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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.