Pore size classification and prediction based on distribution of reservoir fluid volumes utilizing well logs and deep learning algorithm in a complex lithology

Pore size analysis plays a pivotal role in unraveling reservoir behavior and its intricate relationship with confined fluids.Traditional methods for predicting pore size distribution(PSD),relying on drilling cores or thin sections,face limitations associated with depth specificity.In this study,we introduce an innovative framework that leverages nuclear magnetic resonance(NMR)log data,encompassing clay-bound water(CBW),bound volume irreducible(BVI),and free fluid volume(FFV),to determine three PSDs(micropores,mesopores,and macropores).Moreover,we establish a robust pore size classification(PSC)system utilizing ternary plots,derived from the PSDs.Within the three studied wells,NMR log data is exclusive to one well(well-A),while conventional well logs are accessible for all three wells(well-A,well-B,and well-C).This distinction enables PSD predictions for the remaining two wells(B and C).To prognosticate NMR outputs(CBW,BVI,FFV)for these wells,a two-step deep learning(DL)algorithm is implemented.Initially,three feature selection algorithms(f-classif,f-regression,and mutual-info-regression)identify the conventional well logs most correlated to NMR outputs in well-A.The three feature selection algorithms utilize statistical computations.These algorithms are utilized to systematically identify and optimize pertinent input features,thereby augmenting model interpretability and predictive efficacy within intricate data-driven endeavors.So,all three feature selection algorithms introduced the number of 4 logs as the most optimal number of inputs to the DL algorithm with different combinations of logs for each of the three desired outputs.Subsequently,the CUDA Deep Neural Network Long Short-Term Memory algorithm(CUDNNLSTM),belonging to the category of DL algorithms and harnessing the computational power of GPUs,is employed for the prediction of CBW,BVI,and FFV logs.This prediction leverages the optimal logs identified in the preceding step.Estimation of NMR outputs was done first in well-A(80%of data as training and 20%as testing).The correlation coefficient(CC)between the actual and estimated data for the three outputs CBW,BVI and FFV are 95%,94%,and 97%,respectively,as well as root mean square error(RMSE)was obtained 0.0081,0.098,and 0.0089,respectively.To assess the effectiveness of the proposed algorithm,we compared it with two traditional methods for log estimation:multiple regression and multi-resolution graph-based clustering methods.The results demonstrate the superior accuracy of our algorithm in comparison to these conventional approaches.This DL-driven approach facilitates PSD prediction grounded in fluid saturation for wells B and C.Ternary plots are then employed for PSCs.Seven distinct PSCs within well-A employing actual NMR logs(CBW,BVI,FFV),in conjunction with an equivalent count within wells B and C utilizing three predicted logs,are harmoniously categorized leading to the identification of seven distinct pore size classification facies(PSCF).this research introduces an advanced approach to pore size classification and prediction,fusing NMR logs with deep learning techniques and extending their application to nearby wells without NMR log.The resulting PSCFs offer valuable insights into generating precise and detailed reservoir 3D models.

FMLogs:基于模板匹配的日志解析方法

日志主要记录软硬件的运行信息,通过查看系统日志,可以找到系统出现的问题及原因,确保系统的稳定性和正常运行。日志解析的目的是将半结构化的原始日志解析为可阅读的日志模板,现有解析方法往往只注重于对原始日志的解析,而忽略了后期模板处理,导致结果的精度不能进一步提高。自此,提出了一种日志解析方法FMLogs(logs parsing based on frequency and MinHash algorithm)。该方法通过设计正则表达式和调节阈值参数以获得最佳性能,同时采用了字符级频率统计和MinHash方法对长度相同和不同的日志模板进行合并。FMLogs在七个真实数据集上进行了广泛的实验,取得了0.924的平均解析准确率和0.983的F 1-Score。实验结果表明,FMLogs是一种有效的日志解析方法,在解析日志的同时具有较高的准确性和效率,并能保证性能的稳定。

Automatic discrimination of sedimentary facies and lithologies in reef-bank reservoirs using borehole image logs

Reef-bank reservoirs are an important target for petroleum exploration in marine carbonates and also an essential supplemental area for oil and gas production in China. Due to the diversity of reservoirs and the extreme heterogeneity of reef-banks, it is very difficult to discriminate the sedimentary facies and lithologies in reef-bank reservoirs using conventional well logs. The borehole image log provides clear identification of sedimentary structures and textures and is an ideal tool for discriminating sedimentary facies and lithologies. After examining a large number of borehole images and cores, we propose nine typical patterns for borehole image interpretation and a method that uses these patterns to discriminate sedimentary facies and lithologies in reeI^bank reservoirs automatically. We also develop software with user-friendly interface. The results of applications in reef-bank reservoirs in the middle Tarim Basin and northeast Sichuan have proved that the proposed method and the corresponding software are quite effective.

基于logs2intrusions与Web Log Explorer的综合取证分析研究

随着互联网应用的迅猛增长,其受到的安全威胁也越来越严重,尤其是网络入侵攻击事件造成了极大的危害。目前,对入侵行为检测的一种必要手段是对日志数据进行分析,网站日志文件是记录Web服务器接收处理请求以及运行时错误等各种原始信息的文件,但目前来看网络日志文件的作用还有待进一步提升。文章分析了logs2intrusions、Web Log Explorer、光年SEO日志分析系统、逆火网站分析器这四种日志分析工具的特性,提出了基于logs2intrusions和Web Log Explorer两个工具优势的综合取证分析技术,实现了对大批量入侵攻击日志数据的快速分析处理,提高了对网络入侵攻击行为识别的准确率。

基于静电势电荷的抗击埃博拉病毒新药物法匹拉韦及其衍生物水溶解度logS值预测

运用分子中的原子理论(AIM),探讨了以法匹拉韦分子羰基氧原子值为目标,测试了不同基组和泛函选择的依赖性。然后用密度泛函理论(DFT B3LYP)和6-31+G(d,p)基组,优化了20种法匹拉韦及其常见衍生物的分子结构,分别得到11号羰基氧的密立根电荷(MUL-O)、自然原子轨道电荷(NBO-O)、何秀巴赫电荷(HIR-O)和静电势电荷(ESP-O)值,发现11号氧原子的ESP-O电荷值与用ACD Lab6.0预测出来的log S值相关性最好,相关系数达0.986;计算了法匹拉韦及其11种未知衍生物的ESP-O电荷值,代入相关最佳线性方程,发现所得结果与ACD Lab6.0预测结果十分接近,最大误差绝对对数值仅为0.08;分子的静电势图也显示法匹拉韦及其甲基法匹拉韦发挥其药理毒理作用可能的部位在电负性强的羰基氧原子上。

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。

Reservoir heterogeneity and fracture parameter determination using electrical image logs and petrophysical data(a case study, carbonate Asmari Formation, Zagros Basin, SW Iran)

Assessment of reservoir and fracture parameters is necessary to optimize oil production,especially in heterogeneous reservoirs.Core and image logs are regarded as two of the best methods for this aim.However,due to core limitations,using image log is considered as the best method.This study aims to use electrical image logs in the carbonate Asmari Formation reservoir in Zagros Basin,SW Iran,in order to evaluate natural fractures,porosity system,permeability profile and heterogeneity index and accordingly compare the results with core and well data.The results indicated that the electrical image logs are reliable for evaluating fracture and reservoir parameters,when there is no core available for a well.Based on the results from formation micro-imager(FMI)and electrical micro-imager(EMI),Asmari was recognized as a completely fractured reservoir in studied field and the reservoir parameters are mainly controlled by fractures.Furthermore,core and image logs indicated that the secondary porosity varies from 0%to 10%.The permeability indicator indicates that zones 3 and 5 have higher permeability index.Image log permeability index shows a very reasonable permeability profile after scaling against core and modular dynamics tester mobility,mud loss and production index which vary between 1 and 1000 md.In addition,no relationship was observed between core porosity and permeability,while the permeability relied heavily on fracture aperture.Therefore,fracture aperture was considered as the most important parameter for the determination of permeability.Sudden changes were also observed at zones 1-1 and 5 in the permeability trend,due to the high fracture aperture.It can be concluded that the electrical image logs(FMI and EMI)are usable for evaluating both reservoir and fracture parameters in wells with no core data in the Zagros Basin,SW Iran.

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。

Inversion of Array Induction Logs and Its Application

With the help of the modified geometrical factor theory, the Marquardt method was used to calculate the true electrical parameters of the formation from array induction logs. The inversion results derived from the assumed model and some practical cases show that the rebuilt formation profile determined by 2-ft resolution array induction logs is reasonable when the formation thickness is greater than 1 m, which thus indicates that the inversion method is reliable and can provide quantitative information for the discrimination of oil/gas or water zone.

Efficient Deviation Detection Between a Process Model and Event Logs

Business processes described by formal or semi-formal models are realized via information systems.Event logs generated from these systems are probably not consistent with the existing models due to insufficient design of the information system or the system upgrade.By comparing an existing process model with event logs,we can detect inconsistencies called deviations,verify and extend the business process model,and accordingly improve the business process.In this paper,some abnormal activities in business processes are formally defined based on Petri nets.An efficient approach to detect deviations between the process model and event logs is proposed.Then,business process models are revised when abnormal activities exist.A clinical process in a healthcare information system is used as a case study to illustrate our work.Experimental results show the effectiveness and efficiency of the proposed approach.

Effect of Environmental Gradients on the Quantity and Quality of Fallen Logs in Tsuga longibracteata Forest in Tianbaoyan National Nature Reserve, Fujian Province,China

We investigated the quantity and quality 0f fallen l0gs in different Tsuga l0ngibracteata f0rest c0mmunities in the Tianba0yan Nati0nal Nature Reserve. We used redundancy analysis t0 determine the spatial distributi0n 0f fallen l0gs in the different f0rest c0mmunities and t0 analyze the relati0nships am0ng stand structure, t0p0graphic fact0rs and human disturbance. The v0lume, c0vered area, mean l0g length and number 0f fallen l0gs differed significantly am0ng f0rest types （P 〈 0.05）, but mean diameter at breast height sh0wed n0 significant difference （P 〉 0.05）. The l0g v0lume and c0vered area in different f0rest types sh0wed the f0ll0wing trend： T. l0ngibracteata pure f0rest 〈 T. l0ngibracteata ＋ Olig0staehyum scabrifl0rur 〈 T. l0ngibraeteata ＋ hardw00d 〈 Rh0d0dendr0n simiarum ＋ T. l0ngibraeteata 〈 T. l0ngibraeteata ＋ Phyll0stachys heter0cycla pubescens. The spatial distributi0n patterns 0f l0gs quantity and quality indicated that l0g v0lume and c0vered area were str0ngly affected by envir0nmental fact0rs in the f0ll0wing 0rder： human disturbance 〉 elevati0n 〉 sl0pe p0siti0n 〉 b0le height 〉 tree height 〉 sl0pe aspect 〉 density 〉 basal area 〉 sl0pe gradient. The relative c0ntributi0n 0f envir0nmental variables 0n the t0tal variance was t0p0graphy （76%） 〉 disturbance （42%） 〉 stand structure （35%）. T0p0graphy and disturbance c0mbined explained 8.2% 0f the variance. Fallen l0~s auantitv and aualitvwere negatively related t0 elevati0n and sl0pe p0siti0n, and p0sitively ass0ciated t0 human disturbance. The l0g v0lume decreased fr0m n0rthern t0 s0uthern sl0pes. Envir0nmental fact0rs had the highest impact 0n class I （slightly decayed）, and l0west impact 0n class V （highly decayed）.

Fluid Property Discrimination in Dolostone Reservoirs Using Well Logs

The Ordovician Majiagou Formation is one of the main gas-producing strata in the Ordos Basin,China.The identification of hydrocarbon-bearing intervals via conventional well logs is a challenging task.This study describes the litholog of Ma 5(Member 5 of Majiagou Formation)dolostones,and then analyzes the responses of various conventional well logs to the presences of natural gas.The lithology of the gas bearing layers is dominantly of the dolomicrite to fine to medium crystalline dolomite.Natural gas can be produced from the low resistivity layers,and the dry layers are characterized by high resistivities.Neutron-density crossovers are not sensitive to the presences of natural gas.In addition,there are no significant increases in sonic transit times in natural gas bearing layers.NMR(nuclear magnetic resonance)logs,DSI(Dipole Sonic Imager)logs and borehole image logs(XRMI)are introduced to discriminate the fluid property in Majiagou dolostone reservoirs.The gas bearing intervals have broad NMR T2(transverse relaxation time)spectrum with tail distributions as well as large T2gm(T2 logarithmic mean values)values,and the T2 spectrum commonly display polymodal behaviors.In contrast,the dry layers and water layers have low T2gm values and very narrow T2 spectrum without tails.The gas bearing layers are characterized by low Vp/Vs ratios,low Poisson’s ratios and low P-wave impedances,therefore the fluid property can be discriminated using DSI logs,and the interpretation results show good matches with the gas test data.The apparent formation water resistivity(AFWR)spectrum can be derived from XRMI image logs by using the Archie’s formula in the flushed zone.The gas bearing layers have broad apparent formation water resistivity spectrum and tail distributions compared with the dry and water layers,and also the interpretation results from the image logs exhibit good agreement with the gas test data.The fluid property in Majiagou dolostone reservoirs can be discriminated through NMR logs,DSI logs and borehole image logs.This study helps establish a predictable model for fluid property in dolostones,and have implications in dolostone reservoirs with similar geological backgrounds worldwide.

Identification and Evaluation of Low Resistivity Pay Zones by Well Logs and the Petrophysical Research in China

This paper presents an overview of petrophysical research and exploration achievements of low resistivity pay （LRP） zone by well logs in China. It includes geological characteristics and characteristics of well log response of the low resistivity pay zones discovered and evaluated in recent years, as well as the problems in recognizing and evaluating low resistivity pay zones by well logs. The research areas mainly include the Neogene formations in the Bohai Bay Basin, the Triassic formations in the northern Tarim Basin and the Cretaceous formations in the Junggar Basin, The petrophysical research concerning recognition and evaluation of the low resistivity pays, based on their genetic types, is introduced in this paper.

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。

Synthetic well logs generation via Recurrent Neural Networks

To supplement missing logging information without increasing economic cost, a machine learning method to generate synthetic well logs from the existing log data was presented, and the experimental verification and application effect analysis were carried out. Since the traditional Fully Connected Neural Network(FCNN) is incapable of preserving spatial dependency, the Long Short-Term Memory(LSTM) network, which is a kind of Recurrent Neural Network(RNN), was utilized to establish a method for log reconstruction. By this method, synthetic logs can be generated from series of input log data with consideration of variation trend and context information with depth. Besides, a cascaded LSTM was proposed by combining the standard LSTM with a cascade system. Testing through real well log data shows that: the results from the LSTM are of higher accuracy than the traditional FCNN; the cascaded LSTM is more suitable for the problem with multiple series data; the machine learning method proposed provides an accurate and cost effective way for synthetic well log generation.

Experimental Studies on Debris Flow with Logs Focusing on Specific Weight Difference of Log Species

There are many experimental approaches,field investigations and numerical calculations for movements of woods in a clear water and debris flow.However,kinematic conditions for accumulated logs and the interactions between a main flow and logs have not been fully evaluated.Mitigations for woods need taking into account the characteristics of tree species such as conifer and broad-leaf trees and of shapes such as root swells and crown.In the present study,we focus on the differences in specific weight of conifer and broad-leaf trees with some moisture in a sediment-water mixture flow with narrow flow width,and consider that conifer and broad-leaf tree are floating and submerged solid phase,respectively.Flume tests are conducted in steady flow of clear and debris flow over a rigid bed in order to evaluate conifer and broad-leaf tree movement in clear water and debris flow.Experimental data indicates that dimensionless transverse diffusion coefficient can be 0.1 to 0.4 and 0.3 to 0.9 in flow direction.Those diffusive characteristics seem to be independent of Reynolds number and Froude number,but dependent of bed slope,i.e.,gravity,though detailed considerations are needed to discuss about flow characteristics such as spatial eddy structures,momentum transfer induced by interactions of logs and so on.

A new methodology for identification of potential pay zones from well logs: Intelligent system establishment and application in the Eastern Junggar Basin, China

In recent years, as the exploration practices extend into more complicated formations, conventional well log interpretation has often shown its inaccuracy and limitations in identifying hydrocarbons. The Permian Wutonggou Formation hosts typical clastic reservoirs in the Eastern Junggar Basin. The sophisticated lithology characteristics cause complex pore structures and fluid properties. These all finally cause low well testing agreement rate using conventional methods. Eleven years＇ recent statistics show that 12 out of 15 water layers have been incorrectly identified as being oil or oil/water layers by conventional well log interpretation. This paper proposes a methodology called intelligent prediction and identification system （IPIS）. Firstly, parameters reflecting lithological, petrophysical and electrical responses which are greatly related to reservoir fluids have been selected carefully. They are shale content （Vsh）, numbered rock type （RN）, porosity （φ）, permeability （K）, true resistivity （RT） and spontaneous-potential （SP）. Secondly, Vsh, φ and K are predicted from well logs through artificial neural networks （ANNs）. Finally, all the six parameters are input into a neuro-fuzzy inference machine （NFIM） to get fluids identification results. Eighteen new layers of 145.3 m effective thickness were examined by IPIS. There is full agreement with well testing results. This shows the system＇s accuracy and effectiveness.

Total Organic Carbon Enrichment and Source Rock Evaluation of the Lower Miocene Rocks Based on Well Logs: October Oil Field, Gulf of Suez-Egypt

October oil field is one of the largest hydrocarbon-bearing fields which produces oil from the sand section of the Lower Miocene Asl Formation. Two marl (Asl Marl) and shale (Hawara Formation) sections of possible source enrichment are detected above and below this oil sand section, respectively. This study aims to identify the content of the total organic carbon based on the density log and a combination technique of the resistivity and porosity logs (Δlog R Technique). The available geochemical analyses are used to calibrate the constants of the TOC and the level of maturity (LOM) used in the (Δlog R Technique). The geochemical-based LOM is found as 9.0 and the calibrated constants of the Asl Marl and Hawara Formation are found as 11.68, 3.88 and 8.77, 2.80, respectively. Fair to good TOC% content values (0.88 to 1.85) were recorded for Asl Marl section in the majority of the studied wells, while less than 0.5% is recorded for the Hawara Formation. The lateral distribution maps show that most of the TOC% enrichments are concentrated at central and eastern parts of the study area, providing a good source for the hydrocarbons encountered in the underlying Asl Sand section.

A deep kernel method for lithofacies identification using conventional well logs

How to fit a properly nonlinear classification model from conventional well logs to lithofacies is a key problem for machine learning methods.Kernel methods(e.g.,KFD,SVM,MSVM)are effective attempts to solve this issue due to abilities of handling nonlinear features by kernel functions.Deep mining of log features indicating lithofacies still needs to be improved for kernel methods.Hence,this work employs deep neural networks to enhance the kernel principal component analysis(KPCA)method and proposes a deep kernel method(DKM)for lithofacies identification using well logs.DKM includes a feature extractor and a classifier.The feature extractor consists of a series of KPCA models arranged according to residual network structure.A gradient-free optimization method is introduced to automatically optimize parameters and structure in DKM,which can avoid complex tuning of parameters in models.To test the validation of the proposed DKM for lithofacies identification,an open-sourced dataset with seven con-ventional logs(GR,CAL,AC,DEN,CNL,LLD,and LLS)and lithofacies labels from the Daniudi Gas Field in China is used.There are eight lithofacies,namely clastic rocks(pebbly,coarse,medium,and fine sand-stone,siltstone,mudstone),coal,and carbonate rocks.The comparisons between DKM and three commonly used kernel methods(KFD,SVM,MSVM)show that(1)DKM(85.7%)outperforms SVM(77%),KFD(79.5%),and MSVM(82.8%)in accuracy of lithofacies identification;(2)DKM is about twice faster than the multi-kernel method(MSVM)with good accuracy.The blind well test in Well D13 indicates that compared with the other three methods DKM improves about 24%in accuracy,35%in precision,41%in recall,and 40%in F1 score,respectively.In general,DKM is an effective method for complex lithofacies identification.This work also discussed the optimal structure and classifier for DKM.Experimental re-sults show that(m_(1),m_(2),O)is the optimal model structure and linear svM is the optimal classifier.(m_(1),m_(2),O)means there are m KPCAs,and then m2 residual units.A workflow to determine an optimal classifier in DKM for lithofacies identification is proposed,too.

Fracture identification and evaluation using conventional logs in tight sandstones:A case study in the Ordos Basin,China

Fractures are of great significance to tight oil and gas development.Fracture identification using conventional well logs is a feasible way to locate the underground fractures in tight sandstones.However,there are three problems affecting its interpretation accuracy and practical application,namely weak well log responses of fractures,a lack of specific logs for fracture prediction,and relative change omission in log responses.To overcome these problems and improve fracture identification accuracy,a fracture indicating parameter(FIP)method composed of a comprehensive index method(CIM)and a comprehensive fractal method(CFM)is introduced.The CIM tries to handle the first problem by amplifying log responses of fractures.The CFM addresses the third one using fractal dimensions.The flexible weight parameters corresponding to logs in the CIM and CFM make the interpretation possible for wells lacking specific logs.The reconstructed logs in the CIM and CFM try to solve the second problem.It is noted that the FIP method can calculate the probability of fracture development at a certain depth,but cannot show the fracture development degree of a new well compared with other wells.In this study,a formation fracture intensity(FFI)method is also introduced to further evaluate fracture development combined with production data.To test the validity of the FIP and FFI methods,fracture identification experiments are implemented in a tight reservoir in the Ordos Basin.The results are consistent with the data of rock core observation and production,indicating the proposed methods are effective for fracture identification and evaluation.