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.
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Research Article|
July 25, 2024
Mapping hydrodynamic structure with sparse or no well data
S. A. Stewart
1
Saudi Aramco
, Dhahran 31311, Saudi Arabia
Correspondence: [email protected]
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Correspondence: [email protected]
Publisher: Geological Society of London
Received:
27 Aug 2023
Revision Received:
01 Jun 2024
Accepted:
14 Jun 2024
First Online:
01 Jul 2024
© 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
© 2024 The Author(s)
Geoenergy (2024) 2 (1): geoenergy2023-028.
Article history
Received:
27 Aug 2023
Revision Received:
01 Jun 2024
Accepted:
14 Jun 2024
First Online:
01 Jul 2024
Citation
S. A. Stewart; Mapping hydrodynamic structure with sparse or no well data. Geoenergy 2024;; 2 (1): geoenergy2023–028. doi: https://doi.org/10.1144/geoenergy2023-028
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