Acoustic reflection well logging modeling using the frequency-domain finite-element method with a hybrid PML

In this paper, we propose a hybrid PML （H-PML） combining the normal absorption factor of convolutional PML （C-PML） with tangential absorption factor of Mutiaxial PML （M-PML）. The H-PML boundary conditions can better suppress the numerical instability in some extreme models, and the computational speed of finite-element method and the dynamic range are greatly increased using this HPML. We use the finite-element method with a hybrid PML to model the acoustic reflection of the interface when wireline and well logging while drilling （LWD）, in a formation with a reflector outside the borehole. The simulation results suggests that the PS- and SP- reflected waves arrive at the same time when the inclination between the well and the outer interface is zero, and the difference in arrival times increases with increasing dip angle. When there are fractures outside the well, the reflection signal is clearer in the subsequent reflection waves and may be used to identify the fractured zone. The difference between the dominant wavelength and the model scale shows that LWD reflection logging data are of higher resolution and quality than wireline acoustic reflection logging.

Comparison of different inversion methods of D-T_(2)two-dimensional nuclear magnetic resonance logging and applicability analysis

D-T_(2)two-dimensional nuclear magnetic resonance(2D NMR)logging technology can distinguish pore fluid types intuitively,and it is widely used in oil and gas exploration.Many 2D NMR inversion methods(e.g.,truncated singular value decomposition(TSVD),Butler-Reds-Dawson(BRD),LM-norm smoothing,and TIST-L1 regularization methods)have been proposed successively,but most are limited to numerical simulations.This study focused on the applicability of different inversion methods for NMR logging data of various acquisition sequences,from which the optimal inversion method was selected based on the comparative analysis.First,the two-dimensional NMR logging principle was studied.Then,these inversion methods were studied in detail,and the precision and computational efficiency of CPMG and diffusion editing(DE)sequences obtained from oil-water and gas-water models were compared,respectively.The inversion results and calculation time of truncated singular value decomposition(TSVD),Butler-Reds-Dawson(BRD),LM-norm smoothing,and TIST-L1 regularization were compared and analyzed through numerical simulations.The inversion method was optimized to process SP mode logging data from the MR Scanner instrument.The results showed that the TIST-regularization and LM-norm smoothing methods were more accurate for the CPMG and DE sequence echo trains of the oil-water and gas-water models.However,the LM-norm smoothing method was less time-consuming,making it more suitable for logging data processing.A case study in well A25 showed that the processing results by the LM-norm smoothing method were consistent with GEOLOG software.This demonstrates that the LM-norm smoothing method is applicable in practical NMR logging processing.

Self-adaptive inversion method of electromagnetic-propagation resistivity logging while drilling data

Because of the constraint mode of the inversion objective function in the traditional resistivity-inversion method of electromagnetic-propagation resistivity logging while drilling(EPR-LWD),obvious differences appear in the radial and vertical investigation characteristics between the amplitude-ratio and phase difference,which affect the practical application of EPR-LWD data.In this paper,according to the EPR-LWD data,a self-adaptive constraint resistivity-inversion method,which adopts a self-adaptive constraint weighted expression in the objective function to balance the contributions of the phase difference and amplitude attenuation,is proposed.A particle swarm optimization algorithm is also introduced to eliminate the dependence of the accuracy and convergence on the initial value of the inversion.According to the inversion results of multiple classical formation models for EPR-LWD,the differences between the adaptive constraint inversion-resistivity logs with the traditional amplitude-ratio and the phase difference of the resistivity logs are discussed in detail.The results demonstrate that the adaptive resistivity logs take into account the advantages of the amplitude-ratio logs in the radial investigation and phase difference logs in the vertical resolution.Further,it is superior in thin-layer identification and invasion-effect appraisal compared with the single-amplitude-ratio and phase difference logs.The inversion results can provide a theoretical reference for research on the resistivity-inversion method of electromagnetic wave LWD.

A SPECTRAL STREAMLINE DIFFUSION FINITE ELEMENT COUPLING METHOD OF UNSTEADY TRANSPORT EQUATION IN THE FIELD OF NEUTRON LOGGING

In this paper, a new numerical method, the coupling method of spherical harmonic function spectral and streamline diffusion finite element for unsteady Boltzmann equation in the neutron logging field, is discussed. The convergence and error estimations of this scheme are proved. Its applications in the field of neutron logging show its effectiveness.

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.

Corrections for downhole NMR logging

Nuclear magnetic resonance logging （NMR） is an open well logging method. Drilling mud resistivity, formation resistivity and sodium ions influence its radio frequency （RF） field strength and NMR logging signals. Research on these effects can provide an important basis for NMR logging data acquisition and interpretation. Three models, water-based drilling mud--water bearing formation, water- based drilling mud--oil bearing formation, oil-based drilling mud--water bearing formation, were studied by finite element method numerical simulation. The influences of drilling mud resistivity and formation resistivity on the NMR logging tool RF field and the influences of sodium ions on the NMR logging signals were simulated numerically. On the basis of analysis, RF field correction and sodium ion correction formulae were proposed and their application range was also discussed. The results indicate that when drilling mud resistivity and formation resistivity are 0.02 Ω·m and 0.2 Ω·m respectively, the attenuation index of centric NMR logging tool is 8.9% and 9.47% respectively. The RF field of an eccentric NMR logging tool is affected mainly by formation resistivity. When formation resistivity is 0.1 Ω·m, the attenuation index is 17.5%. For centric NMR logging tools, the signals coming from sodium ions can be up to 31.8% of total signal. Suggestions are proposed for further research into NMR logging tool correction method and response characteristics.

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.

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.

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.

Study on Calculation Method of Compressional Velocities Based on Field Well Logs

In the past, most of the studies for compressional velocities are based on experimental measurements, which lack the support of field data. The purpose of this study is to estimate the compressional velocities based on well log data of delta front subfacies of Lower Tertiary ages of Ji-Dong oil field, China. At initial stage, we have chosen the well log parameters (effect factors) which strongly influence on compressional velocities and established a new modified equation for compressional velocities, which is based on these effect factors. Then Gardner, De-hua Han and this newly established equation were utilized to calculate the compressional velocities in each well. Finally, Least-square regression was carried out to check the fitting of each equation. Regression results clearly indicate that our purposed equation shows better fitting as compared to Gardner and De-hua Han equations.

A new method for fluid identification and saturation calculation of low contrast tight sandstone reservoir

The resistivity difference between oil and gas layers and the water layers in low contrast tight sandstone reservoirs is subtle. Fluid identification and saturation calculation based on conventional logging methods are facing challenges in such reservoirs. In this paper, a new method is proposed for fluid identification and saturation calculation in low contrast tight sandstone reservoirs. First, a model for calculating apparent formation water resistivity is constructed, which takes into account the influence of shale on the resistivity calculation and avoids apparent formation water resistivity abnormal values.Based on the distribution of the apparent formation water resistivity obtained by the new model, the water spectrum is determined for fluid identification in low contrast tight sandstone reservoirs.Following this, according to the average, standard deviation, and endpoints of the water spectrum, a new four-parameter model for calculating reservoir oil and gas saturation is built. The methods proposed in this paper are applied to the low contrast tight sandstone reservoirs in the Q4 formation of the X53 block and X70 block in the south of Songliao Basin, China. The results show that the water spectrum method can effectively distinguish oil-water layers and water layers in the study area. The standard deviation of the water spectrum in the oil-water layer is generally greater than that in the water layer. The new four-parameter model yields more accurate oil and gas saturation. These findings verify the effectiveness of the proposed methods.

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。

“反推ΔlogR”法及其在生油岩有机质丰度评价中的应用

测井方法是非均质生油岩精细评价的常用方法,但现有的评价方法对生油岩成熟度及密度等因素考虑不全。文中提出了综合考虑生油岩成熟度和各种测井响应的"反推ΔlogR"法,并以Kar7井为例,应用该方法计算了2285～2985m内生油岩的有机碳含量,计算结果与实测值较为吻合,证明了该方法的正确性和可行性。

Turbo译码中的Log-MAP折线逼近法

为了在降低Turbo系统译码延时的同时仍保证其优良的低误比特率性能,本文提出折线逼近法方案。该方案只用比较器和加减法,就能在保证Turbo码低误比特率的前提下,有效降低系统延时和译码复杂度。仿真结果表明:折线逼近法的译码性能大大优于Max-Log-MAP方法,同时比二次幂逼近算法降低1.5倍以上的系统延时。

UMTS Turbo码的滑动窗-线性-Log-MAP算法及VLSI设计

讨论UMTS中turbo码的设计,提出采用前向状态度量作为初始化精确值的滑动窗方法和改进的Log-MAP(线性Log-MAP)相结合的算法(称为滑动窗-线性-Log-MAP算法),设计出turbo码的VLSI结构,且在归一化处理时,介绍了一种可节约12.5%存储量的方法。FPGA设计结果显示在BER=10-5时,与最优算法Log-MAP相比,性能损失0.2dB,硬件资源节约30%,译码时延小,吞吐量大。

有风险控制的log-最优投资组合问题的一个黎曼几何随机算法

针对有风险控制的log-最优投资组合问题,提出了一个自适应的随机算法.该算法通过引进松弛变量,把对风险控制的不等式约束化为等式约束;再通过引进罚参数,运用罚函数法对风险控制的等式约束进行处理,从而将原来的问题化为一系列新的随机优化问题,再利用黎曼流形上的随机优化算法对其进行自适应求解.最后,使用该算法对上海证券交易所的实际数据进行了模拟计算,得到了很好的计算效果.

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.

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.

基于改进ΔlogR方法估算低阻海相页岩储层总有机碳含量——以桂中坳陷A地区B组为例

总有机碳含量(total organic carbon,TOC)是确定烃源岩质量的最重要参数。为解决传统ΔlogR方法应用于沉积环境较复杂的海相页岩气储层精度低的问题,以桂中坳陷A区B组海相页岩气储层为研究对象,在分析研究区低阻原因的基础上,对测井曲线按照扩径范围进行井径校正,并利用自然伽马能谱测井中Th/K数据,与ΔlogR建立多元拟合关系,提出基于井径校正和自然伽马能谱测井的改进ΔlogR方法。结果表明:声波时差曲线经过校正后,降低了井径扩径带来的测井数据失真的影响,同时结合自然伽马能谱测井,能够适应研究区复杂的地质背景和较强的非均质性,大大提高总有机碳质量分数的预测精度,具有较好的推广性和适用性。该方法可以为桂中坳陷A区B组海相页岩气储层评价提供参考。

Application of well log normalization in coalfield seismic inversion

During the process of coal prospecting and exploration, different measurement time, different logging instruments and series can lead to systematic errors in well logs. Accordingly, all logging curves need to be normalized in the mining area. By studying well-logging normalization methods, and focusing on the characteristics of the coalfield, the frequency histogram method was used in accordance with the condition of the Guqiao Coal Mine. In this way, the density and sonic velocity at marker bed in the non-key well were made to close to those in the key well, and were eventually equal. Well log normalization was completed when this method was applied to the entire logging curves. The results show that the scales of logging data were unified by normalizing coal logging curves, and the logging data were consistent with wave impedance inversion data. A satisfactory inversion effect was obtained.