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1.发明申请Method and System of Processing Gamma County Rate Curves Using Neural Networks 有权使用神经网络处理伽马县速率曲线的方法和系统公开(公告)号：US20110137566A1
公开(公告)日：2011-06-09
申请号：US12740726
申请日：2008-08-26
CPC分类号： G01V5/101
摘要： Processing gamma count rate decay curves using neural networks. At least some of the illustrative embodiments are methods comprising 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 the gamma count rate decay curves to input nodes of a neural network, predicting by the neural network a geophysical parameter of the formation surrounding the borehole, repeating the obtaining, applying and predicting for a plurality of borehole depths, and producing a plot of the geophysical parameter of the formation as a function of borehole depth.
摘要翻译： 使用神经网络处理伽马计数率衰减曲线。 示例性实施例中的至少一些是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）的γ计数率衰减曲线，应用伽马计数率 衰减曲线到神经网络的输入节点，由神经网络预测围绕钻孔的地层的地球物理参数，重复获取，应用和预测多个井眼深度，并产生地层的地球物理参数的图 作为钻孔深度的函数。
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2.发明授权Method and system of processing gamma count rate curves using neural networks 有权使用神经网络处理伽马计数率曲线的方法和系统公开(公告)号：US08660796B2
公开(公告)日：2014-02-25
申请号：US12740726
申请日：2008-08-26
CPC分类号： G01V5/101
摘要： Processing gamma count rate decay curves using neural networks. At least some of the illustrative embodiments are methods comprising 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 the gamma count rate decay curves to input nodes of a neural network, predicting by the neural network a geophysical parameter of the formation surrounding the borehole, repeating the obtaining, applying and predicting for a plurality of borehole depths, and producing a plot of the geophysical parameter of the formation as a function of borehole depth.
摘要翻译： 使用神经网络处理伽马计数率衰减曲线。 示例性实施例中的至少一些是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）的γ计数率衰减曲线，应用伽马计数率 衰减曲线到神经网络的输入节点，由神经网络预测围绕钻孔的地层的地球物理参数，重复获取，应用和预测多个井眼深度，并产生地层的地球物理参数的图 作为钻孔深度的函数。
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3.发明授权Neural network training data selection using memory reduced cluster analysis for field model development 失效使用内存的神经网络训练数据选择降低了现场模型开发的聚类分析公开(公告)号：US08374974B2
公开(公告)日：2013-02-12
申请号：US10393641
申请日：2003-03-21
申请人： Dingding Chen , John A. Quirein , Jacky M. Wiener , Jeffery L. Grable , Syed Hamid , Harry D. Smith, Jr.
发明人： Dingding Chen , John A. Quirein , Jacky M. Wiener , Jeffery L. Grable , Syed Hamid , Harry D. Smith, Jr.
CPC分类号： G06K9/6298 , G01V11/00 , G06N3/08
摘要： A system and method for selecting a training data set from a set of multidimensional geophysical input data samples for training a model to predict target data. The input data may be data sets produced by a pulsed neutron logging tool at multiple depth points in a cases well. Target data may be responses of an open hole logging tool. The input data is divided into clusters. Actual target data from the training well is linked to the clusters. The linked clusters are analyzed for variance, etc. and fuzzy inference is used to select a portion of each cluster to include in a training set. The reduced set is used to train a model, such as an artificial neural network. The trained model may then be used to produce synthetic open hole logs in response to inputs of cased hole log data.
摘要翻译： 一种用于从一组多维地球物理输入数据样本中选择训练数据集的系统和方法，用于训练模型以预测目标数据。 输入数据可以是在情况良好的多个深度点上由脉冲中子测井工具产生的数据集。 目标数据可能是露天测井工具的响应。 输入数据被分成簇。 来自训练井的实际目标数据与集群有关。 对链接的聚类进行方差分析等，并使用模糊推理来选择每个聚类的一部分以包括在训练集中。 缩减集用于训练模型，如人造神经网络。 然后可以使用经过训练的模型来响应于套管孔日志数据的输入而产生合成开孔日志。
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4.发明授权Neural network based well log synthesis with reduced usage of radioisotopic sources 有权基于神经网络的井记录综合，减少放射性同位素来源的使用公开(公告)号：US07587373B2
公开(公告)日：2009-09-08
申请号：US11270284
申请日：2005-11-09
CPC分类号： G06N3/086 , G06N3/0454
摘要： Logging systems and methods are disclosed to reduce usage of radioisotopic sources. Some embodiments comprise collecting at least one output log of a training well bore from measurements with a radioisotopic source; collecting at least one input log of the training well bore from measurements by a non-radioisotopic logging tool; training a neural network to predict the output log from the at least one input log; collecting at least one input log of a development well bore from measurements by the non-radioisotopic logging tool; and processing the at least one input log of the development well bore to synthesize at least one output log of the development well bore. The output logs may include formation density and neutron porosity logs.
摘要翻译： 公开了记录系统和方法以减少放射性同位素源的使用。 一些实施例包括从具有放射性同位素源的测量中收集训练井的至少一个输出日志; 从非放射性同位素测井工具的测量中收集训练井的至少一个输入日志; 训练神经网络以从至少一个输入日志预测输出日志; 从非放射性同位素测井工具的测量中收集开发井眼的至少一个输入日志; 以及处理所述开发井的所述至少一个输入对数以合成所述显影井的至少一个输出对数。 输出原木可包括地层密度和中子孔隙度日志。
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公开(公告)号：US07613665B2
公开(公告)日：2009-11-03
申请号：US11165892
申请日：2005-06-24
CPC分类号： G06N3/0454 , G06N3/086
摘要： Methods of creating and using robust neural network ensembles are disclosed. Some embodiments take the form of computer-based methods that comprise receiving a set of available inputs; receiving training data; training at least one neural network for each of at least two different subsets of the set of available inputs; and providing at least two trained neural networks having different subsets of the available inputs as components of a neural network ensemble configured to transform the available inputs into at least one output. The neural network ensemble may be applied as a log synthesis method that comprises: receiving a set of downhole logs; applying a first subset of downhole logs to a first neural network to obtain an estimated log; applying a second, different subset of the downhole logs to a second neural network to obtain an estimated log; and combining the estimated logs to obtain a synthetic log.
摘要翻译： 公开了创建和使用鲁棒神经网络集合的方法。 一些实施例采用基于计算机的方法的形式，其包括接收一组可用输入; 接收培训数据; 为所述一组可用输入中的至少两个不同子集中的每一个训练至少一个神经网络; 以及提供至少两个经训练的神经网络，其具有可用输入的不同子集，作为被配置为将可用输入转换成至少一个输出的神经网络集合的组件。 神经网络集合可以作为对数合成方法应用，包括：接收一组井下测井; 将第一个井下日志子集应用于第一神经网络以获得估计的对数; 将第二个不同的井下日志子集应用于第二神经网络以获得估计的对数; 并组合估计的日志以获得合成日志。
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6.发明授权System and method of predicting gas saturation of a formation using neural networks 有权使用神经网络预测地层气饱和度的系统和方法公开(公告)号：US08898045B2
公开(公告)日：2014-11-25
申请号：US13146437
申请日：2009-04-21
申请人： Dingding Chen , Weijun Guo , Larry A. Jacobson
发明人： Dingding Chen , Weijun Guo , Larry A. Jacobson
CPC分类号： G01V5/125
摘要： 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.
摘要翻译： 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。
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7.发明申请SYSTEM AND METHOD OF PREDICTING GAS SATURATION OF A FORMATION USING NEURAL NETWORKS 有权使用神经网络预测气体饱和度的系统和方法公开(公告)号：US20110282818A1
公开(公告)日：2011-11-17
申请号：US13146437
申请日：2009-04-21
申请人： Dingding Chen , Weijun Guo , Larry A. Jacobson
发明人： Dingding Chen , Weijun Guo , Larry A. Jacobson
IPC分类号： G06N3/02
CPC分类号： G01V5/125
摘要： 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.
摘要翻译： 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。
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8.发明申请公开(公告)号：US20150184510A1
公开(公告)日：2015-07-02
申请号：US14419435
申请日：2012-08-31
申请人： Li Gao , Wei Zhang , Michael T. Pelletier , Christopher Michael Jones , Dingding Chen , David Earl Ball
发明人： Li Gao , Wei Zhang , Michael T. Pelletier , Christopher Michael Jones , Dingding Chen , David Earl Ball
CPC分类号： E21B49/08 , E21B47/10 , G01N11/14 , G01N27/74 , G01N2011/147
摘要： An apparatus to determine fluid viscosities downhole in real-time includes a housing and an excitation element positioned therein. Electrical circuitry provides a drive signal that excites movement of the excitation element. A detector produces a response signal correlating to the detected rotational or oscillating movement of the excitation element. Circuitry onboard the apparatus utilizes the drive and response signals to determine the fluid viscosity.
摘要翻译： 实时测定井下流体粘度的装置包括壳体和定位在其中的激励元件。 电路提供激励激发元件运动的驱动信号。 检测器产生与检测到的激励元件的旋转或振荡运动相关的响应信号。 设备上的电路利用驱动和响应信号来确定流体粘度。
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公开(公告)号：US20150176403A1
公开(公告)日：2015-06-25
申请号：US14403079
申请日：2012-06-13
CPC分类号： E21B49/008 , E21B47/06
摘要： A system for pressure testing a formation includes a downhole tool configured to measure formation pressure, storage containing pressure parameters of a plurality of simulated formation pressure tests, and a formation pressure test controller coupled to the downhole tool and the storage. For each of a plurality of sequential pressure testing stages of a formation pressure test, the formation pressure test controller 1) retrieves formation pressure measurements from the downhole tool; 2) identifies one of the plurality of simulated formation pressure tests comprising pressure parameters closest to corresponding formation pressure values derived from the formation pressure measurements; and 3) determines a flow rate to apply by the downhole tool in a next stage of the test based on the identified one of the plurality of simulated formation pressure tests.
摘要翻译： 用于对地层进行压力测试的系统包括被配置为测量地层压力的井下工具，包含多个模拟地层压力测试的压力参数的储存器以及耦合到井下工具和储存器的地层压力测试控制器。 对于地层压力试验的多个连续压力试验阶段中的每一个，地层压力试验控制器1）从井下工具检索地层压力测量值; 2）识别多个模拟地层压力测试中的一个，其包括最接近从地层压力测量得出的对应地层压力值的压力参数; 以及3）基于所述多个模拟地层压力测试中的所述一个确定在所述测试的下一阶段中确定由所述井下工具施加的流量。
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10.发明授权公开(公告)号：US08355873B2
公开(公告)日：2013-01-15
申请号：US11288826
申请日：2005-11-29
申请人： Ali Mese , Syed Hamid , Dingding Chen , Harry D. Smith, Jr. , John Howard , Neal Skinner
发明人： Ali Mese , Syed Hamid , Dingding Chen , Harry D. Smith, Jr. , John Howard , Neal Skinner
IPC分类号： G01V9/00
CPC分类号： G01V11/00
摘要： Reservoir characterization based on observations of displacements at the earth's surface. One method of characterizing a reservoir includes the steps of: detecting a response of the reservoir to a stimulus, the stimulus causing a pressure change in the reservoir; and determining a characteristic of the reservoir from the response to the stimulus. The response may be the pressure change which varies periodically over time, or a set of displacements of a surface of the earth. In another example, a method includes the steps of: detecting a set of displacements of the earth's surface corresponding to a pressure change in the reservoir; and determining a characteristic of the reservoir from the surface displacements. In yet another example, a method includes the steps of: detecting a set of displacements of the earth's surface corresponding to a change in volume of the reservoir; and determining a characteristic of the reservoir from the surface displacements.
摘要翻译： 基于地球表面位移观测的油藏特征。 表征储层的一种方法包括以下步骤：检测储层对刺激的响应，所述刺激导致储层中的压力变化; 以及从所述刺激的响应确定所述储层的特性。 响应可以是随时间周期性地变化的压力变化或地球表面的一组位移。 在另一示例中，一种方法包括以下步骤：检测对应于储层中的压力变化的地球表面的一组位移; 以及从表面位移确定储层的特征。 在又一示例中，一种方法包括以下步骤：检测对应于储层体积变化的地球表面的一组位移; 以及从表面位移确定储层的特征。
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