Make it all Profitable:  Oil, Gas, and Carbon Emissions
Data Science & Analytics

Make it all Profitable: Oil, Gas, and Carbon Emissions

Rosemary Jackson  •  

Drilling for Carbon: A complex process involving multiple stages of discovery and development. Numerous analytical methods are employed to understand economic advantages and tradeoffs in production and completions. One of those is the decline curve.

Reporting for Carbon: Reporting is required which means measuring and tracking carbon emissions. It’s the added complexity in an industry that creates environmental hazards. Tough to measure, hard to estimate, Petro.ai sees the answer to reporting carbon emissions as an engineering solution associated with production forecasts over time.

We need to capture information about our carbon emissions. If we think about emissions as a production stream, then the industry can start making profit-oriented decisions about emissions. Use the concept of reporting not as a way to confess our sins, but as a method to flip the script on the cost of carbon emissions and be more strategic with the process. Create an offensive strategy. Petro.ai believes that anticipating carbon reserve emissions allows for future planning rather than just a tale of already committed woes.

In a recent Journal of Petroleum Technology editorial, Petro.ai science advisor, Dr. Nathan Meehan, explained, “Many operators already report the carbon intensity of their activities, usually prior-year activities. Carbon intensity is the carbon emissions per unit of energy or per barrel. A variety of regulatory bodies and others argue the definitions of such reporting. We are arguing for reporting estimated carbon intensity of reserves (CIRMS).

“There are various carbon-estimation methods, life cycle assessment tools (LCA)—including Stanford’s Oil Production Greenhouse Gas Emissions Estimator (OPGEE) and MIT’s Sustainable Energy System Analysis Modeling Environment (SESAME)—that provide a modeling of emissions. Calculations of carbon LCA require many simplified assumptions and are not as accurate as measurements. There are no widely accepted industry standards.”

Petro.ai in collaboration with Dr. Meehan is developing the CIRMS initiative. This initiative scheduled for delivery to our clients in Q1 of 2022 will be based off CIRMS standards and guidelines. Petro.ai is facilitating the reporting in workflows directly tied to a company’s GOR, Gas/Oil Ratio.

The reduction of emissions will be understood through related data and visualized in a decline analysis that is optimized in the same way as forecasts of oil, gas, and water.

Meehan continues, “Actual data collection is invariably more accurate than LCA predictions. Emissions data are available from fixed-point ground sensors, satellites, plane flyovers, drones, hand-point sensors, and cameras. The diversity of emissions data is very similar to completions where you work off your treatment pressure, microseismic, production data, and communication tracers between wells.

The data are pulled together for the complete completions or drainage calculation models; this is exactly how emissions data can be coalesced for a complete CIRMS strategy. Just as data analytics and machine-learning techniques are improving production and completions engineering, emissions optimization will be improved by these evolving tools.

“Realizing that the expense and time of adding instrumentation to every well for carbon measurements is impractical, a CIRMS model needs to be informed by both a production forecast, available emissions measurements, and LCA assumptions. LCA tools fill in the gaps of understanding, measurement, and extrapolation.”

The opportunity of carbon is to make it a profit center for oil and gas operators,” Dr. Troy Ruths, CEO of Petro.ai adds, “Right now they’re seeing it as a cost. They’re seeing it like water. But if you can get your arms around it, if you can be more strategic with it, if you can think about it from an engineering perspective, it doesn’t need to be the cost or burden that we think it is.”

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