稀疏井网下隔夹层精细预测方法研究及效果分析

在薄层预测领域, 地质统计学和波形指示反演技术是当前的两大主流方法。然而, 地质统计学反演在井点稀疏区域构建精确变差函数方面存在挑战, 难以实现对薄层的准确预测。尽管波形指示反演对井网分布的依赖性较小, 但其高频成分在描述地震振幅的空间变化方面存在不足。为了克服这些限制, 提出了叠前构形反演技术。该技术通过对比地震数据体中能量和频率特征的横向变化, 有效预测目标岩性的空间分布。充分利用了地震数据体的横向信息, 其高频信息与地震数据体的空间能量变化高度吻合。通过射线域弹性阻抗分析, 该方法能够提取更多弹性参数, 其结果与AVO特征相一致, 从而更准确地反映叠前地震数据不同射线域能量的横向变化, 精确预测多种岩性类型。在稀疏井网的研究区, 对于复杂岩性隔夹层的预测具有显著优势。该方法在多个油田的实际应用中已显示出卓越的效果, 对于1 m以上夹层的预测准确率超过80%, 明显优于传统的反演技术, 特别适合于稀疏井网区域的复杂岩性隔夹层预测。

In the field of thin layer prediction, geostatistical methods and waveform inversion techniques are currently the two main approaches.However, geostatistical inversion faces challenges in constructing accurate variograms in areas with sparse well points, making it difficult to accurately predict thin layers.Although waveform inversion is less dependent on the distribution of well networks, its high-frequency components are insufficient in describing the spatial variation of seismic amplitudes.To overcome these limitations, this paper proposes pre-stack seismic structure morphology inversion technology, an innovative reservoir prediction method.This technique effectively predicts the spatial distribution of target lithologies by comparing the lateral changes in energy and frequency characteristics within the seismic data volume.It fully utilizes the lateral information of the seismic data volume, with high-frequency information that closely matches the spatial energy variations of the seismic data.Through ray elastic impedance analysis, the method is able to extract more elastic parameters, and its results are consistent with Amplitude Variation with Offset (AVO) characteristics, thereby enhancing the accuracy in reflecting the lateral changes in energy across different ray domains in pre-stack seismic data.This enables it to accurately predict various lithology types.In study areas with sparse well networks, this method demonstrates a marked advantage in predicting complex lithological interlayers.The method has shown excellent results in practical applications in multiple oil fields, with a prediction accuracy rate of over 80% for interlayers of more than 1 meter.Compared with traditional inversion techniques, it is more conducive for predicting complex lithological interlayers in areas with sparse well networks.