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    • 7. 发明申请
    • SYSTEM AND METHOD OF PREDICTING GAS SATURATION OF A FORMATION USING NEURAL NETWORKS
    • 使用神经网络预测气体饱和度的系统和方法
    • US20110282818A1
    • 2011-11-17
    • US13146437
    • 2009-04-21
    • Dingding ChenWeijun GuoLarry A. Jacobson
    • Dingding ChenWeijun GuoLarry A. Jacobson
    • G06N3/02
    • G01V5/125
    • Predicting gas saturation of a formation using neural networks. At least some of the illustrative embodiments include obtaining a gamma count rate decay curve one each for a plurality of gamma detectors of a nuclear logging tool (the gamma count rate decay curves recorded at a particular borehole depth), applying at least a portion of each gamma count rate decay curve to input nodes of a neural network, predicting a value indicative of gas saturation of a formation (the predicting by the neural network in the absence of a formation porosity value supplied to the neural network), and producing a plot of the value indicative of gas saturation of the formation as a function of borehole depth.
    • 使用神经网络预测地层的气体饱和度。 至少一些示例性实施例包括获得针对核测井工具的多个伽马检测器(在特定钻孔深度处记录的伽马计数速率衰减曲线)中的伽马计数率衰减曲线,每个伽马计数率衰减曲线应用至少一部分每个 伽马计数速率衰减曲线到神经网络的输入节点,预测指示地层的气体饱和度的值(在没有提供给神经网络的地层孔隙度值的情况下由神经网络预测),并且产生 表示地层气体饱和度的值作为钻孔深度的函数。
    • 9. 发明授权
    • System and method of predicting gas saturation of a formation using neural networks
    • 使用神经网络预测地层气饱和度的系统和方法
    • US08898045B2
    • 2014-11-25
    • US13146437
    • 2009-04-21
    • Dingding ChenWeijun GuoLarry A. Jacobson
    • Dingding ChenWeijun GuoLarry A. Jacobson
    • G06E1/00G01V5/12
    • G01V5/125
    • Predicting gas saturation of a formation using neural networks. At least some of the illustrative embodiments include obtaining a gamma count rate decay curve one each for a plurality of gamma detectors of a nuclear logging tool (the gamma count rate decay curves recorded at a particular borehole depth), applying at least a portion of each gamma count rate decay curve to input nodes of a neural network, predicting a value indicative of gas saturation of a formation (the predicting by the neural network in the absence of a formation porosity value supplied to the neural network), and producing a plot of the value indicative of gas saturation of the formation as a function of borehole depth.
    • 使用神经网络预测地层的气体饱和度。 至少一些示例性实施例包括获得针对核测井工具的多个伽马检测器(在特定钻孔深度处记录的伽马计数速率衰减曲线)中的伽马计数率衰减曲线,每个伽马计数率衰减曲线应用至少一部分每个 伽马计数速率衰减曲线到神经网络的输入节点,预测指示地层的气体饱和度的值(在没有提供给神经网络的地层孔隙度值的情况下由神经网络预测),并且产生 表示地层气体饱和度的值作为钻孔深度的函数。
    • 10. 发明授权
    • Method and system for calculating extent of a formation treatment material in a formation
    • 用于计算地层中地层处理材料的程度的方法和系统
    • US08044342B2
    • 2011-10-25
    • US12397506
    • 2009-03-04
    • James E. GalfordLarry A. JacobsonJerome A. Truax
    • James E. GalfordLarry A. JacobsonJerome A. Truax
    • G01V5/10
    • G01V5/101
    • A method and system for calculating extent of a formation treatment material in a formation. At least some of the illustrative embodiments are methods comprising releasing neutrons into a formation from a neutron source of a logging tool within a borehole having an axis, sensing energies of gammas produced by materials in the formation, the sensing by a gamma detector on the logging tool, generating a measured spectrum of the energies of the gammas sensed by the gamma detector, determining elemental concentrations of materials in the formation based on a basis spectrum, and calculating axial extent of a formation treatment material in the formation in relation to the axis of the borehole based on the elemental concentrations of at least some materials in the formation.
    • 一种用于计算地层中地层处理材料的程度的方法和系统。 示例性实施例中的至少一些是包括从具有轴线的井眼内的测井工具的中子源将地层中子释放到地层中的方法,感测由地层中的材料产生的伽马的能量,伽马探测器在测井 产生由伽马检测器感测到的伽马的能量的测量光谱,基于基础光谱确定地层中材料的元素浓度,以及计算地层中的地层处理材料的轴向范围 基于地层中至少一些材料的元素浓度的钻孔。