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    SYSTEMS AND METHODS TO DETERMINE BIOT COEFFICIENT AND EFFECTIVE STRESS DEPENDENCE COEFFICIENT IN ROCK
    2.
    发明申请
    SYSTEMS AND METHODS TO DETERMINE BIOT COEFFICIENT AND EFFECTIVE STRESS DEPENDENCE COEFFICIENT IN ROCK 有权

    公开(公告)号：US20250003856A1

    公开(公告)日：2025-01-02

    申请号：US18343684

    申请日：2023-06-28

    Applicant: ARAMCO SERVICES COMPANY

    Inventor： Jilin Jay Zhang ,  Hui-Hai Liu ,  Jewel Duncan

    IPC: G01N15/08 ,  G01N33/24

    Abstract: Methods and systems are disclosed. The methods may include obtaining, from a subterranean region of interest, a rock sample having a rock type and defining a sequence of pore pressure, confining stress (PPCS) pairs such that a sequence of effective stresses monotonically changes. The methods may further include determining a sequence of permeabilities by subjecting the rock sample to the sequence of PPCS pairs and determining a relationship between the sequence of PPCS pairs and the sequence of permeabilities. The methods may further still include determining a parameter using the relationship and a permeability model, where the permeability model includes the parameter and determining an in situ permeability for an in situ rock in the subterranean region of interest using, at least in part, the parameter and the permeability model, where the in situ rock is of the rock type.

    METHOD AND SYSTEM FOR PREDICTING HYDROCARBON DATA FOR UNCONVENTIONAL RESERVOIRS USING MACHINE LEARNING
    3.
    发明申请
    METHOD AND SYSTEM FOR PREDICTING HYDROCARBON DATA FOR UNCONVENTIONAL RESERVOIRS USING MACHINE LEARNING 有权

    公开(公告)号：US20250003325A1

    公开(公告)日：2025-01-02

    申请号：US18344666

    申请日：2023-06-29

    Applicant: ARAMCO SERVICES COMPANY

    Inventor： Rabah Mesdour ,  Jilin Jay Zhang ,  Hui-Hai Liu ,  Faisal F. Almalki

    IPC: E21B44/00

    Abstract: A method may include obtaining reservoir data, hydraulic fracturing data, and static wellbore data for a geological region of interest. The method may further include obtaining temporal production data for the geological region of interest. The temporal production data may include a predetermined production rate with respect to a predetermined period of time. The method may further include determining various temporal features based on the temporal production data and an extraction process. The extraction process may include a deconvolution function that separates a portion of the temporal features from the predetermined production rate. The method may further include determining, using a machine-learning model, predicted hydrocarbon-in-place (HIP) data for the geological region of interest using the reservoir data, the hydraulic fracturing data, the static wellbore data, and the temporal features. The method may further include transmitting a command to a well control system based on the predicted HIP data.

    PREDICTING WELL PERFORMANCE USING NEURAL NETWORKS
    4.
    发明公开
    PREDICTING WELL PERFORMANCE USING NEURAL NETWORKS 审中-公开

    公开(公告)号：US20230385604A1

    公开(公告)日：2023-11-30

    申请号：US17664961

    申请日：2022-05-25

    Applicant: ARAMCO SERVICES COMPANY

    Inventor： Hui-Hai Liu ,  Jilin Jay Zhang ,  Jodel Cornelio ,  Syamil Mohd-Razak

    IPC: G06N3/04 ,  G06N3/08 ,  G01V1/28

    CPC classification number: G06N3/0454 ,  G06N3/0481 ,  G06N3/084 ,  G01V1/282

    Abstract: A system and methods for predicting well performance are disclosed. The method includes obtaining first geoscience data and first performance data, obtaining second geoscience data and second performance data, and obtaining new geoscience data for a new well. The method further includes training a first neural network and determining predicted second performance data using the first neural network. The method still further includes determining a residual between the second performance data and the predicted second performance data and training a second neural network. The method still further includes determining predicted new performance data for the new well by inputting a subset of the new geoscience data into the first neural network, determining a new residual for the new well by inputting the new geoscience data into the second neural network, and updating the predicted new performance data using the new residual.

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