多分量数据全波形反演在气藏检测中的关键问题——以苏里格气田为例

苏里格气田是中国典型的致密砂岩气藏,构造简单、平缓,横向非均质性强,有效储层与围岩声学特征差别小,地震响应不明显,常规地震监测方法预测难度大,但气田含气砂岩泊松比低,是地震气藏检测的有效参数。利用弹性全波形反演精度高和能处理复杂非均质介质的优势,反演地层拉梅常数、剪切模量和密度,并计算泊松比,从而进行气藏预测。重点阐述了苏里格气田多分量数据全波形反演初始模型建模、先验模型建模和地震数据预处理3个关键问题的处理方法。二维三分量数据反演和"甜点"预测结果表明:①对于具有强非均质性的苏里格气田,利用全波形反演获得精度较高的地层弹性参数能显著提高气藏预测的准确度;②苏里格地区构造简单、平缓,利用常规叠加速度并结合构造解释可以建立比较好的初始模型,从而有效地解决了周波跳跃和局部极小的难题;③先验知识的约束和地震数据的预处理是全波形反演成功应用于苏里格气田气藏检测的关键。

Key issues of multi-component seismic full waveform inversion in the detection of gas reservoirs: a case study of Sulige gas field

Sulige gas field is the typical tight sand gas reservoirs in China, characterized with simple and gentle structure and strongly lateral heterogeneity; due to the subtle difference of acoustic characteristics between the reservoir and surrounding rocks, seismic response is unobvious. This leads to a big difficulty in predicting the conventional seismic monitoring technique. The Poisson's ratio of gas-bearing sand in gas field is small, and can be used as an effective parameter to detect gas reservoirs. Gas reservoir prediction is achieved by taking the advantages of high-precision elastic full waveform inversion (FWI) and the ability to process complex heterogeneous media, by inversion of the lamé constant, shear modulus and density of formations, as well as calculation of the Poisson's ratio. This paper focuses on the three key problems of multi-component full waveform inversion in Sulige gas field, i.e., initial modeling, prior modeling and seismic data preprocessing. The results of 2D3C data inversion and sweet points prediction show that: (1) The high-precision stratigraphic elastic parameters obtained by FWI can significantly improve the accuracy of gas reservoir prediction in the Sulige gas field with strong heterogeneity; (2) The Sulige area has simple and gentle structure, and the conventional stack velocity is used in combination with structural interpretation to create a good initial model, so as to effectively solve such problems as cycle skip and local minimum; (3) the prior knowledge constraint and the preprocessing of seismic data are also key factors for successfully applying FWI in the gas reservoir detection of Sulige gas field.