Deep learning based reservoir modeling

    公开(公告)号:US11599790B2

    公开(公告)日:2023-03-07

    申请号:US16614858

    申请日:2017-07-21

    Abstract: Embodiments of the subject technology for deep learning based reservoir modelling provides for receiving input data comprising information associated with one or more well logs in a region of interest. The subject technology determines, based at least in part on the input data, an input feature associated with a first deep neural network (DNN) for predicting a value of a property at a location within the region of interest. Further, the subject technology trains, using the input data and based at least in part on the input feature, the first DNN. The subject technology predicts, using the first DNN, the value of the property at the location in the region of interest. The subject technology utilizes a second DNN that classifies facies based on the predicted property in the region of interest.

    METRIC-BASED SUSTAINABILITY INDEX FOR WELLBORE LIFE CYCLE

    公开(公告)号:US20210388717A1

    公开(公告)日:2021-12-16

    申请号:US17039327

    申请日:2020-09-30

    Abstract: A system can assign a value to one or more sustainability factors for a wellbore operation based on historical data. The system can determine, for each of the one or more sustainability factors, a weight. The system can determine a sustainability index corresponding to a predicted carbon footprint for the wellbore operation based on the weight and the value for each of the one or more sustainability factors. The system can output a command for adjusting the wellbore operation based on the sustainability index.

    Deep Learning Based Reservoir Modeling
    3.
    发明申请

    公开(公告)号:US20200160173A1

    公开(公告)日:2020-05-21

    申请号:US16614858

    申请日:2017-07-21

    Abstract: Embodiments of the subject technology for deep learning based reservoir modelling provides for receiving input data comprising information associated with one or more well logs in a region of interest. The subject technology determines, based at least in part on the input data, an input feature associated with a first deep neural network (DNN) for predicting a value of a property at a location within the region of interest. Further, the subject technology trains, using the input data and based at least in part on the input feature, the first DNN. The subject technology predicts, using the first DNN, the value of the property at the location in the region of interest. The subject technology utilizes a second DNN that classifies facies based on the predicted property in the region of interest.

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