Paleo‐stratigraphic permeability anisotropy controls supergene mimetic martite goethite deposits

• Published in: Basin research Vol. 35; no. 2; pp. 572 - 591
• Main Authors: Poulet, Thomas, Giraldo, Juan Felipe, Ramanaidou, Erick, Piechocka, Agnieszka, Calo, Victor M.
• Format: Journal Article
• Language: English
• Published: Oxford Wiley Subscription Services, Inc 01.04.2023
• Subjects: Anisotropy; Deposits; Distribution; Drilling; Exploration; Finite element method; finite elements simulation; fluid flow modelling; Goethite; Groundwater table; Haematite; Hematite; Iron; iron ore; Iron ores; martite goethite; Methods; Mineral resources; Mineralization; numerical stabilization; Ores; Permeability; Robustness (mathematics); Sedimentary basins; Strata; Stratigraphy; supergene genetic model; Tensors; Transport processes; Water table
• ISSN: 0950-091X; 1365-2117; 1365-2117
• DOI: 10.1111/bre.12723

The Hamersley Basin in Western Australia is one of the world's largest iron ore‐producing regions, hosting two types of ore in banded iron formations: the high‐grade martite‐microplaty haematite and the supergene martite‐goethite ores. With the high‐grade ores almost entirely mined in the last decade, the supergene ores have more recently become the dominant resource of interest. Consequently, understanding the genesis of these martite‐goethite deposits is a critical step for exploration. Yet, although various models exist, there is still no consensus on how these mineral resources formed, complicating the prediction of resource volume and location. Here, we show that the paleo‐stratigraphic permeability anisotropy (with higher permeability along strata than across) controls the supergene mimetic enrichment transport process and, subsequently, the mineralisation distribution. We introduce a flow model that implicitly represents strata with a potential function that orients the permeability tensor accurately. The numerical solver uses automatic mesh adaptivity to deliver robust solutions. By accurately reproducing the mineralisation patterns in specific deposits, we identify and quantify the paleo‐water table level and permeability anisotropy ratio as the two main controlling parameters for the mineralisation distribution. These insights provide new timing constraints for the mineralisation and the physical process of iron enrichment, suggesting much more potential mineralisation volume in the paleo‐reconstructed zones than previously anticipated. These flow models allow us to draw geological conclusions with few a priori assumptions required for the genetic model in which the transport component is dominant. The predictive power of this methodology will allow targeted drilling to narrow down the prospective areas and lower exploration costs. Furthermore, the methodology's generality applies to other commodities in sedimentary basins involving supergene processes and will improve our understanding of various genetic models. Supergene mimetic iron ore deposits are mainly controlled by the paleo‐stratigraphic permeability anisotropy. Numerical flow simulations provide spatial and temporal mineralisation constraints, showing that chemistry is not necessarily required to explain mineralisation patterns. This schematic drawing of reconstructed strata for one of the scenarios modelled highlights the role of the paleo‐water table (horizontal dashed line) as top boundary condition for the fluid flow (black arrows), whose direction and intensity are strongly affected by the strata orientation and anisotropy.