas production parameters from predictor variables, highlighting the complexity of gas formations and the need for data on microscale petrophysical characteristics. In contrast, oil production 1

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Section: Banff/exshawsupporting

confidence: 83%

Section: Uticasupporting

confidence: 64%

Section: Uticamentioning

confidence: 87%

Section: Predictor and Response Data Characterizing Shale Playsmentioning

confidence: 99%