Oil & gas production—optimized.

We perform a dynamic modeling of the production system that enables us to perform a real-time mass, energy, and momentum balance to obtain production per well, as well as the pseudo-dynamic production behavior, capturing events and perturbations in the process. Once this model is built, we can use it to optimize artificial lift systems in real-time, find flow assurance problems in real-time, and generate predictive recommendations for well intervention.

We automate systems to monitor artificial lift systems for lower-producing wells to predict operational problems in real time by applying machine learning and real-time simulation techniques to data already in SCADA systems. Alerts can be programmed to send to operators automatically, proactively servicing the most critical wells first, while optimizing resources. As low as a 12% improvement in these wells would mean almost a 10% improvement in asset production.

We use technologies such as industrial IoT and sensors to provide real-time monitoring of operations and program alerts about unwanted emissions like unsafe atmospheres, or methane emissions that exceed limits under current regulations. Connecting H2S or CO2 sensors directly to the cloud is highly effective, has no impact on the operator's control infrastructure, and involves low operational costs, while boosting operational safety and compliance with regulations.

We combine SCADA data with machine learning models and physical models with automatic parametric tuning to generate real-time virtual three-phase flow and pressure measurements at any point in the production networks. This allows us to predict undesirable flow assurance events and trigger automatic alerts for the operator to proactively mitigate or eliminate production loss.