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    • 1. 发明授权
    • Method of keying a borehole in a seismic block
    • 在地震块中键入钻孔的方法
    • US06970788B2
    • 2005-11-29
    • US10661727
    • 2003-09-12
    • Naamen KeskesFrederic Mansanne
    • Naamen KeskesFrederic Mansanne
    • G01V1/30G01V1/50G01V1/28
    • G01V1/30G01V1/50
    • The aim of the present invention is the determination of the best possible correlation (keying) for electric recordings obtained in a borehole with seismic recordings obtained in a volume of subsoil (the seismic block) containing the bore. This is accomplished by defining in a neighborhood of the trajectory of the bore a set of layered networks, the entries of which are the seismic signals and the exits of which are electric signals. Accordingly, a quality measure is awarded with regard to the convergence. The best convergence quality then permits the determination of the best place in the neighborhood which produces the best keying.
    • 本发明的目的是确定在井眼中获得的电记录的最佳可能相关性(键控),其中所述地震记录是在包含孔的底土体积(地震块)中获得的。 这是通过在孔的轨迹的邻域中定义一组分层网络来实现的,它们的条目是地震信号,并且其出口是电信号。 因此,就融合而言,可以给出质量度量。 最好的收敛质量然后允许确定最好的地方在附近产生最好的键控。
    • 6. 发明授权
    • Method for automatic detection of planar heterogeneities crossing the stratification of an environment
    • 自动检测穿越环境分层的平面异质性的方法
    • US06266618B1
    • 2001-07-24
    • US09367161
    • 1999-08-09
    • Shin-Ju YePhilippe RabillerNaamen Keskes
    • Shin-Ju YePhilippe RabillerNaamen Keskes
    • G01V338
    • G01V3/38
    • Method for automatic detection of planar heterogeneities crossing the stratification of an environment. This method for automatic detection of planar heterogeneities crossing the stratification of an environment, from images of borehole walls or developments of core samples of the said environment, in which an original image defined in a system of axes (X1, Y1, Z1) which is associated with a borehole whose axis is Z1 is used, the said image containing, for a zone of the environment through which the borehole passes, planar heterogeneities consisting of stratification planes (2 to 14) and of planar heterogeneities (15 to 23) intersecting the stratification planes, is characterized in that it consists in: determining a dominant orientation of the stratification planes which lie in at least one part of the said original image, filtering the original image in order to eliminate the planar heterogeneities of the stratification planes (2 to 14) which lie in the dominant direction, and determining, on the said filtered image, at least contour segments (15′ to 23′) of the heterogeneities which intersect the stratification planar heterogeneities.
    • 自动检测穿越环境分层的平面异质性的方法。 这种用于自动检测穿过环境分层的平面异质性的方法,从井壁的图像或所述环境的岩心样品的发展开始,其中在轴系(X1,Y1,Z1)中定义的原始图像是 与使用轴为Z1的钻孔相关联,所述图像包含钻孔通过的环境区域,由分层面(2〜14)和平面异质性(15〜23)组成的平面异质性与 分层平面的特征在于它包括:确定位于所述原始图像的至少一部分中的分层平面的主导方向,对原始图像进行滤波,以消除分层平面(2至 14),并且在所述滤波图像上确定非均匀性的至少轮廓段(15'至23') 反映了分层平面异质性。
    • 7. 发明授权
    • Method for obtaining a representation of a geological structure
    • 获得地质结构表征的方法
    • US6011557A
    • 2000-01-04
    • US836753
    • 1997-05-16
    • Naamen KeskesPhilippe RabillerShinju Ye
    • Naamen KeskesPhilippe RabillerShinju Ye
    • E21B41/00G01V1/30G01V1/34G01V1/38G06T7/40
    • G01V1/34G01V1/301G01V1/38E21B2041/0028
    • A method for obtaining a representation of the textures of a geological structure, characterized in that images characteristic of the sedimentology of the environment are formed, parameters corresponding to the nature of the images are estimated at every point of each image and in a spatial domain around the point so as to determine a texture vector for each of the points and to obtain a set of texture vectors. The method also includes the steps of selecting texture vectors representative of the characteristic textures of the geological environment in the set of texture vectors; and using a neural network formed of cells distributed in two dimensions which contains as many cells as characteristic textures. The selected texture vectors are used to submit the neural network to a learning process so that a final topology map of the textures characteristic of the geological environment is obtained.
    • PCT No.PCT / FR96 / 01397 Sec。 371日期:1997年8月4日 102(e)1997年8月4日PCT PCT 1996年9月11日PCT公布。 公开号WO97 / 11393 日期1997年3月27日一种用于获得地质结构纹理表示的方法,其特征在于,形成环境沉积学特征的图像,在每个图像的每个点处估计与图像的性质相对应的参数, 在所述点周围的空间域中,以便确定每个点的纹理向量并且获得一组纹理向量。 该方法还包括以下步骤:在纹理矢量集中选择表示地质环境的特征纹理的纹理矢量; 并且使用由分布在二维中的细胞形成的神经网络,其包含与特征纹理一样多的细胞。 所选择的纹理向量用于将神经网络提交到学习过程,从而获得地质环境纹理特征的最终拓扑图。
    • 8. 发明授权
    • Automatic seismic pattern recognition method
    • 自动地震模式识别方法
    • US5940777A
    • 1999-08-17
    • US836754
    • 1997-08-04
    • Naamen Keskes
    • Naamen Keskes
    • G01V1/00G01V1/28G01V1/30G06F19/00
    • G01V1/301Y10S706/929
    • An automatic seismic pattern recognition method includes the steps of: determining a given number of seismic patterns to be recognized; providing a set of seismic trace portions for the region; defining a pattern recognition parameter common to all the trace portions, and determining the value of the parameters for each of the traces portions of the set. The method also includes the steps of: selecting trace portions of the set; selecting a one-dimensional neural network containing as many cells as there are patterns to be recognized where each cell is assigned a value of the recognition parameter; and submitting the neural network to a learning process with the selected trace portions so that at the end of the process each cell matches a pattern to be recognized and so that the patterns are progressively ordered. The method also includes the steps of: presenting each trace portion of the set to be processed to the classified and ordered neural network and attributing to each trace portion presented to the network the number of the cell closest to it.
    • PCT No.PCT / FR96 / 01396 Sec。 371日期:1997年8月4日 102(e)1997年8月4日PCT PCT 1996年9月11日PCT公布。 公开号WO97 / 11392 日期1997年3月27日自动地震模式识别方法包括以下步骤:确定要识别的给定数量的地震模式; 为该地区提供一组地震迹线部分; 定义所有迹线部分共同的模式识别参数,以及确定该组的每个迹线部分的参数值。 该方法还包括以下步骤:选择集合的跟踪部分; 选择包含与要识别的模式一样多的单元的一维神经网络,其中每个单元被分配有识别参数的值; 并且将所述神经网络提交到具有所选择的跟踪部分的学习过程,使得在所述过程结束时,每个单元匹配要识别的模式,并且使得所述模式被逐行排序。 该方法还包括以下步骤:将要处理的集合的每个跟踪部分呈现给分类和排序的神经网络,并将呈现给网络的每个跟踪部分归因于最接近它的信元的数量。
    • 10. 发明授权
    • Method for detecting chaotic structures in a given medium
    • 在给定介质中检测混沌结构的方法
    • US06628806B1
    • 2003-09-30
    • US09600469
    • 2000-07-17
    • Naamen KeskesFabien Pauget
    • Naamen KeskesFabien Pauget
    • G06K900
    • G06T7/20G01V1/28
    • Method of detecting chaotic structures in a given medium. It is of the type consisting in: calculating the components of the light intensity gradient vector E at every point of a window F, centered on a point of a block representative of the medium, and it is characterized in that it furthermore consists in summing elementary matrices M for all the points of the window F, diagonalizing said sum matrix A so as to determine its eigenvalues &lgr;1, &lgr;2, &lgr;3, quantifying, at the center of the window, the minimum of the said eigenvalues &lgr;1, &lgr;2, &lgr;3, and with the constraint UT×D=1, eliminating the contribution of the largest eigenvalue, defining a multidirectional error by integrating it in the plane defined by the eigenvectors corresponding to the remaining eigenvalues, assigning the multidirectional error to the image point on which the window F is centered, and calculating the multidirectional errors assigned to all the image points of the image block.
    • 在给定介质中检测混沌结构的方法,其类型包括:计算窗口F的每个点处的光强度梯度矢量E的分量,以代表介质的块的点为中心,并且 其特征在于,它还包括对窗口F的所有点的基本矩阵M求和,对所述和矩阵A进行对角化,以便确定其特征值lambd1,lambd2,lambd3,在窗口的中心量化最小值 所述特征值lambd1,lambd2,lambd3,并且具有约束U×T = 1,消除了最大特征值的贡献,通过将其定义在由对应于剩余特征值的特征向量定义的平面中来定义多方向误差,分配 窗口F所在的图像点的多向错误,并计算分配给图像块的所有图像点的多向错误。