Data Analytics in Drilling Rig Fuel Management and Reduction
Fuel consumption of diesel engines is one of the main greenhouse gas emissions at a rig site. As part of the company’s ESG initiatives, Parker Wellbore is taking proactive approaches to reduce fuel consumption at rig sites. This white paper demonstrates a case study of using a data-driven workflow to mine fuel consumption and rig operational data, extract insights and feedback to the rig crew for actions.
An Automated Digital Analytic Platform (ADAPT™) quantifies fuel consumption for each drilling operation and benchmarks both low-side and high-side limits for better forecasting fuel consumption and carbon footprint. The main advantage of this digital approach is to jump-start ESG effort immediately with little to no capital investment and wide application to most rigs. In the end, the field application shows a reduction of 16 000 gal. of diesel fuel per well, which is equivalent to 358 000 lb. of carbon emissions. Besides the digital solution, Parker Wellbore is actively exploring other options for carbon reduction such as intelligent power management, remote operations support and geothermal energy.
The main advantage of this approach is to start ESG immediately with little to no capital investment and wide application to most rigs. Finally, the field application shows a reduction of 16 000 gal. of diesel per well, which is equivalent to 358 000 lb. of carbon emissions.
In this paper, a data pipeline has been built to integrate fuel consumption data and drilling operation data such as daily drilling reports (DDR). This step is critical because it not only tracks fuel consumption but also more importantly links the fuel computation with each drilling operation. Second, a cloud-based analytics platform processes the data and visualises the results via Power BI dashboards. Third, the results are communicated with the rig crew and customers to convert insights into actions. For instance, after seeing reaming consume much more fuel than other operations, the rig crew cut the unnecessary reaming operation. The other practical outcome is to benchmark both low-side and high-side carbon emission limits such that the Parker ESG team can better budget the carbon emissions for the organisation.
This case study is one of Parker’s early ESG initiatives. The future work includes improving automation in the data pipeline, adding machine learning (ML) models to predict fuel consumption and auto-reporting across different GeoMarkets. Besides digital implementation, Parker Wellbore is actively exploring other options for carbon reduction.