摘要
Buiding data-driven models using machine learning methods has gradually become a common approach for studying reservoir parameters.Among these methods,deep learning methods are highly effective.From the perspective of multi-task learning,this paper uses six types of logging data—acoustic logging(AC),gamma ray(GR),compensated neutron porosity(CNL),density(DEN),deep and shallow lateral resistivity(LLD)and shallow lateral resistivity(LLS)—that are inputs and three reservoir parameters that are outputs to build a porosity saturation permeability network(PSP-Net)that can predict porosity,saturation,and permeability values simultaneously.These logging data are obtained from 108 training wells in a medium₋low permeability oilfield block in the western district of China.PSP-Net method adopts a serial structure to realize transfer learning of reservoir-parameter characteristics.Compared with other existing methods at the stage of academic exploration to simulating industrial applications,the proposed method overcomes the disadvantages inherent in single-task learning reservoir-parameter prediction models,including easily overfitting and heavy model-training workload.Additionally,the proposed method demonstrates good anti-overfitting and generalization capabilities,integrating professional knowledge and experience.In 37 test wells,compared with the existing method,the proposed method exhibited an average error reduction of 10.44%,27.79%,and 28.83%from porosity,saturation,permeability calculation.The prediction and actual permeabilities are within one order of magnitude.The training on PSP-Net are simpler and more convenient than other single-task learning methods discussed in this paper.Furthermore,the findings of this paper can help in the re-examination of old oilfield wells and the completion of logging data.
机器学习方法构建模型已经逐渐成为一类研究储层参数的方法,在这类方法中,以深度学习方法效果较为突出。本文以多任务学习为视角,用我国西部地区某中低渗透率区块108口井的纵波时差(AC)、自然伽马(GR)、补偿中子孔隙度(CNL)、密度(DEN)、深浅侧向电阻率(LLD和LLS)六种测井资料作为输入,构建了一种能够同时以孔隙度、饱和度和渗透率三种储层参数为目标输出的神经网络学习方法——Porosity Saturation Permeability Network(PSP-Net),这种方法采用串行结构来实现储层参数特征迁移学习。与其他现有智能方法相比,该方法克服了常见单任务储层参数预测模型易过拟合、模型训练工作量大的缺点,具有较好的抗过拟合能力和泛化能力,融合了专业知识经验。在37口预测井中,对比测试的智能方法大都还处于学术探讨到模拟工业应用的阶段,相比较于经验公式的方法,三项任务降低平均误差的百分比分别为10.44%、27.79%和28.83%,预测的渗透率和实际渗透率在一个数量级范围内。训练简便性和易用性要优于文中其他几种单任务方法,对于油田老井复查、测井资料补全等工作均能提供帮助。
