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    DRILL BIT REPAIR TYPE PREDICTION USING MACHINE LEARNING
    2.
    发明申请
    DRILL BIT REPAIR TYPE PREDICTION USING MACHINE LEARNING 审中-公开

    公开(公告)号：US20200149354A1

    公开(公告)日：2020-05-14

    申请号：US16611817

    申请日：2018-08-31

    Applicant: LANDMARK GRAPHICS CORPORATION

    Inventor： Ajay Pratap Singh ,  Roxana Nielsen, Jr. ,  Satyam Priyadarshy ,  Ashwani Dev ,  Geetha Gopakumar Nair ,  Suresh Venugopal

    IPC: E21B12/02 ,  G06N3/08 ,  E21B10/62

    Abstract: The subject disclosure provides for a mechanism implemented with neural networks through machine learning to predict wear and relative performance metrics for performing repairs on drill bits in a next repair cycle, which can improve decision making by drill bit repair model engines, drill bit design, and help reduce the cost of drill bit repairs. The machine learning mechanism includes obtaining drill bit data from different data sources and integrating the drill bit data from each of the data sources into an integrated dataset. The integrated dataset is pre-processed to filter out outliers. The filtered dataset is applied to a neural network to build a machine learning based model and extract features that indicate significant parameters affecting wear. A repair type prediction is determined with the applied machine learning based model and is provided as a signal for facilitating a drill bit operation on a cutter of the drill bit.

    FAULT DETECTION BASED ON SEISMIC DATA INTERPRETATION
    3.
    发明申请
    FAULT DETECTION BASED ON SEISMIC DATA INTERPRETATION 审中-公开

    公开(公告)号：US20200064507A1

    公开(公告)日：2020-02-27

    申请号：US16489286

    申请日：2018-07-18

    Applicant: Landmark Graphics Corporation

    Inventor： Youli Mao ,  Bhaskar Mandapaka ,  Ashwani Dev ,  Satyam Priyadarshy

    IPC: G01V1/50 ,  G06K9/62 ,  G06N3/08 ,  G06N20/00

    Abstract: A method for determining a position of a geological feature in a formation includes acquiring a seismic dataset, wherein the seismic dataset is based on signals of one or more seismic sensors and determining a set of indicators of candidate discontinuities in the formation based on the seismic dataset. The method also includes labeling a subset of the set of indicators of candidate discontinuities using a neural network with a label based on the set of indicators of candidate discontinuities, wherein the label distinguishes an indicator of a candidate discontinuity between being an indicator of a target discontinuity or being an indicator of a non-target discontinuity and determining the position of the geological feature in the formation, wherein the geological feature in the formation is associated with at least one target discontinuity based on the subset of the set of indicators of candidate discontinuities.

    RESERVOIR SIMULATION SYSTEMS AND METHODS TO DYNAMICALLY IMPROVE PERFORMANCE OF RESERVOIR SIMULATIONS
    4.
    发明申请
    RESERVOIR SIMULATION SYSTEMS AND METHODS TO DYNAMICALLY IMPROVE PERFORMANCE OF RESERVOIR SIMULATIONS 有权

    公开(公告)号：US20210230977A1

    公开(公告)日：2021-07-29

    申请号：US16651640

    申请日：2019-03-05

    Applicant: Landmark Graphics Corporation

    Inventor： Raja Vikram R. Pandya ,  Satyam Priyadarshy ,  Keshava Prasad Rangarajan

    IPC: E21B41/00 ,  G06F30/28

    Abstract: The disclosed embodiments include reservoir simulation systems and methods to dynamically improve performance of reservoir simulations. The method includes obtaining input variables for generating a reservoir simulation of a reservoir, and generating the reservoir simulation based on the input variables. The method also includes determining a variance of computation time for processing the reservoir simulation. In response to a determination that the variance of computation time is less than or equal to a threshold, the method includes performing a first sequence of Bayesian Optimizations of at least one of internal and external parameters that control the reservoir simulation to improve performance of the reservoir simulation. In response to a determination that the variance of computation time is greater than the threshold, the method includes performing a second sequence of Bayesian Optimizations of at least one of the internal and external parameters.

    Fault detection based on seismic data interpretation
    5.
    发明授权
    Fault detection based on seismic data interpretation 有权

    公开(公告)号：US11378710B2

    公开(公告)日：2022-07-05

    申请号：US16489286

    申请日：2018-07-18

    Applicant: Landmark Graphics Corporation

    Inventor： Youli Mao ,  Bhaskar Mandapaka ,  Ashwani Dev ,  Satyam Priyadarshy

    IPC: G01V1/50 ,  G06N20/00 ,  G06K9/62 ,  G06N3/08

    Abstract: A method for determining a position of a geological feature in a formation includes acquiring a seismic dataset, wherein the seismic dataset is based on signals of one or more seismic sensors and determining a set of indicators of candidate discontinuities in the formation based on the seismic dataset. The method also includes labeling a subset of the set of indicators of candidate discontinuities using a neural network with a label based on the set of indicators of candidate discontinuities, wherein the label distinguishes an indicator of a candidate discontinuity between being an indicator of a target discontinuity or being an indicator of a non-target discontinuity and determining the position of the geological feature in the formation, wherein the geological feature in the formation is associated with at least one target discontinuity based on the subset of the set of indicators of candidate discontinuities.

    METHOD FOR IMPROVING RESERVOIR PERFORMANCE BY USING DATA SCIENCE
    7.
    发明申请
    METHOD FOR IMPROVING RESERVOIR PERFORMANCE BY USING DATA SCIENCE 审中-公开

    公开(公告)号：US20180306030A1

    公开(公告)日：2018-10-25

    申请号：US15769306

    申请日：2015-12-22

    Applicant: Landmark Graphics Corporation

    Inventor： Satyam Priyadarshy

    IPC: E21B49/00 ,  G06F17/50 ,  G01V1/50

    CPC classification number: E21B49/005 ,  G01V1/50 ,  G01V2210/612 ,  G06F17/50 ,  G06F17/5009

    Abstract: In accordance with presently disclosed embodiments, systems and methods for generating a reservoir fluid flow simulation are disclosed. The method includes: obtaining prior reservoir fluid flow simulations generated for the reservoir and a plurality of associated input attributes used to generate the prior simulations; analyzing a variability of the input attributes among the prior reservoir fluid flow simulations; obtaining actual reservoir performance data and associated fluid flow attributes over time; analyzing a variability of the fluid flow attributes; and comparing the variability of the input attributes generated using the prior simulations to the corresponding fluid flow attributes from the actual reservoir performance data.

    RESERVOIR FLUID PROPERTY MODELING USING MACHINE LEARNING
    9.
    发明申请
    RESERVOIR FLUID PROPERTY MODELING USING MACHINE LEARNING 有权

    公开(公告)号：US20220307357A1

    公开(公告)日：2022-09-29

    申请号：US17293454

    申请日：2020-06-12

    Applicant: LANDMARK GRAPHICS CORPORATION

    Inventor： Ajay Pratap Singh ,  Suryansh Purwar ,  Ashwani Dev ,  Satyam Priyadarshy

    IPC: E21B43/16 ,  E21B47/06 ,  E21B47/003

    Abstract: System and methods for tuning equation of state (EOS) characterizations are presented. Pressure-volume-temperature (PVT) data is obtained for downhole fluids within a reservoir formation. A component grouping for an EOS model of the downhole fluids is determined, based on the obtained PVT data. The component grouping is used to estimate properties of the downhole fluids for a current stage of a downhole operation within the formation. A machine learning model is trained to minimize an error between the estimated properties and actual fluid properties measured during the current stage of the operation, where the component grouping for the EOS model is iteratively adjusted by the machine learning model until the error is minimized. The EOS model is tuned using the adjusted component grouping. Fluid properties are estimated for one or more subsequent stages of the downhole operation to be performed along the wellbore, based on the tuned EOS model.

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