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油气钻井工程作为油气勘探开发的核心环节，其高效、安全、智能化发展一直是石油工程领域的研究热点。随着全球油气勘探开发不断向深层、深水、非常规油气藏迈进，智能钻井装备研发、随钻智能感知、施工风险智能预警、钻井参数智能优化等领域的研究持续深入，为油气钻井行业的提质增效与可持续发展筑牢了理论与技术根基。近年来，人工智能、数字孪生、大数据分析、精准测控等前沿技术的深度应用，进一步推动了钻井工程智能化领域的技术创新与产业落地。

本期精选文章系统梳理了智能钻头技术两大核心路径的研究进展，明确了其未来主流发展方向与重点攻关领域；同时提出了密集堆叠岩屑图像颗粒细粒度分割方法，有效解决了岩屑图像分割精度低、小目标漏检等行业痛点。此外，本期精选文章构建了吉木萨尔页岩油压裂施工曲线智能预测模型，验证了基于钻井工程参数的随钻岩性智能预测方法的可行性，还全面总结了中国海油海上智能钻井导航系统的关键技术进展与未来发展方向。接下来，读者可随着阅读深入这些前沿研究成果，探索石油钻井工程智能化发展的技术奥秘。

1

智能钻头技术研究现状与前景展望

Research Status and Prospect of Intelligent Drill Bit Technology

【摘要】智能钻头技术通过集成感知、自适应与决策控制功能，显著提升钻井效率、延长钻头使用寿命并保障钻井安全，是钻井工程智能化发展的核心方向。系统梳理了智能钻头2大技术路径的研究进展：仪器化钻头以近钻头高频数据采集为核心，从振动监测发展为多参数集成测量（如哈利伯顿Cerebro Force、贝克休斯MultiSense 2.0），实现了钻头动力学全参数感知，但实时传输与地层特性识别能力仍有待突破；自适应钻头通过被动式液压机械结构（如TerrAdapt钻头的切削深度控制套筒）或主动调节机构（如NOV可伸缩刀翼原型）动态优化切削深度和钻头形态，有效抑制了粘滑振动，但调节精度与主动响应能力受限。探讨了智能钻头的发展前景，指出仪器化、自适应技术和决策控制系统的深度融合是未来智能钻头发展的主流方向，并针对智能钻头技术当前面临的挑战提出了该技术的重点攻关方向。

【Abstract】Intelligent drill bit technology, as the core direction of the intelligent development of drilling engineering, integrates perception, self-adaption, and decision-making control functions, significantly enhancing drilling efficiency, prolonging drill bit run life, and ensuring operational safety. The research progress of the two major technical paths of intelligent drill bits was systematically reviewed: Instrumented drill bits focus on near-bit high-frequency data acquisition. They have evolved from vibration monitoring to multi-parameter integrated measurement (such as Halliburton Cerebro Force and Baker Hughes MultiSense 2.0), achieving full-parameter perception of the drill bit’s dynamics. However, the real-time transmission and the ability to identify formation characteristics still need to be overcome. Adaptive drill bits achieve dynamic optimization of the depth of cut and drill bit shape through passive hydraulic mechanical structures (such as the depth of cut control cartridge of the TerrAdapt drill bit) or active adjustment mechanisms (such as the prototype of the NOV retractable blade). This effectively suppresses stick-slip vibrations. However, the adjustment accuracy and active response capability are limited. The development prospect of intelligent drill bits was discussed. It is pointed out that the deep integration of instrumentation, adaptive technology, and decision-making control systems is the mainstream direction of the future development of intelligent drill bits. Based on the current challenges of intelligent drill bit technology, the key technological research directions for the future are proposed.

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2

密集堆叠岩屑图像颗粒细粒度分割方法

Fine-Grained Particle Segmentation Method for Dense Stacked Rock Debris Images

【摘要】为解决岩屑分布密集图像边界存在重叠导致图像分割效率低下、小目标漏检和边界分割精度较差等问题，研制了岩屑图像颗粒细粒度分割网络。该网络基于Mask R-CNN网络架构，采用DW轻量卷积构建主干网络，利用坐标注意力机制构建了多尺度特征金字塔，以增强特征选择性和空间位置信息的捕捉能力，并提升不同尺寸目标的检测能力；结合基于距离图引导的边界增强损失函数，进一步优化网络对细粒度边界特征的学习，提升分割结果的精确度。采用丰谷、新场等区块采集的岩屑图像样本对改进后的岩屑分割网络进行测试，平均精度为92.1%，平均交并比为83.2%。研究表明，改进后的岩屑图像颗粒细粒度分割网络的推理速度及精度较现有分割网络明显提升，为自动化岩屑图像分析提供了技术支持。

【Abstract】To solve the problems of low image segmentation efficiency, missed detection of small targets, and poor boundary segmentation accuracy caused by overlapping boundaries in images with dense rock debris distribution, a fine-grained segmentation network for rock debris images was developed. This model was based on the Mask R-CNN network architecture. It used DW lightweight convolution to build the backbone network and utilized coordinate attention mechanism to construct a multi-scale feature pyramid to enhance feature selectivity and capture spatial position information, thereby improving the detection ability of targets of different sizes. By combining the distance graph-guided boundary enhancement loss function, the model’s learning of fine-grained boundary features was further optimized, and the accuracy of segmentation results was improved. The improved rock debris segmentation network was tested using rock debris image samples collected from blocks such as Fenggu and Xinchang. The average precision was 92.1%, and the mean intersection over union (mIOU) was 83.2%. The results show that the improved rock debris segmentation network has significantly improved inference speed and accuracy compared to existing segmentation networks, providing technical support for automated rock debris image analysis.

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3

吉木萨尔页岩油压裂施工曲线智能预测方法

Intelligent Prediction Method of Fracturing Treatment Curves for Jimsar Shale Oil

【摘要】砂堵严重制约了吉木萨尔页岩油示范区的压裂施工安全和施工效率，施工压力变化是砂堵最直接的响应信号，准确预测施工压力是超前预警砂堵的关键。基于LSTM、BiLSTM和GRU等3种机器学习方法，建立了多变量时序施工压力预测模型，利用网格搜索算法和贝叶斯优化算法优化模型的超参数，同时使用五折交叉验证方法，防止模型过拟合。以均方误差、均方根误差和平均绝对误差为评价指标，评估了预测模型的性能，并以实际压裂段开展了模型应用。研究结果表明：具有五折交叉验证和丢弃率等于0.30的BiLSTM模型，相比于具有相同约束的LSTM和GRU模型，均方误差分别降低了67.6%和89.9%，均方根误差分别降低了43.1%和68.3%，平均绝对误差分别降低了28.6%和67.6%。由此可知，具有五折交叉验证和丢弃率等于0.30的BiLSTM模型具有较好的泛化能力和鲁棒性，预测施工压力更加可靠。研究结果可为吉木萨尔页岩油示范区压裂施工压力的预测提供模型和方法。

【Abstract】The screenout severely restricts the safety and efficiency of fracturing treatment in Jimsar Shale Oil. The change in treatment pressure is the most direct response signal of the screenout. Accurate prediction of treatment pressure is the key to achieving early warning against screenout. Based on three machine learning methods, namely, LSTM, BiLSTM, and GRU, a multivariate time-series treatment pressure prediction model was established. Grid search algorithms and Bayesian optimization algorithms were used to optimize the model’s hyperparameters, while five-fold cross-validation was employed to prevent overfitting. The performance of the prediction model was evaluated using mean squared error, root mean squared error, and mean absolute error as metrics, and the model was applied to actual fracturing sections. The research results indicate that the BiLSTM model with five-fold cross-validation and dropout rate set to 0.3 outperforms the LSTM and GRU models under the same constraints, with mean squared error reductions of 67.6% and 89.9%, mean squared root error reductions of 43.1% and 68.3%, and mean absolute error reductions of 28.6% and 67.6%, respectively. Therefore, the BiLSTM model with five-fold cross-validation and dropout rate set to 0.3 demonstrates superior generalization capability and robustness, making it more reliable for predicting treatment pressure. The research findings provide a model and method for the prediction of treatment pressure in Jimsar Shale Oil fracturing treatments.

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4

基于工程参数的随钻岩性智能预测方法研究

Intelligent Lithology Prediction While Drilling Using Engineering Parameters

【摘要】传统岩性识别主要依赖随钻测井数据，但施工成本高、传输效率低，限制了现场应用效果。为验证仅利用钻井工程参数进行岩性识别的可行性，开展了室内真三轴钻进试验。在不同钻压与转速条件下，对不同岩性的岩样进行了钻进模拟，采集钻压、转速、扭矩等工程参数，并分析其在不同岩性下的响应规律；在此基础上，以工程参数为特征值、岩性为分类标签，采用XGBoost方法构建了随钻岩性智能预测模型，并进行了准确性评价。试验结果表明，不同钻压条件下，随着转速增大，扭矩呈下降趋势，转速与钻速之间呈正相关；相同钻压条件下，低硬度岩石的钻速明显高于高硬度岩石，且钻速随着转速增大而增大，低硬度岩石的增幅更显著；以工程参数为特征值的XGBoost岩性随钻识别模型的识别准确率超过90%。研究结果表明，基于工程参数的岩性随钻智能感知方法可以较为准确地预测岩性变化，为工程现场岩性录井提供了新的方法。

【Abstract】Traditional lithology identification mainly relies on logging-while-drilling (LWD) data. However, the high operation costs and low transmission efficiency of LWD limit its field application. To verify the feasibility of lithology identification using only drilling engineering parameters, true triaxial drilling experiments were conducted in the laboratory. Rock samples of different lithologies were drilled under varying weight-on-bit (WOB) and rotational speed conditions, during which engineering parameters such as WOB, rotary speed, and torque were collected and analyzed to reveal their response patterns to lithological differences. On this basis, an intelligent lithology prediction model while drilling was constructed using XGBoost, with engineering parameters as feature variables and lithology as classification labels, and its accuracy was evaluated. The experimental results show that under different WOB conditions, torque decreases with increasing rotary speed, while rotary speed and rate of penetration (ROP) exhibit a positive correlation. Under the same WOB, the ROP of low-hardness rocks is significantly higher than that of high-hardness rocks, and the increase in ROP with rotary speed is more pronounced for low-hardness rocks. The XGBoost lithology identification model, built on engineering parameters, achieves a recognition accuracy of over 90%. Studies have shown that the intelligent lithology identification method, while drilling based on engineering parameters, can accurately predict changes in lithology, offering a new approach for lithology logging in drilling operations.

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5

中国海油海上智能钻井导航系统关键技术进展与发展思考

Key Technologies and Development Considerations of CNOOC’s Offshore Intelligent Drilling Navigation System

【摘要】智能钻井通过数字孪生、人工智能与智能钻机等技术与装备，实现钻井全程的自主决策与闭环控制，达到提质、增效、降本与保障安全的目标。智能钻井装备是实现智能钻井的关键，国内外石油公司在智能钻井关键装备、智能钻井平台建设方面取得了一定的进展。中国海油通过技术攻关，初步构建了基于实时数据的海上智能钻井导航系统，基本实现了地层-井筒三维可视化、岩石可钻性智能表征、卡钻风险智能预警、机械钻速智能预测和钻井参数实时优化等5项功能，并在渤海某井深层钻井中获得很好的提速效果。分析表明，海上智能钻井导航系统仍面临着井下海量真实数据获取、数据高速传输、井下作业闭环调控3个方面的技术挑战，需要开展近钻头参数测量装置、智能钻杆与智能钻机等关键工具及技术研发，以实现基于井筒数字孪生的钻井智能决策与动态调控，提高海上深水深层钻井效率与质量，推动海洋油气钻井智能化发展。

【Abstract】Intelligent drilling utilizes digital twins, artificial intelligence, and intelligent drilling equipment and technologies to enable autonomous decision-making and closed-loop control throughout the drilling process, improving quality, increasing efficiency, reducing costs, and ensuring safety. Intelligent drilling equipment is the key to achieving intelligent drilling. Chinese and foreign oil companies have made certain progress in the key equipment for intelligent drilling and the construction of intelligent drilling platforms. China National Offshore Oil Corporation (CNOOC), through technological research and development, has initially established an offshore intelligent drilling navigation system (IDNS) based on real-time data. It has basically achieved five functions: three-dimensional visualization of the formation and wellbore, intelligent characterization of rock drillability, intelligent early warning of drill pipe sticking risks, intelligent prediction of rate of penetration (ROP), and real-time optimization of drilling parameters. This system has achieved excellent speed-up effects in deep drilling operations at a certain well in the Bohai Sea. The analysis indicates that the offshore IDNS still faces three technical challenges: the acquisition of massive real data from the wellbore, the high-speed transmission of data, and the closed-loop control of well operations. To address these challenges, it is necessary to conduct research and development on key tools such as near-bit parameter measurement devices, intelligent drill pipes, and intelligent drilling rigs, thereby achieving intelligent decision-making and dynamic control of drilling based on the digital twin of the wellbore, improving the efficiency and quality of offshore deepwater drilling, and promoting the intelligent development of marine oil and gas drilling.

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《石油钻探技术》是由中国石油化工集团有限公司主管、中国石化集团石油工程技术研究院有限公司主办，期刊主编为王敏生教授级高工。主要报道国内外石油工程(包括钻井、完井、钻井液、固井、测井、录井、开采等专业)、 信息技术（人工智能、大数据、云计算、边缘计算、数字孪生、数字现实和物联网等技术在石油工程中的应用）、“双碳”目标（地热、干热岩、CCS/CCUS等技术研究进展，新能源技术在石油工程中的应用）、战略规划（能源政策方针、石油工程战略规划等）以及钻探机械设备与自动化方面的科技进展和现场经验。

主要栏目包括：专家视点、钻井完井、油气开发、测井录井，低碳减碳，战略规划。

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