Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.769408
Title: Generic modelling of reservoir heterogeneity and its impact on flow
Author: Debbabi, Yacine
ISNI:       0000 0004 7657 6233
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2017
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Abstract:
This thesis develops a framework to interpret and qualitatively predict the impact of reservoir heterogeneity on flow through the use of dimensionless layered models of reservoir heterogeneity. Understanding the impact of reservoir heterogeneity on flow is of great importance in many industrial and environmental processes, including geologic CO2 storage, hydrocarbon production and contaminant remediation. We identify flow regimes applicable in layered porous media in terms of a small number of key dimensionless scaling groups obtained by inspectional analysis of the governing flow equations, and report their impact on storage efficiency (fraction of the moveable pore volume occupied by the injected phase), which is numerically equivalent to the recovery efficiency (fraction of the moveable pore volume initially in place recovered). Results are directly applicable to both geologic CO2 storage and hydrocarbon production. Examination of parts of the parameter space which were previously ignored reveals additional flow behaviours and parameter interactions which have been overlooked. We then demonstrate how to estimate the scaling groups between well pairs in a realistic reservoir model including a large number of wells, and extrapolate results from the layered models to interpret flow between well pairs. The results show that a small suite of generic dimensionless models can be used to classify flow behaviours into a small number of flow regimes characterized by the prevailing force balance and the dimensionless geometry of heterogeneity. Although generic models might not be sufficient to quantitatively predict reservoir performance or its sensitivity to parameters, their use provides qualitative predictions which guide, and therefore reduce the use of, time-consuming numerical or laboratory experiments performed in numerous reservoir engineering workflows.
Supervisor: Jackson, Matthew ; Hampson, Gary ; Fitch, Peter Sponsor: Exxon Mobil Corporation
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.769408  DOI:
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