Gas-Liquid Separation Processes for Mud Logging Systems

The TRU-Vision system,developed by Baker Hughes,analyzes the gas extracted from drilling mud to estimate the hydrocarbons composition in drilled rock formations.Several separation processes had been surveyed in order to enhance the gas extraction at the gas trap,namely,mechanical stirring,vacuum,air sparging,membrane separation processes,ultrasounds,and cyclones.Mechanical stirring devices(one propeller,one flat-blade turbine,and two baffles sets),a vacuum generator,and an air bubble generator were designed and assembled to increase the efficiency and the response stability of TRU-Vision system.

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.

Different lymph node staging systems for predicting the prognosis of colorectal neuroendocrine neoplasms

BACKGROUND Colorectal neuroendocrine neoplasms(NENs)are a rare malignancy that primarily arises from the diffuse distribution of neuroendocrine cells in the colon and rectum.Previous studies have pointed out that the status of lymph node may be used to predict the prognosis.AIM To investigate the predictive values of lymph node ratio(LNR),positive lymph node(PLN),and log odds of PLNs(LODDS)staging systems on the prognosis of colorectal NENs treated surgically,and compare their predictive values.METHODS This cohort study included 895 patients with colorectal NENs treated surgically from the Surveillance,Epidemiology,and End Results database.The endpoint was mortality of patients with colorectal NENs treated surgically.X-tile software was utilized to identify most suitable thresholds for categorizing the LNR,PLN,and LODDS.Participants were selected in a random manner to form training and testing sets.The prognosis of surgically treating colorectal NENs was examined using multivariate cox analysis to assess the associations of LNR,PLN,and LODDS with the prognosis of colorectal NENs.C-index was used for assessing the predictive effectiveness.We conducted a subgroup analysis to explore the different lymph node staging systems’predictive values.RESULTS After adjusting all confounding factors,PLN,LNR and LODDS staging systems were linked with mortality in patients with colorectal NENs treated surgically(P<0.05).We found that LODDS staging had a higher prognostic value for patients with colorectal NENs treated surgically than PLN and LNR staging systems.Similar results were obtained in the different G staging subgroup analyses.Furthermore,the area under the receiver operating characteristic curve values for LODDS staging system remained consistently higher than those of PLN or LNR,even at the 1-,2-,3-,4-,5-and 6-year follow-up periods.CONCLUSION LNR,PLN,and LODDS were found to significantly predict the prognosis of patients with colorectal NENs treated surgically.

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.

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.

An ARM-based debugging system for multipole array acoustic logging tools

This paper presents a debugging system for multi-pole array acoustic logging （MPAL） tools. The debugging system proposed in this study can debug the MPAL tool system, sub-system and local electronics. In the test equipment, we have used principal and subordinate structures, and interconnected the host computer and the front-end machine via Ethernet. The front-end machine is based on the ARM7 （advanced reduced instruction set computing （RISC） machine） technique, the processor of which runs an embedded operating system, namely, uClinux OS. We have analyzed the system telecommunication, human-machine interface circuit, transmitter mandrel interface circuit, receiver mandrel interface circuit, and board-level test interface circuit. The software used in the system consists of the embedded front-computer software and the host application software. We have explained in detail the flow chart of the boot loader in the embedded front-computer software. The host application software is composed of four application subroutines, which match with the functional modules of the system hardware. A net communication program based on the server^client mode is implemented by means of socket programming and multi-thread programming. Test results indicate that the data transmission rate of the system is higher than 1 MB/s, which completely meets the current requirements of the data transmission rate between the tool system and the wireline telemetry device. Application of the debugging system, which includes multiple level test methods, shows that the proposed system can fully meet the test requirements of MPAL at various levels.

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.

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.

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.

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.

Logformer: Cascaded Transformer for System Log Anomaly Detection

Modern large-scale enterprise systems produce large volumes of logs that record detailed system runtime status and key events at key points.These logs are valuable for analyzing performance issues and understanding the status of the system.Anomaly detection plays an important role in service management and system maintenance,and guarantees the reliability and security of online systems.Logs are universal semi-structured data,which causes difficulties for traditional manual detection and pattern-matching algorithms.While some deep learning algorithms utilize neural networks to detect anomalies,these approaches have an over-reliance on manually designed features,resulting in the effectiveness of anomaly detection depending on the quality of the features.At the same time,the aforementioned methods ignore the underlying contextual information present in adjacent log entries.We propose a novel model called Logformer with two cascaded transformer-based heads to capture latent contextual information from adjacent log entries,and leverage pre-trained embeddings based on logs to improve the representation of the embedding space.The proposed model achieves comparable results on HDFS and BGL datasets in terms of metric accuracy,recall and F1-score.Moreover,the consistent rise in F1-score proves that the representation of the embedding spacewith pre-trained embeddings is closer to the semantic information of the log.

An integrated foot transducer and data logging system for dynamic assessment of lower limb exerted forces during agricultural machinery operations

Agricultural machinery typically requires lower limb actuation forces for operations such as treadling,pedaling and tractor based.However,limited systems exist for assessment of such forces that have ergonomic influence.This study,therefore developed and evaluated a single board computer integrated foot transducer(IFT)and autonomous data logging and visualization systemtomonitor dynamic lower limb exerted forces.The systemconsists of custom developed load sensors sandwiched into foot shaped units that fit operator's both feet.Stamped forces at crank angles for operations typical to pedaling while at height(above ground level)for operation representing typical treadling operations were recorded on-board amemory card and displayed on a liquid crystal display.Evaluations were conducted by imposing external loads that significantly increased(p b 0.05)the foot exerted forces.Force trends were periodic with peaks of 73,85,110.5 and 145.4 N for left foot and 41,50,131.7 and 145.4 N for right foot at loads of 10,30,50 and 70 N,respectively during pedaling operations.Similarly,the left lower actuation limb exerted forces of 139,249 and 255 N at 5,10 and 15 N of imposed loads,respectively during treadling operation.System was also evaluated for tractor operations and exerted forces ranged from 92 to 164 and 107–176 N for clutch pedal engagement at lower to higher tractor speeds on farm and tarmacadam roads,respectively.Similarly,for brake pedal engagement,such forces ranged from106 to 173 and 120–204 N on farm and tarmacadamroads.These forces varied significantly at different forward speeds.Results suggest potential of such system for foot exerted force assessments typical to agricultural machinery systems in real field.Designsmay be evaluated or reconsidered tominimizemusculoskeletal disorder risks during prolonged operations.Work-rest schedules protocols can be developed by ergonomists for safe,efficient and comfortable operations.

Research and Application of Log Defect Detection and Visualization System Based on Dry Coupling Ultrasonic Method

In order to optimize the wood internal quality detection and evaluation system and improve the comprehensive utilization rate of wood,this paper invented a set of log internal defect detection and visualization system by using the ultrasonic dry coupling agent method.The detection and visualization analysis of internal log defects were realized through log specimen test.The main conclusions show that the accuracy,reliability and practicability of the system for detecting the internal defects of log specimens have been effectively verified.The system can make the edge of the detected image smooth by interpolation algorithm,and the edge detection algorithm can be used to detect and reflect the location of internal defects of logs accurately.The content mentioned above has good application value for meeting the requirement of increasing demand for wood resources and improving the automation level of wood nondestructive testing instruments.

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.

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.

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.

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.

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。

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。

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。