Transfer Learning Approach to Prediction of Rate of Penetration in Drilling

Abstract

AbstractThe rate of penetration (ROP) is a key performance indicator in the oil and gas drilling industry as it directly translates to cost savings and emission reductions. A prerequisite for a drilling optimization algorithm is a predictive model that provides expected ROP values in response to surface drilling parameters and formation properties. The high predictive capability of current machine-learning models comes at the cost of excessive data requirements, poor generalization, and extensive computation requirements. These practical issues hinder ROP models for field deployment. Here we address these issues through transfer learning. Simulated and real data from the Volve field were used to pre-train models. Subsequently, these models were fine-tuned with varying retraining data percentages from other Volve wells and Marcellus Shale wells.Four out of the five test cases indicate that retraining the base model would always produce a model with lower mean absolute error than training an entirely new model or using the base model without retraining. One was on par with the traditional approach. Transfer learning allowed to reduce the training data requirement from a typical 70% down to just 10%. In addition, transfer learning reduced computational costs and training time. Finally, results showed that simulated data could be used in the absence of real data or in combination with real data to train a model without trading off model’s predictive capability.KeywordsRate of penetration modelTransfer learningDeep learning