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.

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。

Well logging evaluation of fine-grained hydrate-bearing sediment reservoirs: Considering the effect of clay content

Hydrate reservoirs are different from the host reservoirs of all other fossil energy sources because the characteristics of hydrate reservoirs are generally controlled by deep-sea fine-grained sedimentation. In such reservoirs, the reliability of the classical logging evaluation models established for diagenetic reservoirs is questionable. This study used well W8 in the Qiongdongnan Basin to explore the clay content, porosity, saturation, and hydrate-enriched layer identification of a logging-based hydrate reservoir, and it was found that considering the effect of the clay content on the log response is necessary in the logging evaluation of hydrate reservoirs. In the evaluation of clay content, a method based on the optimization inversion method can obtain a more reliable clay content than other methods. Fine-grained sediment reservoirs have a high clay content, and the effect of clay on log responses must be considered when calculating porosity. In addition, combining density logging and neutron porosity logging data can obtain the best porosity calculation results, and the porosity calculation method based on sonic logging predicted that the porosity of the studied reservoir was low. It was very effective to identify hydrate layers based on resistivity, but the clay distribution and pore structure will also affect the relationship between resistivity, porosity and saturation, and it was suggested that the factors effecting the resistivity of different layers should be considered in the saturation evaluation and that a suitable model should be selected. This study also considered the lack of clarity of the relationships among the lithology, physical properties, hydrate-bearing occurrence properties, and log response properties of hydrate reservoirs and the lack of specialized petrophysical models. This research can directly help to improve hydrate logging evaluation.

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。

Total organic carbon content logging prediction based on machine learning:A brief review

The total organic carbon content usually determines the hydrocarbon generation potential of a formation.A higher total organic carbon content often corresponds to a greater possibility of generating large amounts of oil or gas.Hence,accurately calculating the total organic carbon content in a formation is very important.Present research is focused on precisely calculating the total organic carbon content based on machine learning.At present,many machine learning methods,including backpropagation neural networks,support vector regression,random forests,extreme learning machines,and deep learning,are employed to evaluate the total organic carbon content.However,the principles and perspectives of various machine learning algorithms are quite different.This paper reviews the application of various machine learning algorithms to deal with total organic carbon content evaluation problems.Of various machine learning algorithms used for TOC content predication,two algorithms,the backpropagation neural network and support vector regression are the most commonly used,and the backpropagation neural network is sometimes combined with many other algorithms to achieve better results.Additionally,combining multiple algorithms or using deep learning to increase the number of network layers can further improve the total organic carbon content prediction.The prediction by backpropagation neural network may be better than that by support vector regression;nevertheless,using any type of machine learning algorithm improves the total organic carbon content prediction in a given research block.According to some published literature,the determination coefficient(R^(2))can be increased by up to 0.46 after using machine learning.Deep learning algorithms may be the next breakthrough direction that can significantly improve the prediction of the total organic carbon content.Evaluating the total organic carbon content based on machine learning is of great significance.

Relational Logging Design Pattern

Observability and traceability of developed software are crucial to its success in software engineering.Observability is the ability to comprehend a system’s internal state from the outside.Monitoring is used to determine what causes system problems and why.Logs are among the most critical technology to guarantee observability and traceability.Logs are frequently used to investigate software events.In current log technologies,software events are processed independently of each other.Consequently,current logging technologies do not reveal relationships.However,system events do not occur independently of one another.With this perspective,our research has produced a new log design pattern that displays the relationships between events.In the design we have developed,the hash mechanism of blockchain technology enables the display of the logs’relationships.The created design pattern was compared to blockchain technology,demonstrating its performance through scenarios.It has been determined that the recommended log design pattern outperforms blockchain technology in terms of time and space for software engineering observability and traceability.In this context,it is anticipated that the log design pattern we provide will strengthen the methods used to monitor software projects and ensure the traceability of relationships.

Response analyses on the drill-string channel for logging while drilling telemetry

Downhole acoustic telemetry(DAT),using a long drill string with periodical structures as the channel,is a prospective technology for improving the transmission rate of logging while drilling(LWD)data.Previous studies only focused on the acoustic property of a free drill string and neglected the coupling between pipes and fluid-filled boreholes.In addition to the drill-string waves,a series of fluid waves are recorded in the DAT channel,which has not been investigated yet.Unpredictable channel characteristics result in lower transmission rates and stability than expected.Therefore,a more realistic channel model is needed considering the fluid-filled borehole.In this paper,we propose a hybrid modeling method to investigate the response characteristics of the DAT channel.By combining the axial wavenumbers and excitation functions of mode waves in radially layered LWD structures,the channel model is approximated to the 1-D propagation,which considers transmission,reflection,and interconversion of the drillstring and fluid waves.The proposed 1-D approximation has been well validated by comparing the 2-D finite-difference modeling.It is revealed that the transmitted and converted fluid waves interfere with the drill-string wave,which characterizes the DAT channel as a particular coherent multi-path channel.When a fluid-filled borehole surrounds the drill string,the channel responses exhibit considerable delay as well as strong frequency selectivity in amplitude and phase.These new findings suggest that the complexity of the channel response has been underestimated in the past,and therefore channel measurements on the ground are unreliable.To address these channel characteristics,we apply a noncoherent demodulation strategy.The transmission rate for synthetic data reaches 15 bps in a 94.5 m long channel,indicating that the acoustic telemetry is promising to break the low-speed limitation of mud-pulse telemetry.

Logging curves characteristics and response mechanisms of basaltic facies and sub-facies: A case study from eastern sag of Liaohe depression, Northeast China

Based on five types of conventional logging curves including GR,RLLD,CNL,DEN and AC,and 39 core samples from 30 representative boreholes,the logging characteristics and lithofacies and sub-facies of the basaltic rocks were studied.Three basaltic facies and four sub-facies are recognized from the well logs,includ-ing volcanic conduit facies(post intrusive sub-facies),explosive facies,and effusive lava flow facies(tabular flow,compound flow and hyaloclastite sub-facies).The post intrusive,tabular flow and compound flow sub-facies logging curves are mainly controlled by the distribution of vesiculate zones and vesiculate content,which are characterized by four curves with good correlation.Post intrusive sub-facies are characterized by high RLLD,high DEN,with a micro-dentate logging curve pattern,abrupt contact relationships at the top and base.Tabular flow sub-facies are characterized by high RLLD,high DEN,with a bell-shaped log curve pattern,abrupt contact at the base and gradational contact at the top.Compound flow sub-facies are characterized by medium-low RLLD,with a micro-dentate or finger-like logging curve pattern,abrupt contact at the base and gradational contact at the top.Explosive facies and hyaloclastite sub-facies logging curves are mainly controlled by the distribution of the size and sorting of rock particles,which can be recognized by four kinds of logging curves with poor cor-relation.Explosive facies are characterized by low RLLD,medium-low CNL and low DEN,with a micro-dentate logging curve pattern.Hyaloclastite sub-facies are characterized by low RLLD,high CNL,low DEN and high AC,with a micro-dentate logging curve pattern.The present research is beneficial for the prediction of basaltic reser-voirs not only in the Liaohe depression but also in the other volcanic-sedimentary basins.

Characterization of reservoir properties and pore structure based on micro-resistivity imaging logging: Porosity spectrum, permeability spectrum, and equivalent capillary pressure curve

According to the capillary theory,an equivalent capillary model of micro-resistivity imaging logging was built.On this basis,the theoretical models of porosity spectrum(Ф_(i)),permeability spectrum(K_(i))and equivalent capillary pressure curve(pe)were established to reflect the reservoir heterogeneity.To promote the application of the theoretical models,the Archie's equation was introduced to establish a general model for quantitatively characterizing bi,K,and pei.Compared with the existing models,it is shown that:(1)the existing porosity spectrum model is the same as the general equation of gi;(2)the Ki model can display the permeability spectrum as compared with Purcell's permeability model;(3)the per model is constructed on a theoretical basis and avoids the limitations of existing models that are built only based on the component of porosity spectrum,as compared with the empirical model of capillary pressure curve.The application in the Permian Maokou Formation of Well TsX in the Central Sichuan paleo-uplift shows that the Ф_(i),K_(i),and p_(ci) models can be effectively applied to the identification of reservoir types,calculation of reservoir properties and pore structure parameters,and evaluation of reservoir heterogeneity.

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.

Soil Seed Bank Characteristics in Congolese Rainforests and Implications for Post-Logging Plots Reforestation

The soil seed bank is considered as an important mechanism for the natural regeneration, resilience and conservation of the forests after disturbances. This study evaluates the characteristics of the soil seed bank in two post-logging plots of Loundoungou-Toukoulaka Forest Management Unit: one plot exploited in 2008 and another exploited in 2021. In each study plot, 40 samples were collected per soil layer (0 - 5 cm, 5 - 10 cm, 10 - 15 cm, 15 - 20 cm and 20 - 25 cm depth). The species diversity and abundance of the soil seed bank were estimated after soil samples were brought to germination. The results demonstrated that 347 seedlings belonging to 37 species in the plot exploited in 2008 and 418 seedlings belonging to 27 species in that exploited in 2021 germinated during 20 weeks of monitoring. The total densities of the seedlings identified were respectively 1446 seedlings/m2 and 1742 seedlings/m2. The plot exploited in 2021 presented a higher proportion of herbaceous species (93.78%) compared to that exploited in 2008 (82.71%). Two pioneer species were recorded in the plot exploited in 2008. These are Macaranga barteri (0.29%) in the 0 - 5 cm layer and Musanga cecropioides (2.31%) up to 20 cm deep. On the other hand, in the plot exploited in 2021, Macaranga spinosa (0.96%) in the 0 - 5 cm layer and M. cecropioides (0.96%) up to 20 cm deep were identified. In the plot exploited in 2008, the 20 - 25 cm layer demonstrated important proportions in woody species (9%), these are in particular Rubiaceae sp.4 and Nauclea diderrichii. While that exploited in 2021, presented 19% of woody species, namely the species of Rubiaceae sp.4, Rubiaceae sp.5 and N. diderrichii, greatly exceeding the proportions obtained in the 15 - 20 cm layer of the two plots. Nonetheless, N. diderrichii was the only commercial species recorded with densities of 108 seedlings/m2 and 4 seedlings/m2, respectively in the plot exploited in 2008 and that exploited in 2021. Commercial tree species are poorly represented in the soil seed bank. Consequently, the study suggests that to improve the natural regeneration of the commercial species, silvicultural interventions based on planting techniques in the exploited plots should be more effective in order to sustainably manage these production forests.

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。

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.

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。

Numerical simulation and response analysis of microspherical focused logging in inclined micro-fractured formation

Tight and unconventional reservoirs have become the focus with the progress of petroleum exploration and development.Micro-fractures in these reservoirs can effectively improve reservoir permeability,and well-developed micro-fractures can serve to directly improve productivity.Compared with the centered electrical well logging method,the Micro Spherical Focused Logging(MSFL)is more suitable for microfracture identification due to its high resolution and near borehole wall measuring method.In this study,an anisotropic model is used to depict micro-fractured formation.First,a forward model with microfractured formation,borehole,logging instrument and surrounding rock is established.Subsequently,MSFL responses under different micro-fracture porosity,resistivity,dip angle and borehole radius,are calculated based on the finite element method(FEM).Finally,the MSFL responses under different microfracture parameters are analyzed with the response laws clarified.

基于改进LOG算子的雷达图像边缘检测算法

雷达图像中固定地物杂波的边缘轮廓对运动目标检测有重要作用.针对传统的高斯-拉普拉斯(LOG)边缘检测算法在对雷达图像进行边缘检测时对噪声敏感,易影响图像边缘轮廓信息的提取问题,提出了一种改进的LOG边缘检测算法.首先采用改进的均值滤波和双边滤波对图像进行平滑去噪;然后用拉普拉斯算子计算二阶方向导数,计算零交叉点得到图像的边缘位置信息,从而获得连续、完整的地物边缘轮廓;最后对雷达图像采用原始LOG和本文改进的LOG算法进行了对比实验.实验结果表明,在强地物杂波环境下,与原始LOG算法相比,改进的LOG算法提高了边缘的检测精度,改善了图像边缘连续性,从而可提高雷达目标的检测概率.

Project Level Pavement Evaluation Using FWD,GPR,and Video Logging and Its Application in Pavement Rehabilitation in Indiana

Pavement rehabilitation is a major activity for all highway agencies.Accurate and efficient measurement of the rehabilitated pavement performances becomes more and more important in this procedure.In the last 10 years,significant improvements have been made in pavement nondestructive evaluation.NDT(non-destructive testing)has gained popularity because of its advantage in comparison to laboratory testing.Some of these advantages include minimal or no damage to structure,in-situ full-scale testing,relatively low operational cost,and short test duration.The INDOT(Indiana Department of Transportation)has a project level pavement evaluation program that began several years ago.This project level evaluation program employs FWD(falling weight deflectometer),GPR(ground penetration radar)and video logging.The program provides valuable information about pavement performance characteristics and offers useful tools for developing pavement rehabilitation strategies,specifically overlays and pavement underseals.On the other hand,the state of Indiana had rehabilitated its flexible,rigid or composite pavement almost exclusively with asphalt.This AC(asphalt concrete)overlay can improve the condition of existing pavement and extend the service life of the existing pavement structure.This paper thus describes the experiences of pavement overlay with AC thickness design for the INDOT(Indiana State Department of Transportation)using the AASHTO(American Association of State Highway and Transportation Officials)1993 Guide,the MEPDG(Mechanistic-Empirical Pavement Design Guide).In order to do that,backcalculation program was compared and evaluated to obtain subgrade resilient modulus and k value for pavement rehabilitation using FWD data.Video logging provides IRI(international roughness index)and rut depth for existing pavement condition and GPR provides thickness or pavement bonding conditions in pavement.Emphasis is placed on observations and issues encountered using the current AASHTO 1993 Guide and the MEPDG.

TRGATLog:基于日志时间图注意力网络的日志异常检测方法

为解决现有日志异常检测方法往往只关注定量关系模式或顺序模式的单一特征,忽略了日志时间结构关系和不同特征之间的相互联系,导致较高的异常漏检率和误报率问题,提出基于日志时间图注意力网络的日志异常检测方法。首先,通过设计日志语义和时间结构联合特征提取模块构建日志时间图,有效整合日志的时间结构关系和语义信息。然后,构造时间关系图注意力网络,利用图结构描述日志间的时间结构关系,自适应学习不同日志之间的重要性,进行异常检测。最后,使用三个公共数据集验证模型的有效性。大量实验结果表明,所提方法能够有效捕获日志时间结构关系,提高异常检测精度。

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.

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。