AI Case Study
Bonanza Creek Energy avoids production shutdown and fines by anticipating when certain emissions are going to occur using a machine learning system
Bonanza Creek Energy has implemented a system that anticipates increasing risk of volatile organic compounds and emissions being released by monitoring real-time data from each of its production locations.
Oil And Gas
"To avoid fines against credits, the system identifies what volatile organic compounds are involved and what steps, if taken, would lead to emissions. A machine-learning solution that can be used by subject-domain experts allows Denver-based Bonanza Creek Energy to monitor data drawn from its supervisory control and data acquisition (SCADA) system to detect increasing risk of volatile organic compounds (VOC) or carbon-dioxide emissions. In doing so the system identifies the potential emissions source and allows the situation to be addressed before emissions occur."
Specifics undisclosed: "At Bonanza Creek, ambient air temperature was identified as an important factor when gauging the possibility of emissions. The results over time could influence the company to move to tankless systems, since it is with the storage tanks that the greatest possibility of emissions arises."
Specifics undisclosed: "multi-variate time-series data to discover patterns, recognize conditions, and predict critical events in industrial operations"
"Bonanza Creek's operations are focused in Colorado's Wattenberg Field. Every well the company drills is extended horizontally and is fracture-stimulated. It's important to address the potential for emissions effectively and pre-emptively... To take an example, if a vapor control system exceeds the permitted level of emissions, as defined by a given production output, per the regulations, operators must either shut down production for a period of time or pay hefty fines."
"Live, streaming well data is monitored by a SCADA/telemetry system. Pressures, levels, and temperatures are monitored, involving about 30 instruments at each production location, with signals that vary from 30 seconds to 15 minutes based on the criticality of the equipment involved."