摘要
以测井仪器遇阻预测为典型教学案例,针对仪器遇阻导致相关测井数据(涉及油气开发核心信息)不足且获取渠道受限问题,提出一种基于生成式对抗网络(GAN)的预测方法,通过在教学过程中生成测井数据以补充样本,为学生提供大量训练数据集,从而提升深度学习模型的预测性能,助力教学任务的开展。实验结果表明,该实验过程能够帮助学生理解生成式对抗网络的原理及其应用,培养学生的动手实践能力与科研思维能力。此外,该方法在复杂地层条件下的预测精度显著优于传统方法,展示出良好的应用潜力。
[Objective]This study addresses the critical challenge of limited logging data availability for predicting tool string sticking in petroleum engineering,a problem exacerbated by the confidentiality constraints and restricted access to core oilfield data.The research aims to enhance the predictive performance of deep learning models through synthetic data generation using Generative Adversarial Networks(GAN),while simultaneously providing students with practical training in applying artificial intelligence to real-world engineering challenges.[Methods]A tailored GAN framework was designed to synthesize logging data,integrated with three deep learning architectures:Convolutional Neural Networks(CNN),Recurrent Neural Networks(RNN),and Transformer models.The experimental dataset comprised 120 historical logging records from oilfields,evenly balanced between obstruction and non-obstruction cases.Critical parameters encompassed well depth,lithology,porosity,permeability,formation pressure,borehole diameter,mud properties,inclination angle,and azimuth.Data preprocessing included outlier removal,missing value imputation,and feature standardization.The GAN architecture utilized 1D convolutional layers for both the generator and discriminator.The generator incorporated two convolutional layers with kernel sizes of 5 and 3,32 and 64 filters,LeakyReLU activation with a slope parameter of 0.2,and Tanh normalization for output scaling.The discriminator adopted a similar structure but employed Sigmoid activation for binary classification.Training spanned 100 epochs using the Adam optimizer configured with a learning rate of 0.0002 and a batch size of 64,augmented by label smoothing techniques where real labels were calibrated to 0.9 to stabilize training dynamics.Students engaged in generating customized datasets by systematically varying wellbore geometries and geological conditions,subsequently training and evaluating CNN,RNN,and Transformer models using an 80:20 training-testing split.[Results]The integration of GAN-generated logging data significantly enhanced model performance across all architectures,with marked improvements in accuracy,stability,and generalization.The CNN model achieved a 13.9%accuracy increase(from 61.1%to 75.0%)alongside higher precision,recall,and F1-scores,reflecting improved feature learning and reduced misclassification.While RNN exhibited slower initial convergence,its accuracy and F1-score rose by 11.2%and 11.4%,respectively,with stabilized training dynamics in later stages.The Transformer model demonstrated the most substantial gains,surging 16.7%in accuracy(to 86.1%)and 20.9%in F1-score(to 88.5%),highlighting its superior capability to exploit synthetic data for capturing complex hierarchical patterns.Training without GAN data led to pronounced volatility in CNN and RNN during early epochs,whereas Transformer maintained rapid,stable convergence regardless of data source.These results underscore that GAN-based augmentation not only mitigates overfitting and training instability but also empowers high-capacity models like Transformer to excel in scenarios requiring intricate feature modeling,achieving the highest overall metrics in accuracy,recall,and F1-score.[Conclusions]This study establishes that GAN-driven synthetic data generation effectively addresses data scarcity limitations,achieving a 16.7%accuracy improvement for Transformer models in complex geological environments.The experimental framework successfully integrates academic training objectives with industrial requirements,enabling students to develop expertise in synthetic data generation,model optimization through systematic adjustment of learning rates and batch sizes,and comprehensive performance evaluation.By simulating realistic drilling scenarios,students developed enhanced capabilities in interpreting model outputs and strategically addressing trade-offs between false-positive and false-negative predictions.The methodology demonstrates broad applicability to data-constrained domains within petroleum engineering,advancing both pedagogical objectives and technical innovation in AI-driven solutions.Future directions include the integration of real-time drilling parameters and expansion into multi-modal data synthesis to further refine predictive accuracy under complex wellbore conditions.This pedagogical framework aligns with application-oriented educational paradigms,equipping students with industry-relevant skills while contributing to advancements in intelligent oilfield technologies.
作者
高雅田
刘志文
GAO Yatian;LIU Zhiwen(School of Computer&Information Technology,Northeast Petroleum University,Daqing 163318,China;School of Mechanical Science and Engineering,Northeast Petroleum University,Daqing 163318,China)
出处
《实验技术与管理》
北大核心
2025年第6期211-217,共7页
Experimental Technology and Management
基金
双一流专业建设背景下软件工程专业应用型本科实践教学体系改革研究项目(SJGYB2024457)。
关键词
生成式对抗网络
仪器遇阻
测井数据
深度学习
教学案例
generative adversarial networks
tool string sticking
logging data
deep learning
teaching case
作者简介
高雅田(1979-),女,黑龙江大庆,博士,副教授,主要研究方向为大数据、数字挖掘,gaoyatian1979@163.com;通信作者:刘志文(1997-),男,黑龙江齐齐哈尔,博士研究生,主要研究方向为大数据与智慧油田,1205799268@qq.com。
