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    基于导航金字塔地震数据地质特征增强的相控建模方法研究及应用以LHC地区长兴组生物礁为例

    Research and application of facies controlled modeling based on seismic data processed by steerable pyramid method for geological feature enhancement: A case study of organic reef of Changxing Formation in LHC area

    • 摘要: 四川盆地川东北LHC地区长兴组生物礁是该地区天然气的主力产层,经过多年勘探已进入开发中后期,剩余潜力需通过地质建模进行开采潜力的精准评估。但深层生物礁因地震成像分辨率不足导致礁体边界模糊、内部结构响应不清,且生物礁与非礁体在相同阻抗背景下孔隙度存在显著差异,因此,传统地质建模方法难以实现储层参数的精准表征。为此,提出了基于导航金字塔地震数据地质特征增强的相控建模方法。首先,采用导航金字塔方法对地震数据进行多尺度、多方向分解及重构,从而增强地质特征,提升生物礁识别精度;然后,结合生物礁地震响应特征及古地貌恢复结果,建立生物礁三维有利相带模型;最后,提取储层反演结果并融合到地质模型,进行贝叶斯相控建模以提升地质规律和井中、地震信息一致性,实现礁体孔隙度精细刻画。将该方法应用于实际地震数据建模,得到的相控模型孔隙度分布呈点状,更符合生物礁分布特征,并且与测井结果吻合较好,可用于后续储量精细计算与描述。

       

      Abstract: The organic reef of Changxing Formation in the LHC area of northeastern Sichuan Basin, as the main production layer, has shifted from the exploration stage to the development stage after several years of development. In addition, an accurate assessment of their development potential is crucial and needs to be achieved through geological modeling. However, deep organic reefs have blurred boundaries and unclear internal structural responses due to insufficient seismic imaging resolution. Moreover, there is a significant difference in porosity between organic reefs and non-reefs under the same impedance background, making traditional modeling methods fail to achieve accurate characterization of reservoir parameters. To solve this problem, a facies controlled modeling method based on seismic data processed by a steerable pyramid method for geological feature enhancement was proposed. First, the steerable pyramid method decomposed seismic data into multiple scales and multiple directions and reconstructed the data for geological feature enhancement, improving the accuracy of organic reef identification. Secondly, according to the seismic response characteristics of the organic reef and the restoration results of palaeogeomorphology, the three-dimensional favorable facies zone model of the organic reef was established. Finally, the inversion results of the reservoir were extracted into the geological model, and Bayesian facies controlled modeling was carried out to improve the consistency of geological rules with in-well and seismic information and realize the fine characterization of reef porosity. The application test shows that the porosity distribution of the facies controlled model is point-like, which is more consistent with the distribution characteristics of the organic reef and has a good agreement with the logging results, which proves that the proposed method is reliable. This method can facilitate the precise calculation and description of reservoir reserves in the future.

       

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