The Jiyang depression in the southeastern part of the Bohai Bay Basin has a relatively large scale set of shale oil in the Paleogene Shahejie Formation, but the complex internal components lead to narrow frequency bands, low resolution and difficulty in reservoir information extraction. Impedance is important information for reservoir characterization, and how to predict high-resolution impedance using available information is particularly important. Deep learning, known for its effectiveness in addressing non-linear problems, has found extensive applications in various fields of oil and gas exploration. However, the challenges of overfitting and poor generalization persist due to the limited availability of training datasets. Besides, existing methods often use networks to solve a single problem in fact, deep learning can deal with a series of problems intelligently. In order to partially solve the above problems, an intelligent storage prediction network framework is proposed in this paper. Physical information is introduced to realize data-driven and model-based approaches, thus solving the problem of difficult construction of training datasets. The processing part accomplishes the high-resolution processing of seismic records, thus solving the problems of narrow bandwidth and low resolution. Initial model constraints are introduced so as to obtain more stable inversion results. Finally, the well data is compared and analysed to identify and predict the lithology and complete the intelligent prediction of unconventional reservoirs. The results are compared with the traditional model-driven inversion method, revealing that the approach presented in this paper exhibits higher resolution in predicting dolomite. This contributes to the establishment of a robust data foundation for reservoir evaluation.

中文翻译：

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基于数据驱动和模型的高分辨率储层预测方法

渤海湾盆地东南部济阳坳陷古近系沙河街组页岩油规模较大，但内部成分复杂，导致频段窄、分辨率低，储层信息提取困难。阻抗是储层表征的重要信息，如何利用现有信息预测高分辨率阻抗尤为重要。深度学习以其解决非线性问题的有效性而闻名，在石油和天然气勘探的各个领域都有广泛的应用。然而，由于训练数据集的可用性有限，过度拟合和泛化能力差的挑战仍然存在。此外，现有方法通常使用网络来解决单个问题，事实上，深度学习可以智能地处理一系列问题。为了部分解决上述问题，本文提出了一种智能存储预测网络框架。引入物理信息来实现数据驱动和基于模型的方法，从而解决训练数据集构建困难的问题。处理部分完成了地震记录的高分辨率处理，解决了地震记录带宽窄、分辨率低的问题。引入初始模型约束以获得更稳定的反演结果。最后对井数据进行对比分析，识别和预测岩性，完成非常规油藏的智能预测。结果与传统的模型驱动反演方法进行比较，表明本文提出的方法在预测白云石方面具有更高的分辨率。这有助于为油藏评价建立强大的数据基础。