摘要
随着页岩油资源勘探开发的不断深入,测井数据在储层评价中的重要性愈发突出.然而,由于测井设备故障、成本限制等因素,常出现测井曲线缺失或异常的问题,严重影响地质解释与资源开发的精度.针对测井曲线缺失与异常问题,通过引入Transformer编码器以增强全局特征表达,并结合双向门控循环单元(bidirectional gated recurrent unit,BiGRU)的时序建模能力与Inception模块的多尺度特征提取能力,设计了一种深度学习模型IBT(Inception-BiGRU-Transformer),该模型通过多尺度特征提取与时序建模机制,有效提升了测井曲线的重构精度与稳定性.在大庆古龙页岩油区12口井的实测数据集上开展了单目标与多目标测井曲线重构实验.实验结果表明,IBT模型在RMSE、MAE、MAPE和R^(2)等多项评价指标上均优于现有主流模型,具备更强的预测精度与泛化能力.消融实验进一步验证了各个模块在提升预测性能方面的有效性.
With the continuous advancement of shale oil exploration and development,well logging data has become increasingly important in reservoir evaluation.However,due to factors such as logging equipment failures and cost constraints,missing or abnormal well log curves frequently occur,which severely impact the accuracy of geological interpretation and resource development.To address the issues of missing and abnormal well log curves,a deep learning model termed Inception-BiGRU-Transformer(IBT)is proposed by integrating a Transformer encoder to enhance global feature representation,a bidirectional gated recurrent unit(BiGRU)for temporal modeling,and an Inception module for multi-scale feature extraction.This model effectively improves the reconstruction accuracy and stability of well log curves through its combined multi-scale feature extraction and sequential modeling mechanisms.Experiments are conducted on measured data from twelve wells in the Daqing Gulong shale oil region,involving both single-target and multi-target well log curve reconstruction tasks.The results demonstrate that the IBT model outperforms mainstream models in terms of RMSE,MAE,MAPE,and R^(2),exhibiting superior predictive accuracy and generalization capability.Furthermore,ablation studies confirm the effectiveness of each component in enhancing the model’s predictive performance.
作者
李文昊
李刚
高冉
孟杨
LI Wen-Hao;LI Gang;GAO Ran;MENG Yang(College of Computer&Information Technology,Northeast Petroleum University,Daqing 163318,China;No.1 Oil Production Plant,Daqing Oilfield Co.Ltd.,Daqing 163001,China)
出处
《计算机系统应用》
2026年第1期263-275,共13页
Computer Systems & Applications
基金
黑龙江省政府与大庆油田首批“揭榜挂帅”科技攻关项目(DQYT-2022-JS-750)。
