Session 25. Oral Presentation for: Deep bed filtration and formation damage by particles with distributed properties
Nastaran Khazali A *A
Nastaran Khazali is currently a PhD student at the University of Adelaide. She holds a Master’s degree in reservoir engineering and two Bachelor’s degrees in petroleum and industrial engineering, all from Amirkabir University of Technology (Tehran Polytechnic). She was awarded the privilege of a dual degree, studying as an exceptional talent student when she was an undergraduate student at Amirkabir University. A coupled background in petroleum and industrial engineering was the main motivation for the pursuit of her Bachelors’ and Master’s theses in the field of datamining/machine-learning and artificial intelligence and their applications in reservoir engineering. For her PhD thesis, she is working on size-distributed suspension/colloidal flow in porous media. Contact email: nastaran.khazali@adelaide.edu.au. |
Abstract
Presented on Thursday 23 May: Session 25
Current models for deep bed filtration describe particles with uniform properties. Yet, the sizes, densities, and mineral composition of particles vary significantly in the same injection well. The aim of this work is to provide an effective mathematical model for water injection of particles with distributed properties and formation damage prediction. We average the set of traditional population balance equations for single-property particles and obtain one upscaled equation. The upscaled equation for particle retention rate contains a non-linear function of suspended concentration, which we call the 'suspension function'. We derive analytical solutions for the upscaled equation for linear (coreflood) and radial (well injectivity) flows. Then we treat lab coreflood data to determine the model suspension function and provide a model for well injectivity prediction. The retention profile for the flow of uniform particles has an exponential form. Frequently reported in the literature, hyper-exponential forms have been hypothetically explained by multiple particle properties. The inverse solution allows revealing the individual filtration coefficients for binary mixtures from total breakthrough concentrations during coreflood. Treatment of the data from lab experiments reveals individual filtration coefficients that belong to common intervals. For the first time, deep bed filtration of particles with distributed properties is upscaled and presented using a single equation that reflects the particle property distribution. This equation provides an effective mathematical model for tuning lab coreflood data, determines the model function, and uses it for injectivity decline prediction.
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Keywords: deep bed filtration, filtration function, hyper-exponential retention, particle property distribution, size-distributed colloids, suspension function, suspension/colloidal flow, upscaling.
Nastaran Khazali is currently a PhD student at the University of Adelaide. She holds a Master’s degree in reservoir engineering and two Bachelor’s degrees in petroleum and industrial engineering, all from Amirkabir University of Technology (Tehran Polytechnic). She was awarded the privilege of a dual degree, studying as an exceptional talent student when she was an undergraduate student at Amirkabir University. A coupled background in petroleum and industrial engineering was the main motivation for the pursuit of her Bachelors’ and Master’s theses in the field of datamining/machine-learning and artificial intelligence and their applications in reservoir engineering. For her PhD thesis, she is working on size-distributed suspension/colloidal flow in porous media. Contact email: nastaran.khazali@adelaide.edu.au. |
