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    INTEGRATION OF UPHOLES WITH INVERSION-BASED VELOCITY MODELING
    1.
    发明申请
    INTEGRATION OF UPHOLES WITH INVERSION-BASED VELOCITY MODELING 有权

    公开(公告)号：US20230125277A1

    公开(公告)日：2023-04-27

    申请号：US17522145

    申请日：2021-11-09

    Applicant: Saudi Arabian Oil Company

    Inventor： Daniele Colombo ,  Ersan Turkoglu ,  Ernesto Sandoval-Curiel ,  Diego Rovetta ,  Apostolos Kontakis ,  Weichang Li

    IPC: G01V1/28 ,  G01V1/30 ,  G06N3/08 ,  G06N3/04

    Abstract: Disclosed are methods, systems, and computer-readable medium to perform operations including: receiving for a plurality of common midpoint-offset bins each comprising a respective plurality of seismic traces, respective candidate pilot traces representing the plurality of common midpoint-offset bins; generating, based on the respective candidate pilot traces, a respective plurality of corrected seismic traces for each of the plurality of common midpoint-offset bins; grouping the respective pluralities of corrected seismic traces into a plurality of enhanced virtual shot gathers (eVSGs); generating, based on the plurality of common midpoint-offset bins, a common-midpoint (CMP) velocity model; calibrating the CMP velocity model using uphole velocity data to generate a pseudo-3 dimensional (3D) velocity model; performing, based on the plurality of enhanced virtual shot gathers and the pseudo-3D velocity model, a 1.5-dimensional full waveform inversion (FWI); and determining the subsurface velocity model based on the 1.5 dimensional FWI.

    TRAINING DATASET GENERATION PROCESS FOR MOMENT TENSOR MACHINE LEARNING INVERSION MODELS
    2.
    发明公开
    TRAINING DATASET GENERATION PROCESS FOR MOMENT TENSOR MACHINE LEARNING INVERSION MODELS 审中-公开

    公开(公告)号：US20240240554A1

    公开(公告)日：2024-07-18

    申请号：US18096402

    申请日：2023-01-12

    Applicant: Saudi Arabian Oil Company

    Inventor： Diego Rovetta ,  Daniele Colombo ,  Eddy Revelo Obando ,  Ersan Turkoglu

    IPC: E21B47/18 ,  G01V1/28

    CPC classification number: E21B47/18 ,  G01V1/282 ,  E21B2200/22

    Abstract: Methods and systems for training a machine learning model to process microseismic data recorded during fracturing of a subterranean geological formation are configured for selecting a volume in the subterranean geological formation, the volume comprising a set of vertices and a center, the set of vertices defining a first dimension; determining seismogram data for sources at the vertices of the volume and at the center of the volume; generating training data from the seismogram data, the training data relating values of seismogram data to values of moment tensor components; training a machine learning model using the training data; and determining, based on the trained machine learning model, a second dimension defined for the set of vertices, the second dimension being a maximum value enabling an accuracy for outputs of the trained machine learning model that satisfies a threshold.

    MACHINE LEARNING INVERSION USING BAYESIAN INFERENCE AND SAMPLING
    5.
    发明公开
    MACHINE LEARNING INVERSION USING BAYESIAN INFERENCE AND SAMPLING 审中-公开

    公开(公告)号：US20230289499A1

    公开(公告)日：2023-09-14

    申请号：US17693261

    申请日：2022-03-11

    Applicant: SAUDI ARABIAN OIL COMPANY

    Inventor： Daniele Colombo ,  Anton Egorov ,  Ersan Turkoglu

    IPC: G06F30/27 ,  G01V1/28

    CPC classification number: G06F30/27 ,  G01V1/282

    Abstract: A system and methods for determining an updated geophysical model of a subterranean region of interest are disclosed. The method includes obtaining a preprocessed observed geophysical dataset based, at least in part, on an observed geophysical dataset of the subterranean region of interest, and forming a training dataset composed of a plurality of geophysical training models and corresponding simulated geophysical training datasets. The method further includes iteratively determining a simulated geophysical dataset from a current geophysical model, determining a data loss function between the preprocessed observed geophysical dataset and the simulated geophysical dataset, training a machine learning (ML) network, using the training dataset, to predict a predicted geophysical model and determining a model loss function between the current and predicted geophysical models. The method still further includes updating the current geophysical model based on an inversion using the data loss and model loss functions.

    METHOD FOR PREDICTING A SEISMIC MODEL
    6.
    发明公开
    METHOD FOR PREDICTING A SEISMIC MODEL 审中-公开

    公开(公告)号：US20230288592A1

    公开(公告)日：2023-09-14

    申请号：US17654565

    申请日：2022-03-11

    Applicant: SAUDI ARABIAN OIL COMPANY

    Inventor： Daniele Colombo ,  Diego Rovetta ,  Weichang Li ,  Ersan Turkoglu

    IPC: G01V1/30 ,  G01V99/00 ,  E21B44/00 ,  E21B49/00

    CPC classification number: G01V1/306 ,  G01V1/303 ,  G01V99/005 ,  E21B44/00 ,  E21B49/00 ,  E21B2200/20 ,  E21B2200/22 ,  E21B7/04

    Abstract: A system and methods for determining a refined seismic model of a subterranean region are disclosed. The method includes obtaining an observed seismic dataset and a current seismic model for the subterranean region and training a machine learning (ML) network using seismic training models and corresponding seismic training datasets and predicting, using the trained ML network, a predicted seismic model from the observed seismic dataset. The method further includes determining a simulated seismic dataset from the current seismic model and a seismic wavelet, a data penalty function based on a difference between the observed and the simulated seismic datasets and a model penalty function from the difference between the current the predicted seismic models. The method still further includes determining the refined seismic model based on an extremum of a composite penalty function based on a weighted sum of the data penalty function and the model penalty function.

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