Automation of greenhouse gas emissions quantification in natural gas production for operational flexibility
Hillmert Solano, Disha Gadre, Pallav Sarma
🤖 gxceed AI 要約
日本語
米国アパラチア盆地の天然ガス生産を対象に、GHG排出量算定を自動化するケーススタディ。CO2、CH4、N2Oの包括的なインベントリをリアルタイムで生成し、スプレッドシートの人的ミスを削減。将来の排出予測や運用変更の影響評価も可能とし、オーストラリアの報告制度への適用可能性も示唆。
English
This case study from the US Appalachian Basin demonstrates automated GHG calculations for natural gas operations, mapping activity data to emission models for CO2, CH4, N2O inventories. It reduces spreadsheet errors, enables near real-time identification of emission-intensive processes, and supports forecasting and operational optimization. The paper also discusses integration with Australian reporting frameworks.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
In the global GX context
This paper provides a practical framework for automated GHG quantification that aligns with global disclosure standards (ISSB, SEC, CSRD). It demonstrates how operational data can be leveraged for compliance and methane mitigation, offering a scalable approach for upstream and midstream assets worldwide.
👥 読者別の含意
🔬研究者:The automated methodology and case study provide a replicable framework for emissions quantification research, especially for methane mitigation in natural gas systems.
🏢実務担当者:Corporate sustainability teams can adopt this automation to improve accuracy, reduce manual errors, and enhance real-time emissions reporting and forecasting.
🏛政策担当者:The paper shows how automated workflows can support compliance with reporting schemes like Australia's NGERS, informing policy design for digital MRV systems.
📄 Abstract（原文）
This paper covers a case study demonstrating how automating greenhouse gas (GHG) calculations can improve accuracy, transparency, and efficiency across natural gas operations. This US case study from the Appalachian Basin shows how operational activity data and measurement inputs can be mapped directly into emissions estimation models, producing comprehensive inventories for carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). The automated methodology reduces human error common in spreadsheet-based reporting, integrates source-level emissions factors, and enables near real-time identification of emission-intensive processes. Beyond inventory generation, the framework supports emissions forecasting, allowing operators to model the impacts of operational changes, reduction technologies, and production planning on future carbon and methane intensities. The paper also discusses the potential of how requirements from Australian reporting frameworks, including the National Greenhouse and Energy Reporting Scheme, can be incorporated into automated workflows. Overall, the approach positions automated GHG quantification as both a compliance and operational intelligence capability supporting methane mitigation across upstream and midstream assets.
🔗 Provenance — このレコードを発見したソース
- crossref https://doi.org/10.1071/ep25079first seen 2026-05-14 22:35:41
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