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1.发明申请METHODS AND RELATED SYSTEMS OF BUILDING MODELS AND PREDICTING OPERATIONAL OUTCOMES OF A DRILLING OPERATION 有权建筑模型的方法与相关系统和预测钻井作业的运行成果公开(公告)号：US20140351183A1
公开(公告)日：2014-11-27
申请号：US14359596
申请日：2013-06-07
Applicant: Landmark Graphics Corporation
Inventor： Olivier Germain , Keshava P. Rangarajan , Amit K. Singh , Hermanus Teunissen , Ram N. Adari
CPC classification number: G06F17/5009 , E21B7/00 , E21B44/00 , G06N99/005
Abstract: Building models and predicting operational outcomes of a drilling operation. At least some of the illustrative embodiments are methods including: gathering sensor data regarding offset wells and context data regarding the offset wells, and placing the sensor data and context data into a data store; creating a reduced data set by identifying a correlation between data in the data store and an operational outcome in a drilling operation; creating a model based on the reduced data set; and predicting the operational outcome based on the model.
Abstract translation: 建立模型并预测钻井作业的运行结果。 示例性实施例中的至少一些是包括：收集关于偏移井的传感器数据和关于偏移井的上下文数据，以及将传感器数据和上下文数据放入数据存储器中; 通过识别数据存储中的数据与钻井操作中的操作结果之间的相关性来创建减少的数据集; 基于简化数据集创建模型; 并根据模型预测运行结果。
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2.发明申请公开(公告)号：US20200320386A1
公开(公告)日：2020-10-08
申请号：US16642452
申请日：2017-12-26
Applicant: Landmark Graphics Corporation
Inventor： Andrey Filippov , Jianxin Lu , Avinash Wesley , Keshava P. Rangarajan , Srinath Madasu
Abstract: System and methods for training neural network models for real-time flow simulations are provided. Input data is acquired. The input data includes values for a plurality of input parameters associated with a multiphase fluid flow. The multiphase fluid flow is simulated using a complex fluid dynamics (CFD) model, based on the acquired input data. The CFD model represents a three-dimensional (3D) domain for the simulation. An area of interest is selected within the 3D domain represented by the CFD model. A two-dimensional (2D) mesh of the selected area of interest is generated. The 2D mesh represents results of the simulation for the selected area of interest. A neural network is then trained based on the simulation results represented by the generated 2D mesh.
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公开(公告)号：US09934338B2
公开(公告)日：2018-04-03
申请号：US14359596
申请日：2013-06-07
Applicant: Landmark Graphics Corporation
Inventor： Olivier Germain , Keshava P. Rangarajan , Amit K. Singh , Hermanus Teunissen , Ram N. Adari
CPC classification number: G06F17/5009 , E21B7/00 , E21B44/00 , G06N99/005
Abstract: Building models and predicting operational outcomes of a drilling operation. At least some of the illustrative embodiments are methods including: gathering sensor data regarding offset wells and context data regarding the offset wells, and placing the sensor data and context data into a data store; creating a reduced data set by identifying a correlation between data in the data store and an operational outcome in a drilling operation; creating a model based on the reduced data set; and predicting the operational outcome based on the model.
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4.发明授权公开(公告)号：US12061980B2
公开(公告)日：2024-08-13
申请号：US16642452
申请日：2017-12-26
Applicant: Landmark Graphics Corporation
Inventor： Andrey Filippov , Jianxin Lu , Avinash Wesley , Keshava P. Rangarajan , Srinath Madasu
Abstract: System and methods for training neural network models for real-time flow simulations are provided. Input data is acquired. The input data includes values for a plurality of input parameters associated with a multiphase fluid flow. The multiphase fluid flow is simulated using a complex fluid dynamics (CFD) model, based on the acquired input data. The CFD model represents a three-dimensional (3D) domain for the simulation. An area of interest is selected within the 3D domain represented by the CFD model. A two-dimensional (2D) mesh of the selected area of interest is generated. The 2D mesh represents results of the simulation for the selected area of interest. A neural network is then trained based on the simulation results represented by the generated 2D mesh.
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