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.

The Maari oil field, the first OMV operated offshore oil field development, has showcased OMV's impressive technical skills. Following the completion of a field re-development drilling campaign in August 2015, the well configuration currently consists of 10 producers and 1 water injector (with the option to convert a producer into a water injector in the future). Electric Submersible Pumps (ESPs) are installed on all 10 producing wells to provide lift of reservoir fluids to surface. A SCADA system and associated Production Historian Database (PHD) was included in order to capture the high frequency data for well & reservoir surveillance and daily production optimisation of the field. However, there were many challenges in utilising this live data stream from the offshore facility. In particular, it was vital to continuously and effectively monitor and optimise ESP performance in order to improve run life, reduce downtime and ultimately increase production. An integrated decision support system was therefore required for real-time data collection, production monitoring, ESP health check and KPI analysis for proactive decision making and limiting the number of manual processes involved. This paper describes how these challenges were overcome by creating an integrated workflow and aligning the existing system architecture in order to meet the business needs. The system is based on full workflow automation, and has been deployed for data acquisition, validation and analysis by optimising the components of integrated asset management. The system includes an integrated framework connected to various live data sources with different time increments, allowing data aggregation to a reliable intra-day hub. Automated job scheduling has also been built in with a decision support dashboard setup for production analysis and ESP performance monitoring. Based on historical trends, an optimum operating envelope was defined and automatic rules were configured for anomaly detection. The system has provided standardized data access throughout the asset team, streamlining their entire process and resulting in improved efficiency, which has optimised the engineers time for core operational activities. With a secure and automated workflow, and the ability for multiple users to work simultaneously, the system has minimised their downtime, thus improving overall productivity. Utilizing the live data feed for updating of simulation models has allowed quicker comparisons of numerical predictions with analytical forecasts, hence helping to streamline the overall reservoir management of the field. The system has not only assisted the team in meeting their production reporting deadlines, but has also alleviated bottlenecks in their decision-making processes helping to boost overall asset productivity.

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.

With a vision of innovation, integrity and agility, Nexus Energy began first production of Longtom field in October 2009. The Longtom gas field is located in the Gippsland Basin, offshore Victoria where the produced gas is transported to Santos’ Patricia Baleen gas processing plant. All production data is acquired by Santos with the supervisory control and data acquisition (SCADA) system. The challenge for Nexus Energy was to monitor the field remotely in the absence of a data historian and to support the operational people proactively. Data acquisition from Santos, validation, and storage in a secured centralised repository were therefore key tasks. A system was needed that would not only track accurate production volumes to meet the daily contractual quantity (DCQ) production targets but that would also be aligned with Nexus’s vision for asset optimisation. We describe how real-time data is acquired, validated, and stored automatically in the absence of a data historian for Longtom field, and how the deployed system provides a framework for an integrated Production Operation System (iPOS). The solution uses an integrated methodology that allows effective monitoring of real-time data trends to anticipate and prevent potential well and equipment problems, thus assisting in meeting DCQ targets and providing effective analysis techniques for decision making. Based on full workflow automation, the system is deployed for acquisition, allocation, reporting and analysis. This has increased accuracy, accountability and timely availability of quality data, which has helped Nexus improve productivity. The comprehensive reporting tool provides access to operational and production reports via email for managers, output reports in various formats for joint venture partners, and nontechnical users without direct access to the core application. A powerful surveillance tool, integrated with the operational database, provides alarms and notifications on operation issues, which helps engineers make proactive operational decisions. The framework allows a streamlined data flow for dynamic updates of well and simulation models, improving process integration and reducing the runtime cycle.

This paper discusses a new workflow to stochastically estimate the performance of future production in coal seam gas (CSG) developments. Usually performance evaluations for CSG wells are conducted using either much-generalised statistical methods or numerical simulation. Both approaches have significant drawbacks; the former methods are quick but very often lack accuracy, while the latter is very accurate however also usually highly complex in set-up and computation. The presented workflow is a new approach to well performance prediction that combines speed and reasonable accuracy. The workflow generates a set of key performance indicators of existing wells derived from historic dynamic data (water and gas production rates, pressures, etc.), static data (initial coal and reservoir properties, etc.) and predicted data (simplified production forecasts). The wells are then grouped according to the similarity of their KPIs. The production profiles of the wells within the same group are combined to a type curve that is described by the most likely production profile and an associated uncertainty range. A data-driven expert system is used to identify and capture the correlations of the parameters such as geographic locations, well spacing, reservoir properties and the group membership (equivalent to type curve). This expert system can then be applied to any location in the field in order to determine the most likely group membership of a potential well. The classification of a new well to a group is hereby not necessarily unique; the expert system might classify a new well into several groups and assign a probability of occurrence for each of the groups. A Monte Carlo routine is then applied to forecast the performance of the new well locations honoring the respective probability of occurrence of each type curve.