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Multilabel classification for inflow profile monitoring

Multilabel classification for inflow profile monitoring

MACSPro'2019 - Modeling and Analysis of Complex Systems and Processes, Vienna
21 - 23 March 2019

Ivan Vrabie, Dmitry Ignatov, Pavel Spesivtsev, Vladimir Zyuzin, Dmitry Kurgansky, Svyatoslav Elizarov

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March 22, 2019
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  1. Schlumberger-Private
    Multilabel Classification for Inflow Profile Monitoring
    Dmitry I. Ignatov, Pavel Spesivtsev, Dmitry Kurgansky, Ivan Vrabie, Svyatoslav Elizarov, Vladimir Zyuzin
    22.03.2019

    View Slide

  2. Schlumberger-Private
    The inflow zones (sources) problem
    Active inflow source
    Inactive inflow
    source
    Goal:
    • Determine the active
    and inactive inflow
    sources
    Effects:
    • Efficiency decrease
    Applications:
    • Decision making:
    reperforating
    • Understanding
    performance: A small
    amount of active inflow
    sources or small
    productivity of the
    sources.
    BHP
    WHP Q

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  3. Schlumberger-Private
    The machine learning approach
    Target variable: The binary vector of size N of active and
    inactive inflow sources.
    Features: Time series of surface flowrates, BHP and WHP.
    Models: Random Forest, XGBoost, SVM, kNN, CNN and LSTM
    Auxiliary methods:
    • Feature engineering
    • Dimensionality reduction
    • Ensemble of algorithms
    • Cascade algorithms

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  4. Schlumberger-Private
    Data
    Numerical simulator
    Input parameters:
    • Wellbore geometry
    • Distribution of volume fractions of phases
    • Choke size
    • …
    Output variables
    • Surface flowrates
    • Bottomhole pressure
    • Wellhead pressure
    • Vector of active and non-active sources
    5000 numerical simulations
    Train test split: 4:1
    • Wellbore
    geometry
    • Distribution
    of volume
    fractions of
    phases
    • Choke size
    • Qo, Qg, Qw
    • BHP
    • WHP
    • Target vector
    Numerical
    simulator

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  5. Schlumberger-Private
    Feature generation
    Features obtained using TSFresh:
    • Average
    • Standard deviation
    • Median
    • Dispersion
    • Min/Max value
    • Trend
    • Number of min/max values
    • …
    • ~ 1200 features
    Features used for solving the multi-label
    classification problem

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  6. Schlumberger-Private
    Experiment 1
    The approach of independent classifiers for each of the inflow sources.
    Normalization: standard, min-max
    Dimension reduction: PCA, ICA
    Methods: RF, SVM, kNN and XGBoost
    Best results: XGBoost + standard normalization + PCA
    0/1 loss: 0.36

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  7. Schlumberger-Private
    Experiment 2
    Ensemble approach: top 10 algorithms + majority voting. 0/1 loss: 0.31
    Algorithms:
    1. XGBoost + PCA + standard norm.
    2. RF + PCA + standard norm.
    3. XGBoost + PCA + min-max norm.
    4. RF + PCA + min-max norm.
    5. XGBoost + PCA
    6. RF + ICA
    7. SVM + ICA + standard norm.
    8. RF + PCA
    9. SVM + PCA + min-max norm.
    10. kNN + ICA + standard norm.
    Accuracy matrix

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  8. Schlumberger-Private
    Experiment 3
    Cascade classifier approach:
    1. Predict the number of working sources (100% accuracy).
    2. Obtain probabilities of class 1 for each source separately.
    3. Sort probabilities in descending order.
    4. Select the sources with the highest probabilities.
    0/1 loss: 0.44

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  9. Schlumberger-Private
    Experiment 4
    Enlarged feature space approach
    • 300 ICA components over the time series
    • 300 PCA components over the new features
    • Number of active sources
    Method: XGBoost
    0/1 loss: 0.26

    View Slide

  10. Schlumberger-Private
    Summary and Conclusions
    • The problem of inflow profile monitoring is hard to handle.
    • The XGBoost method over initial data + extracted features achieved a 0/1 loss of 0.26
    • The sources that are closer to the surface are easier to predict.
    • The results are better than a random guess and they show a potential possibility in
    improvement.
    XGBoost, PCA Ensemble of
    10 algorithms
    Cascade
    classifier
    XGBoost +
    PCA + ICA
    0/1 loss 0.36 0.31 0.44 0.26

    View Slide