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    Information extraction from daily drilling reports using machine learning
    1.
    发明授权
    Information extraction from daily drilling reports using machine learning 有权

    公开(公告)号：US12129755B2

    公开(公告)日：2024-10-29

    申请号：US17753759

    申请日：2020-09-14

    申请人： Schlumberger Technology Corporation

    发明人： Mohamed Saad Kisra ,  Francisco Jose Gomez ,  Karsten Fischer ,  Ivan Diaz Granados Pertuz ,  Athithan Dharmaratnam

    IPC分类号： E21B47/12 ,  G06F40/284 ,  G06F40/30

    CPC分类号： E21B47/12 ,  G06F40/284 ,  G06F40/30 ,  E21B2200/20 ,  E21B2200/22

    摘要： A system and method are provided for extracting information regarding a drill site including forming one or more documents having one or more raw comments regarding a well site. Raw data may be extracted from the one or more documents to produce extracted raw data. The extracted raw date may be pre-processed by removing ambiguity, artifacts, and/or formatting errors from the one or more raw comments to produce pre-processed data. Topics data may be extracted from the pre-processed data using a natural language processing (NLP) algorithm to produce extracted topics data. Measurement data may also be extracted from the pre-processed data using the NLP algorithm to produce extracted measurement data. The extracted topics data and the extracted measurement data may be aggregated to form a set of discrete data points, such as calibration points, per comment to produce aggregated data and one more calibration points may be identified from the aggregated data. The results of the one or more calibration points may then be presented.

    Classification and control of detected drilling vibrations using machine learning
    4.
    发明授权
    Classification and control of detected drilling vibrations using machine learning 有权

    公开(公告)号：US12116879B2

    公开(公告)日：2024-10-15

    申请号：US17130189

    申请日：2020-12-22

    申请人： Halliburton Energy Services, Inc.

    发明人： Shilin Chen

    IPC分类号： E21B44/02 ,  E21B47/013 ,  E21B47/18 ,  G06N20/20

    CPC分类号： E21B44/02 ,  E21B47/013 ,  E21B47/18 ,  G06N20/20 ,  E21B2200/22

    摘要： A vibrational disfunction machine learning model trainer trains a vibrational disfunction classifier to identify one or more types of vibrational disfunction, or normal drilling, based on measurements of at least one of displacement, velocity, acceleration, angular displacement, angular velocity, and angular acceleration acquired for the drill bit. The vibrational disfunction machine learning model trainer trains the algorithm based on data sets corresponding to characteristic behavior for one or more types of vibrational disfunction and normal drilling. The vibrational disfunction classifier operates in real time, and can operate at the drill bit and communicate vibrational disfunction identification in real time, allowing mitigation of vibrational disfunction through adjustment of drilling parameters.

    FEATURE DETECTION USING MACHINE LEARNING
    5.
    发明公开
    FEATURE DETECTION USING MACHINE LEARNING 审中-公开

    公开(公告)号：US20240328297A1

    公开(公告)日：2024-10-03

    申请号：US18191613

    申请日：2023-03-28

    申请人： SAUDI ARABIAN OIL COMPANY

    发明人： Bingbing Sun ,  Nasher M. Albinhassan

    IPC分类号： E21B44/00 ,  G01V1/28

    CPC分类号： E21B44/00 ,  G01V1/282 ,  E21B2200/20 ,  E21B2200/22 ,  G01V2210/6222

    摘要： Methods and systems are disclosed. The methods may include obtaining M training pairs and training a machine learning (ML) model using the M training pairs. The methods may further include obtaining geological data from a subterranean region of interest and, for each of a sequence of N windows, inputting the geological data and an (n−1)th predicted feature image within an (n−1)th window into the ML model and producing an nth predicted feature image within an nth window from the ML model. The geological data includes a seismic image and a manifestation of a feature within the subterranean region of interest. The methods may still further include determining the predicted feature image for the geological data associated with the subterranean region of interest using the N predicted feature images. The predicted feature image includes a labeled manifestation of the feature.

    Drilling method and drilling apparatus
    6.
    发明授权
    Drilling method and drilling apparatus 有权

    公开(公告)号：US12065923B2

    公开(公告)日：2024-08-20

    申请号：US17219386

    申请日：2021-03-31

    申请人： INSTITUTE OF GEOLOGY AND GEOPHYSICS, CHINESE ACADEMY OF SCIENCES

    发明人： Shouding Li ,  Xiao Li ,  Dong Chen ,  Siyuan Wu

    IPC分类号： E21B44/00 ,  E21B49/00

    CPC分类号： E21B44/005 ,  E21B49/003 ,  E21B2200/22

    摘要： A drilling method and a drilling apparatus are provided. In the method, the feature parameter of the stratum around the well, the feature parameter of the stratum in front of the drill bit, the preset drilling parameter, the preset trajectory parameter, the current pose of the drill bit and the current rate of penetration of the drill bit are obtained; the feature parameter of the reservoir in front of the drill bit is determined based on the feature parameter of the stratum around the well and the feature parameter of the stratum in front of the drill bit; the above parameters are inputted to the pre-trained drilling parameter modification model to obtain drilling trajectory parameter and drilling speed parameters; and the drilling direction and the rate of penetration are regulated based on the above parameters.

    Inference Models for Well Production with Limited Training Data
    7.
    发明公开
    Inference Models for Well Production with Limited Training Data 审中-公开

    公开(公告)号：US20240263555A1

    公开(公告)日：2024-08-08

    申请号：US18432689

    申请日：2024-02-05

    申请人： Novi Labs, Inc.

    发明人： Dillon Niederhut ,  Dawid Szafranski ,  Tim Gilbertson

    IPC分类号： E21B49/08

    CPC分类号： E21B49/087 ,  E21B2200/22

    摘要： Example embodiments involve obtaining training data comprising a first set of features relating to drilling wells, a second set of features relating to geology at the well locations, a third set of features relating to spacings between wells, and a fourth set of features relating to production output of the wells; training a first model to predict the third set of features given the first and second set of features; determining a spacing error based on differences between the third set of features in the training data and as predicted; training a second model to predict the fourth set of features given the first and second set of features; determining production error based on differences between the fourth set of features in the training data and as predicted; and training a third model to predict the production error given the spacing error and the first and second set of features.

    SYSTEM AND METHOD FOR USING AI-BASED MODEL TO PREDICT BOREHOLE SIZE IN HORIZONTAL CARBONATE WELLS
    9.
    发明公开
    SYSTEM AND METHOD FOR USING AI-BASED MODEL TO PREDICT BOREHOLE SIZE IN HORIZONTAL CARBONATE WELLS 审中-公开

    公开(公告)号：US20240254868A1

    公开(公告)日：2024-08-01

    申请号：US18102293

    申请日：2023-01-27

    申请人： Saudi Arabian Oil Company

    发明人： Lailaa Helmi Alshammasi ,  Yacine Meridji ,  Majed Fareed Kanfar ,  Lautaro Rayo

    IPC分类号： E21B43/16

    CPC分类号： E21B43/16 ,  E21B2200/22

    摘要： Some implementations provide a method that includes: accessing a stream of input data from logging tools in a first well-bore, wherein the stream of input data comprises measurements of bore sizes inside the first well-bore; splitting the stream of input data into a training set of input data and a testing set of input data; training a machine learning model using the training set of input data, wherein the machine learning model is configured to predict a bore size parameter based on input features of the training set of input data; evaluating the machine learning model using the testing set of input data; and in response to evaluating the machine learning model as satisfactory, applying the machine learning model to a newly received stream of input data from a second well-bore such that the bore size parameter of the second well-bore is determined independent of measurements of bore sizes inside the second well-bore.