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    Estimating Reservoir Production Rates Using Machine Learning Models for Wellbore Operation Control
    2.
    发明申请
    Estimating Reservoir Production Rates Using Machine Learning Models for Wellbore Operation Control 有权

    公开(公告)号：US20220205354A1

    公开(公告)日：2022-06-30

    申请号：US17136895

    申请日：2020-12-29

    Applicant: Landmark Graphics Corporation

    Inventor： Soumi Chaki ,  Honggeun Jo ,  Terry Wong ,  Yevgeniy Zagayevskiy ,  Dominic Camilleri

    IPC: E21B47/022 ,  E21B47/12 ,  G06N3/08 ,  G01V1/46 ,  G01V1/48

    Abstract: A system is described for estimating well production and injection rates of a subterranean reservoir using machine learning models. The system may include a processor and a non-transitory computer-readable medium comprising instructions that are executable by the processor to cause the processor to perform various operations. The processor may receive a set of static geological data about at least one subterranean reservoir in a subterranean formation. The processor may apply a trained convolutional neural network to the set of static geological data and data on initial states of dynamic reservoir properties to determine dynamic outputs of the subterranean reservoir. The processor may determine well data by extracting the set of static geological data and the dynamic outputs at well trajectories. And, the processor may apply a trained artificial neural network to the well data and subterranean grid information about the subterranean reservoir to generate estimated well production and injection rates.

    DATA-DRIVEN DOMAIN CONVERSION USING MACHINE LEARNING TECHNIQUES
    3.
    发明申请
    DATA-DRIVEN DOMAIN CONVERSION USING MACHINE LEARNING TECHNIQUES 有权

    公开(公告)号：US20210311221A1

    公开(公告)日：2021-10-07

    申请号：US16841890

    申请日：2020-04-07

    Applicant: Landmark Graphics Corporation

    Inventor： Samiran Roy ,  Soumi Chaki ,  Sridharan Vallabhaneni

    IPC: G01V1/48 ,  G01V1/36 ,  G06K9/62 ,  G06N3/08 ,  G06N20/00

    Abstract: Optimizing seismic to depth conversion to enhance subsurface operations including measuring seismic data in a subsurface formation, dividing the subsurface formation into a training area and a study area, dividing the seismic data into training seismic data and study seismic data, wherein the training seismic data corresponds to the training area, and wherein the study seismic data corresponds to the study area, calculating target depth data corresponding to the training area, training a machine learning model using training inputs and training targets, wherein the training inputs comprise the training seismic data, and wherein the training targets comprise the target depth data, computing, by the machine learning model, output depth data corresponding to the study area based at least in part on the study seismic data; and modifying one or more subsurface operations corresponding to the study area based at least in part on the output depth data.

    Data-driven domain conversion using machine learning techniques
    4.
    发明授权
    Data-driven domain conversion using machine learning techniques 有权

    公开(公告)号：US11614557B2

    公开(公告)日：2023-03-28

    申请号：US16841890

    申请日：2020-04-07

    Applicant: Landmark Graphics Corporation

    Inventor： Samiran Roy ,  Soumi Chaki ,  Sridharan Vallabhaneni

    IPC: G01V1/48 ,  G01V1/36 ,  G06N3/08 ,  G06N20/00 ,  G06V10/70 ,  G06V10/82 ,  G06F18/21

    Abstract: Optimizing seismic to depth conversion to enhance subsurface operations including measuring seismic data in a subsurface formation, dividing the subsurface formation into a training area and a study area, dividing the seismic data into training seismic data and study seismic data, wherein the training seismic data corresponds to the training area, and wherein the study seismic data corresponds to the study area, calculating target depth data corresponding to the training area, training a machine learning model using training inputs and training targets, wherein the training inputs comprise the training seismic data, and wherein the training targets comprise the target depth data, computing, by the machine learning model, output depth data corresponding to the study area based at least in part on the study seismic data; and modifying one or more subsurface operations corresponding to the study area based at least in part on the output depth data.

    MACHINE LEARNING BASED RANKING OF HYDROCARBON PROSPECTS FOR FIELD EXPLORATION
    5.
    发明申请
    MACHINE LEARNING BASED RANKING OF HYDROCARBON PROSPECTS FOR FIELD EXPLORATION 有权

    公开(公告)号：US20220414522A1

    公开(公告)日：2022-12-29

    申请号：US17304970

    申请日：2021-06-29

    Applicant: Landmark Graphics Corporation

    Inventor： Samiran Roy ,  Soumi Chaki

    IPC: G06N20/00 ,  G01V1/30

    Abstract: An ensemble of machine learning models is trained to evaluate seismic and risk-related data in order to evaluate, value, or otherwise rank various prospective hydrocarbon reservoir (“prospects”) of a field. A classification machine learning model is trained to classify a prospect or region of a prospect based on the exploration risk level. From the seismic data, a frequency-filtered volume (FFV) for each prospect is calculated, where the FFV is a measure of reservoir volume which takes into account seismic resolution limits. Based on the risk classification and FFV, prospects of the field are ranked based on their economic value which is a combination of the risk associated with drilling and their potential reservoir volume.

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