Kerogen Type Classification in Hydrocarbon Source Rocks Using Hyperspectral Data and Machine Learning

Guimarães, Tainá Thomassim; Kupssinskü, Lucas Silveira; Zanotta, Daniel Capella; Motta, João Gabriel; Spigolon, André Luiz Durante; Gonzaga, Luiz; Veronez, Maurício Roberto

doi:10.1109/igarss47720.2021.9554771

Cited by 2 publications

(2 citation statements)

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“…In the first experiment, SF-01 core data was used in training and validation whereas the second core, SF-02, was reserved for testing. The second experiment is an extended version of the one published in [30] and includes the use of hyperspectral data collected from hand samples from the outcrop, with spectroradiometer data used for training and validation of the models and the hyperspectral images as a test set.…”

Section: Kerogen Type Classificationmentioning

confidence: 99%

“…10 is the uniformity of the classification inside the same sample. Preliminary results published in [30] showed very noisy classified images, which caused some confusion in its interpretations. Here, we decided to perform feature engineering to extract the mean and std values for each band selected as input into models to try attenuate this problem.…”

Section: Experiments 2: Samplementioning

confidence: 99%

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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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Section: Kerogen Type Classificationmentioning

confidence: 99%

Section: Experiments 2: Samplementioning

confidence: 99%