This article demonstrates the implementation of recurrent neural network (RNN) model in optimizing amplitude versus offset (AVO) attributes for indicating lithology and hydrocarbon zone on seismic data. Several drawbacks exist in the conventional implementation of AVO attributes for hydrocarbon exploration, including ambiguous AVO amplitude response as direct hydrocarbon indicators (DHIs), high cost of conventional seismic inversion and ambiguity from a non-absolute range of AVO scale of quality factor of P-wave (AVO SQp) and AVO scale of quality factor of S-wave (AVO SQs) attributes. Hence, this study aimed to optimize the application of AVO attributes by implementing an RNN model that solves nonlinear approximation with a faster, more economical, and reliable approach, supplied with well data as the data control. Sixteen features were extracted from seismic data as input with two targets of SQp and SQs from wells as output in the Angsi field. The model consisted of the improved algorithm of standard RNN called gated recurrent unit layers, followed by a simple RNN layer, and fully connected dense layers to predict SQp and SQs as lithology and hydrocarbon indicators, respectively. The model managed to predict the I-35 hydrocarbon reservoir zone anomalies, characterized by low SQp (sand-prone interval) and high SQs (hydrocarbon-bearing interval). The results indicated that the proposed RNN model performed efficiently as an alternative approach to bypass the conventional seismic inversion method and to optimize the application of AVO attributes in indicating lithology and hydrocarbon zone in seismic data.

本文演示了递归神经网络 (RNN) 模型在优化振幅与偏移 (AVO) 属性以指示地震数据上的岩性和油气层方面的实施。油气勘探中 AVO 属性的常规实施存在一些缺点，包括作为直接油气指标 (DHI) 的 AVO 幅度响应不明确、常规地震反演的成本高，以及来自非绝对范围的 AVO 品质因数 P- 的模糊性。波（AVO SQp）和S波品质因数的AVO标度（AVO SQs）属性。因此，本研究旨在通过实施 RNN 模型来优化 AVO 属性的应用，该模型以更快、更经济和可靠的方法解决非线性近似，并提供井数据作为数据控制。从地震数据中提取了 16 个特征作为输入，其中 Angsi 油田的井中的 SQp 和 SQs 两个目标作为输出。该模型由标准 RNN 的改进算法（称为门控循环单元层）、简单的 RNN 层和完全连接的密集层组成，分别预测 SQp 和 SQs 作为岩性和碳氢化合物指标。该模型成功地预测了 I-35 油气储层异常，其特征是低 SQp（易沙层段）和高 SQs（含油气层段）。结果表明，所提出的 RNN 模型可以有效地作为替代方法绕过传统的地震反演方法，并优化 AVO 属性在地震数据中指示岩性和油气带的应用。