Emission Reduction in Oil & Gas Subsurface Characterization Workflow with AI/ML Enabler

Abstract

According to (McKinsey & Company, 2020), drilling and extraction operations are responsible for 10% of approximately 4 billion
tons of CO2 emitted yearly by Oil and Gas sector. To lower carbon emissions, companies used different strategies including electrifying
equipment, changing power sources, rebalancing portfolios, and expanding carbon-capture-utilization-storage (CCUS). Technology
evolution with digital transformation strategy is essential for reinventing and optimizing existing workflow, reducing lengthy processes
and driving efficiency for sustainable operations.
Details subsurface studies take up-to 6–12 months, including seismic & static analysis, reserve estimation and simulation to support
drilling and extraction operations. Manual and repetitive processes, aging infrastructure with limited computing-engine are factors
for long computation hours. To address subsurface complexity, hundred-thousand scenarios are simulated that lead to tremendous
power consumption. Excluding additional simulation hours, each workstation uses 24k kWh/month for regular 40 hours/month and
produces 6.1kg CO2.
Machine Learning (ML) become crucial in digital transformation, not only saving time but supporting wiser decision-making. An
80%-time-reduction with ML Seismic and Static modeling deployed in a reservoir study. Significant time reduction from days-tohours-
to-minutes with cloud-computing deployed to simulate hundreds-thousands of scenarios. These time savings help to reduce
CO2-emissions resulting in a more sustainable subsurface workflow to support the 2050 goal.