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Building Advanced Predictive Models - Oil and Gas Case Study

Published on ‎11-07-2024 03:29 PM by Community Manager Community Manager | Updated on ‎11-07-2024 05:39 PM

This case study uses JMP and JMP Pro to find optimal geologic and completion parameters in upstream oil and gas processes (identifying, extracting and producing raw materials).

 

 

 

See how to:

  • Understand the goal of the models - to determine point of diminishing return for using additional, expensive solid material (proppant) in the process
  • Understand the  response of interest - gross amount of oil and gas produced from a particular well over one year.
  • Understand the study factors
    • Controlled factors (20 completion parameters, amount of proppant, well perforation depth, # of completion stages, lateral well length)
    • Pseudo-controlled factors (location parameters, county, latitude, longitude)
    • Uncontrolled factors (23 geologic parameters, facies, reservoir thickness, porosity & permeability, (TOC) total organic carbon)
  • Prepare data for analysis
    • Handle missing values using imputation
  • Use Predictor Screening to identify significant predictors out of all factors
    • Rank all predictors using Bootstrap Forest 
  • Build model using Fit Model to rapidly develop simple to complex linear models using various fitting techniques, model parameters, and additional settings including random effects
    • Construct Standard Least Squares model (JMP)
    • Construct Stepwise Regression model (JMP)
    • Construct Logistic Regression model (JMP)
    • Use Generalized Regression (Pro)to create text, validation and training sets and then model correlated and high-dimensional data
  • Use JMP Pro to fit an ensemble model by averaging many decision trees
    • See how each split considers a random subset of the predictors
    • Use Prediction Profiler to identify point of diminishing returns

specifying.JPG



Start:
Tue, Sep 22, 2020 02:00 PM EDT
End:
Tue, Sep 22, 2020 03:00 PM EDT
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