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Research Papers: Petroleum Engineering

Nanopore Confinement and Pore Connectivity Considerations in Modeling Unconventional Resources

[+] Author and Article Information
Alireza Sanaei, Yixin Ma, Ahmad Jamili

Mewbourne School of Petroleum and
Geological Engineering,
University of Oklahoma,
Norman, OK-73019

1Present address: Hildebrand Department of Petroleum and Geosystems Engineering, The University of Texas at Austin, Austin, TX-78712.

Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received March 29, 2016; final manuscript received July 1, 2018; published online August 9, 2018. Assoc. Editor: Daoyong (Tony) Yang.

J. Energy Resour. Technol 141(1), 012904 (Aug 09, 2018) (8 pages) Paper No: JERT-16-1151; doi: 10.1115/1.4040791 History: Received March 29, 2016; Revised July 01, 2018

Gas and liquid production from nanopore shale resources substantially increased during the past decade due to the advances in horizontal drilling and multistage hydraulic fracturing. Transport properties and mechanisms deviate from their bulk behavior when the pore sizes in unconventional formations are in the order of nanoscale. This is due to the dominant molecule–pore wall interaction effects comparing to molecule–molecule interactions in nanopores. Thus, the physics of multiphase flow in current commercial simulators should be changed to include the effect of pore size on both transport mechanisms and fluid properties. In this study, we analyze the effect of fluid confinement on phase behavior, fluid properties, and condensate banking around the hydraulic fracture where nanopores perform as the dominate storage region and dispersed with pores with bulk behavior. We modified critical properties of the fluid components for different pore sizes in the phase behavior calculations. Using experimental results, we developed a new correlation for estimating mean pore size as a function of permeability and porosity. Moreover, we considered pore size distribution of a shale sample to divide the reservoir into different regions. For each region, a specific permeability is assigned using the new developed correlation. Three different types of connectivity are considered between pores and its impact on production mechanisms is analyzed. Results of this study indicated that neglecting nanopore confinement effect on phase behavior results in an underestimation of the production while neglecting permeability change with pore size results in an overestimation of hydrocarbon production. The connectivity of different pore sizes has a significant impact on reservoir performance and determines the dominant factor.

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Figures

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Fig. 2

Pore-throat size distribution (MICP) of ten Eagle Ford shale samples

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Fig. 1

Critical temperature shift as a function of ratio of pore size to effective molecular diameter [41], data points from Refs. [19,42,43]

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Fig. 6

Condensate viscosity profile after 15 years of production

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Fig. 7

Gas viscosity profile after 15 years of production

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Fig. 8

Condensate saturation profile versus distance from the fracture after 15 years of production

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Fig. 9

Comparing reservoir performance of confined versus unconfined cases: (a) cumulative gas production and (b) cumulative condensate production

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Fig. 3

The geometry of reservoir, horizontal wellbore, hydraulic fracture, and gridding using logarithmically spaced and locally refined technique (left figure): (a) computational grid and (b) zoomed view on grid refinement around fractures

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Fig. 4

Oil–water and oil–gas relative permeability curves according to a tabulated data set

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Fig. 5

Two-phase envelope change for Eagle Ford gas condensate sample

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Fig. 11

Mean pore size versus permeability/porosity (from permeability, porosity, and MICP experiments on Eagle Ford shale samples)

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Fig. 12

Cumulative condensate production (different PVT regions are considered)

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Fig. 13

Cumulative condensate production (different PVT and permeability regions are considered)

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Fig. 10

Pore size distribution with different connectivities: model (1) pore sizes from largest to smallest connected to the fracture in series, model (2) pore sizes from smallest to largest connected to the fracture in series, and model (3) completely random distribution

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