Neural Network and Lithofacies Prediction

 

Determined predictor variables (GR, ILD, ILM, PE, NPhi, DPhi) and lithofacies categories were used to build Neural Network models. The size of network and damping parameters were optimized using cross-validation and repeat testing with whole training data and randomly chosen partial data sets.

 

Predicted Lithofacies Scorecard (Counts) for 10 key trained wells with PE Curve

 

The results of model with network size (number of hidden layers) 35 and damping parameter 0.05 for 10 key wells with PE log. The absolute accuracy for this model is 91%. For a few wells without PE curve, the results decreased but an absolute accuracy of approximately 85% is still attained.

 

 

Cross validation performed on different network size and damping parameters with objective function and mad parameter, which is mean absolute difference between the predicted and actual facies number. Neural network model (size=35, damping =0.05) was selected for facies prediction.