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Samarang is a 35-year-old offshore oilfield in Malaysia, operated by Petronas. By 2003, with the declining production trends anddwindling reserves, the field abandonment was on the horizon. SamarangRedevelopment Project was kicked-off in 2012 with a vision to implementIntegrated Operations (IO) as an Asset Management Decision Support tool byimplementing a real time production, reservoir and process surveillance system. For the IO implementation, various intelligent asset decisionprocesses referred as workflow were identified and in various reservoir, production, and operations domains suchas Flow Assurance, Well Performance, Artificial Lift, Production and facilityPlanning and EOR Optimization. These workflow groups are linked to provide an integrated asset decision support system whichwill improve decision quality and operational efficiency. workflowm will deliver value with enhanced asset management by focusing on decisions toimprove reservoir drainage, production, and operations. In the initial phase, well performance workflow group, by acquiring data from the SCADA system andprocessing the real-time analysis workflows, enables the asset team to movefrom a reactive to a pro-active asset management system. ‘Well Status andUptime Calculation’ and ‘Well Rate Estimation’ workflow enable engineers to know in real-time the performance andstatus of wells to reduce the production deferment. The ‘Well Test Validation’ workflow will provide online validation, enhanced test quality andimproved confidence in back-allocation and reservoir modeling leading to betterField Management. ‘GasLift Surveillanceand Optimization’ workflow monitorslift performance, performs diagnostics and optimizes gaslift distribution. UsingReal Time data and integrated software, this solution will enable efficientdecisions with a shorter turnaround timeThus workflows equipthe asset team with cutting edge technology and collaborationenvironment for operational decision making. The IO implementation will help to extend the fieldlife and increase recoverable reserves while facilitating an effectivereservoir management strategy.

Accurate prediction of individual well potential and estimation of field capacity are the key for managing Coal Seam Gas (CSG) wells and its deliverability to Liquefied Natural Gas (LNG) plant. Because there are no downhole gauges in these wells there is limited reservoir data. The associated uncertainty, the absence of fast predictive wellbore models and challenges in generating accurate well performance predictions add to the deliverability challenge. This paper presents a method used to estimate CSG well performance for Australian CSG assets using neural network (NN) and proxy modeling. Traditional methods for prediction of well potential, such as numerical simulation or statistical techniques, have significant limitations. Numerical prediction is traditionally accurate but very complex in setup and computation; statistical techniques have the advantage of being fast but often lack accuracy. The approach starts with the automatic acquisition, validation, and quality control of static and dynamic production parameters in proxy modeling. Based on the relationship and similarity of key performance indicator (KPI) profiles, the wells are grouped into various clusters using a NN self-organizing map (SOM) technique. For each cluster group, a workflow is defined to estimate various well parameters used to predict individual well potential. A generic proxy model can be designed for each cluster for future prediction and capacity modeling. The workflow also allows tracking of capacity at different operating points on a daily basis for wells, group of wells, and fields and can be leveraged further by generating scenarios for various constraints on production parameters and facility controls. The entire workflow process can be standardized, scheduled to run automatically, and stored in a workflow library for secure deployment and sharing across the organization.

A case study of the Spring Gully coal seam gas (CSG) field illustrates how an integrated production data management and analysis system has consolidated scattered data sources into a unified repository to provide easy access for reporting and analysis. Automated surveillance workflows with notification and alarm capabilities have simplified production performance tracking and enabled proactive decision making. Current analysis techniques and expert knowledge have been captured in predefined templates and workflows to identify trend violations, flagging the issues and optimizing production. The new system has provided a foundation layer for managing the production and deliverability of thousands of wells now and in the future, helping to translate data into information. Enabling timely decision making through the use of accurate, validated data and automated workflows has helped engineers focus on problem solving and analysis. The streamlined process will continue to help the operator's CSG teams improve the efficiency and productivity of all their assets.