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    WORKFLOW TO EVALUATE THE TIME-DEPENDENT PROPPANT EMBEDMENT INDUCED BY FRACTURING FLUID PENETRATION
    1.
    发明公开
    WORKFLOW TO EVALUATE THE TIME-DEPENDENT PROPPANT EMBEDMENT INDUCED BY FRACTURING FLUID PENETRATION 审中-公开

    公开(公告)号：US20230399932A1

    公开(公告)日：2023-12-14

    申请号：US17806030

    申请日：2022-06-08

    Applicant: Aramco Services Company

    Inventor： Yanhui Han ,  Feng Liang

    IPC: E21B43/267 ,  E21B49/00 ,  E21B49/08

    CPC classification number: E21B43/267 ,  E21B49/008 ,  E21B49/006 ,  E21B49/087 ,  E21B2200/20

    Abstract: A method is used to determine the permeability of a hydraulic fracture. The method includes obtaining formation parameters and a plurality of formation samples, dividing the plurality of formation samples into a first group and a second group, measuring mechanical and hydraulic properties of the first group, soaking the second group in a fracturing fluid for a plurality of time periods, and measuring, after each soaking time period, the mechanical and hydraulic properties of the second group. The soaking the second group in the fracturing fluid includes soaking the second group in a plurality of different fracturing fluids. The method further includes building, using a computer processor, a proppant-rock interaction model based, at least in part, on the mechanical and hydraulic properties of the first group and the second group, and determining, using the computer processor, the permeability of a hydraulic fracture based, at least in part, on the proppant-rock interaction model and the formation parameters.

    Determination of stimulated reservoir volume and estimated ultimate recovery of hydrocarbons for unconventional reservoirs
    2.
    发明授权
    Determination of stimulated reservoir volume and estimated ultimate recovery of hydrocarbons for unconventional reservoirs 有权

    公开(公告)号：US11702924B2

    公开(公告)日：2023-07-18

    申请号：US17144677

    申请日：2021-01-08

    Applicant: ARAMCO SERVICES COMPANY

    Inventor： Hui-Hai Liu ,  Jilin Zhang ,  Huseyin Onur Balan ,  Feng Liang

    IPC: E21B43/26 ,  E21B47/003

    CPC classification number: E21B47/003 ,  E21B43/26 ,  E21B2200/20

    Abstract: A method for determining SRV and EUR includes: monitoring an amount and a density of a hydrocarbon fluid produced from the production well; obtaining a cumulative amount of the fluid that has accumulated from a beginning of production; obtaining a relationship between the cumulative amount and a square root of the time; determining a deviation point where the relationship changes from linear to non-linear; determining a deviation amount of the fluid corresponding to the deviation point; determining a first density of the hydrocarbon fluid at the beginning of production, a second density at a pore pressure equal to a bottom hole pressure in the production well, a first porosity at the beginning of production, and a second porosity for a pore pressure equal to the bottom hole pressure; and determining SRV and the EUR based on the deviation amount, the first and second densities, and the first and second porosities.

    PREDICTING WELL PRODUCTION BY TRAINING A MACHINE LEARNING MODEL WITH A SMALL DATA SET
    3.
    发明公开
    PREDICTING WELL PRODUCTION BY TRAINING A MACHINE LEARNING MODEL WITH A SMALL DATA SET 审中-公开

    公开(公告)号：US20230196089A1

    公开(公告)日：2023-06-22

    申请号：US17556549

    申请日：2021-12-20

    Applicant: ARAMCO SERVICES COMPANY

    Inventor： Hui-Hai Liu ,  Jilin Zhang ,  Feng Liang

    IPC: G06N3/08 ,  G06N3/04 ,  E21B49/00 ,  E21B41/00

    CPC classification number: G06N3/08 ,  G06N3/0454 ,  E21B49/00 ,  E21B41/00 ,  E21B2200/20 ,  E21B2200/22

    Abstract: A method for predicting well production is disclosed. The method includes obtaining a training data set for a machine learning (ML) model that generates predicted well production data based on observed data of interest, generating multiple sets of initial guesses of model parameters of the ML model, using an ML algorithm applied to the training data set to generate multiple individually trained ML models based the multiple sets of initial model parameters, comparing a validation data set and respective predicted well production data of the individually trained ML models to generate a ranking, selecting top-ranked individually trained ML models based on the ranking, using the data of interest as input to the top-ranked individually trained ML models to generate a set of individual predicted well production data, and generating a final predicted well production data based on the set of individual predicted well production data.

    DETERMINATION OF STIMULATED RESERVOIR VOLUME AND ESTIMATED ULTIMATE RECOVERY OF HYDROCARBONS FOR UNCONVENTIONAL RESERVOIRS
    8.
    发明申请
    DETERMINATION OF STIMULATED RESERVOIR VOLUME AND ESTIMATED ULTIMATE RECOVERY OF HYDROCARBONS FOR UNCONVENTIONAL RESERVOIRS 有权

    公开(公告)号：US20220220839A1

    公开(公告)日：2022-07-14

    申请号：US17144677

    申请日：2021-01-08

    Applicant: ARAMCO SERVICES COMPANY

    Inventor： Hui-Hai Liu ,  Jilin Zhang ,  Huseyin Onur Balan ,  Feng Liang

    IPC: E21B47/003 ,  E21B43/26

    Abstract: A method for determining SRV and EUR includes: monitoring an amount and a density of a hydrocarbon fluid produced from the production well; obtaining a cumulative amount of the fluid that has accumulated from a beginning of production; obtaining a relationship between the cumulative amount and a square root of the time; determining a deviation point where the relationship changes from linear to non-linear; determining a deviation amount of the fluid corresponding to the deviation point; determining a first density of the hydrocarbon fluid at the beginning of production, a second density at a pore pressure equal to a bottom hole pressure in the production well, a first porosity at the beginning of production, and a second porosity for a pore pressure equal to the bottom hole pressure; and determining SRV and the EUR based on the deviation amount, the first and second densities, and the first and second porosities.

    METHOD FOR MANAGING SANDING VOLUME EXPECTATION IN WEAK SANDSTONE BASED ON PLASTIC ZONE VOLUME
    9.
    发明申请
    METHOD FOR MANAGING SANDING VOLUME EXPECTATION IN WEAK SANDSTONE BASED ON PLASTIC ZONE VOLUME 有权

    公开(公告)号：US20250110031A1

    公开(公告)日：2025-04-03

    申请号：US18478668

    申请日：2023-09-29

    Applicant: ARAMCO SERVICES COMPANY

    Inventor： Yanhui Han ,  Feng Liang

    IPC: G01N3/08 ,  C09K8/575 ,  E21B33/138 ,  E21B49/02 ,  G01N33/24

    Abstract: Described is a method for managing an expectation on sanding volume in weak sandstone. In situ data related to a region of sandstone proximate a well is acquired. Measured property data corresponding to samples from the region of sandstone are obtained, and a chemical consolidation treatment is performed on the samples. Chemical consolidation treatment data is then obtained. The measured property data and the chemical consolidation treatment data is supplied as inputs to a simulator. The simulator performs simulations with and without chemical consolidation treatment. Based on the simulations, a correlation between a well flow rate and an effect of the chemical consolidation treatment is determined.

    PREDICTING WELL PERFORMANCE FROM UNCONVENTIONAL RESERVOIRS WITH THE IMPROVED MACHINE LEARNING METHOD FOR A SMALL TRAINING DATA SET BY INCORPORATING A SIMPLE PHYSICS CONSTRAIN
    10.
    发明申请
    PREDICTING WELL PERFORMANCE FROM UNCONVENTIONAL RESERVOIRS WITH THE IMPROVED MACHINE LEARNING METHOD FOR A SMALL TRAINING DATA SET BY INCORPORATING A SIMPLE PHYSICS CONSTRAIN 有权

    公开(公告)号：US20240403775A1

    公开(公告)日：2024-12-05

    申请号：US18325777

    申请日：2023-05-30

    Applicant: ARAMCO SERVICES COMPANY

    Inventor： Jilin Zhang ,  Hui-Hai Liu ,  Feng Liang ,  Moemen Abdelrahman

    IPC: G06Q10/0637 ,  G06Q50/02

    Abstract: A method and a system for predicting well production of a reservoir using machine learning models and algorithms is disclosed. The method includes obtaining a training data set for training a machine learning (ML) model and selecting an artificial neural network model structure, the model structure including a number of layers and a number of nodes of each layer. Further, the method includes generating a plurality of individually trained ML models and calculating a model performance of each trained model by evaluating a difference between a model prediction and a well performance data. The plurality of top-ranked individually trained ML models is constrained using one or multiple known physical rules. A plurality of individual predicted well production data is generated using the geological, the completion, and the petrophysical data of interest and a final predicted well production data is generating based on the plurality of individual predicted well production data.

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