Building Advanced Predictive Models - Oil and Gas Case Study

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