scholarly journals Stochastic-based liquid apparent permeability model of shale oil reservoir considering geological control

2021 ◽  
Author(s):  
Jilong Xu ◽  
Wendong Wang ◽  
Bing Ma ◽  
Yuliang Su ◽  
Han Wang ◽  
...  
Keyword(s):  
Grain Size ◽  
Pore Size ◽  
Size Distribution ◽  
Grain Size Distribution ◽  
Slip Length ◽  
Distribution Model ◽  
Shale Oil ◽  
Apparent Permeability ◽  
Permeability Model ◽  
Structure Generation

AbstractShale is a complex porous medium composed of organic matter (OM) and inorganic minerals (iOM). Because of its widespread nanopores, using Darcy’s law is challenging. In this work, a two-fluid system model is established to calculate the oil flow rate in a single nanopore. Then, a spatial distribution model of shale components is constructed with a modified quartet structure generation set algorithm. The stochastic apparent permeability (AP) model of shale oil is finally established by combining the two models. The proposed model can consider the effects of various geological controls: the content and grain size distribution of shale components, pore size distribution, pore types and nanoconfined effects (slip length and spatially varying viscosity). The results show that slip length in OM nanopores is far greater than that in iOM. However, when the total organic content is less than 0.3 ~ 0.4, the effect of the OM slip on AP increases first and then decreases with the decrease in mean pore size, resulting in that the flow enhancement in shale is much smaller than that in a single nanopore. The porosity distribution and grain size distribution are also key factors affecting AP. If we ignore the difference of porosity between shale components, the error of permeability estimation is more than 200%. Similarly, the relative error can reach 20% if the effect of grain size distribution is ignored. Our model can help understand oil transport in shale strata and provide parameter characterization for numerical simulation.

The Influence of Microstructures on the Thermal Conductivity of Polycrystalline UO2

2016 ◽  
Author(s):  
Enze Jin ◽  
Chen Liu ◽  
Heming He
Keyword(s):  
Thermal Conductivity ◽  
Finite Element Method ◽  
Grain Size ◽  
Finite Element ◽  
Pore Size ◽  
Size Distribution ◽  
Grain Size Distribution ◽  
Structural Changes ◽  
Property Changes ◽  
Design Guidance

The thermal conductivity is one of the most important properties for UO2. The influences of microstructure are especially important for UO2 due to the severe structural changes under irradiation conditions. In this study, we have investigated the thermal conductivity of UO2 with different microstructures using Finite Element Method. The thermal conductivity increases with increasing grain size. The grain size distribution has obvious influence on the thermal conductivity especially when there are pores in the polycrystal. The influences of porosity and pore size are very sensitive to the position of the pores. The results obtained in this study are useful for prediction of property changes of UO2 fuel in pile and important to gain some design guidance to tune the properties through the control of the microstructure.

A new multifractal-based grain size distribution model

Geoderma ◽  
2021 ◽  
Vol 404 ◽  
pp. 115294
Author(s):  
Filip Stanić ◽  
Ioulia Tchiguirinskaia ◽  
Pierre-Antoine Versini ◽  
Yu-Jun Cui ◽  
Pierre Delage ◽  
...  
Keyword(s):  
Grain Size ◽  
Size Distribution ◽  
Grain Size Distribution ◽  
Distribution Model

Modeling of Injection-Induced Particle Movement Leading to the Development of Thief Zones

2021 ◽  
Author(s):  
Qianru Qi ◽  
Iraj Ershaghi
Keyword(s):  
Grain Size ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Grain Size Distribution ◽  
Size Exclusion ◽  
Published Data ◽  
Particle Movement ◽  
Grain Sizes ◽  
Modeling Approach

Abstract This paper is a contribution to failure prediction of unconsolidated intervals that could have a negative impact on injection efficiency because of susceptibility to structural changes under fluid injection processes. In unconsolidated formations, formation fines may be subjected to drag forces by injected water because of poor cementation. This results in small grain moments, and continuation can result in a gradual increase in permeability and eventual development of washed-out or thief zones. This paper presents a new modeling approach using information from profile surveys and grain and pore size distribution to model the process of injection and the induced particle movement. The motivation came from field observations and realization of permeability increase from profile surveys and substantial fines movement, leading to an increase in rock permeability. A series of case studies based on realistic published data on pore and grain size distribution are included to demonstrate the estimated increases in formation permeability. In our modeling approach, once we establish the range of grain sizes that fits the criterion for particle movement, a probabilistic algorithm, developed for the study, is applied to track changes in porosity and associated variations in permeability. This algorithm, presented for the first time, considers a stochastic approach to monitor the reservoir particle movements, pore size exclusion by particle accumulation and their resultant changes in rock properties. For this methodology, we ignored potential effects of wettability and clay swelling, and considered perfect spheres to represent the various grain sizes. Predictions made using various realizations of channel formation and petrophysical alterations show the significance of having access to three sources of information; pore size distribution, grain size distribution, and profile surveys. Through inverse modeling using these pieces of information for a particular formation, we demonstrate how we can predict realistic changes and map rock transport properties.

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