Spinal Vertebral Fracture Detection and Fracture Level Assessment Based on Deep Learning

This paper addresses the common orthopedic trauma of spinal vertebral fractures and aims to enhance doctors’diagnostic efficiency.Therefore,a deep-learning-based automated diagnostic systemwithmulti-label segmentation is proposed to recognize the condition of vertebral fractures.The whole spine Computed Tomography(CT)image is segmented into the fracture,normal,and background using U-Net,and the fracture degree of each vertebra is evaluated(Genant semi-qualitative evaluation).The main work of this paper includes:First,based on the spatial configuration network(SCN)structure,U-Net is used instead of the SCN feature extraction network.The attention mechanismandthe residual connectionbetweenthe convolutional layers are added in the local network(LN)stage.Multiple filtering is added in the global network(GN)stage,and each layer of the LN decoder feature map is filtered separately using dot product,and the filtered features are re-convolved to obtain the GN output heatmap.Second,a network model with improved SCN(M-SCN)helps automatically localize the center-of-mass position of each vertebra,and the voxels around each localized vertebra were clipped,eliminating a large amount of redundant information(e.g.,background and other interfering vertebrae)and keeping the vertebrae to be segmented in the center of the image.Multilabel segmentation of the clipped portion was subsequently performed using U-Net.This paper uses VerSe’19,VerSe’20(using only data containing vertebral fractures),and private data(provided by Guizhou Orthopedic Hospital)for model training and evaluation.Compared with the original SCN network,the M-SCN reduced the prediction error rate by 1.09%and demonstrated the effectiveness of the improvement in ablation experiments.In the vertebral segmentation experiment,the Dice Similarity Coefficient(DSC)index reached 93.50%and the Maximum Symmetry Surface Distance(MSSD)index was 4.962 mm,with accuracy and recall of 95.82%and 91.73%,respectively.Fractured vertebrae were also marked as red and normal vertebrae were marked as white in the experiment,and the semi-qualitative assessment results of Genant were provided,as well as the results of spinal localization visualization and 3D reconstructed views of the spine to analyze the actual predictive ability of the model.It provides a promising tool for vertebral fracture detection.

Automatic fracture–vug identification and extraction from electric imaging logging data based on path morphology

We present a path morphology method to separate total rock pore space into matrix, fractures and vugs and derive their pore structure spectrum. Thus, we can achieve fine pore evaluation in fracture–vug reservoirs based on electric imaging logging data. We automatically identify and extract fracture–vug information from the electric imaging images by adopting a path morphological operator that remains flexible enough to fit rectilinear and slightly curved structures because they are independent of the structuring element shape. The Otsu method was used to extract fracture–vug information from the background noise caused by the matrix. To accommodate the differences in scale and form of the different target regions,including fracture and vug path, operators with different lengths were selected for their recognition and extraction at the corresponding scale. Polynomial and elliptic functions are used to fit the extracted fractures and vugs, respectively, and the fracture–vug parameters are deduced from the fitted edge. Finally, test examples of numerical simulation data and several measured well data have been provided for the verification of the effectiveness and adaptability of the path morphology method in the application of electric imaging logging data processing. This also provides algorithm support for the fine evaluation of fracture–vug reservoirs.

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.

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.

An attention-based cascade R-CNN model for sternum fracture detection in X-ray images

Fracture is one of the most common and unexpected traumas.If not treated in time,it may cause serious consequences such as joint stiffness,traumatic arthritis,and nerve injury.Using computer vision technology to detect fractures can reduce the workload and misdiagnosis of fractures and also improve the fracture detection speed.However,there are still some problems in sternum fracture detection,such as the low detection rate of small and occult fractures.In this work,the authors have constructed a dataset with 1227 labelled X-ray images for sternum fracture detection.The authors designed a fully automatic fracture detection model based on a deep convolution neural network(CNN).The authors used cascade R-CNN,attention mechanism,and atrous convolution to optimise the detection of small fractures in a large X-ray image with big local variations.The authors compared the detection results of YOLOv5 model,cascade R-CNN and other state-of-the-art models.The authors found that the convolution neural network based on cascade and attention mechanism models has a better detection effect and arrives at an mAP of 0.71,which is much better than using the YOLOv5 model(mAP=0.44)and cascade R-CNN(mAP=0.55).

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.

A new laboratory method for evaluating formation damage in fractured carbonate reservoirs

Natural carbonate core samples with artificial fractures are often used to evaluate the damage of fractured carbonate formations in the laboratory. It is shown that the most frequent error for evaluation results directly from the random width characterized by the artificial fractures. To solve this problem, a series of simulated fractured core samples made of stainless steel with a given width of fracture were prepared. The relative error for the width of artificial fracture decreased to 1%. The width of natural and artificial fractures in carbonate reservoirs can be estimated by image log data. A series of tests for formation damage were conducted by using the stainless steel simulated core samples flushed with different drilling fluids, such as the sulfonate/polymer drill-in fluid and the solids-flee drill-in fluid with or without ideal packing bridging materials. Based on the experimental results using this kind of simulated cores, a novel approach to the damage control of fractured carbonate reservoirs was presented. The effective temporary plugging ring on the end face of the simulated core sample can be observed clearly. The experimental results also show that the stainless steel simulated cores made it possible to visualize the solids and filtrate invasion.

Bedding parallel fractures in fine-grained sedimentary rocks:Recognition,formation mechanisms,and prediction using well log

Core,thin section,conventional and image logs are used to provide insights into distribution of fractures in fine grained sedimentary rocks of Permian Lucaogou Formation in Jimusar Sag.Bedding parallel fractures are common in fine grained sedimentary rocks which are characterized by layered structures.Core and thin section analysis reveal that fractures in Lucaogou Formation include tectonic inclined fracture,bedding parallel fracture,and abnormal high pressure fracture.Bedding parallel fractures are abundant,but only minor amounts of them remain open,and most of them are partly to fully sealed by carbonate minerals(calcite)and bitumen.Bedding parallel fractures result in a rapid decrease in resistivity,and they are recognized on image logs to extend along bedding planes and have discontinuous surfaces due to partly-fully filled resistive carbonate minerals as well as late stage dissolution.A comprehensive interpretation of distribution of bedding parallel fractures is performed with green line,red line,yellow line and blue line representing bedding planes,induced fractures,resistive fractures,and open(bedding and inclined)fractures,respectively.The strike of bedding parallel fractures is coinciding with bedding planes.Bedding parallel fractures are closely associated with the amounts of bedding planes,and high density of bedding planes favor the formation of bedding parallel fractures.Alternating dark and bright layers have the most abundant bedding parallel fractures on the image logs,and the bedding parallel fractures are always associated with low resistivity zones.The results above may help optimize sweet spots in fine grained sedimentary rocks,and improve future fracturing design and optimize well spacing.

An intelligent prediction method of fractures in tight carbonate reservoirs

An intelligent prediction method for fractures in tight carbonate reservoir has been established by upgrading single-well fracture identification and interwell fracture trend prediction with artificial intelligence,modifying construction of interwell fracture density model,and modeling fracture network and making fracture property equivalence.This method deeply mines fracture information in multi-source isomerous data of different scales to reduce uncertainties of fracture prediction.Based on conventional fracture indicating parameter method,a prediction method of single-well fractures has been worked out by using 3 kinds of artificial intelligence methods to improve fracture identification accuracy from 3 aspects,small sample classification,multi-scale nonlinear feature extraction,and decreasing variance of the prediction model.Fracture prediction by artificial intelligence using seismic attributes provides many details of inter-well fractures.It is combined with fault-related fracture information predicted by numerical simulation of reservoir geomechanics to improve inter-well fracture trend prediction.An interwell fracture density model for fracture network modeling is built by coupling single-well fracture identification and interwell fracture trend through co-sequential simulation.By taking the tight carbonate reservoir of Oligocene-Miocene AS Formation of A Oilfield in Zagros Basin of the Middle East as an example,the proposed prediction method was applied and verified.The single-well fracture identification improves over 15%compared with the conventional fracture indication parameter method in accuracy rate,and the inter-well fracture prediction improves over 25%compared with the composite seismic attribute prediction.The established fracture network model is well consistent with the fluid production index.

Fracture detecting based on Ant Colony Algorithm

Image processing plays an important role in engineering treatment. The authors mainly introduced the feature recognition of borehole image process based on Ant Colony Algorithm (ACA). The most important geological structure-fracture on the borehole image was identified, and quantitative parameters were obtained by HOUGH transform. Several case studies show that the method is feasible.

融合裂缝形态信息的电阻率测井成像图空白条带填充方法

电阻率测井成像图中存在宽度和位置不定的空白条带,而目前常用的空白条带反距离加权插值填充法仅依靠像素点距离参数计算插值,往往导致填充后区域与实测区域纹理不连贯,纹理易呈现直线状,与实际状况不符。为此,将裂缝区域形态与分布信息作为参量,构建插值计算模型,形成融合裂缝形态分布信息的空白条带填充法。该填充算法能根据插值点与裂缝形态拟合线的相对位置关系,动态调整参考点对插值点插值的权重系数,填充后的裂缝区域纹理连贯平滑,更符合裂缝实际形态和分布的规律。实际测井数据应用表明,相较于传统填充方法,采用该方法填充后的裂缝分布拟合线与霍夫变换后曲线相似度提升25%以上,不连贯填充点数量下降88%以上,性能提升明显。该方法的应用成果可为后续测井图像中各种裂缝的智能识别和精确分割提供更好的数据支撑。

光纤传感技术在油气田开发中的应用

分布式光纤监测技术在油气田开发中具有无可比拟的优势。从分布式光纤传感技术基本原理和井中布设方式出发,介绍了光纤传感技术在水力压裂、连续油管产液剖面测试、邻井压裂监测、油气井注采监测、储气库安全监测和套损监测等多个石油工程领域应用情况。整体表明光纤监测是一种经济高效的监测技术,具备实时监控油气井全生命周期的潜力,是具有科学性、时代特性和安全高效的生产测井技术。同时指出分布式光纤传感技术在油田应用的未来发展应聚焦于提升传感器性能、丰富光纤监测场景应用、提高资料处理解释水平能力,降低应用成本,进一步发挥光纤在油气田开发中的作用。

致密储层水平井外裂缝与地层界面远探测声波测井响应特征研究

致密砂岩和页岩等非常规储层中常发育有密集小尺度和高角度裂缝.这些裂缝一方面起到增加储层渗透性的积极作用,另一方面在油气开采、水力压裂、注二氧化碳等过程中,它们又可能引起水平井孔套变和邻井压窜等风险.因此,考察这些裂缝的水平井远探测声波测井响应特征,以及进一步利用远探测方法识别这些裂缝具有重要研究意义.本文构建了致密储层水平井外存在裂缝的三维数值模型,并开展了正演模拟研究.结果表明,当裂缝与井孔夹角较小时,井孔内接收器主要接收裂缝反射波.当夹角大于某临界值后,接收波形主要为来自裂缝两个端点的散射波,它们分裂为两个波包.进一步提出广义SH散射波概念,即声源的偏振方向与裂缝走向平行的横波,它总是代表了所有方位上散射能量最强的方位.水平井外同时存在裂缝和强声阻抗地层界面是更为普遍的情况.相关模拟结果表明,裂缝散射波的主要能量集中在广义SH散射波上.随着地层界面和裂缝走向差异的增大,裂缝对应的广义SH散射波更容易识别.最后,通过一口水平井远探测实例验证了正演模拟结论的准确性.本文研究对识别和评价非常规储层水平井外小尺度裂缝具有重要理论意义.

基于成像测井的深层陆相页岩油储层天然裂缝有效性评价

准噶尔盆地玛湖凹陷二叠系风城组陆相页岩油储层埋深超过4500 m,天然裂缝作为重要的储集空间和渗流通道,对页岩油的富集和高产至关重要。尽管前人对风城组裂缝进行了表征,但是在裂缝有效性评价方面缺少系统研究,严重制约了玛湖凹陷页岩油下一步的勘探开发。基于微电阻率成像测井资料,对研究区天然裂缝的发育规律和有效性进行了系统研究。研究表明:准噶尔盆地玛湖凹陷风城组陆相页岩油储层天然裂缝有穿层裂缝和层内裂缝两类。穿层裂缝规模较大,裂缝高度通常超过米级,受断层的控制;层内裂缝发育在脆性地层内,裂缝高度受岩层厚度的控制,多小于50 cm。垂向上单井裂缝发育程度与脆性矿物含量呈正相关关系;平面上随距断层距离的增加,裂缝发育程度逐渐降低。不同方位裂缝的充填情况差异较大,北西-南东向裂缝多数未被矿物充填,有效性好。随着埋深增加,裂缝开度整体呈现减小的趋势。评价认为北东东-南西西向裂缝的开度最大,有效性最好。

亚波长尺度裂缝地震散射分辨分析

裂缝储层地震特征的准确识别已成为当前地震勘探的难点之一,特别是深层礁滩或碳酸盐岩储层,其大部分裂缝都在亚波长尺度,按照传统地震反射四分之一波长几何分辨准则是不能完全分辨的.传统研究裂缝地震响应的方法主要基于各向异性等效介质理论,但由于亚波长尺度裂缝微小且其地震反射弱并存在噪声干扰等问题,故该传统方法逼近精度有限.然而,这类部分可分辨裂缝可在地震散射范畴来分析和预测,本文基于散射聚焦分辨分析理论,提出了表征裂缝系统空间分布和形态特征的波数域散射成像矩阵方法,据此计算了含裂缝与不含裂缝的波数域成像矩阵之间的振幅差异性和结构差异性,从散射能量及其各向异性的角度分析了亚波长尺度裂缝的地震可识别性.均匀和复杂背景介质下的算例表明,振幅差异性随裂缝厚度和密度的变化规律显著,结构差异性随裂缝角度变化规律更加明显.同时讨论了随机噪声和排列长度等因素对裂缝系统地震散射可识别性的影响.排列长度和噪声对于裂缝厚度响应影响较小,主要影响高角度裂缝的识别,同时要求排列长度大于三倍目标深度才可应用该方法进行裂缝参数识别.

砂砾岩尺寸及密度对压裂缝网扩展的影响

为明确乌尔禾组砂砾岩特征及其对压裂缝网扩展的影响,文中综合岩心、成像测井和常规测井等多种数据,通过砂砾岩离散模拟,明确了砾石线密度、砾石等效半径对压裂缝网扩展的影响,并利用灰色关联度分析优选对砂砾岩敏感的常规测井,建立砾石线密度和等效半径预测模型。结果表明:JL2井区乌尔禾组砂砾岩储层中天然裂缝不发育,砾石大小和密度是影响水力压裂缝网扩展的重要因素。水力压裂缝遇到砾石时,缝网扩展形态可分为终止延伸、偏转转向和切穿砾石3种类型;乌尔禾组砾石线密度为10~16个/m,砾石等效半径为16~24 mm;优选对砂砾岩敏感的声波时差、补偿中子和自然伽马曲线,建立砂砾岩特征参数预测模型,据此预测的结果与成像测井、岩心解释的砂砾岩特征参数一致,为乌尔禾组砂砾岩压裂层段优选和施工参数优化设计提供了依据。

基于拉东变换的多尺度断裂识别方法及其应用

针对中深层低信噪比区断裂识别难的问题,研究形成一套基于拉东变换多尺度断裂识别技术组合,即对叠后地震数据进行压噪处理以提高初始地震资料信噪比,压制随机干扰和高频噪声,去掉岩性扰动等造成的弱边界信息;通过分频处理检测不同频带地震数据对不同序级断裂的响应,然后利用敏感性分析优选不同尺度断裂的敏感地震属性;最后利用拉东变换技术进行边缘检测增强,提高断层边缘效应。其核心思路是将基于图像重建理论的拉东变换引入到断裂三维解释中,以区别常规边缘检测梯度算法,拉东变换是一种积分运算,具有线性增强、抗噪性强的特点。将该方法应用于珠江口盆地珠一坳陷浅水区断裂解释,对不同序级断裂研究认为:①相比于常规蚂蚁体切片,显著提高了裂缝预测的精度和分辨率,指示了陆丰13洼LF_A潜山构造主要发育北北西和北东向两组裂缝,指导了花岗岩优质储层分布预测与评价;②精细落实了阳江东凹EP_B构造断裂发育条件,认为是受走滑作用控制的左阶雁列构造;③陆丰C洼存在一条大型北东东向中央走滑断裂带,与北西向调节断裂共同控制中央鼻状带圈闭形成,新模式下发现了一批构造圈闭,提高了洼陷勘探潜力。

运用GMS算法自动识别相似模拟材料实验裂隙场

通常采用沙盘物理模型与近距离摄影测量相结合的方法来探索上覆地层的断裂机制,但由于沙土物理模型裂缝图像的信噪比较低,该算法往往提取得不完整。鉴于此,提出了一种基于计算机视觉的图像梯度原理、图像匹配和区域筛选技术(GMS算法)的裂隙检测算法。首先利用峰峰市九龙煤矿15235工作面的钻孔柱状图建立沙盘物理模型;再利用定焦数码相机拍摄该模型的正射影像;最后根据模型图像的纹理特征,分别采用图像匹配技术、区域筛选技术分割规则的大面积非裂缝纹理和微小非裂缝纹理。研究结果表明,GMS算法具有很强的抗干扰能力,能准确识别并自动提取相似模拟材料中上覆地层的裂隙场。

基于随钻电阻率成像测井分析的礁滩灰岩储层裂缝及次生溶蚀的精细表征——以珠江口盆地陆丰油田为例

陆丰X-1油田是近年来珠江口盆地东部勘探发现的优质礁滩灰岩油田,蕴含着丰富的油气资源,但灰岩储层岩性复杂、孔隙结构复杂及非均质性极强,为储层评价及油田整体开发带来了极大的挑战。通过将随钻高分辨率电阻率成像测井技术引入对礁滩灰岩沉积构造特征、裂缝精细表征、次生溶蚀定量计算及产能主控因素的研究中。结果表明:陆丰X-1灰岩油层纵向上可细分为ZJ10A及ZJ10B灰岩段,ZJ10A灰岩段主要为礁灰岩沉积,沉积构造主要表现为块状及层状特征;ZJ10B灰岩段主要为生物碎屑滩沉积,沉积构造以层状结构为主。以高分辨率电阻率成像资料为基础,确定了ZJ10A灰岩段主要发育高角度构造缝,裂缝有效性及纵向沟通性好;ZJ10B灰岩段裂缝类型以溶蚀缝及孤立缝为主,裂缝有效性及纵向沟通性相对较差。陆丰X-1灰岩油层发育斑杂状、团块状、蜂窝状及顺层状等多种次生溶蚀类型,利用电阻率频谱分析方法及PoroTex方法对陆丰X-1灰岩油层次生溶蚀孔隙度及视面孔率进行了定量计算,优质储层段次生溶蚀孔隙度及视面孔率均较高。综合电阻率成像测井解释成果以及动态生产资料对陆丰X-1灰岩油层产能主控因素进行了分析,次生溶蚀对产能是正向贡献而高角度构造缝则是负向贡献。高分辨率成像测井技术在陆丰X-1油田礁滩灰岩层段的成功应用既满足了储层精细表征的需求,又为陆丰X-1油田后续整体开发提供了坚实的数据基础。

坚硬顶板综放工作面导水裂隙带高度研究

当顶板导水裂隙带发育至水体时,水体会沿着裂隙进入工作面,破坏矿井的生态环境,影响工作安全及效益。以盘城岭煤矿15#煤层150105工作面为研究对象,通过经验计算公式分析导水裂隙带的发育高度,并结合仰孔注水测漏法及钻孔电视成像法,综合确定15#煤层导水裂隙带的发育高度。结果表明,经验计算公式预测导水裂隙带最大发育高度为81.56 m,仰孔注水测漏法为79.99 m,钻孔电视成像法为79.46 m。最终得出经验公式计算的高度与现场实测的结果高度一致,均可为盘城岭煤矿15#煤层水害防治及安全生产提供指导。