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Title: Pore-Scale Modeling : Stochastic Network Generation and Modeling of Rate Effects in Waterflooding
Author: Idowu, Nasiru Abiodun
ISNI:       0000 0004 2680 6918
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2009
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Pore scale network modeling has been used to predict transport flow properties formultiphase flow successfully. The prediction is based on having geologically realisticnetworks that are computationally expensive to generate and normally represent onlya very small section of the rock sample. We present a new method to generatestochastic random networks representing the pore space of different rocks with giveninput pore and throat size distributions and connectivity ? these distributions can beobtained from an analysis of pore-space images. The stochastic networks can bearbitrarily large and hence are not limited by the size of the original image. The basic assumption made in the prediction of transport flow properties using mostpore-scale models is that the flow is capillary dominated. This implies that the viscouspressure drop is insignificant compared to the capillary pressure. However, at the fieldscale, gravity and viscous forces dominate displacement processes. We develop arate-dependent network model that accounts for viscous forces by solving for thewetting and non-wetting phase pressure and which allows wetting layer swelling nearan advancing flood front. We propose a new time-dependent algorithm by accountingfor partial filling of elements. We use the model to study the effects of capillary number and mobility ratio onimbibition displacement patterns, saturation and velocity profiles. We also investigatethe effects of capillary number and mobility ratio on the water fractional flow curve,cumulative oil production and residual oil saturation for water-wet and mixed-wetsystems. By using large networks we reproduce Buckley-Leverett profiles directlyfrom pore-scale modeling thereby providing a bridge between pore-scale and macroscaletransport.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral