Recurrent neural networks for short-term and long-term prediction of geothermal reservoirs. (September 2022)

• Record Type: Journal Article
• Authors: Jiang, Anyue; Qin, Zhen; Faulder, Dave; Cladouhos, Trenton T.; Jafarpour, Behnam
• Is Part Of: Geothermics. Volume 104(2022)
• Publication Date: 2022-09
• Subjects: Geothermal reservoirs -- Machine learning -- Recurrent neural networks -- Dynamic performance prediction -- Time-series data analysis; Hydrogeology -- Periodicals; Geothermal resources -- Periodicals; Énergie géothermique -- Périodiques; GEOTHERMAL ENGINEERING; GEOTHERMAL ENERGY; GEOTHERMAL EXPLORATION; Geothermal resources; Hydrogeology; Periodicals; Electronic journals; 621.44
• DOI: 10.1016/j.geothermics.2022.102439
• Languages: English
• ISSNs: 0375-6505

Abstract

Abstract: Accurate prediction of geothermal reservoir responses to alternative energy production scenarios is critical for optimizing the development of the underlying resources. While the conventional physics-based models offer a comprehensive prediction tool, data-driven models provide an efficient alternative to build fit-for-purpose predictive models by extracting and using the statistical patterns in the collected data to make predictions. The recurrent neural network (RNN) is a data-driven model that is commonly applied to predict time series sequences. This paper presents a variant of RNN that also utilizes the efficiency of convolutional neural networks (CNN) for the prediction of energy production from geothermal reservoirs. Specifically, a CNN–RNN architecture is developed that takes historical well controls as input (features) and their corresponding production response data as output (labels) to learn an input-output mapping that can predict the future well production responses/performance for any given future well control inputs. The model is paired with a labeling scheme to handle real field disturbances that create data gaps. In addition to the model structure, we introduce a thorough workflow for applying the model, which includes data pre-processing, feature selection, as well as different training strategies for short-term and long-term prediction. The performance and accuracy of the model are evaluated by applying it to multiple datasets, including a field reservoir model. Highlights: CNN–RNN architecture is developed and applied to geothermal reservoirs. A labeling scheme is introduced to handle real-field disturbances. Feature selection is applied to incorporate the known physics. Short-term model is trained with historical data, long-term model is guided by simulated data. The accuracy and efficiency of the approach are illustrated using a field dataset.