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.

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.

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.

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.

Beyond p-y method:A review of artificial intelligence approaches for predicting lateral capacity of drilled shafts in clayey soils

In 2023,pivotal advancements in artificial intelligence(AI)have significantly experienced.With that in mind,traditional methodologies,notably the p-y approach,have struggled to accurately model the complex,nonlinear soil-structure interactions of laterally loaded large-diameter drilled shafts.This study undertakes a rigorous evaluation of machine learning(ML)and deep learning(DL)techniques,offering a comprehensive review of their application in addressing this geotechnical challenge.A thorough review and comparative analysis have been carried out to investigate various AI models such as artificial neural networks(ANNs),relevance vector machines(RVMs),and least squares support vector machines(LSSVMs).It was found that despite ML approaches outperforming classic methods in predicting the lateral behavior of piles,their‘black box'nature and reliance only on a data-driven approach made their results showcase statistical robustness rather than clear geotechnical insights,a fact underscored by the mathematical equations derived from these studies.Furthermore,the research identified a gap in the availability of drilled shaft datasets,limiting the extendibility of current findings to large-diameter piles.An extensive dataset,compiled from a series of lateral loading tests on free-head drilled shaft with varying properties and geometries,was introduced to bridge this gap.The paper concluded with a direction for future research,proposes the integration of physics-informed neural networks(PINNs),combining data-driven models with fundamental geotechnical principles to improve both the interpretability and predictive accuracy of AI applications in geotechnical engineering,marking a novel contribution to the field.

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.

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.

一种智能化的钻孔煤屑瓦斯突出参数测试仪应用与实践

为了实现钻孔煤屑瓦斯解吸指标△h_(2)(K_(1))测试结果的稳定性和可靠性,减小传统测试方法由于煤样量小而引起的测试误差,降低煤矿瓦斯安全生产误判,采用河南理工大学新研发的YTC500瓦斯突出参数测试仪和传统的MD-2型瓦斯解吸仪进行测试比对。结果表明,YTC500瓦斯突出参数测试仪采用大煤量测试结果更好,而且该仪器还具有智能化测试的特点,测试误差平均小于10%,满足矿井安全生产的需要,可以作为现有测试方法的补充或代替使用。

国内煤矿锚杆钻机应用现状及发展趋势

按照单体锚杆钻机、全液压履带式锚杆钻车及电液自动化锚杆钻车三类介绍了锚杆钻机在国内煤矿的应用现状,指出各类锚杆钻机在实际应用中存在的问题。结合国内煤矿井下巷道掘进的特点,分析了锚杆钻机的技术特点与适用性,研究了国内煤矿锚杆钻机的发展方向,提出了电动锚杆钻车的思路及实现方式。

矿用伽马测井仪在察哈素矿区应用研究

为了实现煤炭智能化开采,采用基于伽马测井技术的煤岩界面高精度探测来构建三维地质模型。设计了基于单片机和无线模块的矿用伽马测井仪,主要介绍了测井仪设计理论、系统组成、工作原理及应用情况。矿用伽马测井仪在煤矿井下底抽巷穿层钻孔中的应用试验表明:该测井仪测量数据准确可靠,能有效地鉴定钻探中遇到的地层岩性变化和出煤点的空间位置,应用效果良好。

Intelligent classification model of surrounding rock of tunnel using drilling and blasting method

Classification of surrounding rock is the cornerstone of tunnel design and construction.The traditional methods are mainly qualitative and manual and require extensive professional knowledge and engineering experience.To minimize the effect of the empirical judgment on the accuracy of surrounding rock classification,it is necessary to reduce human participation.An intelligent classification technique based on information technology and artificial intelligence could overcome these issues.In this regard,using 299 groups of drilling parameters collected automatically using intelligent drill jumbos in tunnels for the Zhengzhou-Wanzhou high-speed railway in China,an intelligent-classification surrounding-rock database is constructed in this study.Based on a machine learning algorithm,an intelligent classification model is then developed,which has an overall accuracy of 91.9%.Finally,using the core of the model,the intelligent classification system for the surrounding rock of drilled and blasted tunnels is integrated,and the system is carried by intelligent jumbos to perform automatic recording and transmission of drilling parameters and intelligent classification of the surrounding rock.This approach provides a foundation for the dynamic design and construction(both conventional and intelligent)of tunnels.

Application and development trend of artificial intelligence in petroleum exploration and development

Aiming at the actual demands of petroleum exploration and development,this paper describes the research progress and application of artificial intelligence(AI)in petroleum exploration and development,and discusses the applications and development directions of AI in the future.Machine learning has been preliminarily applied in lithology identification,logging curve reconstruction,reservoir parameter estimation,and other logging processing and interpretation,exhibiting great potential.Computer vision is effective in picking of seismic first breaks,fault identification,and other seismic processing and interpretation.Deep learning and optimization technology have been applied to reservoir engineering,and realized the real-time optimization of waterflooding development and prediction of oil and gas production.The application of data mining in drilling,completion,and surface facility engineering etc.has resulted in intelligent equipment and integrated software.The potential development directions of artificial intelligence in petroleum exploration and development are intelligent production equipment,automatic processing and interpretation,and professional software platform.The highlights of development will be digital basins,fast intelligent imaging logging tools,intelligent seismic nodal acquisition systems,intelligent rotary-steering drilling,intelligent fracturing technology and equipment,real-time monitoring and control of zonal injection and production.

Research on remote intelligent control technology of throttling and back pressure in managed pressure drilling

In order to reduce the non production time of drilling,improve the efficiency and safety of drilling,improve the economic effect of managed pressure drilling(MPD),and realize the intelligent control construction of digital oilfield.Based on the pressure control in MPD,this paper analyzes the pressure control drilling system,takes the wellhead back pressure as the controlled parameter,calculates the mathematical model of the throttle valve according to the characteristics of the throttle valve,the basic parameters and boundary conditions of pressure control drilling,and puts forward an improved particle swarm Optimization PID neural network(IPSO-PIDNN)model.By means of remote communication,VR technology can realize remote control of field control equipment.The real-time control results of IPSO-PIDNN are compared with those of traditional PID neural network(PIDNN)and traditional Particle Swarm Optimization PID neural network(PSO-PIDNN).The results show that IPSO-PIDNN model has good self-learning characteristics,high optimization quality,high control accuracy,no overshoot,fast response and short regulation time.Thus,the advanced automatic control of bottom hole pressure in the process of MPD is realized,which provides technical guarantee for the well control safety of MPD.

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。