Theoretical Study on Quantitative Characterization of Interlayer Interference in Multi-Layer Commingled Production

X oilfield is a typical layered reservoir with a large vertical span and many oil-bearing formations. There are significant differences in reservoir types and fluid properties among various formations. The interlayer interference is severe in the development process. At present, the interlayer interference research based on dynamic monitoring data cannot meet development adjustment needs. Combined with the field test results, through the indoor physical simulation experiment method, dynamic inversion method, and reservoir engineering method, this paper analyzes the main control factors and interference mechanism of interlayer interference, studies the variation law of interference coefficient, improves and forms the quantitative characteristic Theory of interlayer interference in multi-layer commingled production, and provides theoretical guidance for the total adjustment of the middle strata division in the oilfield.

Parameter Inversions of Multi-Layer Media of Mars Polar Region with Validation of SHARAD Data

HF (high frequency) radar sounder technology has been developed for several missions of Mars surface/subsurface exploration. This paper presents a model of rough surface and stratified sub-surfaces to describe the multi-layer structure of Mars polar deposits. Based on numerical simulation of radar echoes from rough surface/stratified interfaces, an inversion approach is developed to obtain the parameters of Polar Layered Deposits, i.e. layers thickness and dielectric constants. As a validation example, the SHARAD radar sounder data of the Promethei Lingula of Mars South Polar region is adopted for parameters inversion. The result of stratification is also analyzed and compared with the optical photo of the deep cliff of Chasma Australe canyon. Dielectric inversions show that the deposit media are not uniform, and the dielectric constants of the Promethei Lingula surfaces are large, and become reduced around the depth of 20 m - 30 m, below where most of the deposits are nearly pure ice, except a few thin layers with a lot of dust.

Quantitative Schlieren Image-Noise Reduction Using Inverse Process and Multi-Path Integration

This report deals with introducing two new techniques based on a novel concept of complex brightness gradient in quantitative schlieren images, “inverse process” and “multi-path integration” for image-noise reduction. Noise in schlieren images affects the projections (density thickness) images of computerized tomography (CT). One spot noise in the schlieren image appears in a line shape in the density thickness image. Noise effect like an infectious disease spreads from a noisy pixel to the next pixel in the direction of single-path integration. On the one hand, the noise in the schlieren image reduces the quality of the image and quantitative analysis and is undesirable;on the other it is unavoidable. Therefore, the importance of proper noise reduction techniques seems essential and tangible. In the present report, a novel technique “multi-path integration” is proposed for noise reduction in projections images of CT. Multi-path integration is required the schlieren brightness gradient in two orthogonal directions. The 20-directional quantitative schlieren optical system presents only images of schlieren brightness in the horizontal gradient and another 20-directional optical system seems necessary to obtain vertical schlieren brightness gradient, simultaneously. Using the “inverse process”, a new technique enables us to obtain vertical schlieren brightness gradient from horizontal experimental data without the necessity of a new optical system and can be used for obtaining any optional directions of schlieren brightness gradient.

Comparative Study on Coastal Depth Inversion Based on Multi-source Remote Sensing Data

Coastal depth is an important research focus of coastal waters and is also a key factor in coastal environment. Dongluo Island in South China Sea was taken as a typical study area. The band ratio model was established by using measured points and three multispectral images of Landsat-8, SPOT-6(Systeme Probatoire d'Observation de la Terre, No.6) and WorldView-2. The band ratio model with the highest accuracy is selected for the depth inversion respectively. The results show that the accuracy of SPOT-6 image is the highest in the inversion of coastal depth. Meanwhile, analyzing the error of inversion from different depth ranges, the accuracy of the inversion is lower in the range of 0–5 m because of the influence of human activities. The inversion accuracy of 5–10 m is the highest, and the inversion error increases with the increase of water depth in the range of 5–20 m for the three kinds of satellite images. There is no linear relationship between the accuracy of remote sensing water depth inversion and spatial resolution of remote sensing data, and it is affected by performance and parameters of sensor. It is necessary to strengthen the research of remote sensor in order to further improve the accuracy of inversion.

Weighted total variation using split Bregman fast quantitative susceptibility mapping reconstruction method

An ill-posed inverse problem in quantitative susceptibility mapping （QSM） is usually solved using a regularization and optimization solver, which is time consuming considering the three-dimensional volume data. However, in clinical diagnosis, it is necessary to reconstruct a susceptibility map efficiently with an appropriate method. Here, a modified QSM reconstruction method called weighted total variation using split Bregman （WTVSB） is proposed. It reconstructs the susceptibility map with fast computational speed and effective artifact suppression by incorporating noise-suppressed data weighting with split Bregman iteration. The noise-suppressed data weighting is determined using the Laplacian of the calculated local field, which can prevent the noise and errors in field maps from spreading into the susceptibility inversion. The split Bregman iteration accelerates the solution of the Ll-regularized reconstruction model by utilizing a preconditioned conjugate gradient solver. In an experiment, the proposed reconstruction method is compared with truncated k-space division （TKD）, morphology enabled dipole inversion （MEDI）, total variation using the split Bregman （TVSB） method for numerical simulation, phantom and in vivo human brain data evaluated by root mean square error and mean structure similarity. Experimental results demonstrate that our proposed method can achieve better balance between accuracy and efficiency of QSM reconstruction than conventional methods, and thus facilitating clinical applications of QSM.

RECONSTRUCTION OF ONE DIMENSIONAL MULTI-LAYERED MEDIA BY USING A TIME DOMAIN SIGNAL FLOW GRAPH TECHNIQUE

A novel inverse scattering method to reconstruct the permittivity profile of one-dimensional multi-layered media is proposed in this paper.Based on the equivalent network ofthe medium,a concept of time domain signal flow graph and its basic principles are introduced,from which the reflection coefficient of the medium in time domain can be shown to be a series ofDirac δ-functions(pulse responses).In terms of the pulse responses,we will reconstruct both thepermittivity and the thickness of each layer will accurately be reconstructed.Numerical examplesverify the applicability of this

High-resolution bone microstructure imaging based on ultrasonic frequency-domain full-waveform inversion

The main challenge in bone ultrasound imaging is the large acoustic impedance contrast and sound velocity differences between the bone and surrounding soft tissue. It is difficult for conventional pulse-echo modalities to give accurate ultrasound images for irregular bone boundaries and microstructures using uniform sound velocity assumption rather than getting a prior knowledge of sound speed. To overcome these limitations, this paper proposed a frequency-domain fullwaveform inversion(FDFWI) algorithm for bone quantitative imaging utilizing ultrasonic computed tomography(USCT).The forward model was calculated in the frequency domain by solving the full-wave equation. The inverse problem was solved iteratively from low to high discrete frequency components via minimizing a cost function between the modeled and measured data. A quasi-Newton method called the limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm(L-BFGS) was utilized in the optimization process. Then, bone images were obtained based on the estimation of the velocity and density. The performance of the proposed method was verified by numerical examples, from tubular bone phantom to single distal fibula model, and finally with a distal tibia-fibula pair model. Compared with the high-resolution peripheral quantitative computed tomography(HR-p QCT), the proposed FDFWI can also clearly and accurately presented the wavelength scaled pores and trabeculae in bone images. The results proved that the FDFWI is capable of reconstructing high-resolution ultrasound bone images with sub-millimeter resolution. The parametric bone images may have the potential for the diagnosis of bone disease.

Quantitative Inversion of REEs in Ion-Adsorbed Rare Earth Ores from the Liutang Area(South China),Based on Measured Hyperspectral Data

Rare earth minerals are important strategic resources to economic development all over the world.In this study,multiple linear regression and back propagation(BP) neural network methods are used to invert the contents of ion adsorbed rare earth elements(REEs) and exploring the feasibility of quantitative inversion of REEs through measured hyperspectral data in Liutang rare earth mines,South China.The result shows that the spectral curve of the rare earth ore samples has obvious absorption characteristics around 390,930,1 400,1 900 and 2 200 nm,and continuum removal and the 1st derivative treatment can highlight the absorption characteristics.The modeling accuracies of BP neural network are higher than that of multiple linear regression model.The BP neural network model of the 1st derivative data in 400–1 000 nm bands has the best inversion result of the total content of REEs,R2 reaches 0.98,the ratio of the performance to deviation(RPD) is larger than 3.0.The quantitative inversion model of each REE(except for Ce) has high precision,R2 is greater than 0.90 and RPD is greater than 3.0.The results indicate that quantitative inversion of REEs using measured spectra not only has great potential and feasibility in the exploration of rare earth minerals,but also provides a rapid test method for the content of ion-adsorbed rare earth elements.

井下断层活动的定量监测及其对冲击地压的影响研究

为建立断层活动与冲击地压之间的定量关系,实现煤矿冲击地压有效防控。通过理论分析义马矿区地质构造环境,以及耿村煤矿13200工作面与断层影响带的相对位置关系,研究了断裂构造对冲击地压的宏观控制作用。计算了井田构造应力并进行了构造应力区划分,分析了构造应力分区对冲击地压的控制作用。提出了井下断层活动性的定量监测方法,构建了“震源区煤岩体与动力核区尺度等量,震源能量随传递距离逐渐衰减”的模型,建立了大能量微震事件与断层活动的关系,确定了断层活动性对冲击地压的影响。研究结果表明:义马煤田内相对复杂的逆冲推覆构造体系,构成了义马矿区冲击地压的地质构造背景条件。F_(16)断层的影响带范围为7000~7600 m,13200工作面全部处于F_(16)断层的影响带内,在开采活动的影响下进一步增大了冲击地压的发生危险。Ⅰ-2断裂、Ⅲ-4断裂和Ⅳ-7断裂等控制的区域是冲击地压和大能量微震事件显现的主要区域,且冲击地压和大能量微震事件大多位于应力梯度区范围内。在大能量微震事件孕育和发生期间,F_(16)断层位移分别增长50 mm和45 mm;大能量微震事件发生前,断层活动拉力的增幅均相对最高,分别为2.58 kN和2.93 kN,断层位移量的快速增加和较高的应力增幅构成了大能量微震事件的主要能量来源。表明大能量微震事件和冲击地压的发生均与断层的活动联系紧密。井下断层的实际定量监测方法可以广泛应用于矿井冲击地压预测与防控的指导工作中。

基于机载高光谱遥感的长三角一体化示范区水质监测

本文基于机载航空高光谱影像数据对长三角生态绿色一体化发展示范区内“一河三湖”重点水域水质参数进行了反演研究。试验获取了示范区内全谱段高光谱影像数据,通过对机载遥感影像进行预处理,获得水体离水反射信息;在飞行时刻同步采集了水体样本与水体光谱,通过竞争性自适应重加权特征选择方法,选择最优波段参与反演模型的构建,并完成水质专题制图。模型验证集的决定系数R^(2)均达0.6以上,同时与水质监测站数据误差小于10%,实现了长三角一体化示范区的水质精确监测,证实了机载高光谱遥感对于水质参数的反演应用价值,为示范区发展赋能高质量。

基于多角度高光谱指数的花岗岩中长石含量反演研究

花岗岩中长石含量的定量估算有助于其定名和分类,为后续相关地质过程研究提供基础数据。在可见光—近红外—短波红外波段范围内(0.35~2.5μm),传统基于光谱吸收特征参数反演矿物种类和含量的方法,不适用于像长石这类无诊断性吸收特征的矿物。同时,基于物理的辐射传输模型由于计算复杂,在较大程度上限制了该方法在矿物定量反演中的应用。本文基于多角度高光谱数据,通过不同光谱预处理方法及光谱指数类型的组合实验,创建用于估算花岗岩中长石比例的光谱指数模型。结果表明,使用2035 nm波段的反射率二重差分型(CRDDn_(2035))指数模型,在不同实测数据集中均具有良好表现,估算精度达到0.81。本研究创建了一种适用于估算长石占比的光谱指数模型,为定量反演具有弱吸收特征的岩矿信息提供了新的技术手段与思路。

基于国产资源一号02D卫星和机器学习算法的钦州湾滨海土壤盐分反演

以钦州湾滨海地区作为研究区,基于国产资源一号02D(ZY1-02D)多光谱卫星提取的相关特征参数,在Ada Booost、Light GBM、XGBoost、RFR以及Cat Boost五种不同机器学习算法的支持下,设置了5种不同的输入变量组合,并基于决定系数(R^(2))和均方根误差(RMSE)对不同模型的性能进行了评价.结果显示:研究区实测土壤盐分含量范围为0.740~10.352 g/kg,均值为1.739 g/kg;Cat Boost相较于Ada Booost、Light GBM、XGBoost、RFR有更好的预测性能,Cat Boost结合全变量组在预测阶段取得了最高精度(R^(2)=0.8317,RMSE=0.3957 g/kg);在全变量组中,纹理特征中的均值对土壤盐分含量最为敏感,贡献度最高;研究区土壤盐分含量预测值为0~8.784g/kg,均值为2.478g/kg,轻度盐渍土分布广泛,主要集中分布在研究区西部,在东部地区分布较零散.国产资源卫星遥感数据结合Cat Boost模型在钦州湾滨海土壤盐分反演中表现出较好的性能,可为大规模估算土壤盐分含量提供一种新的方法和思路.

基于属性约束的烃源岩地震反演定量预测

烃源岩评价是含油气盆地、凹陷勘探研究的基础。目前常用的地球化学、测井、地震识别三大类评价方法,均能实现烃源岩的评价,但又受资料制约,存在各自局限性。笔者等结合地质、沉积、地化及测井、地震等数据探索了多属性约束下基于地震反演的烃源岩定量预测技术研究,确定了研究区内文昌组烃源岩的发育规模,并对其进行了烃源岩等级评价和生烃量的估算。

中国东北黑土区地表土壤含水量时空分异特征研究

土壤含水量通过影响土壤养分循环进而控制植被分布格局,量化黑土区土壤含水量空间格局对于理解土地覆被特征及生态系统小气候变化等具有重要意义。本研究以Sentinel-1A双极化合成孔径雷达影像数据为基础,辅助同时段Sentinel-2光学影像数据,基于修正水云模型对东北黑土区不同土地覆被类型下复杂地表土壤含水量进行定量反演;借助MOD11A2和MOD13A3产品计算的温度植被干旱指数,挖掘东北黑土区土壤含水量时空变化特征及其控制因素。研究结果表明,借助VV极化的修正水云模型(R^(2)=0.75,R_(e)=0.015)反演东北黑土区土壤含水量精度最高,研究区内的土壤含水量具有较强的空间分异性,整体呈现偏干旱的状态,其中旱地的干旱现象最为严重,但随着时间的变化,干旱有减轻趋势;土壤含水量变化受降雨的影响高于温度,但研究区内复杂的环境致使干旱情况并不是受单一因素控制。

基于岩石物理模型的凝灰质砂岩的识别与刻画--以珠江口盆地惠州凹陷古近系砂岩储层为例

惠州H5油田位于珠江口盆地惠州凹陷,古近系恩平组下段砂岩储层是其主力油层,但储层中因火山作用而发育了致密的凝灰质砂岩。此类致密砂岩导致储层性能下降,使得优质储层的预测面临挑战。准确识别凝灰质砂岩并刻画其分布范围是惠州H5油田优质储层预测的关键。为此,根据已钻井资料和地震资料进行了含凝灰质砂岩的岩石物理建模,得到了识别凝灰质砂岩的敏感岩石物理弹性参数;在此基础上,开展了地震资料叠前反演和人工智能深度学习的储层定量预测;在储层预测结果的基础上,识别并刻画了致密的凝灰质砂岩及其分布范围,从而突出优质砂岩储层。提出了凝灰质砂岩岩石物理建模和岩石物理模型驱动下的人工智能储层预测技术及其流程。该技术应用于惠州H5油田的储层评价,在钻前成功识别了H5-3d井区和H5-5d井区恩平组下段发育的凝灰质砂岩,准确刻画了其分布范围和边界,为后续评价井的钻探和储量申报提供了重要依据。

灯山村滑坡稳定性分析研究

在滑坡灾害中,滑坡稳定性分析至关重要,对其防治效果起着决定性的作用。借助各种科学手段和“定性+定量”评估方法详细勘查滑坡特征,分析滑坡变形的破坏形式和程度,最后分析其稳定性,并得出准确可靠的结论。选择灯山村滑坡为研究案例,借助“定性+定量”评估方式和其他科学合理的方法对该滑坡进行了详细勘查,以勘查结果为基础,综合评价其稳定性,希望能为后期选择科学合理的滑坡防治举措奠定一定的理论基础。

基于分频尺度约束的缝洞体空间定位方法

塔里木盆地奥陶系碳酸盐岩缝洞体尺度预测和空间位置确定存在较大困难,影响油气勘探靶点设计、压裂方案实施等。为此,提出一种有效提高缝洞体预测精度的基于分频尺度约束的缝洞体空间定位方法。首先利用叠前分频数据体,通过高斯函数拟合确定缝洞体的调谐频率,得到缝洞体的尺度参数;然后利用这一尺度参数对缝洞体中心阻抗曲线进行阈值扫描,当低阻抗曲线段时间厚度满足缝洞体尺度参数时,便完成阻抗阈值的约束和标定,从而实现缝洞体的尺度预测和空间定位。模型正演验证了该方法的可靠性和准确性。实际资料应用表明,预测结果能更加精细地描述缝洞体尺度特征和空间位置,井轨迹与缝洞体之间的关系更加明确。该方法可以有效指导井位压裂方案的制定,助力油气勘探。

土壤中元素遥感定量反演研究进展与展望

【研究目的】土壤质量的优劣与人类生活密切相关。鉴于传统的土壤调查方法无法满足大面积土壤质量的动态监测需求,如何发挥高光谱遥感技术宏观、实时、原位、快速等优势进行土壤元素定量反演已成为遥感应用领域的热点和难点。【研究方法】文章围绕土壤元素的直接定量反演、利用土壤中元素相关性的间接定量反演以及基于植物光谱的土壤元素定量反演三种遥感定量反演方法,系统总结了其主要原理、优势与研究现状,从学科交叉融合角度展望了相关领域未来发展方向。【研究结果】当前常用的土壤元素定量反演方法均难于大范围推广应用。相比而言,利用植物叶片或冠层的光谱间接反演土壤元素含量的方法可信度更高。利用生态地球化学领域的研究成果,有助于找到目标元素在不同植物中特有的光谱学效应信息,解码基于植物光谱的土壤元素定量反演原理。【结论】推进学科交叉融合,深化基于大数据挖掘和土壤理化性质的研究,是突破土壤元素广域监测技术瓶颈的有利发展方向。

四步法定量预测烃源岩技术在珠江口盆地L区块的应用

珠江口盆地L区块勘探程度相对较低,而周缘已钻井证实文昌组发育半深—深湖相烃源岩,此套烃源岩的发育规模是L区块资源潜力评价的关键。为了准确预测总有机碳(TOC)含量,以研究区地化取心数据、测井数据和地震数据为基础,利用Δlog R法预测TOC曲线,并建立TOC曲线与敏感弹性参数间的关系,联合应用多属性约束的地震反演技术,以提高少井区反演结果的可靠性,最终将对地化参数TOC的预测转变为对弹性参数的预测。依次从点、线、面、体四方面逐步实现烃源岩的剖面预测到空间展布特征的雕刻,并通过计算优质烃源岩的体积,实现对烃源岩的定量刻画。该技术被称为“点线面体”四步法定量预测烃源岩技术或四步法定量预测烃源岩技术。L区块的应用结果表明,利用四步法定量预测烃源岩技术预测的TOC含量与已钻井处的TOC值吻合度高,并且有效预测了文昌组优质烃源岩及中等烃源岩的发育规模及体积,为L区块资源潜力评价提供了重要依据。

Applying Neural Network Architecture for Inverse Kinematics Problem in Robotics

One of the most important problems in robot kinematics and control is, finding the solution of Inverse Kinematics. Inverse kinematics computation has been one of the main problems in robotics research. As the Complexity of robot increases, obtaining the inverse kinematics is difficult and computationally expensive. Traditional methods such as geometric, iterative and algebraic are inadequate if the joint structure of the manipulator is more complex. As alternative approaches, neural networks and optimal search methods have been widely used for inverse kinematics modeling and control in robotics This paper proposes neural network architecture that consists of 6 sub-neural networks to solve the inverse kinematics problem for robotics manipulators with 2 or higher degrees of freedom. The neural networks utilized are multi-layered perceptron (MLP) with a back-propagation training algorithm. This approach will reduce the complexity of the algorithm and calculation (matrix inversion) faced when using the Inverse Geometric Models implementation (IGM) in robotics. The obtained results are presented and analyzed in order to prove the efficiency of the proposed approach.