Log Prediction

In seismic reservoir characterization, sonic measurements must be available at the wells that will be used to map the lateral extent of the reservoir. The acquisition and processing of sonic data is costly and can easily increase the required budget in a reservoir characterization campaign. Machine learning can help significantly reduce the need to pay for sonic logs.

The figures below are from a sonic and shear sonic prediction case study using existing gamma ray and density logs to train the machine learning model. The learning well is used to build the model. The application well prediction is completely independent of the well information used to build the model. Notice the 85% correlation for DTCO prediction and 83% correlation for DTSM. Histogram and crossplot comparisons between the measured and predicted logs are shown for quality control measures.

Machine learning (ML) compressional sonic log prediction using GR and RHOB as input. The learning well is used to build the ML model. The application well is used solely for quality control and has no input into the ML model. The quality of the result is evaluated using the R2 method and has a correlation of 85%.

Machine learning (ML) shear sonic log prediction using GR and RHOB as input. The learning well is used to build the ML model. The application well is used solely for quality control and has no input into the ML model. The quality of the result is evaluated using the R2 method and has a correlation of 83%.