1887
Volume 2, Issue 1
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Abstract

Hydrodynamic traps are usually mapped using well pressure data to transform structural depth maps, but if well data are sparse then hydrodynamic maps produced this way may have large uncertainties. An alternative approach that does not rely on well data is described here, utilizing simplified, locally planar representation of the potentiometric surface. Ranges of hydraulic gradient magnitudes and azimuths representing different potentiometric surface orientations, together with a range of possible density contrasts between the flowing and trapped fluids, define a three-dimensional array, termed here ‘hydrodynamic space’. This array can be constrained and simplified by reasonable assumptions and the introduction of an additional new concept that combines the hydraulic gradient magnitude and fluid density contrast into a single parameter termed ‘potentiometric transform’. Ranges of these parameters yield a set of hydrodynamic structural maps. The fineness of sampling the hydrodynamic space parameters is limited only by the resources available to support the workflow. The array can be automatically assessed in terms of hydrodynamic trap volumes by applying a structural closure algorithm that isolates and characterizes dip-closed structure. Closure volumes across the map set are ranked by spatial distribution analysis that informs exploration programs relevant to any subsurface fluid management application. The method is described for the first time here and illustrated by application to a real structural dataset.

© 2024 Saudi Arabian Oil Company. Published by The Geological Society of London for GSL and EAGE. All rights, including for text and data mining (TDM), artificial intelligence (AI) training, and similar technologies, are reserved. For permissions: https://www.lyellcollection.org/publishing-hub/permissions-policy. Publishing disclaimer: https://www.lyellcollection.org/publishing-hub/publishing-ethics

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2024-07-25
2025-11-14

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Mapping hydrodynamic structure with sparse or no well data
Geoenergy 2, geoenergy2023-028 (2024); https://doi.org/10.1144/geoenergy2023-028
/content/journals/10.1144/geoenergy2023-028
/content/journals/10.1144/geoenergy2023-028
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