Optimize production and process systems in real-time

Physics-informed AI is a breakthrough hybrid model building technique, that fuses physics-based simulation and process data. This approach addresses the challenge of maintaining and updating models, solving them better, and faster than traditional model-based simulation alone.

Optimizing complex production and process systems requires accuracy in real-time. Traditional oil and gas operations rely on expertise limitations, siloed working, and slow, non-continuous optimization to operate assets.  This leads to excess carbon emissions, process inefficiency, and new net zero technologies that are economically unfeasible. AI offers promise however, training data availability is typically very limited. The non-linear behavior of systems is difficult to capture, and contextual understanding is required to address previously unseen conditions.

Physics-informed AI addresses these challenges, by performing predictive, real-time optimization for a wide spectrum of complex systems across upstream, midstream, and downstream operations.

The solution delivers scientifically consistent results, captures system dynamics, delivers interpretable results, and typically solves orders of magnitude faster than physics-only simulators.

Physics-informed AI optimizes complex oil and gas production and process systems in real time across a wide range of scenarios.

Proof of value has been clearly established in a wide range of case references, including methanol injection optimization, liquid slug mitigation, electric submersible pump (ESP) power optimization, natural gas liquids (NGL) processing, and gas-oil separation plant (GOSP) optimization. Results include: