A welded pyroclastic deposit within the Dinantian Clyde Plateau Volcanic Formation, near Eaglesham, in the East Renfrewshire Hills of the Midland Valley

1998 ◽  
Vol 34 (2) ◽  
pp. 165-172 ◽  
Author(s):  
K. A. T. MacPherson ◽  
E. R. Phillips
Keyword(s):  
Pyroclastic Deposit ◽  
Volcanic Formation

The stratigraphy of a proximal late Hercynian pyroclastic sequence: the Vilancós region of the Pyrenees

1991 ◽  
Vol 128 (2) ◽  
pp. 111-128 ◽  
Author(s):  
J. S. Gilbert
Keyword(s):  
Explosive Eruptions ◽  
Rhyolite Lava ◽  
Geochemical Fingerprinting ◽  
Garnet Composition ◽  
Spanish Pyrenees ◽  
Volcanic Formation ◽  
Flow Deposit ◽  
Hercynian Orogeny ◽  
Mass Flow Deposits ◽  
Crustal Differentiation

AbstractVolcanic activity, the result of crustal differentiation during the Hercynian orogeny, generated eight explosive eruptions in the Vilancós region of the Spanish Pyrenees. The volcanic products comprise the Erill Castell Volcanic Formation of Stephanian age, which crops out as a 20 km long, WNW-trending strip < 2 km wide dipping steeply to the south.The Vilancós region represents a small fragment of an originally extensive regional terrain of silicic centres.The explosive eruptions mainly generated strongly peraluminous and phenocrystal garnet-bearing subaerial ignimbrite facies. Proximal intra-formational breccias represent a substantial volume of the preserved erupted product and one phreatoplinian deposit is exposed. Mass-flow deposits are common, and small-volume basalt, andesite and rhyolite lava flows, minor tuffs and palaesols also occur.Electron microprobe data show that each garnet-bearing member of the Vilancós region has a distinct garnet composition. This is used as geochemical fingerprinting tool to aid mapping and correlation between proximal intra-formational breccias and ignimbrite of the same eruption. Within one debris-flow deposit (the Vilancós Breccia Member) at least three garnet populations occur. Two of these are derived from pyroclastic members within the mapped region, the other comes from an unexposed rhyolite lava source.

scholarly journals Analysis of lunar pyroclastic deposit FeO abundances by LRO Diviner

2012 ◽  
Vol 117 (E12) ◽  
pp. n/a-n/a ◽  
Author(s):  
Carlton C. Allen ◽  
Benjamin T. Greenhagen ◽  
Kerri L. Donaldson Hanna ◽  
David A. Paige
Keyword(s):  
Pyroclastic Deposit

Identification of natural gas fractured volcanic formation by using numerical inversion method

2013 ◽  
Vol 108 ◽  
pp. 172-179 ◽  
Author(s):  
Fengqi Tan ◽  
Hongqi Li ◽  
Zhongchun Sun ◽  
Xiaohe Yu ◽  
Min Ouyang
Keyword(s):  
Natural Gas ◽  
Inversion Method ◽  
Numerical Inversion ◽  
Volcanic Formation

FACIES CLASSIFICATION OF A COMPLEX RESERVOIR USING MACHINE LEARNING: CASE STUDY FROM VOLCANIC FORMATION, THE YURIHARA OIL FIELD, JAPAN

2021 ◽  
Author(s):  
Yuki Maehara ◽  
◽  
Takeaki Otani ◽  
Tetsuya Yamamoto ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Pillow Lava ◽  
Gaussian Mixture ◽  
Oil Field ◽  
Well Logs ◽  
Depth Interval ◽  
Volcanic System ◽  
Facies Classification ◽  
Volcanic Formation ◽  
Borehole Image

Lithological facies classification using well logs is essential in the reservoir characterization. The facies are manually classified from characteristic log responses derived, which is challenging and time consuming for geologically complex reservoirs due to high variation of log responses for each facies. To overcome such a challenge, machine learning (ML) is helpful to determine characteristic log responses. In this study, we classified the lithofacies by applying ML to the conventional well logs for the volcanic formation, onshore, northeast Japan. The volcanic formation of the Yurihara oil field is petrologically classified into five lithofacies: mudstone, hyaloclastite, pillow lava, sheet lava, and dolerite, with pillow lava being predominant reservoir. The former four lithofacies are the members of the volcanic system in Miocene, and dolerite randomly intruded later into those. Understanding the distribution of omnidirectional tight dykes at the well location is important for the estimation of potential near-lateral seal distribution compartmentalizing the reservoir. The facies are best classified by core data, which are unfortunately available in a limited number of wells. The conventional logs, with the help of the borehole image log, have been used for the facies classification in most of the wells. However, distinguishing dolerite from sheet lava by manual classification is very ambiguous, as they appear similar in these logs. Therefore, automated clustering of well logs with ML was attempted for the facies classification. All the available log data was audited in the target well prior to applying ML. A total of 10 well logs are available in the reservoir depth interval. To prioritize the logs for the clustering, the information of each log was first analyzed by Principal Component Analysis (PCA). The dimension of variable space was reduced from 10 to 5 using PCA. Final set of 5 variables, gamma-ray, density, formation photoelectric factor, neutron porosity, and laterolog resistivity, were used for the next clustering process. ML was applied to the selected 5 logs for automated clustering. Cross-Entropy Clustering (CEC) was first initialized using k-means++ algorithm. Multiple initialization processes were randomly conducted to find the global minimum of cost function, which automatically derived the optimized number of classes. The resulting classes were further refined by the Gaussian Mixture Model (GMM) and subsequently by the Hidden Markov Model (HMM), which takes the serial dependency of the classes between successive depths into account. Resulting 14 classes were manually merged into 5 classes referring to the lithofacies defined by the borehole image log analysis. The difference of the log responses between basaltic sheet lava and dolerite was too subtle to be captured with confidence by the conventional manual workflow, while the ML technique could successfully capture it. The result was verified by the petrological analyses on sidewall cores (SWCs) and cuttings. In this study, the automated clustering with the combination of several ML algorithms was demonstrated more efficient and reasonable facies classification. The unsupervised learning approach would provide supportive information to reveal the regional facies distribution when it is applied in the other wells, and to comprehend the dynamic behavior of the fluids in the reservoir.

Integrated Strategy Delivers Secure and Fast Drilling in Deep CO2-rich Gas Reservoir in Volcanic Formation

2011 ◽  
Author(s):  
Rongchao Cheng ◽  
Haige Wang ◽  
Yunhua Ge ◽  
Zhixue Chen ◽  
Lingzhan Zou
Keyword(s):  
Gas Reservoir ◽  
Integrated Strategy ◽  
Volcanic Formation

scholarly journals Analyzing the Magnesium (Mg) Number of Olivine on the Lunar Surface and Its Geological Significance

2019 ◽  
Vol 11 (13) ◽  
pp. 1544
Author(s):  
Zhou ◽  
Zhang ◽  
Chen ◽  
Zhu
Keyword(s):  
Volcanic Glass ◽  
Pyroclastic Deposit ◽  
The Moon ◽  
Estimation Model ◽  
Cooling Crystallization ◽  
Important Constraint ◽  
Fe Content ◽  
Geological Evolution ◽  
Moon Mineralogy Mapper ◽  
The Difference

Olivine formation is directly related to Mg/Fe content. It is also significant in estimating the geological evolution of the moon. In this study, an estimation model of relative Mg number (Fo#) for lunar olivine was presented through multiple linear regression statistics. Sinus Iridum, the Copernicus Crater, and the pyroclastic deposit in the volcanic vents in the southeast of Orientale Basin were selected as the study areas. Olivine distribution was surveyed, and the relative Fo# calculation of olivine was implemented based on Moon Mineralogy Mapper (M3) data. Results demonstrated that olivine in the crater wall of Sinus Iridum and the Copernicus Crater had relatively high Fo#, which reflected the primitive melt. However, the difference in olivine spectral features between Sinus Iridum and the Copernicus Crater indicated different crystallization modes. The olivine in the pyroclastic deposit in the volcanic vents in the southwest of Orientale Basin also presented high Fo#, which indicated that the olivine was formed via rapid cooling crystallization and was accompanied by volcanic glass substances. As a result, the olivine relative Fo# calculated from the estimation model exhibited an important constraint implication for explanation of its causes.

Geology, geochemistry, and age of volcanites of the Tunguda Volcanic Formation: The problem of the Archean-Proterozoic boundary in North Karelia

2013 ◽  
Vol 21 (4) ◽  
pp. 337-358 ◽  
Author(s):  
T. A. Myskova ◽  
N. M. Ivanov ◽  
M. A. Korsakova ◽  
R. I. Mil’kevich ◽  
N. G. Berezhnaya ◽  
...  
Keyword(s):  
Volcanic Formation
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