Abstract
Abstract
IGAS is a project that aims to develop a digital network optimization model capturing the complete gas life-cycle of Reservoir A from its Upstream through the Downstream process flow. This submission focuses on the modeling efforts and the preliminary implementation of the model in a field trial to generate optimized production scenarios aimed to maximize condensate production in a reservoir developed under gas recycle strategy to form the basis for the development of an AI optimization tool.
IGAS model was built using commercially available solutions to integrate the upstream and downstream models. Model development was straight forward with commercial well performance and network modelling tools combined with process workflow simulators to develop upstream and downstream models, respectively. Those were assimilated in a single tool using an interconnection platform to facilitate integration of workflows. Post model integration, a field trial was conducted to validate the model’s accuracy in generating condensate maximization scenarios.
IGAS field trial was conducted over a period of 10 weeks. A scenario was generated in accordance with the expected well availability for duration of the field trial. Post field trial, two primary observations were made: An Adherence Score metric was created, which calculates how well the IGAS scenario was followed considering each well’s CGR and suggested IGAS rate. A direct correlation was observed between Condensate production and Adherence Score objective function, indicating that the more IGAS scenario is adhered to, the more condensate is produced.Condensate history matching exercise was conducted for the trial period duration, which observed an average error of 1.72% (model vs actual production). This was followed with an optimized scenario generated for each day of the trial period (actual wet gas production for given day was set as constraint to ensure fair comparison) in order to quantify the potential condensate production. This exercise observed an average additional condensate production of 2.2%, which could have been produced if the IGAS scenario was adhered to. Unlocking such value would require the installation of well choke actuators to facilitate instantaneous changes to the chokes.
IGAS serves a great example of the potential value that can be unlocked with modern day IAM (integrated asset model) solutions. More importantly, it sheds light to the importance of developing unmanned field operations (well choke actuators in specific), which maximizes the use of IAM solutions such as IGAS enabling instant implementation of suggested scenarios.