A Data Driven Management Strategy to Reduce the Environmental Impact of Upstream Production Plants
- Luca Cadei (Eni SpA) | Danilo Loffreno (Eni SpA) | Giuseppe Camarda (Eni SpA) | Marco Montini (Eni SpA) | Gianmarco Rossi (Eni SpA) | Piero Fier (Eni SpA) | Davide Lupica (Eni SpA) | Andrea Corneo (Eni SpA) | Lorenzo Lancia (Eni SpA) | Diletta Milana (Eni SpA) | Marco Carrettoni (Eni SpA) | Elisabetta Purlalli (Eni SpA) | Francesco Carduccu (The Boston Consulting Group GAMMA) | Gustavo Sophia (The Boston Consulting Group GAMMA)
- Document ID
- Society of Petroleum Engineers
- SPE Norway One Day Seminar, 14 May, Bergen, Norway
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- 7.6 Information Management and Systems, 7.4 Energy Economics, 7.6.4 Data Mining, 7.6.6 Artificial Intelligence, 7.4.4 Energy Policy and Regulation, 7.4.3 Market analysis /supply and demand forecasting/pricing, 7 Management and Information
- Optimization, Machine Learning, Energy Efficiency, Big Data, Artificial Intelligence
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This paper highlights results of a first campaign of tests of an innovative tool to predict the short term trend of the energy efficiency index and the optimal management of an Oil&Gas production plant. The developed tool represents a step towards the Digital Transformation of the production plants by the integration of Big Data Analytics and Machine Learning methodologies with experts’ domain knowledge.
The predictive model has two main features: produces a forecast of the energy efficiency index 3 hours into the future and supports the site engineers to take optimal management choices by highlighting the energy performances of the most energy-intensive equipment of the plant. The operator can use this information to act on the plant and reduce the overall energy consumption.
The energy efficiency index is described by the Stationary Combustion CO2 Emission KPI [tCO2/kboe] that relates the consumed energy and the associated CO2 emission with the total production. A Gradient Boosting regression model is implemented and fed with a mix of autoregressive and exogenous real-time parameters.
The paper shows the results obtained through a series of actions taken by production engineer, using the model's outputs on a real operating oilfield. The methodology entails a real-time run of the model to analyze the Stationary Combustion CO2 Emission Index trend, identifying a subset of equipment with abnormal/atypical consumption. This activity is followed by a monitoring phase, where such subset of equipment is further analyzed in terms of energy consumption and main process parameter. The purpose of this phase is to identify a relevant set of actions. The final step requires interaction with the control room to act on the equipment operative parameters. Preliminary tests show the daily average CO2 emission from stationary combustion were reduced by 0,9%, with a peak reduction of 1,35%.
The main advantages experimented by the first implementation tests are related to the significant reduction of CO2 emissions while granting the highest level of production, allowing a step towards the field carbon neutrality target.
|File Size||1 MB||Number of Pages||12|
Cadei, L., Montini, M., Landi, F., Porcelli, F., Michetti, V., Origgi, M.,… Duranton, S. (2018, November 12). Big Data Advanced Analytics to Forecast Operational Upsets in Upstream Production System. Society of Petroleum Engineers. doi:10.2118/193190-MS
Friedman, Jerome H. "Greedy Function Approximation: A Gradient Boosting Machine." The Annals of Statistics, vol. 29, no. 5, 2001, pp. 1189-1232. JSTOR, JSTOR, www.jstor.org/stable/2699986.