The Moonie onshore oil field discovered in 1961, was the first commercial oil discovery in Australia. The field was purchased by Bridgeport Energy Limited (BEL) from Santos in late 2015. An Australian first initiative by BEL is to enhance oil production from the field using tertiary recovery CO2 miscible flood to maximise field oil recovery. The process involves an evaluation of well injection strategies for a miscible displacement process using reservoir simulation modelling. In addition, the project jointly addresses community concerns regarding the rise in greenhouse gas emissions by sourcing 60000–120000 tonnes/annum of CO2 from a nearby power station and/or an ethanol plant. Justified by laboratory experiments and reservoir compositional simulations, BEL’s project timeline to implement a CO2-enhanced oil recovery (EOR) pilot could start from 2020 followed by a 2–3-year full field oil production acceleration project if additional CO2 can be sourced. Based on incremental recovery and operational consideration, an injection well in the southern end of the field surrounded by six existing producers has been selected as a pilot flood. Positive indicative economics are achieved by the efficient displacement with CO2 of 8000 scf/bbl of incremental oil. Full field dynamic modelling predicts a further 8% oil recovery factor by injecting 60 Bcf of CO2 over five years, which could store in excess of three million tonnes of CO2. For the pilot, more than 90% of the injected CO2 will remain in the Precipice sandstone reservoir. However, the efficiency and viability of a CO2-EOR project is subject to successful implementation of the miscibility modelling, logistics and injection strategy and uncertainty quantification. To propel the project into the execution phase a fast-multiphase reservoir simulator has been implemented to complete a probabilistic range of results in optimal time.

The case study presented in this paper will demonstrate the efficiency of an integrated subsurface and surface model running numerous what-if scenarios in a timely manner. It will also compare the results and the time taken to run the same scenarios with current conventional methods of having separate subsurface and surface modelling packages. Surface network configuration of a field depends on the field development strategies. As fields mature and reservoir conditions alter, these strategies change. This leads to changes in the configuration of the surface facilities, such as re-adjusting the facilities target rate, rerouting gas and products to different branches etc. Most of the current commercial Integrated Asset Modeling (IAM) tools, model reservoirs as a material balance tank. However, in cases of complex reservoirs, the material balance solution may not be able to model the full complexity in the behavior of the reservoirs. In such cases using a reservoir simulator to model the reservoir is a better option. Performance of the reservoirs depends on the pressure and capacity constraints of the facilities and also the configuration of the surface network. This demonstrates the importance of coupling a reservoir simulator and a network modeler to accommodate the changes to the surface network while accurately meeting the criteria of reservoir management. An integrated subsurface – surface model enables the user to evaluate the impact of changes in production policies while honoring all reservoir management constraints. In this paper we present the results of a fully integrated gas planning system coupled with a reservoir simulator, for guiding field development. We defined multiple scenarios for bringing up gas from reservoirs, through the facilities and delivery point to meet the market demand. The case study deals with a market-based approach for modelling 3 gas-condensate reservoirs. The unique, fully integrated reservoir to market approach we demonstrate in this study provides the ability to model and run scenarios against their complete gas network in a relatively very short time. In particular, the back-allocation from the market provides confidence in the ability to meet contractual sales obligations.