The distribution of proppant injected in hydraulic fractures significantly affects the fracture conductivity and well performance.The proppant transport in thin fracturing fluid used during hydraulic fracturing in the...The distribution of proppant injected in hydraulic fractures significantly affects the fracture conductivity and well performance.The proppant transport in thin fracturing fluid used during hydraulic fracturing in the unconventional reservoirs is considerably different from fracturing fluids in the conventional reservoir due to the very low viscosity and quick deposition of the proppants.This paper presents the development of a three-dimensional Computational Fluid Dynamics(CFD)modelling technique for the prediction of proppant-fluid multiphase flow in hydraulic fractures.The proposed model also simulates the fluid leak-off behaviour from the fracture wall.The Euler-Granular and CFD-Discrete Element Method(CFD-DEM)multiphase modelling approach has been applied,and the equations defining the fluid-proppant and inter-proppant interaction have been solved using the finite volume technique.The proppant transport in hydraulic fractures has been studied comprehensively,and the computational modelling results of proppant distribution and other flow properties are in good agreement with the published experimental study.The parametric study is performed to investigate the effect of variation in proppant size,fluid viscosity and fracture width on the proppant transport.Smaller proppants can be injected early,followed by larger proppants to maintain high propping efficiency.This study has enhanced the understanding of the complex flow phenomenon between proppant and fracturing fluid and can play a vital role in hydraulic fracturing design.展开更多
This paper compares the fluid flow phenomena occurring within a fractured reservoir for three different fracture models using computational fluid dynamics.The effect of the fracture-matrix interface condition is studi...This paper compares the fluid flow phenomena occurring within a fractured reservoir for three different fracture models using computational fluid dynamics.The effect of the fracture-matrix interface condition is studied on the pressure and velocity distribution.The fracture models were compared based on the variation in pressure and permeability conditions.The model was developed for isotropic and anisotropic permeability conditions.The results suggest that the fracture aperture can have a drastic effect on fluid flow.The porous fracture-matrix interface condition produces more realistic transport of fluids.By increasing the permeability in the isotropic porous matrix,the pressure drop was significantly higher in both the fracture and reservoir region.Under anisotropic conditions in the 3D fractured reservoir,the effect of the higher longitudinal permeability was found to lower the pressure in the fractured reservoir.Depending on the properties of the fractured reservoir,this study can enhance the understanding of fracture-matrix fluid interaction and provide a method for production optimisation.展开更多
Artificial intelligence (AI) has become increasingly important in geothermal exploration,significantly improving the efficiency of resource identification.This review examines current AI applications,focusing on the a...Artificial intelligence (AI) has become increasingly important in geothermal exploration,significantly improving the efficiency of resource identification.This review examines current AI applications,focusing on the algorithms used,the challenges addressed,and the opportunities created.In addition,the review highlights the growth of machine learning applications in geothermal exploration over the past decade,demonstrating how AI has improved the analysis of subsurface data to identify potential resources.AI techniques such as neural networks,support vector machines,and decision trees are used to estimate subsurface temperatures,predict rock and fluid properties,and identify optimal drilling locations.In particular,neural networks are the most widely used technique,further contributing to improved exploration efficiency.However,the widespread adoption of AI in geothermal exploration is hindered by challenges,such as data accessibility,data quality,and the need for tailored data science training for industry professionals.Furthermore,the review emphasizes the importance of data engineering methodologies,data scaling,and standardization to enable the development of accurate and generalizable AI models for geothermal exploration.It is concluded that the integration of AI into geothermal exploration holds great promise for accelerating the development of geothermal energy resources.By effectively addressing key challenges and leveraging AI technologies,the geothermal industry can unlock cost‐effective and sustainable power generation opportunities.展开更多
文摘The distribution of proppant injected in hydraulic fractures significantly affects the fracture conductivity and well performance.The proppant transport in thin fracturing fluid used during hydraulic fracturing in the unconventional reservoirs is considerably different from fracturing fluids in the conventional reservoir due to the very low viscosity and quick deposition of the proppants.This paper presents the development of a three-dimensional Computational Fluid Dynamics(CFD)modelling technique for the prediction of proppant-fluid multiphase flow in hydraulic fractures.The proposed model also simulates the fluid leak-off behaviour from the fracture wall.The Euler-Granular and CFD-Discrete Element Method(CFD-DEM)multiphase modelling approach has been applied,and the equations defining the fluid-proppant and inter-proppant interaction have been solved using the finite volume technique.The proppant transport in hydraulic fractures has been studied comprehensively,and the computational modelling results of proppant distribution and other flow properties are in good agreement with the published experimental study.The parametric study is performed to investigate the effect of variation in proppant size,fluid viscosity and fracture width on the proppant transport.Smaller proppants can be injected early,followed by larger proppants to maintain high propping efficiency.This study has enhanced the understanding of the complex flow phenomenon between proppant and fracturing fluid and can play a vital role in hydraulic fracturing design.
文摘This paper compares the fluid flow phenomena occurring within a fractured reservoir for three different fracture models using computational fluid dynamics.The effect of the fracture-matrix interface condition is studied on the pressure and velocity distribution.The fracture models were compared based on the variation in pressure and permeability conditions.The model was developed for isotropic and anisotropic permeability conditions.The results suggest that the fracture aperture can have a drastic effect on fluid flow.The porous fracture-matrix interface condition produces more realistic transport of fluids.By increasing the permeability in the isotropic porous matrix,the pressure drop was significantly higher in both the fracture and reservoir region.Under anisotropic conditions in the 3D fractured reservoir,the effect of the higher longitudinal permeability was found to lower the pressure in the fractured reservoir.Depending on the properties of the fractured reservoir,this study can enhance the understanding of fracture-matrix fluid interaction and provide a method for production optimisation.
文摘Artificial intelligence (AI) has become increasingly important in geothermal exploration,significantly improving the efficiency of resource identification.This review examines current AI applications,focusing on the algorithms used,the challenges addressed,and the opportunities created.In addition,the review highlights the growth of machine learning applications in geothermal exploration over the past decade,demonstrating how AI has improved the analysis of subsurface data to identify potential resources.AI techniques such as neural networks,support vector machines,and decision trees are used to estimate subsurface temperatures,predict rock and fluid properties,and identify optimal drilling locations.In particular,neural networks are the most widely used technique,further contributing to improved exploration efficiency.However,the widespread adoption of AI in geothermal exploration is hindered by challenges,such as data accessibility,data quality,and the need for tailored data science training for industry professionals.Furthermore,the review emphasizes the importance of data engineering methodologies,data scaling,and standardization to enable the development of accurate and generalizable AI models for geothermal exploration.It is concluded that the integration of AI into geothermal exploration holds great promise for accelerating the development of geothermal energy resources.By effectively addressing key challenges and leveraging AI technologies,the geothermal industry can unlock cost‐effective and sustainable power generation opportunities.
