Production Forecasting in Conventional Oil Reservoirs Using Deep Learning

Temizel C. T. , Odi U., Al-Sulaiman N., Reddy K., Putra D., Yurukcu M., ...More

2022 SPE Western Regional Meeting, WRM 2022, California, United States Of America, 26 - 28 April 2022, vol.2022-April identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 2022-April
  • Doi Number: 10.2118/209277-ms
  • City: California
  • Country: United States Of America


© Copyright 2022, Society of Petroleum EngineersAccurate estimation of the estimated ultimate recovery (EUR) is critical in decision making processes related to the development of conventional oil reservoirs. Existing methods have limitations when it comes to predicting such long-term production behaviors. This study analyzes the performance of deep learning methods such as long short-term memory (LSTM) neural networks on time-series data, and their effective application to accurately estimate the EUR in conventional reservoirs. Synthetic data that are realistic and representative of many major conventional oil reservoirs were generated for this study. The generated dataset was used by the LSTM model for the purpose of forecasting the EUR. The results of the LSTM model were compared with that of a reservoir simulation model from a full-physics reservoir simulator. EUR forecasts from the physics-based reservoir simulator is used as a benchmark and the LSTM model shows a good predictive accuracy while forecasting the long-term production behavior from a well in a conventional oil reservoir. The LSTM model-based deep learning method can be effectively used with real-field data obtained from wells in conventional reservoirs to accurately predict the EUR, and the study provides a comparative analysis of the results and factors affecting the EUR forecasts from the LSTM model and reservoir simulation model. Deep learning methods such as LSTMs have an inherent advantage in identifying trends in time-series data and making forecasts using the data. The existing literature has a limited number of studies that outline the use of deep learning methods for EUR forecasts and this study covers this gap by providing details analyses, best practices and workflows on the use of such methods for conventional oil reservoirs.