From Real Time Data to Updated Models: The Challenge of Intelligent Fields Applied to Gas Storage Business
- Alessandro Tiani (ENI SPA E&P Division) | Giuseppina Bartolotto (ENI E & P) | Pasquale Strippoli (ENI E & P) | Roberto Latronico (ENI E & P) | Giovanni Spitaleri (Stogit)
- Document ID
- Society of Petroleum Engineers
- Europec/EAGE Conference and Exhibition, 9-12 June 2008, Rome, Italy
- Publication Date
- Document Type
- Conference Paper
- 2008. Society of Petroleum Engineers
- 1.6.9 Coring, Fishing, 5.6.8 Well Performance Monitoring, Inflow Performance, 3.2.8 Well Performance Modeling and Tubular Optimization, 7.4.5 Future of energy/oil and gas, 4.4.2 SCADA, 5.6.4 Drillstem/Well Testing, 5.5 Reservoir Simulation, 4.1.5 Processing Equipment, 3.3 Well & Reservoir Surveillance and Monitoring
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In recent years, Underground Gas Storage (UGS) has earned itself strategic importance as it guarantees energy sustainability in markets suffering from unpredictable supply. Storage management is a complex activity which faces the challenge of combining the variability of daily commercial client requests along with the capability of the reservoirs to deliver. Gas production and re-injection activity requires standard core competencies of the oil and gas industry, however the process is fast paced as compared to conventional hydrocarbon production activity as time scales from data analysis to decision making is reduced to hours.
Stogit Spa, an Italian gas storage company managing 8 depleted gas fields in Italy has implemented an integrated system that dynamically links databases, visualization tools and reservoir/well simulators in order to assist the management process. This system called PERSEO (PErformance Reservoir StoragE Optimization) is aimed at enhancing efficiency by utilising a piece of intelligent fields application.
The large amount of data from the 270+ wells of STOGIT equipped with SCADA systems monitoring real time gas rate and well head pressure, provides information for production/injection management. The challenge has been to extract the maximum information from the available database and computerize the process of updating well/reservoir performance automatically. This allows for faster monitoring, analysis and prediction of future well behaviour.
A workflow was realised to transform available real time data into calibrated models in the following two steps:
* Design an algorithm capable of filtering stabilised gas rates and wellhead pressures for every injection/production period to extract data representative of the well performance
* Automatic updating of well performance (IPR/VLP) model and matching the algorithm filtered data with theoretical and practica1 models (back pressure C, n equation).
This paper describes the outline of the implemented PERSEO system, details of the computerized workflow along with sensitivities and the results obtained.
The concept of "intelligent field?? is a promising scenario for the future of oil and gas industry. What is more or less foreseen today (Gomersall 2007, Murray et al 2006, Unneland et al 2005) is the idea of creating a higher automation level in the management of a production asset.
The increasing amount of available data, among which real-time subsurface sensors, and the trust in the potential of the "digital?? revolution are probably the main drivers of this belief.
However the kind of automation required is something really peculiar with the oil industry, where we should always admit that we don't really know what our main asset - the reservoir - looks like. The intrinsic impossibility to directly measure, to "see?? the reservoir, brings the petroleum engineer/geologist in the continuous interpretation of indirect data in order to feed more or less complex models.
Going from data to models and from those to decisions is the concretization of the value of the whole petroleum engineering activity. Decisions are fastened and enhanced by a value-chain able to define from data a limited number of clear scenarios.
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