Fusion of 3D seismic exploration and seafloor geochemical survey for methane hydrate exploration

2007 ◽  
Vol 38 (1) ◽  
pp. 37-43 ◽  
Author(s):  
Sadao Nagakubo ◽  
Toshiaki Kobayashi ◽  
Tetsuya Fujii ◽  
Takao Inamori
Keyword(s):  
Methane Hydrate ◽  
Seismic Exploration ◽  
3D Seismic ◽  
Geochemical Survey

Fusion on 3D seismic exploration and seafloor geochemical survey

2006 ◽  
Author(s):  
Sadao Nagakubo ◽  
Toshiaki Kobayashi ◽  
Tetsuya Fujii ◽  
Takao Inamori
Keyword(s):  
Seismic Exploration ◽  
3D Seismic ◽  
Geochemical Survey

High Density 3D Seismic Exploration Example in Daqing Oilfield

2014 ◽  
Author(s):  
Wang Yuhua ◽  
Liu Zhenkuan ◽  
Jiang Hongliang
Keyword(s):  
High Density ◽  
Seismic Exploration ◽  
3D Seismic

scholarly journals Practice of High-density 3D Seismic Exploration in Tight Oil Exploration in G Area of Central Sichuan

2021 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Tan Rongbiao ◽  
Wang Xiaolan ◽  
Deng Xiaojiang ◽  
Huang Lijuan ◽  
Li Yangjing ◽  
...  
Keyword(s):  
High Density ◽  
Seismic Exploration ◽  
Tight Oil ◽  
Oil Exploration ◽  
3D Seismic

Software for Development of Seismic Models of Rock Masses Employing 2D-3D Seismic Exploration

2013 ◽  
Author(s):  
O.O. Glukhov ◽  
A.I. Kompanets ◽  
A.V. Antsiferov ◽  
V.A. Antsiferov ◽  
L.A. Kamburova
Keyword(s):  
Seismic Exploration ◽  
3D Seismic ◽  
Rock Masses ◽  
Seismic Models

GIS Analysis Of Pockmarks From 3D Seismic Exploration Surveys

2014 ◽  
Author(s):  
Jean-Baptiste Geldof ◽  
Joana Gafeira ◽  
Julien Contet ◽  
Simon Marquet
Keyword(s):  
Seismic Exploration ◽  
Gis Analysis ◽  
3D Seismic

Technique of high accuracy 3D seismic exploration for fracture‐cavity type reservoirs

2010 ◽  
Author(s):  
Wang Yanfeng ◽  
Yan Feng ◽  
Wang Naijian ◽  
Gao Guocheng ◽  
Zhang Xiangquan
Keyword(s):  
High Accuracy ◽  
Seismic Exploration ◽  
3D Seismic

Edge-aware filtering with Siamese neural networks

2020 ◽  
Vol 39 (10) ◽  
pp. 711-717
Author(s):  
Mehdi Aharchaou ◽  
Michael Matheney ◽  
Joe Molyneux ◽  
Erik Neumann
Keyword(s):  
Neural Networks ◽  
Seismic Data ◽  
Learning Ability ◽  
Seismic Exploration ◽  
Data Sets ◽  
3D Seismic ◽  
New Class ◽  
Seismic Image ◽  
Siamese Networks ◽  
Seismic Images

Recent demands to reduce turnaround times and expedite investment decisions in seismic exploration have invited new ways to process and interpret seismic data. Among these ways is a more integrated collaboration between seismic processors and geologist interpreters aiming to build preliminary geologic models for early business impact. A key aspect has been quick and streamlined delivery of clean high-fidelity 3D seismic images via postmigration filtering capabilities. We present a machine learning-based example of such a capability built on recent advances in deep learning systems. In particular, we leverage the power of Siamese neural networks, a new class of neural networks that is powerful at learning discriminative features. Our novel adaptation, edge-aware filtering, employs a deep Siamese network that ranks similarity between seismic image patches. Once the network is trained, we capitalize on the learned features and self-similarity property of seismic images to achieve within-image stacking power endowed with edge awareness. The method generalizes well to new data sets due to the few-shot learning ability of Siamese networks. Furthermore, the learning-based framework can be extended to a variety of noise types in 3D seismic data. Using a convolutional architecture, we demonstrate on three field data sets that the learned representations lead to superior filtering performance compared to structure-oriented filtering. We examine both filtering quality and ease of application in our analysis. Then, we discuss the potential of edge-aware filtering as a data conditioning tool for rapid structural interpretation.

Geostatistics Method for Identification of Minor Structure Seismic Attribute

2013 ◽  
Vol 690-693 ◽  
pp. 3545-3548
Author(s):  
Li Juan Zhang ◽  
Yong Qiang Ma ◽  
Zi Liang Yu ◽  
Jun Jun Wei
Keyword(s):  
Coal Seam ◽  
Coal Mining ◽  
Economic Value ◽  
Great Influence ◽  
Seismic Exploration ◽  
3D Seismic ◽  
Seismic Attribute ◽  
Qualitative And Quantitative

Minor structure of coal seam has a great influence on safe and economic coal mining. It has been noticed by more and more people that the application of 3D-seismic exploration technology to solve minor structures in coal mining. In this paper, geostatistics method was adopted to solve the relativity between multi-attitude and small structures, qualitative and quantitative predicate the mine small structures, which can improve the interpretation precision, and can promote the interpreter’s efficiency, then created more economic value.

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