Comparison of micro- and macro-wettability measurements and evaluation of micro-scale imbibition rates for unconventional reservoirs: Implications for modeling multi-phase flow at the micro-scale

Deglint, Hanford J.; Clarkson, Christopher R.; Ghanizadeh, Amin; Debuhr, Chris; Wood, James M.

doi:10.1016/j.jngse.2018.11.026

Cited by 41 publications

(20 citation statements)

References 69 publications

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“…The resulting models can then be used for flow modeling (Deglint et al, 2019) or estimating geomechanical properties (Saad et al, 2018). While digital rock physics has been growing in significance, it remains computationally prohibitive and limited by the physical assumptions of the flow modeling.…”

Section: Motivation and Problem Statementmentioning

confidence: 99%

Do We Really Need Deep Learning? A Study on Play Identification using SEM Images

Zhang¹,

Kasumov²,

Devegowda³

et al. 2021

Proceedings of the 9th Unconventional Resources Technology Conference

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Section: Motivation and Problem Statementmentioning

confidence: 99%

Do We Really Need Deep Learning? A Study on Play Identification using SEM Images

Zhang¹,

Kasumov²,

Devegowda³

et al. 2021

Proceedings of the 9th Unconventional Resources Technology Conference

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“…In the case of secondary fractures, other than bi-wing fractures, proppants are unable to enter into the fractured surface due to narrow apertures and thus, these fractures cannot maintain conductivity for a longer period. The effective vertical and horizontal stresses are responsible for the decrease in hydraulic fracture conductivity and an estimated 60% decrease in propped fracture conductivity occurs by increasing effective stress from 6.2 to 34.48 MPa [4].…”

Section: Introductionmentioning

confidence: 99%

Hydraulic Fracture Conductivity in Shale Reservoirs

Khan¹,

Padmanabhan²,

Haq³

2022

Emerging Technologies in Hydraulic Fracturing and Gas Flow Modelling

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Optimum conductivity is essential for hydraulic fracturing due to its significant role in maintaining productivity. Hydraulic fracture networks with required fracture conductivities are decisive for the cost-effective production from unconventional shale reservoirs. Fracture conductivity reduces significantly in shale formations due to the high embedment of proppants. In this research, the mechanical properties of shale samples from Sungai Perlis beds, Terengganu, Malaysia, have been used for computational contact analysis of proppant between fracture surfaces. The finite element code in ANSYS is used to simulate the formation/proppant contact-impact behavior in the fracture surface. In the numerical analysis, a material property of proppant and formation characteristics is introduced based on experimental investigation. The influences of formation load and resulted deformation of formation are calculated by total penetration of proppant. It has been found that the formation stresses on both sides of fractured result in high penetration of proppant in the fracture surfaces, although proppant remains un-deformed.

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“…The imbibition of fracturing fluids into shale is a significant way of losing fracturing fluid. − Imbibed fluid can be trapped in irregular fracture systems by capillary forces in the form of capillary-bound water and can be adsorbed onto the surface of particles or within internal pores via hydrogen bonding and/or electrostatic forces in the form of water film. , Spontaneous imbibition (SI) experiments for shale have been performed by numerous researchers. ,− In shale reservoirs, SI is mainly driven by capillary forces and is influenced by the mineral composition, total organic carbon (TOC) content, and pore structure. ,, In addition, several researchers have found that evaporation in the form of water vapor is also an important pathway for fracturing fluid loss and results from gas expansion, , water drops formed in cavities and asperities in fractures, , and pore confinement. , However, the current understanding of fracturing fluid loss process is not comprehensive due to a lack of comparative studies of SI and WVA, two main pathways in fracturing fluid loss.…”

Section: Introductionmentioning

confidence: 99%

Water Uptake Behavior and Influence Factors of Longmaxi Shale: Implications from Water Physisorption and Imbibition Measurements

Hou

Liu

et al. 2021

Energy Fuels

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Water uptake behavior can greatly impact gas migration in shale reservoirs. In this work, we combined water vapor sorption (including adsorption and desorption) and spontaneous imbibition (SI) to systematically investigate the water uptake behavior and influence factors of samples from the Longmaxi shales in the northern Guizhou Province, China. The model of fracturing fluid loss was divided into two stages: (i) SI stagefracturing fluid is drawn into fractures by capillary forces, then into large pores, and finally into small pores; (ii) water vapor adsorption (WVA) stagehere an increase in relative humidity (RH) causes water vapor molecules to be initially absorbed into hydrophilic pores via monolayer adsorption, followed by multilayer adsorption, and finally cluster formation and/or capillary condensation when both hydrophilic and hydrophobic pores are filled. The results also show that pore size distributions derived from WVA exhibit unimodal distributions, which are generally greater than that obtained from N 2 adsorption because of clay swelling. The illite−smectite mixed-layer mineral is the main WVA site within clay minerals. Moreover, the controlling factors of WVA vary with RH. At low RHs, clay is the main controlling factor for WVA by providing strong intermolecular bonding for monolayer vapor molecules, while at high RHs, porosity and pore volume become the main controlling factors by providing more space for capillary condensation. Specifically, the pores with sizes of around 3 nm mainly contribute to WVA under high RHs. This work should therefore improve the understanding of fracturing fluid loss process and influence factors, contributing to the optimization of shale gas production.

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Comparison of micro- and macro-wettability measurements and evaluation of micro-scale imbibition rates for unconventional reservoirs: Implications for modeling multi-phase flow at the micro-scale

Cited by 41 publications

References 69 publications

Do We Really Need Deep Learning? A Study on Play Identification using SEM Images

Do We Really Need Deep Learning? A Study on Play Identification using SEM Images

Hydraulic Fracture Conductivity in Shale Reservoirs

Water Uptake Behavior and Influence Factors of Longmaxi Shale: Implications from Water Physisorption and Imbibition Measurements

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