scholarly journals A model of the Chicxulub impact basin based on evaluation of geophysical data, well logs, and drill core samples

1996 ◽  
Author(s):  
Virgil L. Sharpton ◽  
Luis E. Marin ◽  
John L. Carney ◽  
Scott Lee ◽  
Graham Ryder ◽  
...  
Keyword(s):  
Geophysical Data ◽  
Well Logs ◽  
Drill Core ◽  
Core Samples ◽  
Chicxulub Impact

Mapping of Quaternary sediments in northeastern Ontario using ground electromagnetic methods

Geophysics ◽  
1990 ◽  
Vol 55 (12) ◽  
pp. 1596-1604 ◽  
Author(s):  
G. J. Palacky ◽  
L. E. Stephens
Keyword(s):  
Cost Effective ◽  
Geophysical Data ◽  
Drill Core ◽  
Geologic Mapping ◽  
Quaternary Sediments ◽  
Core Samples ◽  
Bedrock Topography ◽  
Electromagnetic Methods ◽  
Overburden Thickness ◽  
Cost Effective Alternative

Since 1985, the Geological Survey of Canada has been evaluating the suitability of electromagnetic (EM) methods for Quaternary geologic mapping. Initially, a variety of ground EM instruments were tested at the Val Gagné, Ontario, site. The multifrequency horizontal‐loop equipment (APEX MaxMin I) was selected for transect surveys in the Kapuskasing‐Timmins area. After interpretation of ground EM results, 70 holes were drilled in the area using Rotasonic equipment. A correlation of drilling logs and geophysical data shows that clay, till, and sand have distinct EM responses. A constrained inversion of the ground EM data, in which drilling information was used to fix the layer thicknesses, yielded resistivity estimates for sediments encountered in boreholes. The following average resistivities were obtained: clay, 47 Ω⋅m; till, 123 Ω⋅m; sand, 251 Ω⋅m; the respective standard deviations were 7, 35, and 70 Ω⋅m. Resistivities of clays and tills were also determined in the laboratory on drill core samples. The results of the study indicate that resistivities of Quaternary sediments in northeastern Ontario are sufficiently stable to justify using EM methods for their identification. When layering is not too complex, inversion of EM data can be used to determine overburden thickness. For some applications, for which only qualitative scanning of bedrock topography is required (detection of bedrock valleys), ground EM techniques are a cost‐effective alternative to more accurate, but more expensive, seismic methods.

scholarly journals Drill-Core Mineral Abundance Estimation Using Hyperspectral and High-Resolution Mineralogical Data

2020 ◽  
Vol 12 (7) ◽  
pp. 1218
Author(s):  
Laura Tuşa ◽  
Mahdi Khodadadzadeh ◽  
Cecilia Contreras ◽  
Kasra Rafiezadeh Shahi ◽  
Margret Fuchs ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Ore Deposits ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Support Vector ◽  
Drill Core ◽  
Data Types ◽  
Mineralogical Characterization ◽  
Core Samples

Due to the extensive drilling performed every year in exploration campaigns for the discovery and evaluation of ore deposits, drill-core mapping is becoming an essential step. While valuable mineralogical information is extracted during core logging by on-site geologists, the process is time consuming and dependent on the observer and individual background. Hyperspectral short-wave infrared (SWIR) data is used in the mining industry as a tool to complement traditional logging techniques and to provide a rapid and non-invasive analytical method for mineralogical characterization. Additionally, Scanning Electron Microscopy-based image analyses using a Mineral Liberation Analyser (SEM-MLA) provide exhaustive high-resolution mineralogical maps, but can only be performed on small areas of the drill-cores. We propose to use machine learning algorithms to combine the two data types and upscale the quantitative SEM-MLA mineralogical data to drill-core scale. This way, quasi-quantitative maps over entire drill-core samples are obtained. Our upscaling approach increases result transparency and reproducibility by employing physical-based data acquisition (hyperspectral imaging) combined with mathematical models (machine learning). The procedure is tested on 5 drill-core samples with varying training data using random forests, support vector machines and neural network regression models. The obtained mineral abundance maps are further used for the extraction of mineralogical parameters such as mineral association.

New notions of Vikulovskaya series reservoir model in the area of Krasnoleninskoye field (Western Siberia)

2020 ◽  
pp. 66-74
Author(s):  
T. G. Isakova ◽  
T. F. Diakonova ◽  
A. D. Nosikova ◽  
D. S. Savchenko ◽  
N. I. Korobova ◽  
...  
Keyword(s):  
Western Siberia ◽  
Parameters Estimation ◽  
Well Logs ◽  
Reservoir Model ◽  
New Model ◽  
Core Samples ◽  
New Methods

Detailed lithological, sedimentological, petrophysical studies of columns and core samples of Vikulovskaya series were performed. On the basis of researches the new model of a reservoir was made and new methods of volumetric parameters estimation based on well logs were established.

Application of a convolutional neural network in permeability prediction: A case study in the Jacksonburg-Stringtown oil field, West Virginia, USA

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. B363-B373 ◽  
Author(s):  
Zhi Zhong ◽  
Timothy R. Carr ◽  
Xinming Wu ◽  
Guochang Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Regression Model ◽  
Bulk Density ◽  
Carbon Storage ◽  
Oil Field ◽  
Well Logs ◽  
Storage Site ◽  
Core Samples ◽  
Permeability Prediction

Permeability is a critical parameter for understanding subsurface fluid flow behavior, managing reservoirs, enhancing hydrocarbon recovery, and sequestering carbon dioxide. In general, permeability is measured in the laboratory based on subsurface core samples, calculated from well logs or estimated from well tests. However, laboratory measurements and well tests are expensive, time-consuming, and usually limited to a few core samples or wells in a hydrocarbon field or carbon storage site. Machine-learning techniques are good options for generating a rapid, robust, and cost-effective permeability prediction model because of their strengths to recognize the potential interrelationships between input and output variables. Convolutional neural networks (CNN), as a good pattern recognition algorithm, are widely used in image processing, natural language processing, and speech recognition, but are rarely used with regression problems and even less often in reservoir characterization. We have developed a CNN regression model to estimate the permeability in the Jacksonburg-Stringtown oil field, West Virginia, which is a potential carbon storage site and enhanced oil recovery operations field. We also evaluate the concept of the geologic feature image, which is converted from geophysical well logs. Five variables, including two commonly available conventional well logs (the gamma rays [GRs] and bulk density) and three well-log-derived variables (the slopes of the GR and bulk density curves, and shale content), are used to generate a geologic feature image. The CNN treats the geologic feature image as the input and the permeability as the desired output. In addition, the permeability predicted using traditional backpropagation artificial neural networks, which are optimized by genetic algorithms and particle swarm optimization, is compared with the permeability estimated using our CNN. Our results indicate that the CNN regression model provides more accurate permeability predictions than the traditional neural network.

The magnetization of oceanic crust: contribution to knowledge from the Troodos, Cyprus, ophiolite

1991 ◽  
Vol 28 (11) ◽  
pp. 1812-1826 ◽  
Author(s):  
James M. Hall ◽  
Charles C. Walls ◽  
Jing-Sui Yang ◽  
S. Lata Hall ◽  
Abdul Razzak Bakor
Keyword(s):  
Lower Limit ◽  
Oceanic Crust ◽  
Drill Hole ◽  
Extensive Study ◽  
Drill Core ◽  
Average Depth ◽  
Ocean Drilling Program ◽  
Ocean Drilling ◽  
Core Samples ◽  
Key Questions

An extensive study of a segment of the Troodos, Cyprus, ophiolite using both outcrop and drill-core samples, and extending from the sediment–extrusive interface through sheeted dikes to cumulate ultramafics, has allowed a number of key questions regarding the magnetization of oceanic crust to be addressed. These include the number of strongly magnetized intervals with depth, their lateral variability and controls on their occurrence. Comparison has also been made with the section in Ocean Drilling Program (ODP) hole 504B, and a reinterpretation of its constructional setting is offered.Two strongly magnetized intervals occur in the area studied. The upper is in the extrusive sequence, extends on average from 0.2 to 0.6 km depth, and has a thickness of ~0.4 km. Here magnetization is dominated by remanence. The lower interval extends from the lowest level at which flows occur with dikes (average depth = 0.9 km) into the Sheeted Complex (average depth = 1.2 km) and has a thickness of 0.3 km. Here magnetization is dominantly induced. No other strongly magnetized intervals occur in the section. The extent of dike intrusion is closely related to the position of the lower limit of the high-remanence layer and to the occurrence of the high induced magnetization layer. In both cases the replacement of primary magnetite, which can carry a strong remanence, by magnetically soft secondary magnetite appears to be the controlling process.Comparison of the Troodos and hole 504B magnetization profiles shows close similarity in the upper, remanence-dominated magnetic interval. The absence of the deeper interval of high induced magnetization in the hole 504B profile is interpreted as meaning that sheeted dikes have not been penetrated by the drill hole.

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