Simulation logging experiment and interpretation model of array production logging measurements in a horizontal well

The distributions of local velocity and local phase holdup along the radial direction of pipes are complicated because of gravity differentiation,and the distribution of fluid velocity fi eld changes along the gravity direction in horizontal wells.Therefore,measuring the mixture flow and water holdup is difficult,resulting in poor interpretation accuracy of the production logging output profile.In this paper,oil–water two-phase flow dynamic simulation logging experiments in horizontal oil–water two-phase fl ow simulation wells were conducted using the Multiple Array Production Suite,which comprises a capacitance array tool(CAT)and a spinner array tool(SAT),and then the response characteristics of SAT and CAT in diff erent fl ow rates and water cut production conditions were studied.According to the response characteristics of CAT in diff erent water holdup ranges,interpolation imaging along the wellbore section determines the water holdup distribution,and then,the oil–water two-phase velocity fi eld in the fl ow section was reconstructed on the basis of the fl ow section water holdup distribution and the logging value of SAT and combined with the rheological equation of viscous fl uid,and the calculation method of the oil–water partial phase fl ow rate in the fl ow section was proposed.This new approach was applied in the experiment data calculations,and the results are basically consistent with the experimental data.The total fl ow rate and water holdup from the calculation are in agreement with the set values in the experiment,suggesting that the method has high accuracy.

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

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

Gas hydrate saturation model and equation for logging interpretation

Since gas hydrate exists in three different forms at the same time such as pore filling,particle support and separate stratification,the calculation method of hydrate saturation using traditional shaly sand formation interpretation models is equivalent to considering only the simple case that hydrate exists as pore filling,and does not consider other complex states.Based on the analysis of hydrate resistivity experimental data and the general form of the resistivity-oil(gas)saturation relationship,the best simplified formula of hydrate saturation calculation is derived,then the physical meaning of the three items are clarified:they respectively represent the resistivity index-saturation relationship when hydrate particles are completely distributed in the pores of formation rocks,supported in the form of particles,and exist in layers,corresponding quantitative evaluation method of hydrate saturation is built.The field application shows that the hydrate saturation calculated by this method is closer to that obtained by sampling analysis.At the same time,it also provides a logging analysis basis for the effective development after hydrate exploration.

Earthquake-triggered landslide interpretation model of high resolution remote sensing imageries based on bag of visual word

Traditional visual interpretation is often inefficient due to its excessively workload professional knowledge and strong subjectivity.Therefore,building an automatic interpretation model on high spatial resolution remote sensing images is the key to the quick and efficient interpretation of earthquake-triggered landslides.Aiming at addressing this problem,a landslide interpretation model of high-resolution images based on bag of visual word(BoVW)feature was proposed.The high-resolution images were pre-processed,and then BoVW feature and support vector machine(SVM)was adopted to establish an automatic landslide interpretation model.This model was further compared with the currently widely used Histogram of Oriented Gradient(HoG)feature extraction model.In order to test the effectiveness of the method,typical landslide images were selected to construct a landslide sample library,which was subsequently utilized as the foundation for conducting an experimental study.The results show that the accuracy of landslide extraction using this method reaches as high as 89%,indicating that the method can be used for the automatic interpretation of landslides in disaster-prone areas,and has high practical value for regional disaster prevention and damage reduction.

Data mining and well logging interpretation: application to a conglomerate reservoir

Data mining is the process of extracting implicit but potentially useful information from incomplete, noisy, and fuzzy data. Data mining offers excellent nonlinear modeling and self-organized learning, and it can play a vital role in the interpretation of well logging data of complex reservoirs. We used data mining to identify the lithologies in a complex reservoir. The reservoir lithologies served as the classification task target and were identified using feature extraction, feature selection, and modeling of data streams. We used independent component analysis to extract information from well curves. We then used the branch-and- bound algorithm to look for the optimal feature subsets and eliminate redundant information. Finally, we used the C5.0 decision-tree algorithm to set up disaggregated models of the well logging curves. The modeling and actual logging data were in good agreement, showing the usefulness of data mining methods in complex reservoirs.

AN EFFICIENT NUMERICAL MODELING OF THE DC RESISTIVITY LOGGING: THEORY AND APPLICATIONS

The numerical solution of Green’s function for the potential in 2-D arbitrary in-homogeneous media with axial symmetry has been given by use of efficient half-analytical, half-numerical hybrid method. Then the logging responses of various kinds of the DC resistivity log with axisymmetric excitation have been obtained by using surface integral equation method to match the boundary conditions on the electrodes of the logging sonde. Comparing the results with that obtained by other methods, one can see good precision and efficiency of the given method. Some applications of the numerical modeling have been also discussed.

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.

CCSD Well Logging Engineering Program

This paper introduces briefly the tasks and characteristics of China Continent Science Drilling (CCSD) Well Logging Engineering, the logging methods measured with CCSD, the quality control of original logging information, the logging plan of CCSD, the logging engineering management of CCSD, the logging interpretation and the results and reports made with CCSD.

Optimized Non-hyperbolic Stack Imaging Based on Interpretation Model

In complex media, especially for seismic prospecting in deep layers in East China and in the mountainous area in West China, due to the complex geological condition, the common-mid-point （CMP） gather of deep reflection event is neither hyperbolic, nor any simple function. If traditional normal move-out （NMO） and stack imaging technology are still used, it is difficult to get a clear stack image. Based on previous techniques on non-hyperbolic stack, it is thought in this paper that no matter how complex the geological condition is, in order to get an optimized stack image, the stack should be non time move-out stack, and any stacking method limited to some kind of curve will be restricted to application conditions. In order to overcome the above-mentioned limit, a new method called optimized non-hyperbolic stack imaging based on interpretation model is presented in this paper. Based on CMP/CRP （Common-Reflection-Point） gather after NMO or pre-stack migration, this method uses the interpretation model of reflectors as constraint, and takes comparability as a distinguishing criterion, and finally forms a residual move-out correction for the gather of constrained model. Numerical simulation indicates that this method could overcome the non hyperbolic problem and get fine stack image.

Experimental study of gas-water elongated bubble flow during production logging

In order to improve the interpretation of production log data on gas-water elongated bubble （EB） flow in horizontal wells, a multi-phase flow simulation device was set up to conduct a series of measurement experiments using air and tap water as test media, which were measured using a real production logging tool （PLT） string at different deviations and in different mixed flow states. By understanding the characteristics and mechanisms of gas-water EB flow in transparent experimental boreholes during production logging, combined with an analysis of the production log response characteristics and experimental production logging flow pattern maps, a method for flow pattern identification relying on log responses and a drift-flux model were proposed for gas-water EB flow. This model, built upon experimental data of EB flow, reveals physical mechanisms of gas-water EB flow during measurement processing. The coefficients it contains are the specific values under experimental conditions and with the PLT string used in our experiments. These coefficients also reveal the interference with original downhole flow patterns by the PLT string. Due to the representativeness that our simulated flow experiments and PLT string possess, the model coefficients can be applied as empirical values of logging interpretation model parameters directly to real production logging data interpretation, when the measurement circumstances and PLT strings are similar.

Flow pattern identification in oil wells by electromagnetic image logging

Petroleum production logging needs to determine the interpretation models first and flow pattern identification is the foundation, but traditional flow pattern identification methods have some limitations. In this paper, a new method of flow pattern identification in oil wells by electromagnetic image logging is proposed. First, the characteristics of gas-water and oil-water flow patterns in horizontal and vertical wellbores are picked up. Then, the continuous variation of the two phase flow pattern in the vertical and horizontal pipe space is discretized into continuous fluid distribution models in the pipeline section. Second, the electromagnetic flow image measurement responses of all the eight fluid distribution models are simulated and the characteristic vector of each response is analyzed in order to distinguish the fluid distribution models. Third, the time domain changes of the fluid distribution models in the pipeline section are used to identify the flow pattern. Finally, flow simulation experiments using electromagnetic flow image logging are operated and the experimental and simulated data are compared. The results show that the method can be used for flow pattern identification of actual electromagnetic image logging data.

Production prediction for fracture-vug carbonate reservoirs using electric imaging logging data

Considering the fluid flow non-darcy characteristics in fracture-vug carbonate reservoirs, a new multi-scale conduit flow model production prediction method for fracture-vug carbonate reservoirs was presented using image segmentation technique of electric imaging logging data. Firstly, based on Hagen-Poiseuille's law of incompressible fluid flow and the different cross-sectional areas in single fractures and vugs in carbonate reservoirs, a multi-scale conduit flow model for fracture-vug carbonate reservoir was established, and a multi-scale conduit radial fluid flow equation was deduced. Then, conduit flow production index was introduced. The conduit flow production index was calculated using fracture-vug area extracted from the result of electrical image segmentation. Finally, production prediction of fracture-vug carbonate reservoir was realized by using electric imaging logging data. The method has been applied to Ordovician fracture-vug carbonate reservoirs in the Tabei area, and the predicted results are in good agreement with the oil testing data.

Well Logging and Perforation Technology in China

Well logging, Perforation, Data analysis, Interpretation

Countermeasure Research on the Drainage and Water-logging Prevention of Coastal Cities:A Case Study of Yingping District in Xiamen

According to the drainage problems emerging from the several torrential rainstorm in domestic coastal cities in the last two years, especially the issues that the drainage and waterlogging prevention of coastal cities are susceptible to the tide level of open seas, this paper took the reconstruction of Yingping District in Xiamen, a typical coastal area, as a case study, analyzed the main waterlogging causes of the district under the influences of the average annual tide level and storm tide caused by typhoon with the application of drainage model, and came up with reasonable implementation strategies for reconstruction;besides, this paper also assessed and divided the waterlogging risks of the district under the influences of storm tide and applied engineering measures as well as non-engineering measures to prevent the urban cities from water-logging hazards. It is expected that the study would provide reference for the reconstruction of coastal cities in drainage and water-logging prevention.

A multi-mineral model for predicting petrophysical properties of complex metamorphic reservoirs:Case study of the Bozhong Depression,Bohai Sea

Interpreting reservoir properties through log data and logging responses in complex strata is critical for efficient petroleum exploitation,particularly for metamorphic rocks.However,the unsatisfactory accuracy of such interpretations in complex reservoirs has hindered their widespread application,resulting in severe inconvenience.In this study,we proposed a multi-mineral model based on the least-square method and an optimal principle to interpret the logging responses and petrophysical properties of complex hydrocarbon reservoirs.We began by selecting the main minerals based on a comprehensive analysis of log data,X-ray diffraction,petrographic thin sections and scanning electron microscopy(SEM)for three wells in the Bozhong 19-6 structural zone.In combination of the physical properties of these minerals with logging responses,we constructed the multi-mineral model,which can predict the log curves,petrophysical properties and mineral profile.The predicted and measured log data are evaluated using a weighted average error,which shows that the multi-mineral model has satisfactory prediction performance with errors below 11%in most intervals.Finally,we apply the model to a new well“x”in the Bozhong 19-6 structural zone,and the predicted logging responses match well with measured data with the weighted average error below 11.8%for most intervals.Moreover,the lithology is dominated by plagioclase,K-feldspar,and quartz as shown by the mineral profile,which correlates with the lithology of the Archean metamorphic rocks in this region.It is concluded that the multi-mineral model presented in this study provides reasonable methods for interpreting log data in complex metamorphic hydrocarbon reservoirs and could assist in efficient development in the future.

基于改进24Model-ISM-SNA建筑工人不安全行为关联路径研究

建筑施工现场环境复杂,为有效控制不安全行为发生,基于行为安全“2-4”模型对360份具有代表性的建筑安全事故调查报告进行分析,提取出22个不安全行为的主要影响因素。利用灰色关联分析方法(GRA)改进的集成ISM-SNA模型,将不安全行为风险因素划分为表层、过渡层与深层,然后对风险因素进行可视化分析、中心度分析及凝聚子群分析,揭示了各致因因素间的关联关系和传导路径。结果表明,建筑工人不安全行为影响因素可划分成7级3阶的多级递阶结构,安全意识、现场监管、外部环境是建筑工人不安全行为的关键影响因素,同时现场监管和隐患排查到位能有效降低不安全行为的发生。

基于24Model-D-ISM的地铁站火灾疏散影响因素研究

为预防地铁站火灾事故,深入了解地铁站火灾人员疏散影响因素间的内在联系与层次结构,基于第6版“2-4”模型(24Model)分析63起地铁站火灾疏散事故,充分考虑各个因素之间的交互作用,提取19个影响地铁站人员疏散的关键因素,建立地铁站火灾人员疏散影响因素指标体系;采用算子客观赋权法(C-OWA)改进决策试验与评价实验法(DEMATEL),确定地铁站火灾人员疏散的重要影响因素;在此基础上,采用解释结构模型(ISM)分析各个因素间的层次结构及相互作用路径,构建地铁站火灾人员疏散影响因素的多级递阶结构模型。研究结果表明:疏散引导、恐慌从众行为、人员拥挤为地铁站火灾人员疏散的关键影响因素;地铁站火灾人员疏散受表层因素、中间层因素、深层因素共同作用的影响,其中,疏散教育与培训、设施维护与检查、疏散预案等因素是根源影响因素,重视根源影响因素的改善有利于从本质上预防和控制事故的发生。

Artificial intelligence-driven radiomics study in cancer:the role of feature engineering and modeling

Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’anatomy.However,the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians.Moreover,some potentially useful quantitative information in medical images,especially that which is not visible to the naked eye,is often ignored during clinical practice.In contrast,radiomics performs high-throughput feature extraction from medical images,which enables quantitative analysis of medical images and prediction of various clinical endpoints.Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis,demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine.However,radiomics remains in a developmental phase as numerous technical challenges have yet to be solved,especially in feature engineering and statistical modeling.In this review,we introduce the current utility of radiomics by summarizing research on its application in the diagnosis,prognosis,and prediction of treatment responses in patients with cancer.We focus on machine learning approaches,for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling.Furthermore,we introduce the stability,reproducibility,and interpretability of features,and the generalizability and interpretability of models.Finally,we offer possible solutions to current challenges in radiomics research.

An attention-based teacher-student model for multivariate short-term landslide displacement prediction incorporating weather forecast data

Predicting the displacement of landslide is of utmost practical importance as the landslide can pose serious threats to both human life and property.However,traditional methods have the limitation of random selection in sliding window selection and seldom incorporate weather forecast data for displacement prediction,while a single structural model cannot handle input sequences of different lengths at the same time.In order to solve these limitations,in this study,a new approach is proposed that utilizes weather forecast data and incorporates the maximum information coefficient(MIC),long short-term memory network(LSTM),and attention mechanism to establish a teacher-student coupling model with parallel structure for short-term landslide displacement prediction.Through MIC,a suitable input sequence length is selected for the LSTM model.To investigate the influence of rainfall on landslides during different seasons,a parallel teacher-student coupling model is developed that is able to learn sequential information from various time series of different lengths.The teacher model learns sequence information from rainfall intensity time series while incorporating reliable short-term weather forecast data from platforms such as China Meteorological Administration(CMA)and Reliable Prognosis(https://rp5.ru)to improve the model’s expression capability,and the student model learns sequence information from other time series.An attention module is then designed to integrate different sequence information to derive a context vector,representing seasonal temporal attention mode.Finally,the predicted displacement is obtained through a linear layer.The proposed method demonstrates superior prediction accuracies,surpassing those of the support vector machine(SVM),LSTM,recurrent neural network(RNN),temporal convolutional network(TCN),and LSTM-Attention models.It achieves a mean absolute error(MAE)of 0.072 mm,root mean square error(RMSE)of 0.096 mm,and pearson correlation coefficients(PCCS)of 0.85.Additionally,it exhibits enhanced prediction stability and interpretability,rendering it an indispensable tool for landslide disaster prevention and mitigation.

Log interpretation of carbonate rocks based on petrophysical facies constraints

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