Eni pilots software mapping emissions, fuel use to specific activities
By Stephen Whitfield, Associate Editor
In drilling operations, reducing the fuel consumption of diesel generators at the rig site has become a major focus for both operators and drilling contractors alike, which means obtaining accurate consumption and emissions data is critical to fully understanding a rig’s carbon footprint.
Last year, Eni and Kwantis, a digital services provider, developed a greenhouse gas (GHG) software to track and map the CO2-equivalent emissions associated with the fuel consumption on Eni-operated rigs. The tool collects and analyzes data from sensors placed at high-emissions points throughout the rig and outputs the data in near-real time.
“The main purpose was to analyze the emissions performance of the well construction process,” Daniele Farina, Technology Innovation Project Engineer at Eni, said in a presentation at the 2023 Offshore Technology Conference in May. “Let’s compare the monitoring systems for emissions that our contractors use with this one so we can get a better overall picture of the greenhouse gases coming from the rig and from constructing the well. This will help us find the most emissions-intense activities in our drilling operations.”
The software was installed as a module on the data analytics platform Eni uses to aggregate and analyze data from its operated rigs. The system relies on the aggregation of high-frequency data gathered daily from sensors placed on rig equipment to provide an overall picture of the rig’s fuel consumption and emissions. It collects fuel consumption data from the rig’s engine on a given day, along with sensor data measuring power consumption of various pieces of rig equipment, and combines them with daily drilling report (DDR) data input by rig crews. This allows the system to correlate the fuel usage and power consumption with a given operation listed on the DDR.
This combination of DDR data, equipment power usage and fuel consumption data trains the software to automatically discriminate between different operations, such as tripping, drilling, circulating and reaming. Effectively, the system associates fuel consumption and equipment power usage with a given operation, allowing it to identify the rig state in real time. This data is displayed on an interface along with the power consumption and fuel usage. An algorithm built within the software converts the fuel consumption to GHG emissions, and the system can then associate the emissions to given equipment and activity on the rig. The emissions calculation is also displayed in real time.
In 2021, Eni and Kwantis conducted a field trial covering 12 wells from seven workover rigs and two wells from two land drilling rigs operated by Eni. The average GHG emissions of each well was measured and compared against baseline averages using historical data for the same rigs in 2020. The two sets of values matched closely enough to confirm the consistency of the tool in assessing emissions coming from operated rig activities.
A second field trial was conducted on a jackup in the US Gulf of Mexico in 2022. For this trial, the companies sought to test the tool’s accuracy in allocating GHG emissions to a given activity. Six categories of activities were devised – drilling, drilling connection, reaming/washing, casing run, tripping and other – and combined rig sensor data with power metering to calculate the amount of energy required during each activity category. These values were compared against emissions and fuel consumption estimates provided by the drilling contractor.
The data gathered from the emissions tool in the second field trial showed a 4.8% increase in GHG emissions compared with the drilling contractor estimates, which Mr Farina said was a “reasonable” discrepancy and confirmed the consistency of the emissions tool. The tool also showed that tripping operations accounted for around 75% of the overall emissions, even though only 40% of this trial period was spent on tripping activities. Mr Farina said this was mainly due to several weather-induced operational stops, which impacted tripping at restarts.
Eni and Kwantis are currently evaluating the results of the field tests and determining how to move forward with incorporating the tool into Eni’s operations. Mr Farina said the companies are looking to increase the frequency with which it can acquire fuel consumption data – the sensors used in the field tests processed fuel usage in 5-second intervals – as well as algorithms that can connect the types of fuel blends used for engines with emissions.
“This is continuous work that we’re doing on this system today,” he said. “Our focus right now is on trying to improve the system so we can improve the quality of the operation itself.” DC
