A log-based method for fine-scale evaluation of lithofacies and its applications to the Gulong shale in the Songliao Basin,Northeast China

The Gulong shale demonstrates high clay content and pronounced thin laminations,with limited vertical variability in log curves,complicating lithofacies classification.To comprehend the distribution and compositional features of lithofacies in the Gulong shale for optimal sweet spot selection and reservoir stimulation,this study introduced a lithofacies classification scheme and a log-based lithofacies evaluation method.Specifically,theΔlgR method was utilized for accurately determining the total organic carbon(TOC)content;a multi-mineral model based on element-to-mineral content conversion coefficients was developed to enhance mineral composition prediction accuracy,and the microresistivity curve variations derived from formation micro-image(FMI)log were used to compute lamination density,offering insights into sedimentary structures.Using this method,integrating TOC content,sedimentary structures,and mineral compositions,the Qingshankou Formation is classified into four lithofacies and 12 sublithofacies,displaying 90.6%accuracy compared to core description outcomes.The classification results reveal that the northern portion of the study area exhibits more prevalent fissile felsic shales,siltstone interlayers,shell limestones,and dolomites.Vertically,the upper section primarily exhibits organic-rich felsic shale and siltstone interlayers,the middle part is characterized by moderate organic quartz-feldspathic shale and siltstone/carbonate interlayers,and the lower section predominantly features organic-rich fissile felsic/clayey felsic shales.Analyzing various sublithofacies in relation to seven petrophysical parameters,oil test production,and fracturing operation conditions indicates that the organic-rich felsic shales in the upper section and the organic-rich/clayey felsic shales in the lower section possess superior physical properties and oil content,contributing to smoother fracturing operation and enhanced production,thus emerging as dominant sublithofacies.Conversely,thin interlayers such as siltstones and limestones,while producing oil,demonstrate higher brittleness and pose great fracturing operation challenges.The methodology and insights in this study will provide a valuable guide for sweet spot identification and horizontal well-based exploitation of the Gulong shale.

A Real-time Lithological Identification Method based on SMOTE-Tomek and ICSA Optimization

In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on machine learning and mud logging data is studied in this paper.This method can effectively utilize downhole parameters collected in real-time during drilling,to identify lithology in real-time and provide a reference for optimization of drilling parameters.Given the imbalance of lithology samples,the synthetic minority over-sampling technique(SMOTE)and Tomek link were used to balance the sample number of five lithologies.Meanwhile,this paper introduces Tent map,random opposition-based learning and dynamic perceived probability to the original crow search algorithm(CSA),and establishes an improved crow search algorithm(ICSA).In this paper,ICSA is used to optimize the hyperparameter combination of random forest(RF),extremely random trees(ET),extreme gradient boosting(XGB),and light gradient boosting machine(LGBM)models.In addition,this study combines the recognition advantages of the four models.The accuracy of lithology identification by the weighted average probability model reaches 0.877.The study of this paper realizes high-precision real-time lithology identification method,which can provide lithology reference for the drilling process.

大庆油田CIFLog测井数智云平台建设应用实践

针对大庆油田生产中测井数据量大、类型多和数据来源复杂等问题,以中国石油天然气集团有限公司大型测井处理解释软件CIFLog为基础,以业务需求为主导,采用微服务架构和测井分布式云计算技术体系,研发测井大数据存储管理、中间服务层和云端测井处理解释应用等新功能,形成了大庆油田测井数智云应用平台。目前,平台已全面安装部署到大庆油田相关单位,应用效果显著。特别在大庆油田智能决策中心,平台直接用于重点水平井随钻地质导向的现场决策,大幅提升了Ⅰ类储层的钻遇率。未来平台将重点围绕新功能研发、油田数智化应用场景建设和标准化技术体系构建等开展工作,并将取得的成果及时推广复制到西南油田、塔里木油田等油气田。CIFLog云平台作为中国油气工业软件数智化建设应用的先行典范,必将发挥越来越重要的示范引领作用。

Log4cxx在国产化平台的应用研究

以国产化操作系统、数据库和硬件环境为基础,通过分析和示例详细阐述了使用及扩展Log4cxx日志框架实现日志信息记录和管理的具体方法,为国产化软件的开发和维护提供了高效、可靠、易用的日志解决方案。

基于改进LOG算子与Otsu算法结合的物体表面图像残损检测方法

针对物体表面残损区域存在明显的亮缺陷和不明显的暗缺陷这一特性,构建一种基于改进LOG算子与Otsu算法相结合的物体表面残损区域缺陷的边缘检测方法。首先,针对传统的LOG算子在检测图像亮缺陷边缘时检测结果不准确的问题,引入可根据图像特性自动调整模糊因子的Wiener滤波代替传统LOG算子中的高斯滤波,以提高图像亮缺陷检测的精度;其次,针对检测图像暗缺陷边缘时结果不准确的问题,使用Otsu算法分析图像暗缺陷的灰度直方图来自动确定阈值,以提升暗缺陷边缘检测准确率;最后,采用像素加权平均融合算法对检测出的物体表面图像亮、暗缺陷边缘进行融合,以实现物体表面残损缺陷检测。实验结果表明:相较于单独使用改进的LOG算子和Otsu算法,采用加权融合的方法检测到的缺陷像素点数量与原始图片中基本一致,能够更准确地对图像中物体表面残损区域的亮、暗缺陷边缘进行检测。

含零收益率的金融非对称Log-GARCH模型研究

在实际外汇市场中,由于诸如交易缺失、舍入误差等原因使得收益率序列中出现零值,常见GARCH族模型无法对含零收益率数据进行有效拟合,导致波动率估计结果产生较大偏差。为了更准确地估计汇率波动率,本文对含有零收益率的外汇数据进行建模。首先运用不受条件方差为正限制的log-GARCH模型对汇率市场收益率数据进行拟合,同时提出一个处理含有零收益率的数据处理框架,即将零值视为缺失的观测值。然后通过结合QMLE方法和期望最大化(EM)算法对含缺失观测值的log-GARCH模型进行无偏估计。最后通过实证分析比较零收益率两种不同处理方法——非零值代替零值方法和将零值视为缺失值方法下波动率估计结果的差异。研究结果显示零收益率的存在会增加波动率的估计偏差,将非零值作为缺失值方法得到的估计结果更接近市场真实情况。

多模态遥感图像模板匹配Log-Gabor滤波方法

针对多模态遥感图像匹配难的问题,本文提出了一种基于Log-Gabor滤波的高精度匹配方法。该方法采用由粗到细的多层级密集匹配框架,无须进行特征点检测,避开了多模态图像特征点检测重复率低的问题,能够提取大量高精度匹配点对。本文方法主要分为两步:首先,利用多尺度多角度Log-Gabor滤波器构建对图像间非线性辐射差异稳健的特征金字塔;然后,利用粗尺度的底层特征图进行密集模板匹配,提取大量粗粒度的特征匹配点对,在此基础上再利用特征金字塔,实现粗匹配点自下而上的逐层优化,完成高精度特征匹配点对的提取。同时,针对模板匹配滑窗运算效率不高的问题,提出了一种密集模板匹配的快速实现方式,有效减少了密集模板匹配的运算时间。本文使用多组不同模态的遥感图像进行试验,结果表明,本文方法能够克服图像间非线性辐射差异的影响,在正确匹配数目、匹配准确率与匹配精度上均优于现有多模态图像特征匹配方法。

融合LoG特征的凸焊螺母检测算法

针对目前汽车曲面零部件的紧固连接中常用的凸焊工艺中出现凸焊螺母的漏焊、错焊,以及主要依赖人工目测的低效检测方法等问题,提出了一种基于Faster-RCNN的凸焊螺母检测算法。以Faster-RCNN作为基础模型,针对模型在不同角度下螺母特征各异且难以提取的问题,提出提取LoG特征和原图像自适应融合的方法,以增强模型对螺母特征的提取能力;引入特征金字塔(feature pyramid network,FPN)解决小目标难以被精确检测的问题;为了提升网络在复杂背景中的检测鲁棒性,在FPN中嵌入坐标注意力机制来提升网络对重点目标的关注;设计损失函数,提升训练效果,增强回归框中心点的回归精确度。实验结果表明,所提算法相比原算法,在IoU=0.75时凸焊螺母的检测精确率上升了8.65个百分点,达到90.11%,召回率上升了5.87个百分点,达到79.23%,相比原算法具有明显改善。

Permeability Logging:A Breakthrough from 0 to1

On March 3,2024,the prototype permeability logging instrument independently developed in China successfully completed its first downhole test in Ren 91 standard well in PetroChina Huabei Oilfield.In the open hole section at a depth of 3925 metres and at a temperature of 148℃,the device collected high-quality permeability logging data.This marks a key technological breakthrough from 0 to 1 in permeability logging,and lays the foundation for the next step in developing a complete set of permeability logging equipment.

Identification of reservoir types in deep carbonates based on mixedkernel machine learning using geophysical logging data

Identification of reservoir types in deep carbonates has always been a great challenge due to complex logging responses caused by the heterogeneous scale and distribution of storage spaces.Traditional cross-plot analysis and empirical formula methods for identifying reservoir types using geophysical logging data have high uncertainty and low efficiency,which cannot accurately reflect the nonlinear relationship between reservoir types and logging data.Recently,the kernel Fisher discriminant analysis(KFD),a kernel-based machine learning technique,attracts attention in many fields because of its strong nonlinear processing ability.However,the overall performance of KFD model may be limited as a single kernel function cannot simultaneously extrapolate and interpolate well,especially for highly complex data cases.To address this issue,in this study,a mixed kernel Fisher discriminant analysis(MKFD)model was established and applied to identify reservoir types of the deep Sinian carbonates in central Sichuan Basin,China.The MKFD model was trained and tested with 453 datasets from 7 coring wells,utilizing GR,CAL,DEN,AC,CNL and RT logs as input variables.The particle swarm optimization(PSO)was adopted for hyper-parameter optimization of MKFD model.To evaluate the model performance,prediction results of MKFD were compared with those of basic-kernel based KFD,RF and SVM models.Subsequently,the built MKFD model was applied in a blind well test,and a variable importance analysis was conducted.The comparison and blind test results demonstrated that MKFD outperformed traditional KFD,RF and SVM in the identification of reservoir types,which provided higher accuracy and stronger generalization.The MKFD can therefore be a reliable method for identifying reservoir types of deep carbonates.

Enhancing Log Anomaly Detection with Semantic Embedding and Integrated Neural Network Innovations

System logs,serving as a pivotal data source for performance monitoring and anomaly detection,play an indispensable role in assuring service stability and reliability.Despite this,the majority of existing log-based anomaly detection methodologies predominantly depend on the sequence or quantity attributes of logs,utilizing solely a single Recurrent Neural Network(RNN)and its variant sequence models for detection.These approaches have not thoroughly exploited the semantic information embedded in logs,exhibit limited adaptability to novel logs,and a single model struggles to fully unearth the potential features within the log sequence.Addressing these challenges,this article proposes a hybrid architecture based on amultiscale convolutional neural network,efficient channel attention and mogrifier gated recurrent unit networks(LogCEM),which amalgamates multiple neural network technologies.Capitalizing on the superior performance of robustly optimized BERT approach(RoBERTa)in the realm of natural language processing,we employ RoBERTa to extract the original word vectors from each word in the log template.In conjunction with the enhanced Smooth Inverse Frequency(SIF)algorithm,we generate more precise log sentence vectors,thereby achieving an in-depth representation of log semantics.Subsequently,these log vector sequences are fed into a hybrid neural network,which fuses 1D Multi-Scale Convolutional Neural Network(MSCNN),Efficient Channel Attention Mechanism(ECA),and Mogrifier Gated Recurrent Unit(GRU).This amalgamation enables themodel to concurrently capture the local and global dependencies of the log sequence and autonomously learn the significance of different log sequences,thereby markedly enhancing the efficacy of log anomaly detection.To validate the effectiveness of the LogCEM model,we conducted evaluations on two authoritative open-source datasets.The experimental results demonstrate that LogCEM not only exhibits excellent accuracy and robustness,but also outperforms the current mainstream log anomaly detection methods.

Automatic depth matching method of well log based on deep reinforcement learning

In the traditional well log depth matching tasks,manual adjustments are required,which means significantly labor-intensive for multiple wells,leading to low work efficiency.This paper introduces a multi-agent deep reinforcement learning(MARL)method to automate the depth matching of multi-well logs.This method defines multiple top-down dual sliding windows based on the convolutional neural network(CNN)to extract and capture similar feature sequences on well logs,and it establishes an interaction mechanism between agents and the environment to control the depth matching process.Specifically,the agent selects an action to translate or scale the feature sequence based on the double deep Q-network(DDQN).Through the feedback of the reward signal,it evaluates the effectiveness of each action,aiming to obtain the optimal strategy and improve the accuracy of the matching task.Our experiments show that MARL can automatically perform depth matches for well-logs in multiple wells,and reduce manual intervention.In the application to the oil field,a comparative analysis of dynamic time warping(DTW),deep Q-learning network(DQN),and DDQN methods revealed that the DDQN algorithm,with its dual-network evaluation mechanism,significantly improves performance by identifying and aligning more details in the well log feature sequences,thus achieving higher depth matching accuracy.

A machine learning approach for the prediction of pore pressure using well log data of Hikurangi Tuaheni Zone of IODP Expedition 372,New Zealand

Pore pressure(PP)information plays an important role in analysing the geomechanical properties of the reservoir and hydrocarbon field development.PP prediction is an essential requirement to ensure safe drilling operations and it is a fundamental input for well design,and mud weight estimation for wellbore stability.However,the pore pressure trend prediction in complex geological provinces is challenging particularly at oceanic slope setting,where sedimentation rate is relatively high and PP can be driven by various complex geo-processes.To overcome these difficulties,an advanced machine learning(ML)tool is implemented in combination with empirical methods.The empirical method for PP prediction is comprised of data pre-processing and model establishment stage.Eaton's method and Porosity method have been used for PP calculation of the well U1517A located at Tuaheni Landslide Complex of Hikurangi Subduction zone of IODP expedition 372.Gamma-ray,sonic travel time,bulk density and sonic derived porosity are extracted from well log data for the theoretical framework construction.The normal compaction trend(NCT)curve analysis is used to check the optimum fitting of the low permeable zone data.The statistical analysis is done using the histogram analysis and Pearson correlation coefficient matrix with PP data series to identify potential input combinations for ML-based predictive model development.The dataset is prepared and divided into two parts:Training and Testing.The PP data and well log of borehole U1517A is pre-processed to scale in between[-1,+1]to fit into the input range of the non-linear activation/transfer function of the decision tree regression model.The Decision Tree Regression(DTR)algorithm is built and compared to the model performance to predict the PP and identify the overpressure zone in Hikurangi Tuaheni Zone of IODP Expedition 372.

Integrated Geological and Geophysical Mapping for Groundwater Potential Studies at Ekwegbe-Agu and Environs, Enugu State, Nigeria

The study integrates both the geological and geophysical mapping techniques for groundwater potential studies at Ekwegbe-Agu and the environs, Enugu state, Nigeria for optimal citing of borehole. Located in the Anambra Basin between latitudes 6˚43'N and 6˚47'N and longitudes 7˚28'E and 7˚32'E, it is stratigraphycally underlain by, from bottom to top, the Enugu/Nkporo, Mamu and Ajali Formation respectively, a complex geology that make citing of productive borehole in the area problematic leading to borehole failure and dry holes due to inadequate sampling. The study adopted a field and analytic sampling approach, integrating field geological, electrical resistivity and self-potential methods. The software, SedLog v3.1, InterpexIx1Dv.3, and Surfer v10 were employed for the data integration and interpretation. The result of the geological field and borehole data shows 11 sedimentary facies consisting of sandstone, shales and heterolith of sandstone/shale, with the aquifer zone mostly prevalent in the more porous sand-dominated horizons. Mostly the AK and HK were the dominant curve types. An average of 6 geo-electric layers were delineated across all transects with resistivity values ranging from 25.42 - 105.85 Ωm, 186.38 - 3383.3 Ωm, and 2992 - 6286.4 Ωm in the Enugu, Mamu and Ajali Formations respectively. The resistivity of the main aquifer layer ranges from 1 to 500 Ωm. The aquifer thickness within the study area varies between 95 and 140 m. The western and northwestern part of the study area which is underlain mainly by the Ajali Formation showed the highest groundwater potential in the area and suitable for citing productive boreholes.

Log interpretation of carbonate rocks based on petrophysical facies constraints

The complex pore structure of carbonate reservoirs hinders the correlation between porosity and permeability.In view of the sedimentation,diagenesis,testing,and production characteristics of carbonate reservoirs in the study area,combined with the current trends and advances in well log interpretation techniques for carbonate reservoirs,a log interpretation technology route of“geological information constraint+deep learning”was developed.The principal component analysis(PCA)was employed to establish lithology identification criteria with an accuracy of 91%.The Bayesian stepwise discriminant method was used to construct a sedimentary microfacies identification method with an accuracy of 90.5%.Based on production data,the main lithologies and sedimentary microfacies of effective reservoirs were determined,and 10 petrophysical facies with effective reservoir characteristics were identified.Constrained by petrophysical facies,the mean interpretation error of porosity compared to core analysis results is 2.7%,and the ratio of interpreted permeability to core analysis is within one order of magnitude,averaging 3.6.The research results demonstrate that deep learning algorithms can uncover the correlation in carbonate reservoir well logging data.Integrating geological and production data and selecting appropriate machine learning algorithms can significantly improve the accuracy of well log interpretation for carbonate reservoirs.

Facies logging identification of intermediate-basic volcanic rocks in Huoshiling Formation of Songliao Basin

Volcanic oil and gas reservoirs are generally buried deep,which leads to a high whole-well coring cost,and the degree of development and size of reservoirs are controlled by volcanic facies.Therefore,accurately identifying volcanic facies by logging curves not only provides the basis of volcanic reservoir prediction but also saves costs during exploration.The Songliao Basin is a‘fault-depression superimposed’composite basin with a typical binary filling structure.Abundant types of volcanic lithologies and facies are present in the Lishu fault depression.Volcanic activity is frequent during the sedimentary period of the Huoshiling Formation.Through systematic petrographic identification of the key exploratory well(SN165C)of the Lishu fault-depression,which is a whole-well core,it is found that the Huoshiling Formation in SN165C contains four facies and six subfacies,including the volcanic conduit facies(crypto explosive breccia subfacies),explosive facies(pyroclastic flow and thermal wave base subfacies),effusive facies(upper and lower subfacies),and volcanogenic sedimentary facies(pyroclastic sedimentary subfacies).Combining core,thin section,and logging data,the authors established identification markers and petrographic chart logging phases,and also interpreted the longitudinal variation in volcanic petro-graphic response characteristics to make the charts more applicable to this area's volcanic petrographic interpretation of the Huoshiling Formation.These charts can provide a basis for the further exploration and development of volcanic oil and gas in this area.

Application of Secondary Logging Interpretation—Taking Yan 9 Reservoir in X Area as an Example

Logging data and its interpretation results are one of the most important basic data for understanding reservoirs and oilfield development. Standardized and unified logging interpretation results play a decisive role in fine reservoir description and reservoir development. Aiming at the problem of the conflict between the development effect and the initial interpretation result of Yan 9 reservoir in Hujianshan area of Ordos Basin, by combining the current well production performance, logging, oil test, production test and other data, on the basis of making full use of core, coring, logging, thin section analysis and high pressure mercury injection data, the four characteristics of reservoir are analyzed, a more scientific and reasonable calculation model of reservoir logging parameters is established, and the reserves are recalculated after the second interpretation standard of logging is determined. The research improves the accuracy of logging interpretation and provides an effective basis for subsequent production development and potential horizons.

Development of Long-Range,Low-Powered and Smart IoT Device for Detecting Illegal Logging in Forests

Forests promote the conservation of biodiversity and also play a crucial role in safeguarding theenvironment against erosion,landslides,and climate change.However,illegal logging remains a significant threatworldwide,necessitating the development of automatic logging detection systems in forests.This paper proposesthe use of long-range,low-powered,and smart Internet of Things(IoT)nodes to enhance forest monitoringcapabilities.The research framework involves developing IoT devices for forest sound classification andtransmitting each node’s status to a gateway at the forest base station,which further sends the obtained datathrough cellular connectivity to a cloud server.The key issues addressed in this work include sensor and boardselection,Machine Learning(ML)model development for audio classification,TinyML implementation on amicrocontroller,choice of communication protocol,gateway selection,and power consumption optimization.Unlike the existing solutions,the developed node prototype uses an array of two microphone sensors forredundancy,and an ensemble network consisting of Long Short-Term Memory(LSTM)and ConvolutionalNeural Network(CNN)models for improved classification accuracy.The model outperforms LSTM and CNNmodels when used independently and also gave 88%accuracy after quantization.Notably,this solutiondemonstrates cost efficiency and high potential for scalability.

HiLog:OpenHarmony的高性能日志系统

日志是计算机系统中记录事件状态信息的的重要载体,日志系统负责计算机系统的日志生成、收集和输出.OpenHarmony是新兴的、面向全设备、全场景的开源操作系统.在所述工作之前,包括日志系统在内OpenHarmony有许多关键子系统尚未构建,而OpenHarmony的开源特性使第三方开发者可以为其贡献核心代码.为了解决Open Harmony日志系统缺乏的问题,主要开展如下工作:(1)分析当今主流日志系统的技术架构和优缺点;(2)基于OpenHarmony操作系统的异构设备互联特性设计HiLog日志系统模型规范;(3)设计并实现第1个面向OpenHarmony的日志系统HiLog,并贡献到OpenHarmony主线;(4)对HiLog日志系统的关键指标进行测试和对比试验.实验数据表明,在基础性能方面,HiLog和Log的日志写入阶段吞吐量分别为1500 KB/s和700 KB/s,相比Android日志系统吞吐量提升114%;在日志持久化方面,HiLog可以3.5%的压缩率进行持久化,并且丢包率小于6‰,远低于Log.此外,HiLog还具备数据安全、流量控制等新型实用能力.

ModelLogVis:面向模型服务的日志异常可视分析方法

利用深度学习模型训练和运行维护过程产生的海量日志信息,进行模型的优化与故障排查,是当前人工智能运维的研究热点.针对现有工作缺少模型工作流分析的问题,提出面向模型服务的日志异常可视分析方法ModelLogVis.该方法采用日志异常检测方法定位模型工作流中的潜在故障,帮助用户聚焦主要的故障类型;支持用户从数据流、状态、实例性能和原始日志等多个角度对工作流中的事件进行交互式可视化与分析,快速、准确地排查问题.通过真实的模型服务数据的案例研究和专家访谈,证明ModelLogVis方法可高效地辅助用户快速挖掘日志中的异常信息.