Real-Time Dynamic Model-Based Production System for Operations and Surveillance of Oil & Gas Gathering Networks
Article based on the original paper published by SPE SPE Intelligent Energy Conference and Exhibition held in Utrecht, The Netherlands, 23–25 March 2010.
The upstream oil & gas industry faces the increasing challenge of maximizing overall profitability by enhancing hydrocarbon recovery, maintaining the field and reducing operating costs, and has been actively exploring technology applications that can assist in optimizing the value of the assets. This can only be achieved by refining relevant data into actionable information and delivering it on time to drive critical decisions. Detecting and resolving operational problems quickly can save significant amounts of money every day. The amount of available real-time data obtained from downhole instruments, surface SCADA and DCS systems can rapidly become overwhelming such that operations managers require tools to process these large amounts of data within their time constraints.
Most of the real-time data which is available from increasingly sophisticated measurement and control systems is seldom used for decision making as it mainly supports plant control services; in order to extract more value from these acquisition systems it is necessary to design and implement novel engineering applications that can translate the raw data into actionable information that can be used by engineering groups and management to support better decision-making.
In this article we present a methodology for the synchronized integration of a dynamic simulation model of the production facilities with the real-time online data obtained from the production field. By dynamically simulating the production system, it can be characterized as a transient, multiphase operation capable of modeling time-dependent phenomena. The implementation incorporates development of an accurate dynamic model of the wells and the production network, as well as an online and real-time application to transfer data between historian database and the simulation model at predetermined time steps.
Methodology and Theory
The proposed dynamic production facilities surveillance system can be summarized as a combination of a real-time synchronized and consistent operation of the following three stages:
1.Real-time field data gathering
2.Online transfer of real-time data to a time-synchronized dynamic simulation model of the wells and production system
3.Gathering and storage of the response of the dynamic model to various stimuli and key predicted system parameters including hard to measure variables.
In the data gathering stage a variety of field data, measured periodically from various places in the production system and wells, are transferred and stored in an online central historian SQL database. The collected data are used as:
- Direct input to the model,
- Real-time calibration of the model response,
- Off-line calibration of the equipment in the model.
The online and real-time production facility surveillance system proposed in this study was tested on an operating field and it was shown that it can provide the following benefits:
1. Provides virtual instrumentation for the production system to calculate the values of key variables that are not measured or where instrumentation may not be reliable; this provides an enhanced view into the current operating conditions of the system.
2. Allows look-ahead based on the latest data from the facilities. The dynamic model can be run off-line faster than real time and predict the future state of the system. A set of predefined conditions or user-defined triggers can be set up to launch a look-ahead simulation.
3. Planning by performing what-if analysis can be used to explore the impacts of changes in the system configuration or operating practices; the model is run offline to evaluate start-up and shut-down procedures or to evaluate emergency situations without risk to the facilities
4. Provides valuable information for increasing production and reducing operational costs, while meeting goals for financial revenue, cash flow and profitability.
5. The system can act as a flow-assurance tool for unmeasured key performance indices indicating operating problems such as leakage, equipment inefficiencies, gas hydrate formation, and high/low liquid levels in separators.
6. Identifies under-performing wells and the optimal operating policy for the system (solution candidate recognition).
7. Detects equipment failure: mismatch between model and data, flags for low efficiency operation or instrument failure.
8. A dynamic simulation of the system enables engineers to optimize their operating strategies and to prevent operational problems before they occur.
9. The system provides information of key performance indices that are calculated based on real time data providing a clear picture of the impact of operational decisions.
10.Online dynamic simulation models provide the capability to manage operations based on information of both current and future operating conditions of the system.
11.The predicted information from the model also helps in decision making for production allocation.
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