Continuous Mineralogical Characterization of the Bakken-Three Forks Formations: New Geological Insights from Hyperspectral Core Imaging

Martini, Brigette A.; Bellian, Jerome A.; Katz, David; Fonteneau, Lionel; Carey, Ronell; Guisinger, Mary; Nordeng, Stephan

doi:10.15530/urtec-2019-913

Cited by 3 publications

(3 citation statements)

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“…Several mining companies-BHP corporation at its operations in Chile (Minera Escondida and Minera Spence), for example-have taken hundreds of thousands of single pixel spectral samples from drill core composites using ASD tools [6,7] or similar, and analogous numbers of CoreScan [3] and HyLogger images from drill core trays have been taken. These images and spectra have been used to deliver mineralogical and geological data-mainly for variables such as alteration, clay content, lithology and mineral content-to build better geological and geometallurgical long and medium term models.…”

Section: Laboratory and Field Based Geological Hyperspectral Analysismentioning

confidence: 99%

“…Mining industry has taken advantage of near infrared (NIR) spectrography [1] technology to characterize geological and metallurgical samples. They record drill core trays using hyperspectral single pixel scanners, such as Hylogger [2], or 2D imaging systems such as Corescan [3,4], and they acquire spectra from composites of drill core segments using single pixel hyperspectral instruments such as ASD SpecLab [5][6][7]. In the first case, single pixel spectral analysis is used for semi-quantitative mineralogy, lithology and alteration characterization, supported by several mature unmixing techniques or linear regressions [8,9].…”

Section: Introductionmentioning

confidence: 99%

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A Robust Stochastic Approach to Mineral Hyperspectral Analysis for Geometallurgy

et al. 2020

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Most mining companies have registered important amounts of drill core composite spectra using different acquisition equipment and by following diverse protocols. These companies have used classic spectrography based on the detection of absorption features to perform semi-quantitative mineralogy. This methodology requires ideal laboratory conditions in order to obtain normalized spectra to compare. However, the inherent variability of spectral features—due to environmental conditions and geological context, among others—is unavoidable and needs to be managed. This work presents a novel methodology for geometallurgical sample characterization consisting of a heterogeneous, multi-pixel processing pipeline which addresses the effects of ambient conditions and geological context variability to estimate critical geological and geometallurgical variables. It relies on the assumptions that the acquisition of hyperspectral images is an inherently stochastic process and that ore sample information is deployed in the whole spectrum. The proposed framework is basically composed of: (a) a new hyperspectral image segmentation algorithm, (b) a preserving-information dimensionality reduction scheme and (c) a stochastic hierarchical regression model. A set of experiments considering white reference spectral characterization and geometallurgical variable estimation is presented to show promising results for the proposed approach.

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Section: Laboratory and Field Based Geological Hyperspectral Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Robust Stochastic Approach to Mineral Hyperspectral Analysis for Geometallurgy

et al. 2020

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“…Hyperspectral and Fourier transform spectrometry are already non-destructive alternatives to conventional quantitative analysis in geochemistry, mineralogy, geophysics, and reservoir assessment [1][2][3] . These spectrometry techniques do not require the extraction of samples, reduce the time needed for testing, and can obtain spectral information across the electromagnetic spectrum, from UV (~250 nm) to long-wavelength IR (30,000 nm).…”

Section: Introductionmentioning

confidence: 99%

AI inference of multiphysics properties using hyperspectral data

San Roman Alerigi,

Li,

Sepulveda

et al. 2023

Optical Technology and Measurement for Industrial Applications Conference

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We investigate using machine learning techniques to infer various physical properties of rocks from hyperspectral imaging data. In particular, we demonstrate that deep neural networks (DNN) can infer mechanical and geochemical properties based on high-resolution Fourier transform spectrograms. Our goal is to enable real-time petrophysical analysis of subsurface rocks and fluids. The ongoing work encompasses the development of sensors and algorithms that facilitate nondestructive, fast, and high-resolution mapping of petrophysical properties. We acquired high-resolution mappings of mechanical, chemical, and electromagnetic properties at sub-millimeter scale (> 100 um) using a scanning system fitted with multiphysics probes, including impulse hammer geomechanical probe designed to measure the rebound hardness and the reduced Young modulus; Fourier transform spectrometer (FTIR) to acquire the diffuse reflectance; acoustic transducers to measure unconstrained sonic velocities; and near-surface gas permeability. We have characterized over two hundred thousand samples across various lithologies, including limestone, sandstones, and shales from outcrops and cores from unidentified wells. We present the results of machine-learning models and algorithms that predict, based on the IR reflectance data, the rock types, and the unconstrained geomechanical properties. The method could be extended to characterize other solids from subsurface, terrestrial, or non-terrestrial environments. The combination of photonic measurements and machine learning provides the means to find non-causal relations between materials' electromagnetic/photonic response and their other physical properties under various stress states and environmental configurations. This work presents the foundational blocks to achieve this objective and develop optical sensors for sustainable energy extraction.

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Mineralogy and lithology of the Upper Cretaceous Niobrara Formation determined by hyperspectral core imaging

Birdwell

Fonteneau²,

Martini³

2020

MT GEOL

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Sections of the Upper Cretaceous (Coniacian to Campanian) Niobrara Formation in two cores from Kansas and Colorado, the Amoco Rebecca Bounds and USGS Portland 1, respectively, were examined by hyperspectral core imaging and analysis. A spectral imaging system combining high-resolution photography (50 μm), 3D laser profiling (20 μm), and near-visible + short-wave infrared reflectance spectroscopy (wavelengths from 450 to 2500 nm, 500 μm pixel size) was applied to these cores to provide spectral and textural data facilitating creation of continuous mineral and lithology class maps. In addition, compositing of pixel-based results to group pixels to create mineralogical and lithological logs (0.5-ft resolution) was performed to facilitate comparisons to other geochemical datasets. The results show general correspondence in trends identified by previous geochemistry studies, with some exceptions due to instrumental limitations related to low reflectance of some rock intervals and the limited range of infrared wavelengths examined. This study provides a cursory overview of an extensive dataset meant to demonstrate the utility of hyperspectral core scanning to studies of mudrocks in petroleum systems as well as the kinds of information this technique can provide for detailed examination of stratigraphic features in sedimentary systems more generally.

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Continuous Mineralogical Characterization of the Bakken-Three Forks Formations: New Geological Insights from Hyperspectral Core Imaging

Cited by 3 publications

References 0 publications

A Robust Stochastic Approach to Mineral Hyperspectral Analysis for Geometallurgy

A Robust Stochastic Approach to Mineral Hyperspectral Analysis for Geometallurgy

AI inference of multiphysics properties using hyperspectral data

Mineralogy and lithology of the Upper Cretaceous Niobrara Formation determined by hyperspectral core imaging

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