A data-driven hyperspectral method for sampling of diagenetic carbonate fabrics - a case study using an outcrop analogue of Jurassic Arab-D reservoirs, Saudi Arabia

Abstract

Diagenetic alteration commonly overprints depositional fabrics in carbonate-dominated sediments and impact reservoir quality. Dolomitization is a prevalent diagenetic process observed in subsurface reservoirs that profoundly alters the depositional precursor's pore network, thereby influencing subsurface storage capacity and fluid flow behavior. Typical workflows to characterize the dolomitized sequences, textures, degree and extent of dolomitization rely on mapping, spatial sampling, and time-consuming geochemical, petrographic and petrophysical analysis. In this study, we propose a hyperspectral data-driven workflow for identifying dolomitized horizons and extracting sample sets optimized to characterize textural and chemical variations. Hyperspectral imaging (HSI) data was acquired with 1.5 mm spatial sampling along a 50 m long core drilled behind an outcrop of the Late Jurassic Jubaila-Arab sequence in Wadi Daqlah, Saudi Arabia. Spectral features in the visible (VNIR), shortwave (SWIR), mid-wave (MWIR), and long-wave (LWIR) infrared regions were then used to classify carbonate mineralogy, allowing for the rapid identification of dolomitized zones, and k-means clustering applied exclusively to the dolomitized areas used to identify intra-dolomite variations and suggest representative sample locations. Petrographic and geochemical analyses were carried out on these samples, revealing that clusters identified with the hyperspectral data represent four distinct diagenetic fabrics. These results demonstrate the value of HSI for objective and data-driven sampling, reducing the number of samples required for petrographic, geochemical and geophysical analysis and hence time and costs required to spatially characterize diagenetic alteration.