Department of Natural Resources, New Mexico Highlands University, Las Vegas, Mexico
Mini Review
Oil and Gas Well Production Forecasting Based on Machine Learning Models: The Volve Field Case
Author(s): Moreno Millan*
The current techniques for predicting the oil and gas production flow rates at well and reservoir scales include from
the classical decline curves analysis thru numerical simulation models. The present work proposes the use of the
following Machine Learning Models (MLM): Linear Regression (LR), Support Vector Machines (SVM), Random
Forest (RF), and an Artificial Neural Network (ANN), as an alternative to the conventional methods for forecasting
oil and gas production flow rates. The application of this proposal is demonstrated based on production data
recorded along 8 years in wells from Volve field, located in the Norwegian continental shelf. Thus, the benefits for
each MLM above mentioned are discussed, concluding based on a practical experience that not always the more
complex algorithm is the best choice. It is demonstrated that the alternative of SVM yield be.. View more»