摘要
近些年来,数据分析、深度学习技术取得了长足的发展,并为社会带来了可观的收益。故利用深度学习手段进行岩性识别也成为了一个研究热点。岩性识别是录井解释的核心业务,准确而有效地预测储层性质对石油勘探工作有着重大意义。为解决传统岩性识别方法成本高、耗时长等缺点。论文利用松辽盆地中若干井的测井数据进行模型研究,提出了一种基于PSO-BP的岩性识别方法。通过对测井源数据进行数据预处理、构建网络识别模型、优化岩性识别模型、评价模型输出结果等步骤,实现基于PSO-BP岩性识别方法。经过反复试验,结果表明采用PSO-BP的岩性识别方法对岩性进行识别的平均准确率可达92.2%,为储层预测工作提供了可靠的支撑。
In recent years,data analysis and deep learning technology have made great progress and brought considerable benefits to the society.Therefore,the use of deep learning method for lithology identification has become a research hotspot.Lithology identification is the core business of logging interpretation,accurate and effective prediction of reservoir properties is of great significance to petroleum exploration.However,the traditional lithology identification scheme has some disadvantages,such as high cost,long time and so on.Therefore,this paper uses the logging data of some wells in Songliao basin to study the model,after comparing the lithology identification results of different algorithms,a lithology identification method based on PSO-BP is proposed.Through data preprocessing of logging source data,construction of network identification model,optimization of lithology identification model and evaluation of model output,the lithology identification method based on PSO-BP is realized.After repeated tests,the results show that the average accuracy of lithology identification using PSO-BP method can reach 92.2%,which provides a reliable support for reservoir prediction.
作者
高雅田
杨俊国
GAO Yatian YANG Junguo(School of Computer and Information Technology,Northeast Petroleun University,Daqing 163318)
出处
《计算机与数字工程》
2024年第4期1119-1124,共6页
Computer & Digital Engineering
基金
东北石油大学校培育基金项目(编号:PY120225)资助。
作者简介
高雅田,女,博士,副教授,研究方向:大数据、数据挖掘;杨俊国,男,硕士研究生,研究方向:大数据、数据挖掘。
