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

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

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.

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

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

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.

Sign in / Sign up

Export Citation Format

Share Document