Data Science & Analytics Geology & Geoscience Transfer

Synthetic Well Log: Reconstructing Bad Hole Well Log Results with Machine Learning

Loss of contact with the borehole.  Cave-ins.  Sub-optimal mud types.  Recording errors.  Unit conflicts.  Equipment failure.  A number of things can cause a Well Log to have bad hole readings.  Perhaps the Caliper log indicates a series of unreliable borehole sections, and an expert has flagged them.  Perhaps the expert has run an outlier detection algorithm to identify aberrant well log readings. 

What next?  What to do?  Re-logging the well is often cost prohibitive.

We’ve built a Synthetic Well Log tool in Spotfire that uses machine learning to help replace those bad hole values with more accurate ones.

Our tool uses the theory behind academic studies (e.g. An Artificially Intelligent Technique to Generate Synthetic Geomechanical Well Logs for the Bakken Formation, Synthetic well logs generation via Recurrent Neural Networks, Generating Synthetic Well Logs by Artificial Neural Networks (ANN) Using MISO-ARMAX Model in Cupiagua Field) and supports several machine learning algorithms (Random Forest, Gradient Boosting, and Support Vector Machines).  The algorithms ingest the curves which do not have data integrity issues in order to predict more accurate values of the missing or faulty curve.

Jumping into the machine learning arena might feel daunting, so we developed this tool to help Geo experts with the process.  Better yet, our tool doesn’t just reach into a black box and hand back a reconstructed well log.  The Synthetic Well Log tool:

  1. Works with the Geo expert to build an imputation model
  2. Lets that person examine modeling validation metrics
  3. Displays the predictions and reconstructed logs next to the original for a sanity check    
  4. Exports the chosen reconstructed model

Our tool puts reconstructed curves right next to the originals so they pass the expert’s eye test as well as the modeling diagnostics.