Flow simulation software offers a means to facilitate production optimization by monitoring the flow of oil and gas in wells and pipelines. Traditionally, production optimization focused heavily on maximizing production outputs. In the last decade however, a shift towards cost-effective operations and, more recently, carbon-effective production targets have been observed in the oil and gas market.
With respect to flow simulations, software like ours is built to look at any performance indicators an operator may include as part of their production optimization goals. Production efficiencies are perceived as a key criterion for emission reductions, whereby research indicates that a 10% increase of production efficiency can lead to a 4% reduction in emission intensity (McKinsey Report “Toward a net-zero future”).
We realize we need to turn every stone to reach a net-zero future. Planning is not enough as you cannot ever foresee every eventuality in your future operations. This is why it is important to make the most out of your available data at all times. We make solutions that consume the available data and transform it into knowledge and situational awareness; all of which enable production optimization that impacts the operators’ financial and environmental sustainability.
Flow Simulations – approaching the problem of excess gas
Let us look into an example on flaring emissions. Consider a production platform with liquid-only export capabilities. This means that the produced associated gas must be “consumed” on the platform. If the gas can only be utilized as fuel or lift gas, any excess gas will be flared. According to estimates from the World Bank, 140 billion cubic meters of natural gas were flared in 2019, resulting in 300 million tons of CO2 emitted into the atmosphere. Flaring may seemingly be an unavoidable “dirty” problem. But is it truly unavoidable?
Transient multiphase flow simulations have a long history and can help gain insight into addressing unnecessary flaring emissions. Simulations can be used to determine the facility upgrades required to export the produced gas from the platform along with the liquids; a switch from liquid-only to multiphase export. This implies that continuous flaring can be avoided. Here, it is important to notice that intermittent flaring may still be required for operational safety. However, reductions in such operational flaring can be facilitated by flow simulations and production optimization. For best impact, these workflows must be put online and connected to real-time data streams to make up-to-date insights available anywhere, at any time.
Turbulent Flux Virtual Flow Monitoring explained
To solve complex science and engineering problems such as real-time applications of transient multiphase flow, we need novel methodologies that are able to integrate traditional physics-based modeling with state-of-the-art machine learning techniques. To be more specific, first-principle physics is a necessity to address operational conditions for which we have no data. It is also required to gain a complete understanding of the entire flowing system from inlet to outlet. By applying machine learning on real-life data and simulations from physics models, we can create ML models which mimic the physics and are faster and easier to deploy. Machine learning models can also be used to generate synthetic data, e.g., to compensate for missing sensors, and improve the robustness and accuracy of physics models. In other words, machine learning models assist and self-correct to ever changing conditions over time. By taking such a hybrid approach, we can assure the most accurate flow predictions with easy access to up-to-date simulations to help optimize production – for any KPIs you set, including targets to reduce your carbon footprint.
About Turbulent Flux
Turbulent Flux provides ready-to-use simulation software for flow insights for the oil & gas industry. Our solutions help to accurately measure and optimize our clients’ wells and pipelines whilst reducing associated financial and environmental costs. Simulations undertaken are based on a hybrid model that combines the predictive capabilities of physical models with the speed and self-correcting abilities of data analytics. We are a trusted software provider to clients in all parts of the world.