Research on Well Testing Interpretation of Low Permeability Deformed Dual Medium Reservoir

Considering the influence of quadratic gradient term and medium deformation on the seepage equation, a well testing interpretation model for low permeability and deformation dual medium reservoirs was derived and established. The difference method was used to solve the problem, and pressure and pressure derivative double logarithmic curves were drawn to analyze the seepage law. The research results indicate that the influence of starting pressure gradient and medium deformation on the pressure characteristic curve is mainly manifested in the middle and late stages. The larger the value, the more obvious the upward warping of the pressure and pressure derivative curve;the parameter characterizing the dual medium is the crossflow coefficient. The channeling coefficient determines the time and location of the appearance of the “concave”. The smaller the value, the later the appearance of the “concave”, and the more to the right of the “concave”.

A deep reinforcement learning(DRL)based approach for well-testing interpretation to evaluate reservoir parameters

Parameter inversions in oil/gas reservoirs based on well test interpretations are of great significance in oil/gas industry.Automatic well test interpretations based on artificial intelligence are the most promising to solve the problem of non-unique solution.In this work,a new deep reinforcement learning(DRL)based approach is proposed for automatic curve matching for well test interpretation,by using the double deep Q-network(DDQN).The DDQN algorithms are applied to train agents for automatic parameter tuning in three conventional well-testing models.In addition,to alleviate the dimensional disaster problem of parameter space,an asynchronous parameter adjustment strategy is used to train the agent.Finally,field applications are carried out by using the new DRL approaches.Results show that step number required for the DDQN to complete the curve matching is the least among,when comparing the naive deep Q-network(naive DQN)and deep Q-network(DQN).We also show that DDQN can improve the robustness of curve matching in comparison with supervised machine learning algorithms.Using DDQN algorithm to perform 100 curve matching tests on three traditional well test models,the results show that the mean relative error of the parameters is 7.58%for the homogeneous model,10.66%for the radial composite model,and 12.79%for the dual porosity model.In the actual field application,it is found that a good curve fitting can be obtained with only 30 steps of parameter adjustment.

Expert Consensus on Polymyxin Antimicrobial Susceptibility Testing and Clinical Interpretation

The polymyxins are important antimicrobial agents against antibiotic-resistant gram-negative bacilli.In 2020,the Clinical and Laboratory Standards Institute modified the clinical breakpoints for polymyxin susceptibility test by eliminating the"susceptible"interpretive category,only reporting intermediate(≤2 mg/L)and resistant(≥4 mg/L).However,the European Committee on Antimicrobial Susceptibility Testing recommended the use of clinical breakpoints of W2 mg/L as susceptible and>2 mg/L as resistant.The first-line laboratorians and clinicians in China have been perplexed by the inconsistence of international polymyxin clinical breakpoints and discouraged by the difficulty of conducting polymyxin susceptibility testing.Therefore,it is urgently needed to make it clear for the laboratorians in China to know how to accurately carry out polymyxin susceptibility testing and standardize the interpretation of susceptibility testing results.To this end,the experts from relevant fields were convened to formulate this consensus statement on the testing and clinical interpretation of polymyxin susceptibility.Relevant recommendations are proposed accordingly for laboratorians and clinicians to streamline their daily work.

Quantitative Interpretation for the Magnetic Flux Leakage Testing Data Based on Neural Network

In order to interpret the magnetic flux leakage (MFL) testing data quantitatively and size the defects accurately, some defect profiles inversion methods from the MFL signals are studied on the basis of the neural network.Because the wavelet ba- sis function neural network (WBFNN) has good accuracy in the forward calculation and the radial basis function neural network (RBFNN) has reliable precision in the inversion modeling respectively,a new neural network scheme combining WBFNN and RBFNN is presented to solve the nonlinear inversion problem for the MFL data and reconstruct the defect shapes.And such details as the choice of wavelet basis function,the initialization of the weight value and the input normalization are analyzed,the train- ing and testing algorithm for the network are also studied.The inversion results demonstrate that the proposed network scheme has good reliability to interpret the MFL data for some defects.

Automatic well test interpretation based on convolutional neural network for a radial composite reservoir

An automatic well test interpretation method for radial composite reservoirs based on convolutional neural network(CNN) is proposed, and its effectiveness and accuracy are verified by actual field data. In this paper, based on the data transformed by logarithm function and the loss function of mean square error(MSE), the optimal CNN is obtained by reducing the loss function to optimize the network with "dropout" method to avoid over fitting. The trained optimal network can be directly used to interpret the buildup or drawdown pressure data of the well in the radial composite reservoir, that is, the log-log plot of the given measured pressure variation and its derivative data are input into the network, the outputs are corresponding reservoir parameters(mobility ratio, storativity ratio, dimensionless composite radius, and dimensionless group characterizing well storage and skin effects), which realizes the automatic initial fitting of well test interpretation parameters. The method is verified with field measured data of Daqing Oilfield. The research shows that the method has high interpretation accuracy, and it is superior to the analytical method and the least square method.

A new interpretation and quantitative method for diplopia test： 304 cases of ocular motor nerve palsy for clinical test and verify

We introduce a new interpretation and quantitative method for computerized diplopia test. By comparing this new method to the Hess screen test, we validate its applicability among 304 patients with ocular motor nerve palsy. This new method shows great assistant value as the Hess screen test in making accurate diagnosis and quantitative evaluation the severity of diplopia. Furthermore, it is more convenient and suitable for daily clinical use.

Fracture network types revealed by well test curves for shale reservoirs in the Sichuan Basin,China

Pressure buildup testing can be used to analyze fracture network characteristics and conduct quantitative interpretation of relevant parameters for shale gas wells,thus providing bases for assessing the well productivity and formulating proper development strategies.This study establishes a new well test interpretation model for fractured horizontal wells based on seepage mechanisms of shale reservoirs and proposes a method for identifying fracturing patterns based on the characteristic slopes of pressure buildup curves and curve combination patterns.The pressure buildup curve patterns are identified to represent three types of shale reservoirs in the Sichuan Basin,namely the moderately deep shale reservoirs with high pressure,deep shale reservoirs with ultra-high pressure,and moderately deep shale reservoirs with normal pressure.Based on this,the relationship between the typical pressure buildup curve patterns and the fracture network types are put forward.Fracturing effects of three types of shale gas reservoir are compared and analyzed.The results show that typical flow patterns of shale reservoirs include bilinear flow in primary and secondary fractures,linear flow in secondary fractures,bilinear flow in secondary fractures and matrix,and linear flow in matrix.The fracture network characteristics can be determined using the characteristic slopes of pressure buildup curves and curve combinations.The linear flow in early secondary fractures is increasingly distinct with an increase in primary fracture conductivity.Moreover,the bilinear flow in secondary fractures and matrix and the subsequent linear flow in the matrix occur as the propping and density of secondary fractures increase.The increase in the burial depth,in-situ stress,and stress difference corresponds to a decrease in the propping of primary fractures that expand along different directions in the shale gas wells in the Sichuan Basin.Four pressure buildup curve patterns exist in the Sichuan Basin and its periphery.The pattern of pressure buildup curves of shale reservoirs in the Yongchuan area can be described as 1/2/→1/4,indicating limited stimulated reservoir volume,poorly propped secondary fractures,and the forming of primary fractures that extend only to certain directions.The pressure buildup curves of shale reservoirs in the main block of the Fuling area show a pattern of 1/4/→1/2 or 1/2,indicating greater stimulated reservoir volume,well propped secondary fractures,and the forming of complex fracture networks.The pattern of pressure buildup curves of shale reservoirs in the Pingqiao area is 1/2/→1/4→/1/2,indicating a fracturing effect somewhere between that of the Fuling and Yongchuan areas.For reservoirs with normal pressure,it is difficult to determine fracture network characteristics from pressure buildup curves due to insufficient formation energy and limited liquid drainage.

Some Analyses Concerning Interpretation Quality Assessment Criteria——A Case Study of TEM8 Interpretation Test

his paper,through analyzing the current TEM8 interpretation test,concludes that the present TEM 8 interpretation test has various unsatisfactory points in its interpretation quality assessment criteria.Therefore,in light of the interpretation quality assessment criteria proposed by Butler,this paper put forwards some constructive suggestions in this aspect,which will be helpful to get better and fair assessment results.

Current scenario of the genetic testing for rare neurological disorders exploiting next generation sequencing

Next generation sequencing is currently a cornerstone of genetic testing in routine diagnostics,allowing for the detection of sequence variants with so far unprecedented large scale,mainly in genetically heterogenous diseases,such as neurological disorders.It is a fast-moving field,where new wet enrichment protocols and bioinformatics tools are constantly being developed to overcome initial limitations.Despite the as yet undiscussed advantages,however,there are still some challenges in data analysis and the interpretation of variants.In this review,we address the current state of next generation sequencing diagnostic testing for inherited human disorders,particularly giving an overview of the available high-throughput sequencing approaches;including targeted,whole-exome and whole-genome sequencing;and discussing the main critical aspects of the bioinformatic process,from raw data analysis to molecular diagnosis.

Computer-Aided Design of X-Ray Microtomographic Scanners

The article is to study the development of computer-aided design of X-ray microtomography—the device for investigating the structure and construction of three-dimensional images of organic and inorganic objects on the basis of shadow projections. This article provides basic information regarding CAD of X-ray microtomography and a scheme consisting of three levels. The article also shows basic relations of X-ray computed tomography, the generalized scheme of an X-ray microtomographic scanner. The methods of X-ray imaging of the spatial microstructure and morphometry of materials are described. The main characteristics of an X-ray microtomographic scanner, the X-ray source, X-ray optical elements and mechanical components of the positioning system are shown. The block scheme and software functional scheme for intelligent neural network system of analysis of the internal microstructure of objects are presented. The method of choice of design parameters of CAD of X-ray microtomography aims at improving the quality of design and reducing costs of it. It is supposed to reduce the design time and eliminate the growing number of engineers involved in development and construction of X-ray microtomographic scanners.

X-Ray Image Interpretation Guide for Aviation Aluminum Alloy Castings

The purpose of this paper is to introduce the basic steps of X-ray image interpretation of aviation castings,so as to provide a learning guidance for beginners and reduce the exploration time.Among them,the introduction of specifications,the requirements for image quality,and the application of reference radiographs are just a sorting out the key points.If there is a need for practical application,we should conduct a deeper exploration and understanding of each specification,not just cite this article.

Interpretation of Groundwater Flow into Fractured Aquifer

The region of investigation is part of the western desert of Iraq covering an area of about 12,400 km2, this region includes several large wadis discharging to the Euphrates River. Since the Tectonic features in particular fault zones play a significant role with respect to groundwater flow in hard rock terrains. The present research is focus on investigate lineaments that have been classified as suspected faults by means of remote sensing techniques and digital terrain evaluation in combination with interpolating groundwater heads and MLU pumping tests model in a fractured rock aquifer, Lineaments extraction approach is illustrated a fare matching with suspected faults, moreover these lineaments conducted an elevated permeability zone.

双封隔取样压力数据深入解释方法研究

双封隔器是一种地层测试测压取样工具,取样过程通常需要泵抽几小时甚至十几小时,取得地层流体样品,获得压力和流量数据,但无法真实反映地层渗透率的探测范围。利用油藏模拟软件模拟双封隔器泵抽取样过程,引入Schroeter算法和Levitan算法这两种压力/流量数据反褶积算法,反演渗透率参数波及半径从几厘米到上百米,并使用数值模拟案例验证了只利用取样完成后的压力恢复数据进行反褶积方法的准确性和合理性。该研究应用于双封隔现场测井数据,采用反褶积方法得到渗透率探测半径31m,反褶积处理后的数据可用于产能预测,对正确认识油气藏具有重要意义。

基于停泵压降曲线的压裂窜扰效果定量评价--以吉木萨尔页岩油平台井为例

针对目前缺乏压裂窜扰效果定量评价方法的难题,创新提出一套基于停泵压降曲线的压裂窜扰效果诊断分析方法。结合井组拉链式压裂工艺,提出了4种典型窜扰模式下的停泵压降曲线,用以诊断压裂窜扰类型。选取吉木萨尔页岩油A平台6个典型压裂窜扰段开展实例应用,通过与微地震监测结果对比,验证了方法的有效性。在此基础上,利用压降试井解释方法对吉木萨尔“新井组内部压窜”和“新老井间压窜”两种典型的停泵压降曲线开展拟合分析,反演解释缝网参数。解释结果表明,新井组内部压窜使主次裂缝的改造效果均受到影响,具体影响效果因窜扰类型的不同而存在差异;新老井间压窜主要影响主裂缝的改造效果,导致缝网由“长宽”向“短窄”转变,有效体积降低28%,整体改造效果变差。

断溶体油藏井洞垂向窜流试井解释模型

断溶体油气藏储层类型多样,非均质性极强,常规多重孔隙介质模型以及简单溶洞等势体模型难以广泛应用。文中针对断溶体油藏复杂的地质特征,考虑大溶洞向井筒的垂向窜流以及小尺度缝洞储集体分别向井筒和大溶洞的径向供给作用,建立了双层双重孔隙介质井洞垂向窜流试井解释模型;利用Laplace变换方法获得井底压力解,绘制井底压力典型曲线并划分流动段,对窜流通道传导率、大溶洞半径、裂缝传导系数比等参数进行了敏感性分析。结果表明:窜流通道传导率主要影响早期窜流“下凹”特征,数值过大或过小均会导致早期窜流“下凹”特征减弱甚至消失;大溶洞半径与大溶洞储容比也主要影响早期窜流“下凹”特征,数值越大,“下凹”特征越明显;裂缝系统传导系数比和裂缝储容比主要影响中、晚期的窜流“下凹”特征。该研究结果对复杂断溶体油藏储集体参数评价具有重要意义。

IATG:基于解释分析的自动驾驶软件测试方法

以深度神经网络(deep neural network,DNN)为基础构建的自动驾驶软件已成为最常见的自动驾驶软件解决方案.与传统软件一样,DNN也会产生不正确输出或意想不到的行为,基于DNN的自动驾驶软件已经导致多起严重事故,严重威胁生命和财产安全.如何有效测试基于DNN的自动驾驶软件已成为亟需解决的问题.由于DNN的行为难以预测和被人类理解,传统的软件测试方法难以适用.现有的自动驾驶软件测试方法通常对原始图片加入像素级的扰动或对图片整体进行修改来生成测试数据,所生成的测试数据通常与现实世界差异较大,所进行扰动的方式也难以被人类理解.为解决上述问题,提出测试数据生成方法IATG(interpretability-analysis-based test data generation),使用DNN的解释方法获取自动驾驶软件所做出决策的视觉解释,选择原始图像中对决策产生重要影响的物体,通过将其替换为语义相同的其他物体来生成测试数据,使生成的测试数据更加接近真实图像,其过程也更易于理解.转向角预测模型是自动驾驶软件决策模块重要组成部分,以此类模型为例进行实验,结果表明解释方法的引入有效增强IATG对转向角预测模型的误导能力.此外,在误导角度相同时IATG所生成测试数据比DeepTest更加接近真实图像;与semSensFuzz相比,IATG具有更高误导能力,且IATG中基于解释分析的重要物体选择技术可有效提高semSensFuzz的误导能力.

Automatic Control of Automobile Transmission Performance Test Stand

To realize automatic control of automobile transmission performance test stand Methods The automatic control technique of the lubricant temperature,the program- controll- edautomaticshifting of the transmission,the continuous adjusting of revolution speed and load, data-acquisition and data real-time processing were adopted.Results The lubricant temperature was controlled at the set temperature ±2℃.The automatic shifting of the trans- mission is simple,reliable and accurate.The automatic adjusting of load and rotation speed is rapidandaccurate,the torque divergence is ±1N·m,the rotation speed divergence is ±5r/min Conclusion The four kinds of techniques are applied into the automobile transmission perfor- mance test stand successfully. mancetest stand successfully.

一种基于一维卷积神经网络的试井模型智能识别方法

为提高试井分析工作效率,实现试井模型的自动识别,提出了基于一维卷积神经网络(1D CNN)的试井模型智能识别方法。根据实测数据的特点,提出基于理论曲线构建样本库的原则与方法,并构建了4种常用油藏模型的训练样本库;建立了一维卷积神经网络模型,将样本库中双对数曲线的压力变化和压力导数数据作为输入,油藏类别作为网络输出训练及优化网络,总识别准确率可达99.16%,敏感度均在98%以上。经4口井实例应用,正确识别试井模型的概率大于0.99,与二维卷积神经网络相比,1D CNN显著降低了计算复杂度和时间成本,加快了训练速度。这表明基于试井理论所构建的样本库是有效的,能满足实测数据模型识别的需求;同时证明了方法的有效性、实用性和普适性。

基于图可解释网络的软件错误定位

软件错误定位技术旨在通过挖掘程序与测试用例执行数据,提升定位准确性;针对SBFL技术过于依赖二进制覆盖信息的问题,提出一种基于图可解释网络的软件错误定位方法,将测试执行转化为图结构,利用图注意网络建模深度挖掘代码片段隐含的信息及其相互关系,并采用强化学习思想对图注意力网络学习后的决策过程进行解释,从而确定关键节点,缩小错误定位范围;实验的场景设立在Defects4j数据集的5个项目进行,并与SBFL及未经过解释的深度学习方法进行了对比;结果显示,基于图可解释网络的定位方法在Top-1、Top-3和Top-5指标上分别提升了7.26%、7.56%和9.96%,EXAM指数也提升了8.98%,显著优于其他方法。

海上多段生产水平气井试井解释方法及其应用

以南海北部海域X气田1H气井为例,建立了多段水平气井模型,进行试井解释研究。通过对该井实际试井解释的应用效果对比,分析多段水平气井模型相对于常规水平井模型的优势。研究表明,应用多段水平气井模型可对水平气井的有效段数、长度、间隔等参数进行合理解释,解释结果更符合储层实际。