An effective and comprehensive integrated production-system monitoring framework plays an important role in the Smart Fields philosophy to turn data into actionable information and therefore value. This has been demonstrated by implementing some of the principal requirements for such a framework in the application called Data Broker in Shell. The Data Broker infrastructure has allowed Shell Engineers to perform exception-based surveillance. Used together with other Smart Fields applications such as FieldWare Production Universe, FieldWare Well Test, IPM and Energy Components, automated event recognition and workflows has freed up engineer's time to do more value added work, such as technical analysis and decision-making - time that would otherwise be spent looking for and validating data from many different sources. The system allows for a more efficient handling of data in terms of efficiency, quality and timeliness in reporting to end-users. The advent of real-time algorithms, monitoring tools, better integration software, event and workflow application tools allows the above infrastructure to be realised. Introduction There is a drive in the industry towards more advanced ways of managing hydrocarbon fields and many companies have initiated programs to implement new innovative ways of working [1]. These programs have been called various names: in Shell the program is called Smart Fields. These programs typically involve extending access to, and improving the quality of, production and metering data in the field so operators and engineers are aware of what the field is doing in real time. As fields are getting equipped with more and more gauges, large volumes of data have become available, both in the process control domain, and in the office domain. The industry is faced with the challenge to turn this data into value. The prize contained with the data is reduced deferment, optimised production, accurate reservoir characterisation and better security of supply. If this concept is combined with a right degree of automation it also has the potential to increase the efficiency of staff as they can focus on the real problem areas rather that spending a lot of time trying to analyse the data and identify the problems. The term that has been coined for the latter is " exception based surveillance". Exception based surveillance focuses on the exceptions that require a response from engineering staff. This means staff can spend their time working on real problems, and do not have to spend a lot of time looking for them. Surveys have shown that Engineers spend as much as 60% of their time to gather, QC, format, and convert data. At the heart of the Smart Fields concept are value loops (see Figure 1), which can be limited in scope and have short cycle times (such as control loops in a Process Control system) or cover a large scope and have a long cycle time (such as asset optimisation). In any case the loop needs to be closed, i.e. conclusion needs to be derived from information and action needs to be taken to improve the overall asset performance. If the loop is not closed, then there is a lot of wasted effort and investments are not capitalized on. For example, if you have invested in measuring down hole pressure, but data are not analyzed continuously and events (such as shut-ins) are not detected by staff in a timely fashion, then a lot of deferment can ensue. The loop from measured data to integrated modelling, event detection and follow up actions was not closed in that case. Deferment could have been reduced by a timely response on the basis of the data, and the gauge could have returned the value of purchase, installations and maintenance many times over. This paper first describes principal requirements for a comprehensive monitoring framework. In Shell an application called Data Broker has been developed that has implemented several of these principal requirements. In the description and application section, some of the implementation details of Data Broker are used to illustrate how one can realise a comprehensive monitoring framework. Practical examples are shown in the "Examples" section.

This paper describes the large-scale trial of Real-Time Optimization (RTO) that Shell Malaysia E&P has conducted on the Integrated Gas Production System iln Sarawak, implementing models for real-time monitoring and optimization of wells and facilities on a gas production network spanning more than 100 wells on more than 40 platforms across a number of different Production Sharing Contracts (PSCs). We highlight how Digital Oil Field (DOF) practices enable field-based data to be turned into information, support decision making, and lead to actions that ensure production is optimized continuously. Additionally, this approach replaces the traditional, daily or monthly optimization by a continuous one. The system generates optimal set-points for the control variables which are executed by the operators. Closed loop control is possible with remotely operated chokes which would allow the optimization of the entire gas system to be fully automated and minute-by-minute.

This paper describes the successful application of Real-Time Optimization by Shell Malaysia E&P on the Integrated Gas Production System in Sarawak, implementing models for real-time monitoring and optimization of wells and facilities on a gas production network spanning more than 100 wells on more than 40 platforms across a number of different Production Sharing Contracts. We highlight how Digital Oil Field practices enable field-based data to be turned into information, support decision making, and lead to actions that ensure production is optimized continuously. The technology described in this paper is applied to achieve consistent gas supply to meet demand, maximize revenue, and enable improved and timely operational decisions - striking a balance between short- and long-term value, and taking into account the reality of commercial and contractual constraints, finance, and economics. The optimization is data-driven and covers more than 1,000 variables and features multiple, mutually dependent objectives and constraints. The solution has proven significantly better than prior physical model-based solutions, which deliver optimized field settings, but with inherently unstable results, and not fast enough for application in a real-time decision making environment. Field trials have proven a result of: increased condensate production at current or improved expected Ultimate Recovery, whilst maintaining a stable gas supply, fulfilling quality constraints and contractual LNG nominations. This is one of the first successful attempts to implement truly-real-time optimization in a production environment of this size and complexity, including a complicated set of commercial and contractual constraints, and striking a transparent balance between short-term and long-term value. Having proven that a multi-departmental reality can be successfully captured and modeled, might markt the start of a transformation towards embedding intelligent energy to its true potential.

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