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  • 河北十一选五任五遗漏:Industry Articles

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    后一万能码 2期内中 www.qgqx.net The benefits of CGG technologies and services are regularly featured in the industry press. Find out more by consulting our e-library of published industry articles. Narrow your search by entering at least one search criterion:

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    Overcoming the challenges of a shallow-water sparse wide-azimuth survey to improve deep reservoir imaging in the East China Sea

    The Leading Edge, August, 2019
    Peipei Deng | Yongdeng Xiao | Srujan Poonamalli | Tran Thinh To | Joe Zhou | Jason Sun | Yun Wei | Hua Chen | Senqing Hu | Gang Yao | Yu Jiang
    ?2019 SEG

    A new broadband Wide-Azimuth Towed-Streamer (WATS) survey was acquired in a shallow water region of offshore China to better resolve reservoir compartments. Two side boats were added as additional source boats to form the WATS acquisition geometry to resolve the shortcomings of narrow-azimuth acquisition along strike direction. This WATS acquisition is much sparser than common WATS surveys in deep water environments due to only one-side WATS configuration. The combination of sparse acquisition, shallow water and deep targets imposes challenges on how to optimally utilize the side-gun data as the key adding on to improve the final image. The 3D effect and severe aliasing expected in the Crossline direction pose tremendous difficulties for side gun data processing. A comprehensive flow for resolving these challenges especially in deghosting, demultiple and regularization for sparse and shallow wide-azimuth data is presented in the paper. A tilted orthorhombic (TORT) velocity model is also built with better constraints from the wide azimuth information for better fault positioning and imaging. Side gun data clearly enhances the final target reservoir imaging and better ties with the well due to better illuminations. A new channel is discovered based on the interpretation from the inverted Vp/Vs ratio, which clearly explains the previous misleading prediction that an exceptional well was drilled to a thinner and shallower channel not connected to the main reservoir.

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    Revealing new hydrocarbon potential through Q-compensated prestack depth imaging at Wenchang Field, South China Sea

    The Leading Edge, August, 2019
    Yitao Chen | Xiaodong Wu | Yong Xia Liu | Jason Sun | Lin Li | Lie Li | Tao Xu | Min Ouyang | Yonghao Gai
    ?2019 SEG

    The imaging of the complex fault system plays an important role in hydrocarbon exploration in Wenchang field since the fault system forms a bridge between the source rocks and reservoirs. However, it is challenging to obtain a high quality depth image of the fault system due to the complex depth velocity and Q absorption effect. In this paper, we demonstrate how a combination of Fault Constraint Tomography (FCT) model building flow and Q-compensated High Fidelity Controlled Beam Migration (QHFCBM) work together to provide a step change in the imaging quality and bring significant impact to the reservoir delineation.

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    Machine learning and geophysical inversion – A numerical study

    The Leading Edge, July, 2019
    Brian Russell
    ?2019 SEG

    Much recent work has been done on comparing machine learning and geophysical inversion techniques to the extraction of model parameters from seismic reflection data. In our profession we are used to analyzing the physics of geophysical problems in detail. However, in many of the recent studies the machine learning algorithms are treated almost as “black boxes”. In this study l I will use a straightforward numerical example to illustrate the difference between geophysical inversion and machine learning inversion. In doing so I will try to “demystify” machine learning algorithms and show that, like inverse problems, they have a definite mathematical structure that can be written down and understood. The example used is this tutorial is the extraction of the underlying reflection coefficients from an overlapping wavelet response that was created by convolving a reflection coefficient dipole with a symmetric wavelet. In discussing the solution to this problem I will cover the topics of deconvolution, recursive inversion, linear regression and nonlinear regression using a feedforward neural network. I will present both the full inverse approach as well as gradient descent algorithms, which can be applied to both linear and nonlinear problems. This will lead to a description of the backpropagation algorithm, which is used to train a feedforward neural network. In the final section of the tutorial I will look at the impact of local minima in the search for a global minimum in the backpropagation algorithm.

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    Integration in Exploration

    Oilfield Technology, May, 2019
    Carl Watkins | Daniel Carruthers | Pedro Martinez Duran | Simon Otto | Mark Cowgill
    ?2019 Palladian Publications Ltd

    Integrated exploration, new workflows for new challenges highlighting JumpStart Gabon, Marine Source Predictions and Encontrado reprocessing projects.

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    Consolidating rock-physics classics: A practical take on granular effective medium models

    The Leading Edge, May, 2019
    Fabien Allo
    ?2019 SEG

    The paper presents a review of classic rock physics models used for clastic sedimentary rocks and how they have been combined into extended models through the introduction of a few parameters associated with a compositional or textural property of the rock. The models are used on a variety of real data sets to showcase how rock properties can be inferred from elastic properties.

     Download (PDF, 506.9KB)

  • 股权质押“利剑”高悬 上市公司急寻脱困之道 2019-08-21
  • 数百人吃发芽糙米 三个月收获健康 2019-08-09
  • 古画中的“婴戏”:穿越千年的可爱淘气与天真烂漫 2019-08-09
  • 好坏大家判 事事有人管 2019-08-09
  •  [生活资讯]  [辣妹探店]  [激情试驾]  [创客餐考] 2019-08-08
  • 陕西凤翔“血池”密档:祭祀谜团有望解开 2019-08-08
  • 拥抱新时代 贯彻新思想 展现新气象 沿着党的十九大指引的方向砥砺奋进 2019-08-07
  • 土豆姐姐冯小燕的新农人梦 2019-08-07
  • 险!小孩头卡防盗栏 民警爬窗外托举 成功解救 2019-08-07
  • 韩国:关注平昌冬奥会——朝啦啦队抵达  韩方举办欢迎晚宴 2019-08-04
  • 广州租房市场进入淡季 区域热点板块成交不减 ——凤凰网房产北京 2019-08-04
  • 重庆动物园助动物“冰爽”度夏 2019-08-04
  • 曹建明:坚持有腐必反,坚定不移“打虎”、“拍蝇”、“猎狐” 2019-07-30
  • 厚积薄发 东阳红木家具市场新中式馆华丽蝶变 2019-07-25
  • 2016年第四季度经济增长预测 2019-07-25
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