Intelligent risk identification of gas drilling based on nonlinear classification network

During the transient process of gas drilling conditions,the monitoring data often has obvious nonlinear fluctuation features,which leads to large classification errors and time delays in the commonly used intelligent classification models.Combined with the structural features of data samples obtained from monitoring while drilling,this paper uses convolution algorithm to extract the correlation features of multiple monitoring while drilling parameters changing with time,and applies RBF network with nonlinear classification ability to classify the features.In the training process,the loss function component based on distance mean square error is used to effectively adjust the best clustering center in RBF.Many field applications show that,the recognition accuracy of the above nonlinear classification network model for gas production,water production and drill sticking is 97.32%,95.25%and 93.78%.Compared with the traditional convolutional neural network(CNN)model,the network structure not only improves the classification accuracy of conditions in the transition stage of conditions,but also greatly advances the time points of risk identification,especially for the three common risk identification points of gas production,water production and drill sticking,which are advanced by 56,16 and 8 s.It has won valuable time for the site to take correct risk disposal measures in time,and fully demonstrated the applicability of nonlinear classification neural network in oil and gas field exploration and development.

A real-time intelligent lithology identification method based on a dynamic felling strategy weighted random forest algorithm

Real-time intelligent lithology identification while drilling is vital to realizing downhole closed-loop drilling. The complex and changeable geological environment in the drilling makes lithology identification face many challenges. This paper studies the problems of difficult feature information extraction,low precision of thin-layer identification and limited applicability of the model in intelligent lithologic identification. The author tries to improve the comprehensive performance of the lithology identification model from three aspects: data feature extraction, class balance, and model design. A new real-time intelligent lithology identification model of dynamic felling strategy weighted random forest algorithm(DFW-RF) is proposed. According to the feature selection results, gamma ray and 2 MHz phase resistivity are the logging while drilling(LWD) parameters that significantly influence lithology identification. The comprehensive performance of the DFW-RF lithology identification model has been verified in the application of 3 wells in different areas. By comparing the prediction results of five typical lithology identification algorithms, the DFW-RF model has a higher lithology identification accuracy rate and F1 score. This model improves the identification accuracy of thin-layer lithology and is effective and feasible in different geological environments. The DFW-RF model plays a truly efficient role in the realtime intelligent identification of lithologic information in closed-loop drilling and has greater applicability, which is worthy of being widely used in logging interpretation.

Intelligent Drilling and Completion:A Review

The application of artificial intelligence(AI)has become inevitable in the petroleum industry.In drilling and completion engineering,AI is regarded as a transformative technology that can lower costs and significantly improve drilling efficiency(DE),In recent years,numerous studies have focused on intelligent algorithms and their application.Advanced technologies,such as digital twins and physics-guided neural networks,are expected to play roles in drilling and completion engineering.However,many challenges remain to be addressed,such as the automatic processing of multi-source and multi-scale data.Additionally,in intelligent drilling and completion,methods for the fusion of data-driven and physicsbased models,few-sample learning,uncertainty modeling,and the interpretability and transferability of intelligent algorithms are research frontiers.Based on intelligent application scenarios,this study comprehensively reviews the research status of intelligent drilling and completion and discusses key research areas in the future.This study aims to enhance the berthing of AI techniques in drilling and completion engineering.

Research status and development directions of intelligent drilling fluid technologies

By reviewing the current status of drilling fluid technologies with primary intelligence features at home and abroad,the development background and intelligent response mechanisms of drilling fluid technologies such as variable density,salt response,reversible emulsification,constant rheology,shape memory loss prevention and plugging,intelligent reservoir protection and in-situ rheology control are elaborated,current issues and future challenges are analyzed,and it is pointed out that intelligent material science,nanoscience and artificial intelligence theory are important methods for future research of intelligent drilling fluid technology of horizontal wells with more advanced intelligent features of"self-identification,self-tuning and self-adaptation".Based on the aforementioned outline and integrated with the demands from the drilling fluid technology and intelligent drilling fluid theory,three development suggestions are put forward:(1)research and develop intelligent drilling fluids responding to variable formation pressure,variable formation lithology and fluid,variable reservoir characteristics,high temperature formation and complex ground environmental protection needs;(2)establish an expert system for intelligent drilling fluid design and management;and(3)establish a real-time intelligent check and maintenance processing network.

An intelligent identification method of safety risk while drilling in gas drilling

In view of the shortcomings of current intelligent drilling technology in drilling condition representation, sample collection, data processing and feature extraction, an intelligent identification method of safety risk while drilling was established. The correlation analysis method was used to determine correlation parameters indicating gas drilling safety risk. By collecting monitoring data in the safety risk period of more than 20 wells, a sample database of a variety of safety risks in gas drilling was established, and the number of samples was expanded by using the method of few-shot learning. According to the forms of gas drilling monitoring data samples, a two-layer convolution neural network architecture was designed, and multiple convolution cores of different sizes and weights were set to realize the vertical and horizontal convolution computations of samples to extract and learn the variation law and correlation characteristics of multiple monitoring parameters. Finally, based on the training results of neural network, samples of different kinds of safety risks were selected to enhance the recognition accuracy. Compared with the traditional BP(error back propagation) full-connected neural network architecture, this method can more deeply and effectively identify safety risk characteristics in gas drilling, and thus identify and predict risks in advance, which is conducive to avoid and quickly solve safety risks while drilling. Field application has proved that this method has an identification accuracy of various safety risks while drilling in the process of gas drilling of about 90% and is practical.

Discussion on the Development of Intelligent Drilling Technology and Equipment for Gas Drainage

This paper deals about the application and development of gas drainage intelligent drilling technology and equipment from remote automatic drilling,ground controlled drilling,ground long distance automatic control drilling,downhole remote control drilling,sub-source,and sub-area independent unit management.The main direction of our research is to achieve full automatic drilling,intelligent drilling and drilling robots that can realize gas drainage,and also to promote innovation and development of gas drainage intelligent drilling technology.

Real Time Application of Bearing Wear Prediction Model Using Intelligent Drilling Advisory System

The real-time prediction of bearing wear for roller cone bits using the Intelligent Drilling Advisory system （IDAs） may result in better performance in oil and gas drilling operations and reduce total drilling cost. IDAs is a real time engineering software and being developed for the oil and gas industry to enhance the performance of complex drilling processes providing meaningful analysis of drilling operational data. The prediction of bearing wear for roller cone bits is one of the most important engineering modules included into IDAs to analyze the drilling data in real time environment. The Bearing Wear Prediction module in IDAs uses a newly developed wear model considering drilling parameters such as weight on bit （WOB）, revolution per minute （RPM）, diameter of bit and hours drilled as a function of International Association of Drilling Contractors （IADC） bit bearing wear. The drilling engineers can evaluate bearing wear status including cumulative wear of roller cone bit in real time while drilling, using this intelligent system and make a decision on when to pull out the bit in time to avoid bearing failure. The wear prediction module as well as the intelligent system has been successfully tested and verified with field data from different wells drilled in Western Canada. The estimated cumulative wears from the analysis match close with the corresponding field values.

Logging-while-drilling formation dip interpretation based on long short-term memory

Azimuth gamma logging while drilling(LWD)is one of the important technologies of geosteering but the information of real-time data transmission is limited and the interpretation is difficult.This study proposes a method of applying artificial intelligence in the LWD data interpretation to enhance the accuracy and efficiency of real-time data processing.By examining formation response characteristics of azimuth gamma ray(GR)curve,the preliminary formation change position is detected based on wavelet transform modulus maxima(WTMM)method,then the dynamic threshold is determined,and a set of contour points describing the formation boundary is obtained.The classification recognition model based on the long short-term memory(LSTM)is designed to judge the true or false of stratum information described by the contour point set to enhance the accuracy of formation identification.Finally,relative dip angle is calculated by nonlinear least square method.Interpretation of azimuth gamma data and application of real-time data processing while drilling show that the method proposed can effectively and accurately determine the formation changes,improve the accuracy of formation dip interpretation,and meet the needs of real-time LWD geosteering.

BUILDING OF THE PETROLEUM DRILLING FLUID ENGINEERING DESIGN EXPERT SYSTEM

The targets, importance, difficulties, strategies, general function frame and technology frame of Petroleum DrillingFluid Engineering Design Expert Ssytem(PDFEDES) were discussed. A brief introduction to the special domain and application cases of the PDFEDES are given. A good prospect of artificial intelligence application in petroleum exploration engineering is presented.

Intelligent Petroleum Engineering

Data-driven approaches and artificial intelligence(AI)algorithms are promising enough to be relied on even more than physics-based methods;their main feed is data which is the fundamental element of each phenomenon.These algorithms learn from data and unveil unseen patterns out of it The petroleum industry as a realm where huge volumes of data are generated every second is of great interest to this new technology.As the oil and gas industry is in the transition phase to oilfield digitization,there has been an increased drive to integrate data-driven modeling and machine learning(ML)algorithms in different petroleum engineering challenges.ML has been widely used in different areas of the industry.Many extensive studies have been devoted to exploring AI applicability in various disciplines of this industry;however,lack of two main features is noticeable.Most of the research is either not practical enough to be applicable in real-field challenges or limited to a specific problem and not generalizable.Attention must be given to data itself and the way it is classified and stored.Although there are sheer volumes of data coming from different disciplines,they reside in departmental silos and are not accessible by consumers.In order to derive as much insight as possible out of data,the data needs to be stored in a centralized repository from where the data can be readily consumed by different applications.

An Artificial Intelligence Algorithm for the Real-Time Early Detection of Sticking Phenomena in Horizontal Shale Gas Wells

Sticking is the most serious cause of failure in complex drilling operations.In the present work a novel“early warning”method based on an artificial intelligence algorithm is proposed to overcome some of the known pro-blems associated with existing sticking-identification technologies.The method is tested against a practical case study(Southern Sichuan shale gas drilling operations).It is shown that the twelve sets of sticking fault diagnostic results obtained from a simulation are all consistent with the actual downhole state;furthermore,the results from four groups of verification samples are also consistent with the actual downhole state.This shows that the pro-posed training-based model can effectively be applied to practical situations.

煤矿防冲钻孔机器人全自主钻进系统关键技术

针对高地应力矿井钻孔卸压作业智能化程度低的技术难题,总结分析了国内外钻孔卸压技术和装备的研究现状,指出研发高性能、高可靠、高效率的防冲钻孔机器人全自主钻进系统是破解冲击地压防治难题的重要发展方向。为此,凝练了影响钻进系统性能的“孔位精准识别、钻具姿态精确感知、无线电磁随钻智能检测、钻具运行状态智能识别和钻进系统精确控制”五大关键技术,并给出了解决思路和方法。针对在复杂恶劣环境下卸压孔的精确识别问题,设计了融合图像尺寸调节和多阶段训练模式的卸压孔图像样本扩充SinGAN模型,引入多层特征融合优化的FasterRCNN,构建了基于改进SqueezeNet轻量级网络架构的孔位识别模型,以实现卸压孔位的准确快速识别;针对钻具姿态精确感知问题,提出了基于改进梯度下降法算法优化无迹卡尔曼滤波的惯性测量单元(Inertial Measurement Unit,IMU)初始对准方法,设计了多个IMU的空间阵列布局方式,研究了基于BP神经网络的钻具姿态误差补偿方法,旨在提高钻具姿态的解算精度,实现精准钻孔卸压;针对复杂地质环境下钻进工况的精确检测问题,搭建了煤矿井下随钻测量无线电磁传输系统架构,探讨了微弱电磁波信号自适应调制和随钻高速双向电磁传输技术原理,研究了孔底地质参数、几何参数和工程参数的测量原理和实现过程;针对钻进系统运行状态识别问题,构建了钻进信号时域、频域、时频域的多域特征和深度网络高级特征提取架构,提出了钻进系统关键零部件健康状态评估和故障诊断技术,构建了基于改进蝙蝠优化长短期记忆网络的卡钻风险因子预测模型,实现对卸压钻具卡钻状态的准确预测;针对钻进系统的精确控制问题,分析了钻进系统的液压系统工作原理,构建了考虑煤岩性状的钻进系统精确控制方案,探讨了基于转矩和位置的钻进系统最优控制参数求解原理,旨在实现钻进回转系统和给进系统的智能协同控制和并行作业。

四川盆地超深井钻井关键技术及发展方向

四川盆地海相碳酸盐岩油气资源丰富,但构造地层条件复杂,井筒环境苛刻。近年来,盆地深井超深井钻井关键技术的形成,支撑了油气的增储上产,为特深层油气资源勘探开发奠定了技术基础。特深层油气资源的开发需持续开展地质工程一体化设计技术攻关,通过精细刻画复杂地质体建立三维地质力学模型,指导井身结构和井眼轨道设计,同时开展基于膨胀管、随钻扩眼技术的井身结构拓展研究,并通过岩石可钻性剖面指导钻头及提速工具设计。针对深井超深井井口溢流异常监测识别滞后及钻柱振动剧烈的问题,持续研发基于大数据分析的复杂防控技术。深入开展适用于超高温超高压环境的工具、钻井液研发,研发攻关110钢级及以上膨胀管管材、抗220℃的高密度水基钻井液、抗260℃的油基钻井液体系、抗温240℃以上的水泥浆体系、高性能固井材料以及抗高温堵漏材料。同时,应紧密结合物联网、新型通信、大数据等智能技术,实现钻井全过程运算分析及管理。

钻井利器的故事之“全液压岩心钻机”

钻机作为钻探工程中最重要的地面设备,是当之无愧的钻井利器。全液压岩心钻机是岩心钻机的主要发展方向。本文从科普的角度,介绍了岩心钻机在破岩过程中提供压力和旋转运动的主要作用,类比杠杆分析液压传动的工作原理,回顾了立轴式手把钻机、立轴式油压钻机到全液压岩心钻机的发展历程,阐述模块化轻便岩心钻机的特点及适合绿色勘查要求的优越性,并指出全液压岩心钻机的智能化、高效化、绿色化的发展方向。

油气钻采数字孪生模型构建方法及应用案例

油气钻采过程中地质的不确定性、井下实时工况的不可见性、工程仿真的复杂性阻碍了其科学高效的设计及施工。数字孪生技术能够提供实时智能且可视化的方案设计和工程决策,但缺乏针对油气钻采的系统建模方法。对此,本文首先剖析油气钻采数字孪生的国内外研究及应用现状,进而应用成熟度指标定量评价该技术的发展程度;其次,逐次提出油气钻采数字孪生模型的建模方法,包括建模流程、拆分策略、装配及融合架构、建模工具,并以钻井井壁稳定和海上生产系统为例,介绍数字孪生在钻井与开采方面的应用案例;最后,分析困难与挑战并提出发展建议。研究发现,相对制造业,钻采孪生多处于可视化阶段,整体成熟度偏低。油气钻采系统的复杂需求被拆分为若干清晰且较容易实现的子需求;基于需求分析将建模对象在粒度、维度、生命周期上拆分为不同的子模型,通过模型层、功能层、需求层逐层装配子模型,进而实现多维度、多领域模型间的融合。同时,需要在模型管理、数据管理和工程仿真方面完善方法和提高效率。此外,钻采孪生面临多源异构数据选择与融合困难、子模型定义模糊、模型验证不清的问题,以及复杂动力学过程、多部门多任务协同、自主软件工具开发方面的挑战。综上,本文提出的数字孪生模型构建方法和案例能为油气钻采工程提供方法指导和应用参考。

钻柱振动的主被动控制研究进展与展望

在油气井钻进作业过程中,钻柱长期工作在充满钻井液的狭长井筒里,其受到井底压力、摩擦、岩石硬度变化的影响,受力情况非常复杂,会产生多种形式的振动,这些是影响钻具寿命和钻进效率的最主要因素。为系统分析工程界和学术界长期以来普遍关注的钻柱系统振动的有效控制问题,对近几十年来国内外在钻杆振动控制上使用的各种方法进行了分类和归纳,总结了各种方法的特点、应用场景以及不足之处,并对钻柱系统振动控制技术的发展趋势进行了展望。研究结果表明:(1)现有的钻柱振动主动控制主要集中于单一方向(扭转振动)和2个方向的耦联振动(纵扭耦联振动),且控制参数主要集中于钻压和扭矩,仅控制单方向的振动和单参数的调节,效果将很难保证;(2)被动控制是通过改变钻柱系统自身的结构或设计参数来抑制其振动,控制过程不受结构响应和外部干扰的影响,虽然效果有较大的局限性,但仍应在设计阶段给与足够的重视;(3)今后钻柱振动控制将朝着同时基于地面和井下测量数据,纵、横、扭3个方向联合控制的方向发展。结论认为,钻杆振动往往是几种基本振动形式的耦联,并具有明显的非线性特征,基于数据驱动的钻进系统动力学建模、考虑模型不确定性的现代鲁棒自适应控制技术以及新兴的人工智能技术将会成为未来钻柱振动研究和发展的主流趋势。

智能钻井系统在赵东油田的应用

赵东油田开发后期调整井钻井施工中,需要进行三维防碰绕障,井眼轨道设计复杂,钻进过程中经常出现摩阻扭矩变化大、井眼清洁困难、井壁稳定性差、钻井液漏失等难题。针对精细化井眼轨迹控制和快速识别井下复杂状况等关键技术难点,采取以现场作业人员经验为主、智能钻井系统为辅的作业模式,通过贝克休斯智能钻井系统i-Trak™的量化分析与及时预警,提高了轨迹控制精度及井下复杂状况早发现、早调整的及时性,避免复杂工况的发生,降低非生产作业时间,缩短了建井周期。通过17口井的试验,进一步验证现场“作业人员经验+智能钻井系统”的作业模式能够很好满足安全高效钻井的需求,为海上油田开发后期复杂调整井的钻井施工提供了可借鉴经验。

基于多要素钻进信息的围岩分级方法研究

本研究以交通部科技示范项目“三峡库区奉建高速公路”为背景,开展基于随钻参数的围岩智能分级方法研究,取得以下主要成果:(1)通过现场试验共收集1000个围岩分级指标,建立了围岩智能分级数据样本库;(2)建立了基于SVR与PSO-BP算法的围岩分级指标预测模型,使用样本库训练并实现了基于随钻参数的围岩智能分级;(3)模型预测结果显示,PSO-BP模型的预测值在真实值拟合参考线上的偏离程度小于SVR模型,尤其是在最大误差方面,PSO-BP模型表现出更小的偏离,显示出更高的拟合精度,尤其在预测完整性系数时,PSO-BP模型预测精度更高。

智能钻井多目标协同优化系统研究与应用

在深井超深井钻探过程中,井眼中高强度、高研磨性地层导致的钻头早期磨损、井下钻具振动剧烈、井眼清洁不足等,严重制约了深井超深井的安全快速钻进。针对上述问题,文章提出了一种智能钻井多目标协同优化系统,可以实时跟踪、优化钻井参数,提高钻井性能。同时基于机械钻速和机械比能,定义了钻井性能综合评价指标;结合地应力、钻具振动、摩阻扭矩、井眼清洁和破岩能效等地质、物理模型,提出了包含探索、学习和应用三种模式的钻井参数实时优化流程,训练了融合支持向量回归模型和随机森林回归模型的钻井参数实时优化算法。该系统在深地川科1井进行了现场应用,提速比达到了41.5%,为深井超深井钻井优化提速提供了一种新的技术手段。

大规模定制家具加工中心多钻头并行作业的优化

大规模定制家具超高的生产效率与高度个性化的产品两大特征决定了其需要依赖高水平的信息化与自动化组织生产,是家具制造业智能制造的前沿领域。运用数控加工中心开展钻孔作业是调和个性化产品制造过程中的矛盾、实现柔性生产的重要手段。当前,数控钻孔工序因其作业时间长且板件之间差异较大而往往成为制造过程中的瓶颈。为切实提高生产效率,本研究立足生产实际,从钻头与孔的位置关系中寻求突破口,提出了数控加工中心多钻头并行作业优化问题。以优化钻头排列为主要途径,减少下钻次数为核心目标,基于制造大数据与工艺规则挖掘信息并简化建模,采用差分进化算法求解钻头排列方案,进一步通过聚类算法探索出面向未来高自动化水平下的差异化钻头排列,形成了一套具有实际意义与普适性的优化方法。通过理论与实践验证了该方法的有效性,达到了缩减作业时间、提升加工效率的目的。对打通大规模定制家具制造瓶颈、推动定制家具智能制造具有一定的指导意义。