Physics-informed AI is a breakthrough hybrid model building technique, that fuses physics-based simulation and process data.
In response to increasing regulatory pressure around flaring, our customer—an operator known for its focus on efficiency—sought a solution that could deliver rapid, reliable insights to support operational decisions during events, such as compressor shutdowns and field turnarounds. The objective was clear—optimize the system to reduce flaring without compromising production in a high-well-count shale basin.
To achieve this, SLB developed a surrogate physics-informed AI model—real science supported by artificial intelligence. The model is trained to replicate the results of running a hydraulic simulator—performing approximately 1,000 solves in under six seconds. This enables near-instantaneous optimization, and was equivalent to 50 hours of work using traditional simulation methods.
The SLB solution supports a range of use cases, including total field throughput optimization and flaring forecasting, and allows customers to explore “what-if” scenarios under various constraints, such as pad rates, inlet pressures, and choke settings. An intuitive dashboard provides clear comparisons between current and optimized states, highlighting measurable gains in throughput and reductions in flaring—empowering faster, smarter decisions in the field.
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