scholarly journals NON-TRADITIONAL OIL AND GAS-PERSPECTIVE RESERVOIRS OF THE INTER-SALT COMPLEX OF THE PRIPYAT TROUGH

2018 ◽  
Vol 62 (3) ◽  
pp. 335-340 ◽  
Author(s):  
S. M. Abravets ◽  
I. A. Yashin
Keyword(s):  
Oil And Gas ◽  
Salt Complex ◽  
Salt Deposits ◽  
Organic Carbonate

Lower Famenian inter-salt deposits (Belarus) have been studied comprehensively. The microlithofacies are as follows: laminated organic-carbonate-silica rock; massive limestone; radiolarite; algal limestone; massive dolomite; porous dolomite; dolomite of indistinct lumpy texture. Rocks contain pores of dissolved radiolarians, cracks of fluid auto-fracturing and weakened zones encountered in the secondary transformations. Rocks of Domanic type in the Central oil area of the Pripyat can be oil-bearing and oil-generating ones.

scholarly journals ON THE METHOD OF EXPLORATION OF OIL AND GAS DEPOSITS IN THE SUBSALT SEDIMENTS OF THE CASPIAN DEPRESSION

Neft i gaz ◽  
2020 ◽  
Vol 6 (120) ◽  
pp. 20-34
Author(s):  
О.S. TURKOV ◽  
Keyword(s):  
Oil And Gas ◽  
Sedimentary Cover ◽  
Caspian Basin ◽  
Salt Domes ◽  
Caspian Depression ◽  
Salt Deposits ◽  
Gas Potential ◽  
Oil Gas ◽  
General Method ◽  
Large Deposits

The Caspian Basin is a unique geological object, the most notable features of which are the anomalously large thickness of the sedimentary cover and its enormous hydrocarbon potential. It is generally known that the main reserves in the identified fields and the prospects for discovering new large deposits of oil, gas and gas condensate here are associated with a subsalt complex of terrigenous and carbonate strata, submerged in the central part of the basin to depths of over 10 km. To date, this complex has been most studied in the edges of the depression at depths of 4-5 km. Confident forecasting of oil prospecting operations for deeper-lying objects is constrained by low researched of their structural, lithological, thermobaric and other parameters. In the debate about these very complex problems, one rarely recalls such a simple and at the same time crucial fact as direct signs of oil and gas potential. Indeed, every year we receive more and more new data on the “breathing of resources”, on the hydrocarbons migration from subsalt sediments into post-salt reservoirs, but we do not give them due attention. The article provides convincing, in the author's opinion, examples of the formation of post-salt deposits on salt domes due to the vertical migration of hydrocarbons. Taking this process into account, recommendations were made both НЕФТЬ И ГАЗ 2020 6 (120) 21 ГЕОЛОГИЯ on the general method of oil prospecting in subsalt deposits, and specific proposals for the search for subsalt and post-salt deposits of oil and gas.

scholarly journals Identification of Salt Deposits on Seismic Images Using Deep Learning Method for Semantic Segmentation

2020 ◽  
Vol 9 (1) ◽  
pp. 24 ◽  
Author(s):  
Aleksandar Milosavljević
Keyword(s):  
Oil And Gas ◽  
Semantic Segmentation ◽  
Depth Information ◽  
Oil And Gas Exploration ◽  
Image Patches ◽  
Seismic Image ◽  
Oil And Natural Gas ◽  
Series Of Experiments ◽  
Salt Deposits ◽  
Seismic Images

Several areas of Earth that are rich in oil and natural gas also have huge deposits of salt below the surface. Because of this connection, knowing precise locations of large salt deposits is extremely important to companies involved in oil and gas exploration. To locate salt bodies, professional seismic imaging is needed. These images are analyzed by human experts which leads to very subjective and highly variable renderings. To motivate automation and increase the accuracy of this process, TGS-NOPEC Geophysical Company (TGS) has sponsored a Kaggle competition that was held in the second half of 2018. The competition was very popular, gathering 3221 individuals and teams. Data for the competition included a training set of 4000 seismic image patches and corresponding segmentation masks. The test set contained 18,000 seismic image patches used for evaluation (all images are 101 × 101 pixels). Depth information of the sample location was also provided for every seismic image patch. The method presented in this paper is based on the author’s participation and it relies on training a deep convolutional neural network (CNN) for semantic segmentation. The architecture of the proposed network is inspired by the U-Net model in combination with ResNet and DenseNet architectures. To better comprehend the properties of the proposed architecture, a series of experiments were conducted applying standardized approaches using the same training framework. The results showed that the proposed architecture is comparable and, in most cases, better than these segmentation models.

scholarly journals Geophysical methods of oil and gas exploration in cambrian and precambrian sedimentary rocks of the Siberian Platform

1989 ◽  
Vol 20 (2) ◽  
pp. 37
Author(s):  
M.M. Mandelbaum ◽  
A.I. Shamal
Keyword(s):  
Siberian Platform ◽  
Seismic Data ◽  
Oil And Gas ◽  
Complex Structure ◽  
Geophysical Methods ◽  
Reservoir Properties ◽  
Oil And Gas Exploration ◽  
Salt Complex ◽  
Abrupt Changes ◽  
Low Amplitude

The Siberian Platform is the largest hydrocarbon-bearing sedimentary basin in the USSR. The conditions encountered in geophysical exploration in this basin are uniquely difficult. This very old sedimentary complex is characterised by abrupt changes in physical properties reflecting the presence of dolerites and tuffs, changes in salt thickness, and complex structure. Petroleum traps are controlled by low amplitude structures in the salt complex, although reservoir properties are variable, so that most traps are stratigraphic. This leads to the use of frequency content of seismic data to identify traps and electrical and time domain EM techniques to confirm the presence of the traps.

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