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

2021 ◽  
Author(s):  
Taina Thomassim Guimaraes ◽  
Lucas Silveira Kupssinsku ◽  
Daniel Capella Zanotta ◽  
Joao Gabriel Motta ◽  
Andre Luiz Durante Spigolon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hyperspectral Data ◽  
Source Rocks ◽  
Hydrocarbon Source Rocks ◽  
Kerogen Type ◽  
Type Classification

Oil to Source Rock Correlation Using Biomarker Data Through Pattern Matching and Fingerprinting Analysis in “Kitkat” Field, Jabung Block, South Sumatra Basin

2020 ◽  
Author(s):  
S., R. Muthasyabiha
Keyword(s):  
Pattern Matching ◽  
Source Rock ◽  
Vitrinite Reflectance ◽  
Hydrocarbon Exploration ◽  
Source Rocks ◽  
Maturity Level ◽  
Fingerprinting Analysis ◽  
Kerogen Type ◽  
Oil Characteristics ◽  
Biomarker Data

Geochemical analysis is necessary to enable the optimization of hydrocarbon exploration. In this research, it is used to determine the oil characteristics and the type of source rock candidates that produces hydrocarbon in the “KITKAT” Field and also to understand the quality, quantity and maturity of proven source rocks. The evaluation of source rock was obtained from Rock-Eval Pyrolysis (REP) to determine the hydrocarbon type and analysis of the value of Total Organic Carbon (TOC) was performed to know the quantity of its organic content. Analysis of Tmax value and Vitrinite Reflectance (Ro) was also performed to know the maturity level of the source rock samples. Then the oil characteristics such as the depositional environment of source rock candidate and where the oil sample develops were obtained from pattern matching and fingerprinting analysis of Biomarker data GC/GCMS. Moreover, these data are used to know the correlation of oil to source rock. The result of source rock evaluation shows that the Talangakar Formation (TAF) has all these parameters as a source rock. Organic material from Upper Talangakar Formation (UTAF) comes from kerogen type II/III that is capable of producing oil and gas (Espitalie, 1985) and Lower Talangakar Formation (LTAF) comes from kerogen type III that is capable of producing gas. All intervals of TAF have a quantity value from very good–excellent considerable from the amount of TOC > 1% (Peters and Cassa, 1994). Source rock maturity level (Ro > 0.6) in UTAF is mature–late mature and LTAF is late mature–over mature (Peters and Cassa, 1994). Source rock from UTAF has deposited in the transition environment, and source rock from LTAF has deposited in the terrestrial environment. The correlation of oil to source rock shows that oil sample is positively correlated with the UTAF.

scholarly journals Prediction of Sugar Content in Port Wine Vintage Grapes Using Machine Learning and Hyperspectral Imaging

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1241
Author(s):  
Véronique Gomes ◽  
Marco S. Reis ◽  
Francisco Rovira-Más ◽  
Ana Mendes-Ferreira ◽  
Pedro Melo-Pinto
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Hyperspectral Imaging ◽  
Sugar Content ◽  
Hyperspectral Data ◽  
Machine Learning Algorithms ◽  
Port Wine ◽  
Monitoring And Control ◽  
High Quality ◽  
Harvesting Stage

The high quality of Port wine is the result of a sequence of winemaking operations, such as harvesting, maceration, fermentation, extraction and aging. These stages require proper monitoring and control, in order to consistently achieve the desired wine properties. The present work focuses on the harvesting stage, where the sugar content of grapes plays a key role as one of the critical maturity parameters. Our approach makes use of hyperspectral imaging technology to rapidly extract information from wine grape berries; the collected spectra are fed to machine learning algorithms that produce estimates of the sugar level. A consistent predictive capability is important for establishing the harvest date, as well as to select the best grapes to produce specific high-quality wines. We compared four different machine learning methods (including deep learning), assessing their generalization capacity for different vintages and varieties not included in the training process. Ridge regression, partial least squares, neural networks and convolutional neural networks were the methods considered to conduct this comparison. The results show that the estimated models can successfully predict the sugar content from hyperspectral data, with the convolutional neural network outperforming the other methods.

Small angle neutron scattering signature of oil generation in artificially and naturally matured hydrocarbon source rocks

2000 ◽  
Vol 31 (1) ◽  
pp. 1-14 ◽  
Author(s):  
A.P. Radliński ◽  
C.J. Boreham ◽  
P. Lindner ◽  
O. Randl ◽  
G.D. Wignall ◽  
...  
Keyword(s):  
Neutron Scattering ◽  
Small Angle ◽  
Small Angle Neutron Scattering ◽  
Source Rocks ◽  
Hydrocarbon Source Rocks ◽  
Oil Generation

scholarly journals Can hydrocarbon source rocks be identified on seismic data?

Geology ◽  
2011 ◽  
Vol 39 (12) ◽  
pp. 1167-1170 ◽  
Author(s):  
Helge Løseth ◽  
Lars Wensaas ◽  
Marita Gading ◽  
Kenneth Duffaut ◽  
Michael Springer
Keyword(s):  
Seismic Data ◽  
Source Rocks ◽  
Hydrocarbon Source Rocks

Developmental modes of the Neoproterozoic-Lower Paleozoic marine hydrocarbon source rocks in China

2007 ◽  
Vol 52 (S1) ◽  
pp. 77-91 ◽  
Author(s):  
BaoMin Zhang ◽  
ShuiChang Zhang ◽  
LiZeng Bian ◽  
ZhiJun Jin ◽  
DaRui Wang
Keyword(s):  
Source Rocks ◽  
Lower Paleozoic ◽  
Hydrocarbon Source Rocks

Machine Learning techniques for solar irradiation nowcasting: Cloud type classification forecast through satellite data and imagery

2022 ◽  
Vol 305 ◽  
pp. 117834
Author(s):  
Alfredo Nespoli ◽  
Alessandro Niccolai ◽  
Emanuele Ogliari ◽  
Giovanni Perego ◽  
Elena Collino ◽  
...  
Keyword(s):  
Machine Learning ◽  
Satellite Data ◽  
Machine Learning Techniques ◽  
Solar Irradiation ◽  
Cloud Type ◽  
Learning Techniques ◽  
Type Classification

scholarly journals Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll a, Diatoms, Green Algae and Turbidity

2018 ◽  
Vol 15 (9) ◽  
pp. 1881 ◽  
Author(s):  
Sina Keller ◽  
Philipp Maier ◽  
Felix Riese ◽  
Stefan Norra ◽  
Andreas Holbach ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Chlorophyll A ◽  
Green Algae ◽  
Hyperspectral Data ◽  
Coefficient Of Determination ◽  
Inland Waters ◽  
Learning Approaches ◽  
River Elbe ◽  
Regression Framework

Inland waters are of great importance for scientists as well as authorities since they are essential ecosystems and well known for their biodiversity. When monitoring their respective water quality, in situ measurements of water quality parameters are spatially limited, costly and time-consuming. In this paper, we propose a combination of hyperspectral data and machine learning methods to estimate and therefore to monitor different parameters for water quality. In contrast to commonly-applied techniques such as band ratios, this approach is data-driven and does not rely on any domain knowledge. We focus on CDOM, chlorophyll a and turbidity as well as the concentrations of the two algae types, diatoms and green algae. In order to investigate the potential of our proposal, we rely on measured data, which we sampled with three different sensors on the river Elbe in Germany from 24 June–12 July 2017. The measurement setup with two probe sensors and a hyperspectral sensor is described in detail. To estimate the five mentioned variables, we present an appropriate regression framework involving ten machine learning models and two preprocessing methods. This allows the regression performance of each model and variable to be evaluated. The best performing model for each variable results in a coefficient of determination R 2 in the range of 89.9% to 94.6%. That clearly reveals the potential of the machine learning approaches with hyperspectral data. In further investigations, we focus on the generalization of the regression framework to prepare its application to different types of inland waters.

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