An Integrated Production Modelling process is presented that is used to automate CSG (Coal Seam Gas) production forecasts from reservoir to sales. This involved incorporating type curves representing future reservoir performance into a hydraulic model of the surface network. One of the key challenges was to convert time-dependent decline curve forecasts into time and pressure-dependent reservoir-well models suitable for Integrated Production Modelling process. Results are compared to other methods with reduced simulation time, improved accuracy and scenario analysis. This innovative approach used a combination of techniques to incorporate type curve based reservoir models into two large integrated production models. The first model consisted of 250 wells and a single compressor station and the second model consisted of 500 wells and multiple compressor stations. Different techniques were applied for wells that were pre or post dewatering and incorporated into a surface network modelling tool. This allowed production forecasts to be generated automatically for the single compressor station model and semi-automated for the multiple compressor station model whereby the system was solved to meet demand or to maximise production taking into account constraints. Type curve data was converted to a ‘tight gas’ reservoir model which showed a good match for wells post dewatering but not for wells that were still inclining. A combination of proprietary scripting in a network simulation program and macro code in Excel were used to handle system constraints optimisation. The time taken to run each scenario reduced significantly as the restriction moved from a human to the amount of computing power. Accuracy and repeatability of results also improved due to models being setup and solved in a consistent manner, thereby removing discrepancies associated with manually driven models. This allowed for more scenarios to be modelled in the time allocated to the production forecasting processes allowing for improved analysis and decision making. Previously type curved based reservoir models could not be solved by a surface network modelling tool automatically without human intervention. This was not possible due to the size and complexity of the surface network and because of the lack of time and pressure-dependent reservoir-well models suitable for Integrated Production Modelling process. The process outlined in this paper overcame this challenge.

No abstract

The industry-wide move towards big data and the digital oilfield is underpinned by good data. This paper outlines a suite of data management standards, systems and processes, and provides examples of how these have led to improved decision making. The approach involved the development of standards and streamlined business processes followed by the implementation of systems focusing on production data accessibility, quality and integration. Accessibility was addressed by making real-time data readily available from multiple devices so users spend more time using data instead of locating it. Quality was improved through the implementation of processes such as operational data validation (ODV) and production allocation (PA). Integration was facilitated so that users could view data from various systems in a single location. The implementation of data management standards, systems and processes led to improved decision making in the areas of external reporting, operating cost, safety, environment, commercial, reservoir management, well surveillance, and situational awareness. In particular, implementation of the ODV process ensured the completeness, accuracy and timeliness of data from reservoir to sales. Furthermore, improved accessibility and integration increased situational awareness, reduced troubleshooting time, and improved problem analysis. While the concept of data management and quality control is not new, the novelty is in the approach of developing robust standards, implementation of systems based on these standards, and creating the supporting business process and culture aligned to what drives value in the organisation. This is easily transferable and adaptable across all facets of the petroleum industry.