Hyperspectral imaging for the determination of bitumen content in Athabasca oil sands core samples

Speta, Michelle; Rivard, Benoît; Feng, Jiang; Lipsett, Michael G.; Gingras, Murray K.

doi:10.1306/03021514121

Cited by 27 publications

(10 citation statements)

References 20 publications

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“…Laboratory imaging spectroscopy at the sample scale is rapid and non‐destructive and has numerous applications in the geological sciences (Greenberger, Mustard, Ehlmann, et al., 2015 ). The technique is becoming more common for measurement of drill core owing to the potential for rapid, non‐destructive determination of mineralogy (e.g., Aymerich et al., 2016 ; Hunt et al., 2020 ; Kruse et al., 2012 ; MacLagan et al., 2020 ; Mathieu et al., 2017 ; Speta et al., 2013 , 2015 ) and has garnered considerable interest in the mining industry. Here, we have collected imaging spectroscopy data of all core sections recovered by OmanDP totaling 3.2 km and, for the first time, set up and used the instrument aboard the Japanese IODP drilling vessel Chikyu .…”

Section: Methodsmentioning

confidence: 99%

Hydrothermal Alteration of the Ocean Crust and Patterns in Mineralization With Depth as Measured by Micro‐Imaging Infrared Spectroscopy

et al. 2021

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Imaging spectroscopy efficiently and effectively mapped spatial patterns in hydrothermal alteration mineral occurrence in ocean crust core  Samail ophiolite upper ocean crust cores are dominated by chlorite, amphibole, and epidote, while deeper cores have more zeolite/prehnite  Hydrothermal alteration largely decreases with depth in the ocean crust but is locally intense in major fault zones, even in lower crust Accepted ArticleThis article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

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Section: Methodsmentioning

confidence: 99%

Hydrothermal Alteration of the Ocean Crust and Patterns in Mineralization With Depth as Measured by Micro‐Imaging Infrared Spectroscopy

et al. 2021

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“…Although the geochemical data mentioned provide valuable information for geoscientists, these are obtained from laboratory analysis which are often destructive or requires sample processing, besides involving high costs, and skilled labor [7], [8]. Furthermore, these data suffer from a spatial scale limitation [8], [9], as they are often obtained from point sampling, whether on samples collected in outcrops or cores.…”

Section: Introductionmentioning

confidence: 99%

A Nondestructive Alternative for Kerogen Type Determination in Potential Hydrocarbon Source Rocks Using Hyperspectral Data and Machine Learning

Guimarães

Kupssinskü

Cardoso

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In Petroleum Geology, to assess the hydrocarbon generation potential in source rocks involves the determination of the kerogen type by some destructive method. The usage of such methods is a bottleneck in the process because it is time-consuming, requires specialized tools and personnel, and ends up destroying the rock sample, so it is not possible to do any posterior analysis. This study presents an alternative method for determination of the kerogen type that is fast and non-destructive using hyperspectral data and machine learning techniques. The method is validated using five distinct supervised learning algorithms that were applied to spectral data collected in rock samples from Taubaté Basin, Brazil, of an outcrop whose rocks have a wide range of hydrocarbon generation potential. Cores and samples were collected from the outcrop and had their kerogen type determined by geochemical analyses performed in the laboratory. The robustness of the method is evaluated in two distinct experiments. In the first one, the hyperspectral dataset was collected using a non-imaging spectroradiometer; in the second one, the method uses non-imaging hyperspectral data as training and is tested in hyperspectral images collected. In both experiments, the method was able to establish a relationship between selected spectral features and the kerogen type of the source rocks sampled. The results obtained in this paper are prospective for non-destructive classification of kerogen type (and, consequently, the hydrocarbon generation potential), since most of the models generated achieved accuracy above 0.8 in the validation step and 0.75 in the test step.

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“…If spatial relationships are important in addition to individual spectral points, then hyperspectral images can be collected using spectral cameras. These methods are in current use for the estimation of bitumen in oil sand drill core [13][14][15][16] and have been used for imaging of oil sands outcrops and mine walls [17].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Characteristics of Oil Sand, Part 1: Prediction of Processability and Froth Quality from Measurements of Ore

et al. 2020

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This study is the first of two companion papers using hyperspectral data to generate predictive models of oil sand ore and froth characteristics as a potential new means for process control. In Alberta, Canada, shallow oil sands deposits are accessed by surface mining and crushed ore is transported to a processing plant for extraction of bitumen using flotation processes. The ore displays considerable variability in clay, bitumen, and fines which affects their behavior in flotation units. Using oil sand ore spanning a range of bitumen and fines characteristics, flotation experiments were performed to generate froth in a batch extractor to determine ore processability (e.g., separation performance) and froth characteristics (color, bitumen, solids). From hyperspectral observations of ore, models can predict the %bitumen content and %fines (particle passing at 44 and 3.9 µm) of ore but the models with highest r2 (>0.96) predict the solids/bitumen of froth and the processability of ore. Spectral observations collected on ore upstream of the separation vessels could therefore offer a first order assessment of froth quality for an ore blend before the ore enters the plant. These models could also potentially be used to monitor and control the performance of the blending process as another means to control the performance of the flotation process.

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Hyperspectral imaging for the determination of bitumen content in Athabasca oil sands core samples

Cited by 27 publications

References 20 publications

Hydrothermal Alteration of the Ocean Crust and Patterns in Mineralization With Depth as Measured by Micro‐Imaging Infrared Spectroscopy

Hydrothermal Alteration of the Ocean Crust and Patterns in Mineralization With Depth as Measured by Micro‐Imaging Infrared Spectroscopy

A Nondestructive Alternative for Kerogen Type Determination in Potential Hydrocarbon Source Rocks Using Hyperspectral Data and Machine Learning

Hyperspectral Characteristics of Oil Sand, Part 1: Prediction of Processability and Froth Quality from Measurements of Ore

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