energy

Across hundreds of onshore wells, well, manifold, compressor, and facility dynamics shift faster than current tools and workflows can adjust, leaving the system running below optimum much of the day.

Geminus concurrently optimizes every well (ESP, gas lift, plunger lift, etc), manifold, and network choke setting, including rerouting options across the full network in seconds, all while honoring the constraints you set (like liquid handling, gas handling, and intake pressures). Available data (unstructured, process, simulation, real-time sensor) is transformed into a live decision engine. When a compressor goes down at 2 a.m., operators already have the optimal setpoints for that specific configuration, delivered in seconds. Field-validated at scale, including a public deployment with Hess in the Bakken.

Subsea networks can be complex.  Slugging hurts production and infrastructure. Your transient simulator takes an hour per scenario and still cannot characterize the flow regime well.

The Geminus model-data fusion approach delivers the first millisecond-scale slugging diagnostic: flow regime classified, instability quantified, stable operating point recommended. The same approach extends to gas lift allocation, distributing available gas across the network in real time to maximize production while avoiding hydrate formation.

Produced-water disposal networks, amine units, TEG units, and NGL units all perform critical functions, but are often overlooked as optimization targets, leaving OPEX savings unrealized.

Geminus turns your existing data into live optimizers: produced-water routes through your cost hierarchy automatically, while amine and TEG units find their minimum-energy operating point without compromising on-spec processing.

Fluid catalytic crackers, hydrocrackers, and hydrotreaters are major profitability levers in refining when run even slightly smarter than existing models allow. Reinforcement Learning (RL) approaches recognize the opportunity but are ill-suited to the high dimensionality of these units and the dynamic nature of feed changes and equipment changeouts.

Geminus unlocks your existing investment in chemical kinetic models, replacing them with high-fidelity surrogates that integrate directly into your existing RTO platform, continuously adapting as feeds and catalysts change, with deployment compressed from nine months to nine hours.

Across the entire LNG process, energy consumption is the dominant OPEX driver.

From feed gas pretreatment through liquefaction, fractionation, and NGL recovery, Geminus builds surrogates of your key processes that lower energy and fuel consumption without sacrificing throughput.

Where first-principles simulators are too slow for closed-loop use and data-driven models break down outside historical conditions, Geminus’ physics-native surrogates run fast enough for real-time optimization while maintaining credibility across the full operating range.

Cracking severity, grade transitions, catalyst management, and separation energy are all solvable optimization problems, provided your models can keep up with reality.

Geminus surrogates replace static kinetic models with physics-native representations that adapt continuously to feed changes, catalyst aging, and equipment degradation, integrated directly into your existing RTO and APC infrastructure. No capex, no parallel systems, just sharper models driving better decisions and recovering yield and energy across your complex.