From Real-Time Data to Updated Models: Challenge of Intelligent Fields Applied to Gas Storage
- Dennis Denney (JPT Senior Technology Editor)
- Document ID
- Society of Petroleum Engineers
- Journal of Petroleum Technology
- Publication Date
- May 2009
- Document Type
- Journal Paper
- 57 - 58
- 2009. Society of Petroleum Engineers
- 0 in the last 30 days
- 54 since 2007
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This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 113171, "From Real-Time Data to Updated Models: The Challenge of Intelligent Fields Applied to Gas-Storage Business," by A. Tiani, SPE, G. Bartolotto, P.D. Strippoli, and R. Latronico, Eni E&P, and G. Spitaleri, Stogit, prepared for the 2008 SPE Europec/EAGE Annual Conference and Exhibition, Rome, 9-12 June. The paper has not been peer reviewed.
Underground gas storage (UGS) is a complex activity, which faces the challenge of combining variability of daily commercial-client requests with the reservoirs’ capability to deliver. Stogit has implemented an integrated system that dynamically links databases, visualization tools, and reservoir/well simulators to assist the management process. The challenge has been to extract the maximum information from the available database and to computerize the process of updating well/reservoir performance.
The intelligent-field concept is a promising scenario for the future of the oil and gas industry. The increasing amount of available data, use of real-time subsurface sensors, and the trust in the potential of the digital revolution are the main drivers. However, the kind of automation required is peculiar to the oil industry because the detailed characteristics of the reservoir are unknown. The intrinsic impossibility to measure directly, to “see,” the reservoir requires continuous interpretation of indirect data to feed complex models.
Going from data to models to decisions is the concretization of value of the whole petroleum-engineering activity. Decisions are enhanced by a value chain that is able to define a limited number of clear scenarios from the data. Whereas downstream oil and gas processes have a degree of certainty and automation comparable with other industries, it is a big challenge to automate the subsurface process. In this context, the measure/analyze/decide cycle, as the basis of each feedback loop, encounters processes in which the human factor is mandatory and the work and experience of the petroleum specialist are required. Field surveillance and optimization of the production/storage cycle require fast and effective decision making, supporting the daily-to-hourly market changes.
Performance Surveillance for Gas-Storage FieldsTo monitor well production and performance and to control the production from the wells remotely, many sensors and controls have been deployed to manage the operativity of the wells and fields from a central operations-control room. The main data acquired are pressure and temperature at the wellhead, at cluster nodes, and at the plant manifold; gas rate at the wellhead, cluster nodes, and plant manifold; production of liquids (e.g., water and gas); and status of valves, compression units, treatment facilities, lines, separators, and others.
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