Application of Machine Learning to Oil Production Forecast under Uncertainties-The Linear Model
- Leonardo Kubota (Petrobras) | Fabio Souto (Petrobras)
- Document ID
- Offshore Technology Conference
- Offshore Technology Conference Brasil, 29-31 October, Rio de Janeiro, Brazil
- Publication Date
- Document Type
- Conference Paper
- 2019. Offshore Technology Conference
- Production Forecast, Machine Learning
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- 125 since 2007
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In this paper, we propose an alternative approach to the problem of oil-production forecast based on the most straightforward feature-based machine-learning algorithm: the linear model. The method can be successfully applied to forecast both oil-rate and liquid-rate in oil fields under (i) water injection, (ii) gas injection, and (iii) simultaneous water and steam injection. Our data-driven algorithm learns the underlying reservoir dynamics from 3 sets of time-series, namely, (i) injection-rate, (ii) liquid and oil-rate, and (iii) number of producers. That is all the data we need to make reliable forecasts, no geological model or numerical reservoir simulators were used.
|File Size||1 MB||Number of Pages||11|
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