Flaring reduction & emissions optimisation

Without flaring reduction & emissions optimisation, energy transition teams rely on sampling and after-the-fact review, while the signals in flare and process data sit unread, so problems surface only after the loss, incident or complaint.

Flaring reduction & emissions optimisation predicts and reduces flaring events. The approach relies on ML. It draws on flare and process data and is typically owned by energy transition teams. The main constraint is regulatory targets. Where the human comes in: operations decide. It is scaled at leading organisations and piloted elsewhere; examples include World Bank Zero Routine Flaring initiative; operators.

Flare and process data

Regulatory targets