The Permian Shihezi Formation is located at the LX block at the eastern margin of the Ordos Basin, and it develops tight sandstone reservoirs with fluvial facies. Reservoirs with high gas production feature a porosity of larger than 12%, a permeability of higher than 1 mD, and a gas saturation of more than 50%, and the quantitative evaluation of reservoir parameters shall be urgently carried out to find sweet spots with high production. However, the accuracy of indirectly predicting porosity and other parameters by traditional seismic inversion is low. In addition, the seismic data and well-logging curves of the LX block have inconsistent corresponding relations, and a lot of conflict samples exist, which makes conventional convolutional neural networks difficult to be applied. Therefore, a fully connected network architecture is added to the conventional convolutional neural network, and the seismic data and well-logging data are connected through local Toeplitz network architecture, so as to deal with the indirect correlation between reservoir parameters and seismic data. The fully connected network architecture can address the conflict samples by introducing prior information including the line/channel number, horizon, and seismic facies. Furthermore, a deep learning network model suitable for tight reservoirs is established by introducing prior constraint information such as stratigraphic framework and seismic facies, and a geo-oriented method for selecting the best sample well is developed, so as to quantitatively predict reservoir parameters and describe the plane distribution of the sweet spots in reservoirs with high gas production. The actual application results show that the predicted results of porosity, permeability, and gas saturation are in good agreement with the well-logging data, and the newly deployed five wells are tested and achieve an open-flow capacity of more than 10,000 m~3/d during drilling, which effectively promotes the efficient development of tight gas.

致密储层具有地层薄、孔隙度低、横向非均质性强等特点.现有储层预测技术在解决此类问题时,主要依靠人工从反演属性体中寻找可能的甜点区域.由于地层的砂岩含量、孔隙度值与地震反射特征并无直接关系,导致甜点识别准确率低.为此,根据测井数据和地震数据的空间分布特征和数据分布特征,将全局和局部连接网络相结合,有针对性地创建了适用于致密储层甜点预测的混合深度学习网络结构,其中局部连接网络负责学习数据分布特征,全局连接网络负责学习空间分布特征.在甜点预测时,先预测砂岩储层,在此基础上预测孔隙度值.为解决孔隙度数据分布不均匀、有效值与背景值比例不均衡的问题,以砂岩含量曲线为约束条件设置阈值,筛选高于阈值的对应层段的孔隙度值,建立了砂岩含量遮挡的孔隙度训练样本集构建方法.鄂尔多斯盆地东北部的致密砂岩甜点识别结果表明,孔隙度预测结果准确度高,能有效识别本区的致密储层甜点发育区.

鄂尔多斯盆地东缘LX地区二叠系石盒子组发育河流相致密砂岩储层, 具有低孔、低渗且非均质性强的特点, 优选“甜点”是取得产能突破的关键。勘探结果表明, 基于叠后振幅属性预测气层存在多解性。对比分析井旁地震道集发现, 气层和干层的叠前AVO响应特征存在差异, 且与储层参数具有一定相关性。为此, 从研究区沉积模式入手, 建立泥-砂-泥3层介质模型, 通过开展变参数AVO正演模拟, 分析孔隙度、厚度以及含气饱和度变化对截距属性(P)和梯度属性(G)的影响。模拟结果表明, 孔隙度和厚度是决定AVO响应特征的主控因素。通过构建AVO定量解释模版, 并拟合截距和梯度属性与孔隙度和厚度之间的数学函数关系, 实现孔隙度和厚度的半定量-定量预测。应用结果表明, “甜点”预测结果与已钻井吻合率较高, 孔隙度和厚度定量预测误差控制在20%以内, 依据预测结果部署的新钻井L6井获得了高产天然气, 取得了良好的应用效果。

在地质勘探与开发研究工作中,地震数据是最重要的研究对象.随着地震勘探技术及设备的不断发展与进步,从陆地到海洋、从浅层到深层,采集的地震数据结构越来越复杂,数据的量级也不断增加,因此,地震数据的结构分析将直接影响到勘探与开发的研究进展.本文对多格式地震数据进行剖析,在熟知地震数据基本概念与采集原理的基础上,探索不同格式地震数据的结构转换与数据存取的实现意义.

北海DG油田浅层广泛发育一套近千米厚的高速灰岩屏蔽层,下伏储层只能在近偏移距采集到弱反射地震信号,中远偏移距接收的是折射波、多次波和转换波的复合干扰波.提出了高速灰岩屏蔽层下伏目标储层成像和预测的处理反演一体化解决方案:通过井和地震层速度的一维二维波动方程正演模型确定储层上覆灰岩顶面的临界角;并制定原窄反射角中远角度倾斜干扰波压制处理方案,改进原窄反射角共反射点道集中远角度反射波成像质量;同时统计分析储层敏感岩石物理参数,认识到泊松阻抗属性是DG油田区最敏感的储层指示器.运用新处理宽反射角地震资料和原窄反射角地震资料进行叠前反演泊松阻抗对比,新发现了原先用窄反射角资料漏判的储层,指明了DG油田区下步勘探潜力方向,对其他地区高速块体下伏储层预测具有一定的参考意义.