Deep Learning and Hybrid Approaches Applied to Production Forecasting

Abstract

Reliable forecasting of production rates from mature hydrocarbon fields is crucial both in optimizing their operation (via short-term forecasts) and in making reliable reserves estimations (via long-term forecasts). Several approaches may be employed for production forecasting from the industry standard decline curve analysis, to new technologies such as machine learning. The goal of this study is to assess the potential of utilizing deep learning and hybrid modelling approaches for production rate forecasting. Several methods were developed and assessed for both short-term and long-term forecasts, such as: first-principle physics-based approaches, decline curve analysis, deep learning models and hybrid models (which combine first-principle and deep learning models). These methods were tested on data from a variety of gas assets for different forecasting horizons, ranging from 6 weeks to several years. The results suggest that each model can be beneficial for production forecasting, depending on the complexity of the production behavior, the forecasting horizon and the availability and accuracy of the data used. The performances of both hybrid and physical models were dependent on the quality of the calibration (history matching) of the models employed. Deep learning models were found to be more accurate in capturing the dynamic effects observed during production – this was especially true for mature fields with frequent shut-ins and interventions. For long-term production forecasting, in some cases, the hybrid model produced a greater accuracy due to its consideration of the long-term reservoir depletion process provided by the incorporated material balance model.