
Sultan Abulkhair
School of Chemical Engineering
Faculty of Sciences, Engineering and Technology
Sultan is a Postdoctoral Researcher at the ARC Training Centre for Integrated Operations for Complex Resources, University of Adelaide. He is currently working on a project involving rapid updating of resource knowledge and its application to optimise the mining value chain.
In 2025, he completed a PhD in Mining Engineering focusing on geostatistics and mineral resource modelling. Additionally, he obtained an MSc in Mining Engineering in 2021 and a BEng in Geology and Mineral Exploration in 2019.
Rapid, stochastic updating of resource knowledge with sensor information for high-resolution reconciliation and rapid decision-making.
This project focuses on rapidly updating resource knowledge with upstream (drill) and downstream (belt) sensor information, in collaboration with partner organisations, Think & Act Differently by BHP, Maptek and Petra Data Science. The upstream and downstream information contains the resource knowledge close to the mining stage, allowing for rapid reconciliation of the ore attributes for both upstream and downstream use.
Resource models are generally constructed from directly observed data (e.g., grades of drill cores) that have relatively high accuracy. Resource models, however, are limited by the scale on which the data are collected. As mining progresses, more information becomes available on different scales from various types and sources of data. Drill cuttings from blast holes provide in situ data on smaller scales. Sensors on drilling rigs and on conveyor belts provide data in near real-time, as do draw-point sensing and Load Haul Dump (LHD) sensors.
All these types of data – drill core, drill sensors, draw-point sensors, LHD sensors, belt sensors – measure different volumes in different ways and have different levels of accuracy, all of which must be accounted for when integrating the various types of data. Although most data will be quantitative (e.g., ore grades), some will be qualitative (e.g., structures such as geology or domain types) and non-additive (e.g., geometallurgical attributes related to comminution and processing). Integrating these types of data requires a stochastic approach to account for the different levels of accuracy (uncertainty) and the different volumes of measurement. To enable rapid decision-making, the resource model must be updated with newly acquired data in near real-time.
This project involves data assimilation for updating primary resource variables, as well as multi-Gaussian transformation and Gibbs sampling for converting complex data into variables suitable for data assimilation. Machine learning then predicts the non-additive response variables, such as geometallurgical attributes, using updated models of the primary resource variables.
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Appointments
Date Position Institution name 2025 - ongoing Postdoctoral Research Fellow University of Adelaide 2023 - 2023 Research Intern Petra Data Science 2021 - 2025 PhD Candidate University of Adelaide 2019 - 2021 Research Assistant Nazarbayev University -
Awards and Achievements
Date Type Title Institution Name Country Amount 2025 Scholarship Internship Scholarship University of Adelaide Australia - 2022 Research Award PhD Student Poster Award SAEMC Australia $500 2022 Research Award Mathematical Geosciences Student Award IAMG United States $2,500 USD 2022 Research Award IAMG Travel Grant IAMG United States $2,000 USD -
Language Competencies
Language Competency English Can read, write, speak, understand spoken and peer review Kazakh Can read, write, speak, understand spoken and peer review Russian Can read, write, speak, understand spoken and peer review -
Education
Date Institution name Country Title 2021 - 2025 University of Adelaide Australia Doctor of Philosophy 2019 - 2021 Nazarbayev University Kazakhstan Master of Science 2015 - 2019 Kazakh-British Technical University Kazakhstan Bachelor of Engineering and Technology -
Research Interests
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Journals
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Conference Papers
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Conference Items
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Preprint
Year Citation 2025 Abulkhair, S., Dowd, P., & Xu, C. (2025). Pluri-Gaussian rapid updating of geological domains.
- University of Adelaide Internship Scholarship (2025)
- ARC Training Centre for Integrated Operations for Complex Resources Scholarship (2021 - 2024)
- IAMG Student Travel Grant (2022)
- IAMG Student Research Grant: Mathematical Geosciences Student Award (2022)
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Memberships
Date Role Membership Country 2022 - ongoing Member Society for Mining, Metallurgy & Exploration United States 2020 - ongoing Member International Association of Mathematical Geosciences United States -
Presentation
Date Topic Presented at Institution Country 2024 - 2024 Rapid and stochastic updating of geological domains in a resource model South Australian Exploration & Mining Conference SAEMC Australia 2024 - 2024 A comprehensive rapid updating of resource models with complex geology The 37th International Geological Congress 2024 IGC Korea, Republic of 2022 - 2022 Rapid updating of resource knowledge with sensor information South Australian Exploration & Mining Conference SAEMC Australia 2022 - 2022 Comparison of multiGaussian transforms in multivariate geostatistical simulation 21st Annual Conference of the International Association for Mathematical Geosciences IAMG France 2021 - 2021 Reproduction of inequality constraint between iron and silica for accurate production scheduling Iron Ore 2021 AusIMM Australia 2021 - 2021 Mineral resource modelling of variables with inequality constraints: a case study of an iron ore deposit APCOM 2021 SAIMM South Africa 2021 - 2021 Application of multiple-point statistics for stratigraphic modelling of coal layers 11th International Geostatistics Congress Geostats Canada
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