Integrating Data Analytics and Mass Balance Approaches to Estimate and Understand Regional Methane Emissions: A Study in the Permian Basin

• Author / Creator

  Bian, Yifan

• Satellite-retrieved methane (CH4) concentration data offers a valuable opportunity for large-scale emissions monitoring. However, its widespread adoption remains challenging due to the data volume and varying data quality. A workflow to estimate the methane emission rate of major hydrocarbon plays based on the mass balance principle using publicly available Sentinel-5P satellite data is presented. This workflow estimates the methane emission rate originating from specific regions. The proposed workflow is applied to estimate emissions from the Permian and Appalachian Basins in the United States. Applying the proposed workflow to these regions, the three-year-mean methane emission rates from 2019 to 2021 are estimated to be 3.56 Mt/year for the Permian Basin and 4.46 Mt/year for the Appalachian Basin. The results are compared against volumes estimated by other means and reported in the literature. The proposed method is easy to implement and offers promising potential for practical and reliable estimates for long-term regional methane emission monitoring purposes for operators, governments, investors, and the general public. In addition, this study presents a comprehensive, data-driven approach to analyze and predict methane enhancements in the Permian Basin. Methane enhancement refers to ''the increase in methane concentration above the baseline background level'' (Dlugokencky et al., 2003). Leveraging satellite-retrieved methane (CH4) concentration data and oil and gas related operational data, this research helps to better understand the complex interactions influencing methane emissions. It begins with a descriptive analysis of methane enhancement data attributing to different operators based on their geographical distribution across the basin. Next, multiple supervised and unsupervised learning algorithms are utilized to help predict methane enhancement levels quantitatively, offering insights into influential features contributing to methane emissions. Lastly, impurity-based feature importance and SHAP values are used to evaluate the predictive power and interpretability of these models, decoding the 'black-box' nature and enabling an in-depth understanding of the factors driving methane enhancements. This study explores the complex dynamics of methane emissions in the Permian Basin but also sets a foundation for future investigations aimed at refining our comprehension and prediction capabilities of methane emissions in oil and gas regions.

• Subjects / Keywords

  • Methane
  • Emission
  • GHG

• Graduation date

  Fall 2023

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-931n-h356

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.