Fluid Flow and Heat Transport Computation for Power-Law Scaling Poroperm Media

In applying Darcy’s law to fluid flow in geologic formations, it is generally assumed that flow variations average to an effectively constant formation flow property. This assumption is, however, fundamentally inaccurate for the ambient crust. Well-log, well-core, and well-flow empirics show that cr...

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Main Authors: Peter Leary, Peter Malin, Rami Niemi
Format: Article
Language:English
Published: Hindawi-Wiley 2017-01-01
Series:Geofluids
Online Access:http://dx.doi.org/10.1155/2017/9687325
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spelling doaj-88da095c4007488f9a879c7437ae7a442020-11-25T01:00:17ZengHindawi-WileyGeofluids1468-81151468-81232017-01-01201710.1155/2017/96873259687325Fluid Flow and Heat Transport Computation for Power-Law Scaling Poroperm MediaPeter Leary0Peter Malin1Rami Niemi2Advanced Seismic Instrumentation and Research, 1311 Waterside, Dallas, TX 75218-4475, USAAdvanced Seismic Instrumentation and Research, 1311 Waterside, Dallas, TX 75218-4475, USASt1 Deep Heat Ltd, Purotie 1, 00381 Helsinki, FinlandIn applying Darcy’s law to fluid flow in geologic formations, it is generally assumed that flow variations average to an effectively constant formation flow property. This assumption is, however, fundamentally inaccurate for the ambient crust. Well-log, well-core, and well-flow empirics show that crustal flow spatial variations are systematically correlated from mm to km. Translating crustal flow spatial correlation empirics into numerical form for fluid flow/transport simulation requires computations to be performed on a single global mesh that supports long-range spatial correlation flow structures. Global meshes populated by spatially correlated stochastic poroperm distributions can be processed by 3D finite-element solvers. We model wellbore-logged Dm-scale temperature data due to heat advective flow into a well transecting small faults in a Hm-scale sandstone volume. Wellbore-centric thermal transport is described by Peclet number Pe ≡ a0φv0/D (a0 = wellbore radius, v0 = fluid velocity at a0, φ = mean crustal porosity, and D = rock-water thermal diffusivity). The modelling schema is (i) 3D global mesh for spatially correlated stochastic poropermeability; (ii) ambient percolation flow calibrated by well-core porosity-controlled permeability; (iii) advection via fault-like structures calibrated by well-log neutron porosity; (iv) flow Pe ~ 0.5 in ambient crust and Pe ~ 5 for fault-borne advection.http://dx.doi.org/10.1155/2017/9687325
collection DOAJ
language English
format Article
sources DOAJ
author Peter Leary
Peter Malin
Rami Niemi
spellingShingle Peter Leary
Peter Malin
Rami Niemi
Fluid Flow and Heat Transport Computation for Power-Law Scaling Poroperm Media
Geofluids
author_facet Peter Leary
Peter Malin
Rami Niemi
author_sort Peter Leary
title Fluid Flow and Heat Transport Computation for Power-Law Scaling Poroperm Media
title_short Fluid Flow and Heat Transport Computation for Power-Law Scaling Poroperm Media
title_full Fluid Flow and Heat Transport Computation for Power-Law Scaling Poroperm Media
title_fullStr Fluid Flow and Heat Transport Computation for Power-Law Scaling Poroperm Media
title_full_unstemmed Fluid Flow and Heat Transport Computation for Power-Law Scaling Poroperm Media
title_sort fluid flow and heat transport computation for power-law scaling poroperm media
publisher Hindawi-Wiley
series Geofluids
issn 1468-8115
1468-8123
publishDate 2017-01-01
description In applying Darcy’s law to fluid flow in geologic formations, it is generally assumed that flow variations average to an effectively constant formation flow property. This assumption is, however, fundamentally inaccurate for the ambient crust. Well-log, well-core, and well-flow empirics show that crustal flow spatial variations are systematically correlated from mm to km. Translating crustal flow spatial correlation empirics into numerical form for fluid flow/transport simulation requires computations to be performed on a single global mesh that supports long-range spatial correlation flow structures. Global meshes populated by spatially correlated stochastic poroperm distributions can be processed by 3D finite-element solvers. We model wellbore-logged Dm-scale temperature data due to heat advective flow into a well transecting small faults in a Hm-scale sandstone volume. Wellbore-centric thermal transport is described by Peclet number Pe ≡ a0φv0/D (a0 = wellbore radius, v0 = fluid velocity at a0, φ = mean crustal porosity, and D = rock-water thermal diffusivity). The modelling schema is (i) 3D global mesh for spatially correlated stochastic poropermeability; (ii) ambient percolation flow calibrated by well-core porosity-controlled permeability; (iii) advection via fault-like structures calibrated by well-log neutron porosity; (iv) flow Pe ~ 0.5 in ambient crust and Pe ~ 5 for fault-borne advection.
url http://dx.doi.org/10.1155/2017/9687325
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