Every engineer and manager knows that you can only improve performance that you measure and track. That is why we have key performance indicators (KPIs). Similarly, we can only optimize what we can predict. If we really want to lower carbon emissions, we will need to implement a consistent method of measuring and tracking the right data. There are challenges in improving what we track because tracking comes from so many sources. We need to work on optimizing what we predict if we are going to start making high-value decisions around emissions.
Carbon emissions occur during all phases of the hydrocarbon extraction industry right through to the final use of the product. We call the total life cycle of emissions “well to wheels.” SPE members are generally focused on one phase of the carbon emissions.
The largest contribution is the combustion and use of produced oil, from refinery to wheels. This is typically about 350–400 kg of CO2 equivalent per barrel. We use CO2 equivalent to include the greenhouse-gas (GHG) impact of methane. Then, there is the energy and carbon expenditure of producing that hydrocarbon, well to refinery. This includes drilling, completions, production, and transportation. Carbon emissions from the wells to refinery vary from less than 25 kg to more than 300 kg CO2 equivalent per barrel, averaging about 100. Flaring and fugitive emissions are generally the largest contributors to these emissions.
Environmental, social, and governance (ESG) activism is driving changes in behavior for public investors, private investors, lenders, and management teams. When will the measuring be done? Who will set the industry standards? How will the model be developed?
Carbon emissions from shale production vary dramatically and are also driven by flaring and fugitives. While flaring is preferable to venting, most low-volume flares are inefficient. Operators flare for a variety of reasons including lack of pipeline capacity, upsets, and low value for natural gas.
Fugitive emissions also enter the equation. Fugitive emissions are any leakage or irregular release to the atmosphere of natural gas. This can be caused by human error, mechanical operations (such as pneumatic actuators), or faulty equipment. Fugitive emissions and flaring both factor into the well-to-reservoir carbon footprint.
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.
Carbon reserves definitional issues require the development of a carbon intensity reserves management system (CIRMS) to set industry standards. We need to see what is ahead to make strategic decisions on emissions and carbon reduction now. We need to analyze the carbon intensity of reserves—the potential emissions that are in front of us—not just the carbon intensity of current operations. An engineering solution associated with production forecasts over time offers a framework for thinking through the carbon emissions issue optimally.
We believe the CIRMS initiative, the evaluation of carbon emissions, is an engineering problem. And the answer is in a decline analysis linked directly to wells and reservoirs. Carbon emissions should be considered in the same vein as oil, gas, and water. Emissions from well to refinery are another production stream and can be projected over time. Companies are producing oil, gas, water, and emissions. Engineers already know how to optimize and forecast oil, gas, and water. Emissions are added in the same way to the analysis.
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.
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.
Reducing emissions as an engineering problem also means there need to be channels of cooperation between the engineering teams who are making those evaluations and the ESG teams who are communicating to investors. Collecting data and then translating them into models that reveal carbon-impact decisions is an important part of the internal company process that easily moves to reporting. CIRMS is not a technology. It is a policy that needs to be seen through a ratio such as gas or oil, and correlates the extraction cost of emissions.
We all stand at the global front lines, trying to provide safe, affordable energy while minimizing environmental impact. The key is to understand that making oil and gas production more sustainable will include integrating forecasts of data emissions collection and LCA modeling with our production and reserves forecasts. If we can think about CIRM from an engineering perspective, we can solve the problem and find the benefit with the strategic developmental processes that the science will provide.
You can learn more about carbon intensity reserves management here.
D. Nathan Meehan is president of CMG Petroleum Consulting, an energy advisory firm founded in 2001, and senior technology advisor for Petro.ai, an oilfield data analytics firm. He was formerly president of Gaffney, Cline & Associates and a senior executive at Baker Hughes. He served as the 2016 SPE President. Previously, he was vice president of engineering for Occidental Oil & Gas and general manager, exploration and production services, for Union Pacific Resources. He holds a BSc in physics from the Georgia Institute of Technology, an MSc in petroleum engineering from the University of Oklahoma, and a PhD in petroleum engineering from Stanford University.