Journal of Petroleum & Environmental Biotechnology

Department of Natural Resources, New Mexico Highlands University, Las Vegas, Mexico

Publications

• 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»
  

  DOI: 10.35248/2157-7463.24.15.564

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