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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.10170 |
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| _version_ | 1866929214653988864 |
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| author | Gasmi, Cedric Fraces Long, Wennan |
| author_facet | Gasmi, Cedric Fraces Long, Wennan |
| contents | This paper introduces the Oilfield Pollutant Graphical Model (OPGM), an innovative approach designed to improve the benchmarking and uncertainty analysis of greenhouse gas (GHG) emissions in oilfields. Building on the robust foundation provided by the Oil Production Greenhouse Gas Emission Estimator (OPGEE) framework, OPGM retains all essential functionalities of the latest OPGEE iteration (v3.0c), while offering substantial improvements in user experience and computational performance. Key advances of OPGM include a streamlined user interface for more intuitive interaction, which facilitates transparent visualization of intermediate results and thus contributes to a more interpretable and accessible analysis process. A notable feature of the OPGM is its ability to naturally perform sensitivity analyzes. This is achieved by allowing users to seamlessly transition nodes from deterministic to probabilistic, thereby integrating uncertainty directly into the core structure of the model. OPGM achieves remarkable computational efficiency, executing analyzes at a speed 1e+5 times faster than the Excel-based OPGEE, thus facilitating rapid large-scale emissions assessments. This leap in processing speed represents a significant step forward in emissions modeling, enabling more agile and accurate environmental impact assessments. The integration of OPGM into existing Life Cycle Assessment (LCA) practices holds the promise of significantly improving the precision and speed of environmental impact analyses, offering a vital tool for policymakers and industry stakeholders in their efforts to better understand and manage the environmental impacts of oilfield operations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_10170 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Building a Life Cycle Assessment Model using Bayesian Networks Gasmi, Cedric Fraces Long, Wennan Data Analysis, Statistics and Probability This paper introduces the Oilfield Pollutant Graphical Model (OPGM), an innovative approach designed to improve the benchmarking and uncertainty analysis of greenhouse gas (GHG) emissions in oilfields. Building on the robust foundation provided by the Oil Production Greenhouse Gas Emission Estimator (OPGEE) framework, OPGM retains all essential functionalities of the latest OPGEE iteration (v3.0c), while offering substantial improvements in user experience and computational performance. Key advances of OPGM include a streamlined user interface for more intuitive interaction, which facilitates transparent visualization of intermediate results and thus contributes to a more interpretable and accessible analysis process. A notable feature of the OPGM is its ability to naturally perform sensitivity analyzes. This is achieved by allowing users to seamlessly transition nodes from deterministic to probabilistic, thereby integrating uncertainty directly into the core structure of the model. OPGM achieves remarkable computational efficiency, executing analyzes at a speed 1e+5 times faster than the Excel-based OPGEE, thus facilitating rapid large-scale emissions assessments. This leap in processing speed represents a significant step forward in emissions modeling, enabling more agile and accurate environmental impact assessments. The integration of OPGM into existing Life Cycle Assessment (LCA) practices holds the promise of significantly improving the precision and speed of environmental impact analyses, offering a vital tool for policymakers and industry stakeholders in their efforts to better understand and manage the environmental impacts of oilfield operations. |
| title | Building a Life Cycle Assessment Model using Bayesian Networks |
| topic | Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2401.10170 |