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公开(公告)号：US09587478B2
公开(公告)日：2017-03-07
申请号：US13154921
申请日：2011-06-07
Applicant: David P. Moran , Stuart R. Oliver
Inventor： David P. Moran , Stuart R. Oliver
CPC classification number: E21B44/00 , E21B2041/0028
Abstract: A computer-assisted method for optimizing a drilling tool assembly, the method comprising defining a desired drilling plan; determining current drilling conditions; determining current drilling tool parameters of at least two drilling tool assembly components; analyzing the current drilling conditions and the current drilling tool parameters to define a base drilling condition; comparing the base drilling condition to the desired drilling plan; determining a drilling tool parameter to adjust to achieve the desired drilling plan; and adjusting at least one drilling tool parameter of at least one of the two drilling tool assembly components based on the comparing the base drilling condition to the desired drilling plan.
Abstract translation: 一种用于优化钻具组件的计算机辅助方法，所述方法包括定义所需的钻孔平面图; 确定当前钻井条件; 确定至少两个钻具组件部件的当前钻具参数; 分析当前的钻井条件和当前的钻具参数来定义基础钻井条件; 将基础钻井条件与期望的钻井计划进行比较; 确定钻具参数以进行调整以实现所需的钻孔计划; 并且基于将所述基础钻井条件与期望的钻井计划进行比较来调整所述两个钻削组件部件中的至少一个的至少一个钻孔参数。
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公开(公告)号：US20120316787A1
公开(公告)日：2012-12-13
申请号：US13154921
申请日：2011-06-07
Applicant: David P. Moran , Stuart R. Oliver
Inventor： David P. Moran , Stuart R. Oliver
CPC classification number: E21B44/00 , E21B2041/0028
Abstract: A computer-assisted method for optimizing a drilling tool assembly, the method comprising defining a desired drilling plan; determining current drilling conditions; determining current drilling tool parameters of at least two drilling tool assembly components; analyzing the current drilling conditions and the current drilling tool parameters to define a base drilling condition; comparing the base drilling condition to the desired drilling plan; determining a drilling tool parameter to adjust to achieve the desired drilling plan; and adjusting at least one drilling tool parameter of at least one of the two drilling tool assembly components based on the comparing the base drilling condition to the desired drilling plan.
Abstract translation: 一种用于优化钻具组件的计算机辅助方法，所述方法包括定义所需的钻孔平面图; 确定当前钻井条件; 确定至少两个钻具组件部件的当前钻具参数; 分析当前的钻井条件和当前的钻具参数来定义基础钻井条件; 将基础钻井条件与期望的钻井计划进行比较; 确定钻具参数以进行调整以实现所需的钻孔计划; 并且基于将所述基础钻井条件与期望的钻井计划进行比较来调整所述两个钻削组件部件中的至少一个的至少一个钻孔参数。
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公开(公告)号：US20070185696A1
公开(公告)日：2007-08-09
申请号：US11670696
申请日：2007-02-02
Applicant: David P. Moran , Stuart R. Oliver , Sujian J. Huang , Luis C. Paez , Lei Yan
Inventor： David P. Moran , Stuart R. Oliver , Sujian J. Huang , Luis C. Paez , Lei Yan
IPC: G06G7/48
CPC classification number: E21B44/00 , E21B2041/0028 , G05B13/027
Abstract: A method of optimizing drilling including identifying design parameters for a drilling tool assembly, preserving the design parameters as experience data, and training at least one artificial neural network using the experience data. The method also includes collecting real-time data from the drilling operation, analyzing the real-time data with a real-time drilling optimization system, and determining optimal drilling parameters based on the analyzing the real-time date with the real-time drilling optimization system. Also, a method for optimizing drilling in real-time including collecting real-time data from a drilling operation and comparing the real-time data against predicted data in a real-time optimization system, wherein the real-time optimization includes at least one artificial neural network. The method further includes determining optimal drilling parameters based on the comparing the real-time data with the predicted data in the real-time drilling optimization system.
Abstract translation: 一种优化钻井的方法，包括识别钻具组件的设计参数，将设计参数保留为经验数据，以及使用经验数据训练至少一个人造神经网络。 该方法还包括从钻井作业中收集实时数据，利用实时钻井优化系统分析实时数据，并通过实时钻井优化分析实时日期确定最优钻井参数。 系统。 此外，一种实时优化钻井的方法，包括从钻井操作收集实时数据并将实时数据与实时优化系统中的预测数据进行比较，其中实时优化包括至少一个人工 神经网络。 该方法还包括基于将实时数据与实时钻井优化系统中的预测数据进行比较来确定最佳钻井参数。
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公开(公告)号：US08285531B2
公开(公告)日：2012-10-09
申请号：US12105108
申请日：2008-04-17
Applicant: David P. Moran , Mark P. Frenzel , Roy Duncan
Inventor： David P. Moran , Mark P. Frenzel , Roy Duncan
IPC: G06G7/48
CPC classification number: E21B7/00 , E21B44/00 , E21B2041/0028 , G05B13/027 , G05B17/02
Abstract: A method of optimizing a drilling tool assembly including inputting well data into an optimization system, the optimization system having an experience data set and an artificial neural network. The method further including comparing the well data to the experience data set and developing an initial drilling tool assembly based on the comparing the well data to the experience data, wherein the drilling tool assembly is developed using the artificial neural network. Additionally, the method including simulating the initial drilling tool assembly in the optimization system and creating result data in the optimization system based on the simulating.
Abstract translation: 一种优化钻具组合的方法，包括将井数据输入到优化系统中，所述优化系统具有体验数据集和人造神经网络。 该方法还包括将井数据与经验数据集进行比较，并且基于将井数据与经验数据进行比较来开发初始钻井工具组件，其中使用人造神经网络来开发钻井工具组件。 另外，该方法包括模拟优化系统中的初始钻孔组件，并基于模拟创建优化系统中的结果数据。
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公开(公告)号：US20110232968A1
公开(公告)日：2011-09-29
申请号：US12732301
申请日：2010-03-26
Applicant: Arifin Purwanto , David P. Moran , Lei Yan
Inventor： Arifin Purwanto , David P. Moran , Lei Yan
IPC: E21B7/00
CPC classification number: E21B44/00 , E21B2041/0028
Abstract: A method of optimizing a drilling operating parameter or a drilling system parameter for a drilling assembly employing at least first and second distinct cutting structures includes entering at least one design parameter for each of the cutting structures into a trained artificial neural network. At least one of the design parameters of the first cutting structure may be optionally combined with at least one of the design parameters of the second cutting structure. The combined design parameter may also be entered into the artificial neural network.
Abstract translation: 优选使用至少第一和第二不同切割结构的钻井组件的钻井操作参数或钻井系统参数的方法包括将每个切割结构的至少一个设计参数输入训练的人造神经网络。 第一切割结构的设计参数中的至少一个可以任选地与第二切割结构的设计参数中的至少一个组合。 组合设计参数也可以输入人造神经网络。
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Patent Agency Ranking
公开(公告)号：US08799198B2
公开(公告)日：2014-08-05
申请号：US12732301
申请日：2010-03-26
Applicant: Arifin Purwanto , David P. Moran , Lei Yan
Inventor： Arifin Purwanto , David P. Moran , Lei Yan
CPC classification number: E21B44/00 , E21B2041/0028
Abstract: A method of optimizing a drilling operating parameter or a drilling system parameter for a drilling assembly employing at least first and second distinct cutting structures includes entering at least one design parameter for each of the cutting structures into a trained artificial neural network. At least one of the design parameters of the first cutting structure may be optionally combined with at least one of the design parameters of the second cutting structure. The combined design parameter may also be entered into the artificial neural network.
Abstract translation: 使用至少第一和第二不同切割结构优化用于钻井组件的钻井操作参数或钻井系统参数的方法包括将用于每个切割结构的至少一个设计参数输入训练的人造神经网络。 第一切割结构的设计参数中的至少一个可以任选地与第二切割结构的设计参数中的至少一个组合。 组合设计参数也可以输入人造神经网络。
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公开(公告)号：US20080294606A1
公开(公告)日：2008-11-27
申请号：US12095338
申请日：2007-02-06
Applicant: David P. Moran , Yashodhan Gidh , Lei Yan
Inventor： David P. Moran , Yashodhan Gidh , Lei Yan
CPC classification number: G06F17/2247 , G06F17/2229 , G06F17/2264
Abstract: A method for aggregating data that includes obtaining a log object including a log element, wherein the log element includes oilfield data obtained from a provider, obtaining an aggregation policy for the log element, and aggregating the log element into an aggregated object based on the aggregation policy is disclosed.
Abstract translation: 一种用于聚合数据的方法，包括获得包括日志元素的日志对象，其中所述日志元素包括从提供者获得的油田数据，获得所述日志元素的聚合策略，并且基于所述聚合将所述日志元素聚合成聚合对象 披露政策。
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公开(公告)号：US09388680B2
公开(公告)日：2016-07-12
申请号：US11843929
申请日：2007-08-23
Applicant: David P. Moran
Inventor： David P. Moran
CPC classification number: E21B44/00 , E21B2041/0028
Abstract: A method for providing assistance to a drilling site including receiving, by a remote system, an assistance request from a quick-link communication device, wherein the quick-link communication device is located at the drilling location. The method also including obtaining sensor data from the rig based on the assistance request, analyzing, by the remote system, the sensor data to identify a condition of the rig, and providing assistance to the drilling site for the condition of the rig.
Abstract translation: 一种用于向钻孔现场提供帮助的方法，包括由远程系统接收来自快速连接通信设备的辅助请求，其中快速连接通信设备位于钻井位置。 该方法还包括基于辅助请求从钻机获取传感器数据，由远程系统分析传感器数据以识别钻机的状况，以及为钻机的状况向钻井现场提供帮助。
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公开(公告)号：US20130036077A1
公开(公告)日：2013-02-07
申请号：US13645562
申请日：2012-10-05
Applicant: David P. Moran , Mark P. Frenzel , Roy Duncan
Inventor： David P. Moran , Mark P. Frenzel , Roy Duncan
CPC classification number: E21B7/00 , E21B44/00 , E21B2041/0028 , G05B13/027 , G05B17/02
Abstract: A method of optimizing a drilling tool assembly including inputting well data into an optimization system, the optimization system having an experience data set and an artificial neural network. The method further including comparing the well data to the experience data set and developing an initial drilling tool assembly based on the comparing the well data to the experience data, wherein the drilling tool assembly is developed using the artificial neural network. Additionally, the method including simulating the initial drilling tool assembly in the optimization system and creating result data in the optimization system based on the simulating.
Abstract translation: 一种优化钻具组合的方法，包括将井数据输入到优化系统中，所述优化系统具有体验数据集和人造神经网络。 该方法还包括将井数据与经验数据集进行比较，并且基于将井数据与经验数据进行比较来开发初始钻井工具组件，其中使用人造神经网络来开发钻井工具组件。 另外，该方法包括模拟优化系统中的初始钻孔组件，并基于模拟创建优化系统中的结果数据。
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公开(公告)号：US20080262810A1
公开(公告)日：2008-10-23
申请号：US12105108
申请日：2008-04-17
Applicant: David P. Moran , Mark P. Frenzel , Roy Duncan
Inventor： David P. Moran , Mark P. Frenzel , Roy Duncan
CPC classification number: E21B7/00 , E21B44/00 , E21B2041/0028 , G05B13/027 , G05B17/02
Abstract: A method of optimizing a drilling tool assembly including inputting well data into an optimization system, the optimization system having an experience data set and an artificial neural network. The method further including comparing the well data to the experience data set and developing an initial drilling tool assembly based on the comparing the well data to the experience data, wherein the drilling tool assembly is developed using the artificial neural network. Additionally, the method including simulating the initial drilling tool assembly in the optimization system and creating result data in the optimization system based on the simulating.
Abstract translation: 一种优化钻具组合的方法，包括将井数据输入到优化系统中，所述优化系统具有体验数据集和人造神经网络。 该方法还包括将井数据与经验数据集进行比较，并且基于将井数据与经验数据进行比较来开发初始钻井工具组件，其中使用人造神经网络来开发钻井工具组件。 另外，该方法包括模拟优化系统中的初始钻孔组件，并基于模拟创建优化系统中的结果数据。
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