SYSTEM AND METHOD FOR USING AI-BASED MODEL TO PREDICT BOREHOLE SIZE IN HORIZONTAL CARBONATE WELLS

    公开(公告)号:US20240254868A1

    公开(公告)日:2024-08-01

    申请号:US18102293

    申请日:2023-01-27

    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.

    Autonomous Interpretation of Rock Drill Cuttings

    公开(公告)号:US20230374903A1

    公开(公告)日:2023-11-23

    申请号:US17751040

    申请日:2022-05-23

    IPC分类号: E21B49/00

    CPC分类号: E21B49/005 E21B2200/22

    摘要: A computer-implemented method that autonomously performs rock drill cuttings interpretation is described herein. The method includes obtaining rock drill cuttings representations. The method also includes preprocessing the rock drill cuttings representations. The method also includes performing unsupervised image segmentation in order to obtain masked representations of such images discriminating rock types. The method also includes performing supervised learning through a custom Convolutional Neuronal Network using the segmented pictures as inputs and a continuous or discrete mineralogical or sedimentological variable of interest as the output. Additionally, the method includes autonomously predicting such mineralogical or sedimentological quantity from new rock drill cuttings pictures using the parameters of the unsupervised segmentation and the trained supervised model created for this purpose.