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。

烃源岩有机碳测井预测模型优选及应用——以鄂尔多斯盆地安塞地区延长组长9为例

总有机碳(TOC)质量分数是烃源岩评价的重要指标。为了对鄂尔多斯盆地东南部安塞地区延长组长9烃源岩有机碳进行测井评价,本文先立足于岩心分析实测w(TOC)资料,基于烃源岩对不同测井曲线的响应特征,运用多元回归模型、传统Δlog R模型以及Δlog R模型的改进型和广义型,分别建立烃源岩w(TOC)测井定量预测模型;然后将这几种模型加以分析和组合运用,从改进Δlog R模型中提取拟合叠合系数应用到两种广义Δlog R模型的计算当中,应用效果良好;最后对模型进行对比和优选,提出最适合研究区的烃源岩w(TOC)测井定量预测模型。结果表明:考虑密度的广义Δlog R模型准确度最高,平均相对误差为7.78%;多元回归模型次之,平均相对误差为9.65%。二者均满足w(TOC)测井定量预测的精度要求。

Deep-large faults controlling on the distribution of the venting gas hydrate system in the middle of the Qiongdongnan Basin, South China Sea

Many locations with concentrated hydrates at vents have confirmed the presence of abundant thermogenic gas in the middle of the Qiongdongnan Basin(QDNB).However,the impact of deep structures on gasbearing fluids migration and gas hydrates distribution in tectonically inactive regions is still unclear.In this study,the authors apply high-resolution 3D seismic and logging while drilling(LWD)data from the middle of the QDNB to investigate the influence of deep-large faults on gas chimneys and preferred gasescape pipes.The findings reveal the following:(1)Two significant deep-large faults,F1 and F2,developed on the edge of the Songnan Low Uplift,control the dominant migration of thermogenic hydrocarbons and determine the initial locations of gas chimneys.(2)The formation of gas chimneys is likely related to fault activation and reactivation.Gas chimney 1 is primarily arises from convergent fluid migration resulting from the intersection of the two faults,while the gas chimney 2 benefits from a steeper fault plane and shorter migration distance of fault F2.(3)Most gas-escape pipes are situated near the apex of the two faults.Their reactivations facilitate free gas flow into the GHSZ and contribute to the formation of fracture‐filling hydrates.

基于立体视觉的焊孔识别与定位系统研究

目前对大型轴承焊孔、焊缝的焊接基本都是工人手工焊接,而这种传统的人工焊接方式,效率低且质量不稳定,难以满足大批量的生产需求;并且在高温高压、水下及核工业现场等恶劣环境下,要考虑温度、压强及光的折射等因素的影响,人工焊接更是困难,焊接过程对工人的身体伤害也非常大。针对上述问题,基于立体视觉技术,研究焊孔的自动检测识别与定位系统,先通过双目相机实时拍摄焊孔信息,并对拍摄的图像进行预处理;然后利用模板匹配算法和LOG算子检测技术实现对焊孔的检测识别;最后使用AD-Census算法对焊孔进行三维重建。经实验表明,焊孔三维定位误差在0.5 mm内,满足工厂对焊孔的焊接要求。

Data-driven human and bot recognition from web activity logs based on hybrid learning techniques

Distinguishing between web traffic generated by bots and humans is an important task in the evaluation of online marketing campaigns.One of the main challenges is related to only partial availability of the performance metrics:although some users can be unambiguously classified as bots,the correct label is uncertain in many cases.This calls for the use of classifiers capable of explaining their decisions.This paper demonstrates two such mechanisms based on features carefully engineered from web logs.The first is a man-made rule-based system.The second is a hierarchical model that first performs clustering and next classification using human-centred,interpretable methods.The stability of the proposed methods is analyzed and a minimal set of features that convey the classdiscriminating information is selected.The proposed data processing and analysis methodology are successfully applied to real-world data sets from online publishers.

Effect of stress on fracture development in the Asmari reservoir in the Zagros Thrust Belt

Development and production from fractured reservoirs require extensive knowledge about the reservoir structures and in situ stress regimes.For this,this paper investigates fractures and the parameters(aperture and density)through a combination of wellbore data and geomechanical laboratory testing in three separate wells in the Asmari reservoir,Zagros Belt,Iran.The Asmari reservoir(Oligo-Miocene)consists mainly of calcitic and dolomitic rocks in depths of 2000e3000 m.Based on the observation of features in several wellbores,the orientation and magnitude of the in situ stresses along with their influence on reservoir-scale geological structures and neotectonics were determined.The study identifies two regional tectonic fracture settings in the reservoir:one set associated with longitudinal and diagonal wrinkling,and the other related to faulting.The former,which is mainly of open fractures with a large aperture,is dominant and generally oriented in the N45°-90°W direction while the latter is obliquely oriented relative to the bedding and characterized by N45°-90°E.The largest aperture is found in open fractures that are longitudinal and developed in the dolomitic zones within a complex stress regime.Moreover,analysis of drilling-induced fractures(DIFs)and borehole breakouts(BBs)from the image logs revealed that the maximum horizontal stress(SHmax)orientation in these three wells is consistent with the NE-SW regional trend of the SHmax(maximum principal horizontal stress)in the Zagros Belt.Likewise,the stress magnitude obtained from geomechanical testing and poroelastic equations confirmed a variation in stress regime from normal to reverse,which changes in regard to active faults in the study area.Finally,a relationship between the development degree of open fractures and in situ stress regime was found.This means that in areas where the stress regime is complex and reverse,fractures would exhibit higher density,dip angle,and larger apertures.

Anisotropy measurements and characterization of the Qingshankou shale

Qingshankou shale(Gulong area,China)exhibits strong acoustic anisotropy characteristics,posing significant challenges to its exploration and development.In this study,the five full elastic constants and multipole response law of the Qingshankou shale were studied using experimental measurements.Analyses show that the anisotropy parametersϵandγin the study region are greater than 0.4,whereas the anisotropy parameterδis smaller,generally 0.1.Numerical simulations show that the longitudinal and transverse wave velocities of these strong anisotropic rocks vary significantly with inclination angle,and significant differences in group velocity and phase velocity are also present.Acoustic logging measures the group velocity in dipped boreholes;this differs from the phase velocity to some extent.As the dip angle increases,the longitudinal and SH wave velocities increase accordingly,while the qSV-wave velocity initially increases and then decreases,reaching its maximum value at a dip of approximately 40°.These results provide an effective guide for the correction and modeling of acoustic logging time differences in the region.

Gas hydrate saturation from NGHP 02 LWD data in the Mahanadi Basin

During the Indian National Gas Hydrate Program(NGHP)Expedition 02,Logging-while-drilling(LWD)logs were acquired at three sites(NGHP-02-11,NGHP-02-12,and NGHP-02-13)across the Mahanadi Basin in area A.We applied rock physics theory to available sonic velocity logs to know the distribution of gas hydrate at site NGHP-02-11 and NGHP-02-13.Rock physics modeling using sonic velocity at well location shows that gas hydrate is distributed mainly within the depth intervals of 150-265 m and 100 -215 mbsf at site NGHP-02-11 and NGHP-02-13,respectively,with an average saturation of about 4%of the pore space and the maximum concentration of about 40%of the pore space at 250 m depth at site NGHP-02-11,and at site NGHP-02-13 an average saturation of about 2%of the pore space and the maximum concentration of about 20%of the pore space at 246 m depth,as gas hydrate is distributed mainly within 100-246 mbsf at this site.Saturation of gas hydrate estimated from the electrical resistivity method using density derived porosity and electrical resistivity logs from Archie's empirical formula shows high saturation compared to that from the sonic log.However,estimates of hydrate saturation based on sonic P-wave velocity may differ significantly from that based on resistivity,because gas and hydrate have higher resistivity than conductive pore fluid and sonic P-wave velocity shows strong effect on gas hydrate as a small amount of gas reduces the velocity significantly while increasing velocity due to the presence of hydrate.At site NGHP-02-11,gas hydrate saturation is in the range of 15%e30%,in two zones between 150-180 and 245-265 mbsf.Site NGHP-02-012 shows a gas hydrate saturation of 20%e30%in the zone between 100 and 207 mbsf.Site NGHP-02-13 shows a gas hydrate saturation up to 30%in the zone between 215 and 246 mbsf.Combined observations from rock physics modeling and Archie’s approximation show the gas hydrate concentrations are relatively low(<4%of the pore space)at the sites of the Mahanadi Basin in the turbidite channel system.

Relationship between Natural Fracture and Structural Style and its Implication for Tight Gas Enrichment:A Case Study of Deep Ahe Formation in the Dibei–Tuzi Area,Kuqa Depression

The deep Lower Jurassic Ahe Formation(J_(1a))in the Dibei–Tuzi area of the Kuqa Depression has not been extensively explored because of the complex distribution of fractures.A study was conducted to investigate the relationship between the natural fracture distribution and structural style.The J_(1a)fractures in this area were mainly high-angle shear fractures.A backward thrust structure(BTS)is favorable for gas migration and accumulation,probably because natural fractures are more developed in the middle and upper parts of a thick competent layer.The opposing thrust structure(OTS)was strongly compressed,and the natural fractures in the middle and lower parts of the thick competent layer around the fault were more intense.The vertical fracture distribution in the thick competent layers of an imbricate-thrust structure(ITS)differs from that of BTS and OTS.The intensity of the fractures in the ITS anticline is similar to that in the BTS.Fracture density in monoclinic strata in a ITS is controlled by faulting.Overall,the structural style controls the configuration of faults and anticlines,and the stress on the competent layers,which significantly affects deep gas reservoir fractures.The enrichment of deep tight sandstone gas is likely controlled by two closely spaced faults and a fault-related anticline.

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.

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.

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.

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.

The Upper Jurassic sediments,Marib-Shabwa Basin,Yemen:Lithofacies aspects and sequence stratigraphic analysis

The present study is devoted to understanding the evolution of the Upper Jurassic Sab'atayn Formation in the Marib-Shabwa Basin,Yemen,through a sequence stratigraphic analysis based on integrating datasets of sedimentology,seismic sections,and well logs.The Sab'atayn Formation(Tithonian age)is represented by a series of clastic and evaporites that were deposited under fluvio-deltaic to prodeltaic settings.It is divided into four members including Yah(at the base),upwards to Seen,Alif,and Safir at the top.Two third-order depositional sequences were determined for the Tithonian succession which were separated by three sequence boundaries.These sequences were classified into their systems tracts signifying several sedimentation patterns of progradational,aggradational,and retrogradational parasequence sets.The first depositional sequence corresponds to the early-middle Tithonian Yah and Seen units that can be classified into lowstand,transgressive,and highstand systems tracts.The second sequence comprises the late Tithonian Alif unit that can be subdivided into transgressive and highstand systems tracts.The sandy deposits of the Alif Member(highstand deposits)represent the most productive hydrocarbon reservoir in the basin.The Upper Jurassic sediments in the study area were resulted from a combination of eustatic and tectonic effects.

Evaluation of formation susceptibility and sand production potential in an offshore field,Niger Delta Basin,Nigeria

Wellbore instability and sand production are all common challenges in the Niger Delta oil province,resulting in high drilling and production cost as well as damage to oil facilities.The vulnerability of lithologic formations to wellbore instability and resultant sand production is investigated in the four delineated reservoirs of the“Areo”field,western part of Niger Delta Basin.The foundation for establishing the geomechanical properties in this study was a 1-dimensional mechanical earth model,using gamma ray(GR),density(RHOB),compressional slowness(DTC),and shear slowness(DTS)logs.Within the Areo oil field,two wells(well 001 and well 002)were correlated.The evaluated formations are still primarily composed of compacted shale and unconsolidated sandstone,with reservoir sand units exhibiting lower elastic and rock strength properties than shale units.High compressibility and porosity make sand more brittle,while low compressibility and porosity make shale stiffer due to high moduli.The maximum force that can be applied to a shale unit without causing it to fail is 17.23 MPa,which is the maximum average rock strength of the shale.It means that shale requires more vertical stress or pressure than sand does in order to deform it(15.06 MPa).The three sand prediction approaches used in the analysis of sand production predictions have cut-off values that are higher than the average values of the formations.The Schlumberger sand production index method(S/I)indicates that the reservoir has potential for sand influx in the two wells,with the average of the four reservoirs studied in wells 001 and 002 being 1.551012 psi and 1.141012 psi respectively.However,when a formation's sand production index is less than 1.241012 psi,as it is in this study,the formation is likely to produce sand.These findings support the notion that the defined sandstone units are highly unconsolidated and have a high potential for producing sands;therefore,sand control techniques must be factored into process optimization and cost reduction plans.

Reservoir quality of the Late Cretaceous Volador Formation of the Latrobe group,Gippsland Basin,Australia:Implications from integrated analytical techniques

Increasing demand for energy due to the populous Eastern Australia has driven oil and gas industries to find new sources of hydrocarbons as they are the primary energy suppliers.Intensive study has been done on the Volador Formation in the Gippsland Basin by means of core-based petrophysical,sedimentological,and petrographic analyses as well as well log-based interpretation and capillary pressure test.Five wells from Kipper,Basker and Tuna fields with available dataset were investigated in this study:Kipper-1,Basker-1,Basker-2,Basker-5 and Tuna-4.Overall,the formation has good reservoir quality based on the high porosity and permeability values obtained through core and well log petrophysical analyses.The formation made up of mostly moderate to coarse quartz grains that has experienced strong anti-compaction and is poorly cemented.Montmorillonite and illite clays are seen dispersed in the rock formation,with the minority being mixed clays.These clays and diagenetic features including kaolinite cement and quartz overgrowth that can lead to porosity reduction only have insignificant impact on the overall reservoir quality.In addition,capillary pressure data shows that most samples are found in the transition to good reservoir zones(<50%saturation).The results obtained from this study have shown that the Volador Formation in the Gippsland Basin is worth for hydrocarbon exploration.

Fracture identification of carbonate reservoirs by deep forest model:An example from the D oilfield in Zagros Basin

Identifying fractures along a well trajectory is of immense significance in determining the subsurface fracture network distribution.Typically,conventional logs exhibit responses in fracture zones,and almost all wells have such logs.However,detecting fractures through logging responses can be challenging since the log response intensity is weak and complex.To address this problem,we propose a deep learning model for fracture identification using deep forest,which is based on a cascade structure comprising multi-layer random forests.Deep forest can extract complex nonlinear features of fractures in conventional logs through ensemble learning and deep learning.The proposed approach is tested using a dataset from the Oligocene to Miocene tight carbonate reservoirs in D oilfield,Zagros Basin,Middle East,and eight logs are selected to construct the fracture identification model based on sensitivity analysis of logging curves against fractures.The log package includes the gamma-ray,caliper,density,compensated neutron,acoustic transit time,and shallow,deep,and flushed zone resistivity logs.Experiments have shown that the deep forest obtains high recall and accuracy(>92%).In a blind well test,results from the deep forest learning model have a good correlation with fracture observation from cores.Compared to the random forest method,a widely used ensemble learning method,the proposed deep forest model improves accuracy by approximately 4.6%.

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.