An intelligent data driven approach for production prediction Conference Paper uri icon


  • © 2019, Offshore Technology Conference The objective of this work is to further explore the potential application of Machine Learning algorithms in production prediction and ultimate recovery. Intelligent Machine Learning approaches such as Gradient Boosted Trees (GBT), Adaboost, and Support Vector Regression (SVR) are applied to detect the most important features contributing to cumulative production prediction within the first 12 producing months. The models are applied on a data set composed of 5 wells in the Volve field in the North Sea. The collected data was then filtered and used to structure and train the different Regression algorithms and fine tune the appropriate hyperparameters. The different models were evaluated by measuring the Mean Absolute Error (MAE). The generalization and precision performance of the proposed models were established by comparing the forecasted outcome after cross validation with the field data. The optimized model can predict production response with high accuracy. The data-fitting process comprised of splitting the data into training using 70% of the data set, 15% validation, and 15% for testing. A regression model was constructed on the training set and later validated with the test set. Recurrent application of a "cross-validation" process produced important information concerning the robustness of the regression-modeling method. Six parameters were considered as input factors to build the model. Factors affecting production prediction included: on stream hours, average choke size, bore oil volume, bore gas volume, bore water volume, and finally average wellhead pressure were used as input features. The outcome showed that the developed model provided better prediction compared to analytical models with a 11.71% MAE prediction for SVR. This novel data mining application could be trained on any dataset to help predict future production performance at any condition in any given scenario.

author list (cited authors)

  • Noshi, C. I., Eissa, M. R., Abdalla, R. M., & Schubert, J. J.

publication date

  • January 2019