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