| 地震油气储层的小样本卷积神经网络学习与预测 |
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| 引用本文： | 林年添,张栋,张凯,王守进,付超,张建彬,张冲.地震油气储层的小样本卷积神经网络学习与预测[J].地球物理学报,2018,61(10):4110-4125. |
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| 作者姓名： | 林年添 张栋 张凯 王守进 付超 张建彬 张冲 |
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| 作者单位： | 1. 山东省沉积成矿作用与沉积矿产重点实验室, 山东科技大学地球科学与工程学院, 山东青岛 266590;2. 海底科学与探测技术教育部重点实验室, 中国海洋大学海洋地球科学学院, 山东青岛 266100;3. 海洋国家实验室海洋矿产资源评价与探测技术功能实验室, 山东青岛 266071;4. 中国石化石油物探技术研究院, 南京 211103;5. 山东大学 岩土与结构工程研究中心, 济南 250061;6. 山东正元建设工程有限责任公司潍坊分公司, 山东潍坊 261000 |
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| 基金项目： | 国家科技发展计划863项目（2013AA064201，2012AA061202）与国家自然科学基金项目（41174098）联合资助. |
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| 摘 要： | 地震储层预测是油气勘探的重要组成部分，但完成该项工作往往需要经历多个环节，而多工序或长周期的研究分析降低了勘探效率.基于油气藏分布规律及其在地震响应上所具有的特点，本文引入卷积神经网络深度学习方法，用于智能提取、分类并识别地震油气特征.卷积神经网络所具有的强适用性、强泛化能力，使之可以在小样本条件下，对未解释地震数据体进行全局优化提取特征并加以分类，即利用有限的已知含油气井段信息构建卷积核，以地震数据为驱动，借助卷积神经网络提取、识别蕴藏其中的地震油气特征.将本方案应用于模型数据及实际数据的验算，取得了预期效果.通过与实际钻井信息及基于多波地震数据机器学习所预测结果对比，本方案利用实际数据所演算结果与实际情况有较高的吻合度.表明本方案具有一定的可行性，为缩短相关环节的周期提供了一种新的途径.
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| 关 键 词： | 人工智能 深度学习 卷积神经网络 卷积核 地震数据驱动 油气藏识别 |
| 收稿时间： | 2018-07-06 |
Predicting distribution of hydrocarbon reservoirs with seismic data based on learning of the small-sample convolution neural network |
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| LIN NianTian,ZHANG Dong,ZHANG Kai,WANG ShouJin,FU Chao,ZHANG JianBin,ZHANG Chong.Predicting distribution of hydrocarbon reservoirs with seismic data based on learning of the small-sample convolution neural network[J].Chinese Journal of Geophysics,2018,61(10):4110-4125. |
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| Authors: | LIN NianTian ZHANG Dong ZHANG Kai WANG ShouJin FU Chao ZHANG JianBin ZHANG Chong |
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| Abstract: | Prediction of reservoirs with seismic data plays an important role in oil and gas explorations. However, multiple stages or long periods of work are often required to complete prediction processes, which can lower the efficiency of exploration. To solve this problem, based on features of distribution of hydrocarbon reservoirs and their seismic response, this work introduces a convolution neural network depth learning to intelligently extract, classify and identify characteristics of oil and gas from seismic data. It makes it possible to globally extract and classify features from unexplained seismic data under small sample conditions with strong applicability and generalization of the convolutional neural network. That is, the convolution kernels are obtained from the known drilling data, and then which are used to recognize hydrocarbon characteristics from seismic data driven based on the convolution neural network. This scheme is applied to the checking of model data and actual data, and the expected results are achieved. It can be seen that the calculation results of this scheme have a high degree of agreement with the actual situation by comparing the actual drilling data and the predicted results with multi-component seismic data by machine learning. It shows that the scheme has certain feasibility and provides a new way to shorten the periods of relevant steps. |
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| Keywords: | Artificial intelligence Deep learning Convolution neural network Convolution kernel Seismic data driven Oil and gas reservoir identification |
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