Application of non-linear tomography technology to gas hydrate imaging in the Qiongdongnan area
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摘要: 琼东南海域地震剖面上存在大量的含气特征，同相轴下拉、明显的速度横向变化等，因此水合物之下地层存在成像模糊且归位不准等问题，本文利用基于非线性层析的深度偏移方法提升成像精度。该方法采用全三维体的层析成像反演法建立深度域速度模型，通过对深度偏移道集拾取RMO量，并对其进行反偏移计算运动学不变量；在建立层位、倾角等骨架信息约束的混合模型基础上，利用运动学不变量进行速度层析，使得RMO最小以实现模型更新。该方法避免了常规速度更新的多次迭代偏移，能极大地提升层析效率，并能充分利用剖面骨架和倾角信息，获得高精度的速度模型。在琼东南水合物资料的实际应用中，有效地消除了含气对地层的影响，获得高精度的深度域成像结果。Abstract: There are a substantial amount of gas-bearing sediments occurred in the Qiongdongnan area. Geophysical features, such as in-phase axial pulling down and velocity changes are commonly observed. Therefore, the images of sediments below the gas hydrate layer are always blurring. In this paper, a method based on non-linear tomographic depth offset is adopted to enhance the accuracy and resolution of imaging. The method of full 3D tomography inversion is used to establish the velocity model in depth domain, and the kinematic invariants are calculated by picking up the RMO of depth offset gathers and reverse deflection. Based on the mixed model under the constraints of skeleton information, such as horizons and inclination angles, kinematic invariants are used for velocity tomography to minimize RMO for model updating. This method can avoid multiple iteration PSDM processing while updating velocity and greatly improve the tomographic efficiency. Then the high precision velocity model can be obtained by making full use of the profile skeleton and inclination information. The effect of gas on stratigraphic formation is effectively eliminated in the practical application of hydrate data in the Qiongdongnan area, and high precision depth imaging results are obtained.
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Keywords:
- Non-linear tomography /
- hybrid model /
- kinematic invariant /
- PSDM
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图 2 剩余量拾取
a. 道集中RMO拾取量，b. 剩余曲率属性，c. 质量因子属性。
Figure 2. The remaining quantity picking up
a. RMO quantity is picked up from gathers，b. gamma attribute，c. semblance attribute.
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图 4 样条插值思想
a. 一维速度模型，b. 小网格还原速度情况，c. 大网格还原速度情况，d. 样条插值还原速度情况。
Figure 4. Spline interpolation
a. 1D velocity model，b. small grid reduction velocity model，c. big grid reduction velocity model，d. spline interpolation reduction velocity model.
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图 6 RMO量统计直方图
a. 初始速度模型计算的RMO量统计直方图，b. 最终速度模型计算的RMO量统计直方图。
Figure 6. RMO quantity statistical histogram
a. statistical histogram of RMO quantity calculated by initial velocity model，b. statistical histogram of RMO quantity calculated by final velocity model.
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