基于双域自适应混合专家模型的深海钻探过程声波时差软测量方法

doi:10.3799/dqkx.2026.098

基于双域自适应混合专家模型的深海钻探过程声波时差软测量方法

doi: 10.3799/dqkx.2026.098

陶梓兴^1,3,4,
曹卫华^1,2,3,4, ,,
甘超^1,2,3,4

1. 中国地质大学(武汉)未来技术学院, 湖北武汉 430074;
2. 中国地质大学(武汉)自动化学院, 湖北武汉 430074;
3. 复杂系统先进控制与智能自动化湖北省重点实验室, 湖北武汉 430074;
4. 地球探测智能化技术教育部工程研究中心, 湖北武汉 430074

基金项目:

国家自然科学基金重点项目（No.62333019）.

详细信息

作者简介:
陶梓兴（2001-），男，博士研究生，研究方向为深海资源勘探过程环境融合感知.E-mail： taozixing@cug.edu.cn,ORCID:0009-0006-8559-3993.

通讯作者:
曹卫华（1972-），男，教授.E-mail： weihuacao@cug.edu.cn,ORCID:0000-0002-9677-9586.

中图分类号: TE51
计量
- 文章访问数: 40
- HTML全文浏览量: 1
- PDF下载量: 2
- 被引次数: 0
出版历程
- 收稿日期: 2026-01-14
- 网络出版日期: 2026-05-13

A Dual-Domain Adaptive Mixture-of-Experts-Based Soft Measurement Method for Sonic Transit Time in Deep-Sea Drilling Processes

Zixing Tao^1,3,4,
Weihua Cao^{1,2,3,4
, ,},
Chao Gan^1,2,3,4

1. School of Future Technology, China University of Geosciences (Wuhan), Wuhan 430074, China;
2. School of Automation, China University of Geosciences (Wuhan), Wuhan 430074, China;
3. Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China;
4. Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan, 430074, China

摘要

摘要: 针对深海钻探过程中测井参数（声波时差）获取代价大、且传统数据驱动的声波时差软测量方法在跨井应用中泛化能力不足等问题，提出一种基于双域自适应混合专家模型的深海钻探过程声波时差软测量方法。该方法融合多源钻探数据，根据低频可迁移的趋势域与高频相关的残差域，分别构建声波时差多专家软测量模型；在此基础上，引入自适应混合权重，通过多源无监督信号动态评估两域的可信度，实现声波时差的自适应融合。在多口实际钻井数据上的对比实验表明，所提方法在跨井预测精度与稳定性方面均优于SVR、RF、RNN、LSTM及Transformer等对比模型，平均误差降低约15%。该方法在仅依靠钻前勘探与钻井数据的前提下，有效平衡了预测精度与跨井应用的稳定性，适用于深海钻探环境下的声波时差实时软测量。
- 声波时差 /
- 跨井泛化 /
- 软测量 /
- 双域建模 /
- 自适应专家集成
Abstract: In deepwater drilling operations, the acquisition of logging parameters such as sonic transit time (DTCO) is associated with high cost and operational constraints, while conventional data-driven soft-sensing methods often exhibit limited generalization capability in cross-well applications. To address these challenges, this study proposes a dual-domain adaptive mixture-of-experts-based soft measurement method for sonic transit time in deep-sea drilling processes. This method integrates multi-source drilling data and constructs separate multi-expert soft measurement models for acoustic travel time based on low-frequency migratory trend domains and high-frequency residual domains. Building on this, adaptive mixed weights are introduced to dynamically assess the reliability of the two domains using multi-source unsupervised signals, thereby achieving adaptive fusion of acoustic travel times. Comparative experiments conducted on multiple real drilling datasets demonstrate that the proposed method consistently outperforms benchmark models, including SVR, RF, RNN, LSTM, and Transformer, in terms of cross-well prediction accuracy and stability, achieving an average error reduction of approximately 15%. This method effectively balances prediction accuracy with the stability of cross-well application whilst relying solely on pre-drilling exploration and drilling data, and is suitable for real-time sonic transit time soft measurement in deep-sea drilling environments.
- Sonic slowness /
- Cross-well generalization /
- Soft sensing /
- Dual-domain modeling /
- Adaptive Mixture of Experts

HTML全文

参考文献(30)

Akinyemi O. D., Elsaadany M., Siddiqui N. A., Elkurdy S., Olutoki J. O., Islam M. M., 2023. Machine Learning Application for Prediction of Sonic Wave Transit Time-A Case of Niger Delta basin. Results in Engineering, 20: 101528. doi: https://doi.org/10.1016/j.rineng.2023.101528
Bertini, J. R., Lavi, B., 2025. Enhancing Rate of Penetration Prediction in Drilling Operations: A Data Stream Framework Approach. Engineering Applications of Artificial Intelligence, 143, 110034. doi: https://doi.org/10.1016/j.engappai.2025.110034.
Chen, X., Cao, W. H., Gan, C., Ohyama, Y., She, J. H., Wu, M., 2021. Semi-supervised Support Vector Regression Based on Data Similarity and Its Application to Rock-mechanics Parameters Estimation. Engineering Applications of Artificial Intelligence, 104, 104317. doi: https://doi.org/10.1016/j.engappai.2021.104317.
Chen, X., Cao, W. H., Gan, C., Wu, M., 2022a. A Hybrid Partial Least Squares Regression-based Real Time Pore Pressure Estimation Method for Complex Geological Drilling Process. Journal of Petroleum Science and Engineering, 210, 109771. doi: https://doi.org/10.1016/j.petrol.2021.109771.
Chen, X., Cao, W. H., Gan, C., Wu, M., 2022b. A Hybrid Spatial Model Based on Identified Conditions for 3d Pore Pressure Estimation. Journal of Natural Gas Science and Engineering, 100, 104448. doi: https://doi.org/10.1016/j.jngse.2022.104448.
Du, K., Xi, W. Q., Huang, S., Zhou, J., 2024. Deep-sea Mineral Resource Mining: A Historical Review, Developmental Progress, and Insights. Mining, Metallurgy & Exploration, 41, 173-192. doi: https://doi.org/10.1007/s42461-023-00909-9.
Eaton, B. A., 1972. The Effect of Overburden Stress on Geopressure Prediction from Well Logs. Journal of Petroleum Technology, 24, 929-934. doi: https://doi.org/10.2118/3719-PA.
Feng, Y. W., Ren, Y., Zhang, G. C., Qu, H. J., 2020. Petroleum Geology and Exploration Direction of Gas Province in Deepwater Area of North Carnarvon Basin, Australia. China Geology, 3, 623-632. doi: https://doi.org/10.31035/cg2020064.
Gan, C., Cao, W. H., Wu, M., Chen, X., Hu, Y. L., Liu, K. Z., Wang, F. W., Zhang, S. B., 2019. Prediction of Drilling Rate of Penetration (rop) Using Hybrid Support Vector Regression: A Case Study on the Shennongjia Area, Central China. Journal of Petroleum Science and Engineering, 181, 106200. doi: https://doi.org/10.1016/j.petrol.2019.106200.
Gan, C., Cao, W. H., Liu, K. Z., Wu, M., 2021. A New Spatial Modeling Method for 3d Formation Drillability Field Using Fuzzy C-means Clustering and Random Forest. Journal of Petroleum Science and Engineering, 200, 108371. doi: https://doi.org/10.1016/j.petrol.2021.108371.
Gartrell, A., 2000. Rheological Controls on Extensional Styles and the Structural Evolution of the Northern Carnarvon Basin, North West Shelf, Australia. Australian Journal of Earth Sciences, 47, 231-244. doi: https://doi.org/10.1046/j.1440-0952.2000.00776.x.
Geekiyanage, S. C., Tunkiel, A., Sui, D., 2021. Drilling Data Quality Improvement and Information Extraction with Case Studies. Journal of petroleum Exploration and Production, 11, 819-837. doi: https://doi.org/10.1007/s13202-020-01024-x.
Lothe, A. E., Cerasi, P., Aghito, M., 2020. Digitized Uncertainty Handling of Pore Pressure and Mud-weight Window Ahead of Bit: North Sea Example. SPE Journal, 25, 529-540. doi: https://doi.org/10.2118/189665-PA.
Ning B., Sha Z. B., Li J., Song G., Chen Y. L., Jin X. M., 2026. Current Status and Development Trends of Deepwater Drilling Technology. Earth Science, 1-12 (in Chinese with English abstract).
Ramu, C., Sunkara, S. L., Ramu, R., Sain, K., 2021. An Ann-based Identification of Geological Features Using Multi-attributes: A Case Study from Krishna-godavari Basin, India. Arabian Journal of Geosciences, 14, 1-10. doi: https://doi.org/10.1007/s12517-021-06652-z.
Su Y. A., Dou X. R., Gao W. K., Liu K., 2023. Reflections and Outlook on the Development of Drilling-While-Measuring Technology for Oil and Gas Wells. Bulletin of Petroleum Science, 8(05): 535-554 (in Chinese with English abstract).
Tao, Z. X., Cao, W. H., Gan, C., 2025. Physically Interpretable Pore Pressure Prediction in Marine Strata through Embedded Depositional Mechanisms and Attention-Guided Learning. SPE Journal, 1-17. doi: https://doi.org/10.2118/231425-PA.
Wen, Z. X., Wang, J. J., Wang, Z. M., He, Z. J., Song, C. P., Liu, X. B., Ji, T. Y., 2023. Analysis of the World Deepwater Oil and Gas Exploration Situation. Petroleum Exploration and Development, 50, 1060-1076. doi: https://doi.org/10.1016/S1876-3804(23)60449-5.
Xiang, M., Li, B., He, Y. M., Lin, Z. Q., Hai, W. G., Wang, J., Xue, Q. L., 2024. Research on Simulation Model of Deepwater Drilling and Optimization Method of Drilling Parameters Based on Energy. Ocean Engineering, 312, 119286. doi: https://doi.org/10.1016/j.oceaneng.2024.119286.
Xu C. G., Gao Y. D., Zhang J. W., Zhang X. T., 2025. Recent Exploration Situation, Insights and Development Strategy of CNOOC. China Offshore Oil and Gas, 37(02): 1-13 (in Chinese with English abstract).
Xu C. L., Sun J. M., Dong Xu, Lai F. Q., 2017. Novel Method for Predicting Pore Pressure in Shale Gas Reservoirs via Logging. Acta Petrolei Sinica, 38(06): 666-676 (in Chinese with English abstract).
Xu K., Yang H. J., Zhang H., Zhao B., Yin G. Q., Wang Z. M., Wang H. Q., 2023. Efficient Exploration Techniques for Deep Tight Gas Reservoirs Based on Geomechanical Methods: The Case of the Dibei Gas Reservoir in the Kuqa Depression. Earth Science, 48(02): 621-639 (in Chinese with English abstract).
Yang, H., Feng, Y. C., Shang, G. Y., Li, P., 2024. A Sequence Learning Approach for Real-time and Ahead-of-bit Pore Pressure Prediction Utilizing Drilling Data from the Drilled Section. SPE Journal, 1-20. doi: https://doi.org/10.2118/223962-pa.
Zhang, Z., Sun, B. J., Wang, Z. Y., Pan, S. W., Lou, W. Q., Sun, D. L., 2022. Formation Pressure Inversion Method Based on Multisource Information. SPE Journal, 27, 1287-1303. doi: https://doi.org/10.2118/209206-pa.
中文参考文献
宁波, 沙志彬, 李晶, 等. 2026. 深海钻探技术现状及发展动态. 地球科学, 1-12.
徐珂, 杨海军, 张辉, 等. 2023. 基于地质力学方法的深层致密气藏高效勘探技术：以库车坳陷迪北气藏为例. 地球科学, 48(02): 621-639.
徐长贵, 高阳东, 张锦伟, 等. 2025. 中国海油近期勘探形势、启示与发展战略. 中国海上油气, 37(02): 1-13.
徐春露, 孙建孟, 董旭, 等. 2017. 页岩气储层孔隙压力测井预测新方法. 石油学报, 38(06): 666-676.
苏义脑, 窦修荣, 高文凯, 等. 2023. 油气井随钻测量技术发展思考与展望. 石油科学通报, 8(05): 535-554.

施引文献

资源附件(0)

访问统计

点击查看大图

计量

文章访问数: 40
HTML全文浏览量: 1
PDF下载量: 2
被引次数: 0

姓名
邮箱
手机号码
标题
留言内容
验证码

留言板

基于双域自适应混合专家模型的深海钻探过程声波时差软测量方法

doi: 10.3799/dqkx.2026.098

作者简介:
陶梓兴（2001-），男，博士研究生，研究方向为深海资源勘探过程环境融合感知.E-mail： taozixing@cug.edu.cn,ORCID:0009-0006-8559-3993.

通讯作者:
曹卫华（1972-），男，教授.E-mail： weihuacao@cug.edu.cn,ORCID:0000-0002-9677-9586.

计量

A Dual-Domain Adaptive Mixture-of-Experts-Based Soft Measurement Method for Sonic Transit Time in Deep-Sea Drilling Processes

计量

目录

留言板

基于双域自适应混合专家模型的深海钻探过程声波时差软测量方法

doi: 10.3799/dqkx.2026.098

作者简介: 陶梓兴（2001-），男，博士研究生，研究方向为深海资源勘探过程环境融合感知.E-mail： taozixing@cug.edu.cn,ORCID:0009-0006-8559-3993.

通讯作者: 曹卫华（1972-），男，教授.E-mail： weihuacao@cug.edu.cn,ORCID:0000-0002-9677-9586.

计量

出版历程

A Dual-Domain Adaptive Mixture-of-Experts-Based Soft Measurement Method for Sonic Transit Time in Deep-Sea Drilling Processes

计量

出版历程

目录

作者简介:
陶梓兴（2001-），男，博士研究生，研究方向为深海资源勘探过程环境融合感知.E-mail： taozixing@cug.edu.cn,ORCID:0009-0006-8559-3993.

通讯作者:
曹卫华（1972-），男，教授.E-mail： weihuacao@cug.edu.cn,ORCID:0000-0002-9677-9586.