This is a summary of an article that appears in the June 2019 issue of E&P Magazine. Read the full version here.
Advances in air pollution modeling technology are making their way into industry regulatory requirements, and offshore oil and gas operators should have a working knowledge of the complexity and demands of these tools.
Exploration and Production (E&P) activities off the coasts of Texas, Louisiana, Mississippi and Alabama are regulated by the U.S. Bureau of Ocean Energy Management (BOEM), under the authority of the Secretary of the Interior. If an E&P plan has projected pollutant emissions greater than BOEM’s “exemption threshold” for the plan (a factor related to the distance of the plan offshore), it requires an air quality review. These air quality reviews employ sophisticated modeling of pollutant transport and dispersion to determine their effect onshore. The offshore E&P community performing air quality reviews has been anticipating a significant shift in approved pollutant modeling technology that will transform a typical air quality review into a highly complex computational analysis. This is due to a concerted push by regulators (both onshore and offshore) to evaluate the effects of a complicated set of pollutants called secondary pollutants.
Secondary pollutant effect
An offshore E&P air quality review under BOEM is required to examine so-called “primary” pollutant effects onshore. In other words, one reviews the increase in the concentration of pollutant “X” onshore due to sources of pollutant “X” emissions within a project. However, expanded reviews of “secondary” pollutants are likely in store for offshore projects in the not-so-distant future.
Quantum shift of complexity
Regulatory agencies appear to be moving toward a modeling approach called photochemical grid modeling (PGM). This technology, while a significant advance in the dynamic representation of long-range transport and chemical interactions of a project’s emissions, is a quantum shift of complexity for compounding reasons—sophisticated encoded chemical mechanisms using a substantial amount of input data requiring more extensive computational resources.
The complex chemistry and copious input data of a PGM system all naturally lead to a large draw on computational resources to handle the necessary data processing and model iteration. Some industry practitioners estimate that roughly 10 times the amount of processing power, memory and storage space needed for standard primary pollutant modeling is required to run a PGM. While this seems daunting, today’s cloud computing environments, such as Microsoft’s Azure or Amazon’s Web Services clouds, are available and ameliorate much of the challenge of the increase of necessary resources.
The coming shift to the BOEM-regulated offshore E&P market in approved air pollution modeling technology will usher in potential advances in the science and understanding of offshore emission effects over land. However, along with those possible steps toward an improved scientific representation of air pollution, there are certain steps toward increased complexity and essential resources with protracted schedules that offshore operators will need to navigate.