Abstract
Abstract The Jansz-Io field is part of the ‘Greater Gorgon’ project, situated approximately 120km offshore the northwest coast in the Northern Carnarvon Basin offshore Western Australia. This study presents a two-part workflow for determining petrophysical and grain-size related properties of the late Jurassic Jansz Sandstone in the Jansz-Io Field. This field contains highly variable clays and cements distributed both vertically and spatially due to bioturbation and diagenetic processes. The relationship between saturation and pore structure, important for reservoir evaluation and modeling, is not well understood. The objective of the study is to use class-based machine learning (CbML) and nuclear magnetic resonance (NMR) pore-to-production workflows to improve the understanding of the reservoir properties. This will also reduce uncertainty in gas initially in place (GIIP) and estimated ultimate recovery (EUR) during static and dynamic modeling workflows. The first part of the workflow involves applying CbML to determine petrophysical properties. CbML was used to classify well logs into facies based on rock types. The resulting classification was used to determine core-calibrated continuous petrophysical properties in all wells. In the second part, novel NMR pore-to-production workflows were used to determine grainsize related properties. NMR data from 19 wells were normalized against different acquisition technologies, vendors, and conveyances using factor analysis and fluid substitution methods, to determine textural facies, effective porosity/water saturation, and grain-size distribution across the field. The workflow comprising CbML and NMR pore-to-production methodologies shows how AI/ML can assist alongside conventional workflows to maximize the return on investment of measurement acquisition. The results show that the fluidsubstituted NMR distribution and grain-size distribution by textural facies is integral to understanding the relationship between permeability, porosity, water saturation, and grain size. Insights from this study are being used to update the geological deposition model to reduce uncertainty in GIIP and EUR during static and dynamic modeling workflows. The integrated workflow enables the determination of continuous core-calibrated properties required for not only petrophysical evaluation but also for geological and reservoir modeling to optimize completions and production. The significance of the study lies in its ability to improve the understanding of reservoir properties and reduce uncertainty in GIIP and EUR, leading to more accurate reservoir modeling and optimized production. The novelty of the study lies in the combination of CbML and NMR pore-to-production methodologies to determine petrophysical and grain-size related properties of the Jansz-Io Field.
Published Version
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