We believe that neither physics nor machine learning alone can compete against a unified hybrid approach. Integrating physics and machine learning combines the best of the two worlds, resulting in higher accuracy, better scalability, and cost efficiency. In short, hybrid models will be invaluable in the future of modeling in the industry.
Flow simulations facilitate production optimization by monitoring and planning the flow of oil and gas in wells and pipelines. Production optimization in the oil and gas industry can focus on a wide range of objectives such as maximizing production outputs; cost-effective operations; and, more recently, carbon-effective production targets. Regardless of the core objective, flow simulations can help address production efficiencies and achieve targets.
Real-time access to multiphase flow rates from all wells and pipelines is of critical importance for effective production optimization. This information provides operators situational awareness and allows them to react immediately. More importantly, they can proactively optimize operations and avoid deferred, or even lost, production. A trial-and-error approach to production optimization is not an option.
Virtual flow metering (VFM) can deliver real-time well rates for effective production optimization. For the best possible outcome, it is essential to address the full operating envelope throughout the life of an asset. To deliver the most accurate real-time well rates, we have developed a self-adjusting, hybrid virtual flow meter that combines state-of-the-art physics-based and machine-learning approaches (Fig. 1).
Fig. 1—Hybrid modeling acknowledges that both approaches have strengths and weaknesses.
Source: Turbulent Flux
Physics-Based vs. Machine-Learning VFMs
A physics-based approach to virtual flow metering relies on multiphase flow simulations. Physics-based modeling builds on well-understood concepts in, for example, thermodynamics, fluid dynamics, fluid modeling, and optimization techniques. If the physics model is accurate, this approach is often a good solution. Yet, it requires deep domain knowledge and may incur significant computational cost.
Machine learning solutions are based on learning algorithms that uncover the relationship between sensor data and target variables in a historical dataset. The machine-learning approach does not require deep knowledge about physics, but rather a good understanding of the learning algorithms and statistics. Yet, limited data sets or changes in operational conditions limit general applicability of this approach.
Use Machine Learning To Improve Physics-Based Modeling
Machine-learning methods are designed to exploit large data sets, reduce complexity, and find new features in data.
One way to use machine-learning models is to generate synthetic data for the physics-based models. Where physics-based simulations only consume sensor data directly related to the simulation model, machine learning can incorporate any sensors. By that, machine-learning models can …….