The oil and gas industry strives to improve environmental stewardship and reduce its carbon footprint,but lacks comprehensive global operational data for accurate environmental assessment and decision-making.This chal...The oil and gas industry strives to improve environmental stewardship and reduce its carbon footprint,but lacks comprehensive global operational data for accurate environmental assessment and decision-making.This challenge is compounded by dispersed information sources and the high costs of accessing proprietary databases.This paper presents an innovative framework using Large Language Models(LLMs)–specifically GPT-4 and GPT-4o–to extract critical oil and gas asset information from diverse literature sources.Our framework employs iterative comparisons between GPT-4’s output and a dataset of 129 ground truth documents labeled by domain experts.Through 11 training and testing iterations,we fine-tuned prompts to optimize information extraction.The evaluation process assessed performance using true positive rate,precision,and F1 score metrics.The framework achieved strong results,with a true positive rate of 83.74%and an F1 score of 78.16%on the testing dataset.The system demonstrated remarkable efficiency,processing 32 documents in 61.41 min with GPT-4o,averaging 7.09 s per extraction-a substantial improvement over the manual method.Cost-effectiveness was also achieved,with GPT-4o reducing extraction costs by a factor of 10 compared to GPT-4.This research has significant implications for the oil and gas industry.By creating an organized,transparent,and accessible database,we aim to democratize access to critical information.The framework supports more accurate climate modeling efforts,enhances decision-making processes for operations and investments,and contributes to the sector’s ability to meet environmental commitments.These improvements particularly impact emissions reduction and energy transition strategies,potentially transforming how data is extracted and utilized in this field and beyond.展开更多
Consistent estimation and monitoring of greenhouse gas(GHG)emissions in the Oil and Gas(O&G)industry is challenging due to inaccessible,fragmented,and unstandardized datasets.Earlier efforts in estimating such emi...Consistent estimation and monitoring of greenhouse gas(GHG)emissions in the Oil and Gas(O&G)industry is challenging due to inaccessible,fragmented,and unstandardized datasets.Earlier efforts in estimating such emissions required extensive manual analysis to harmonize diverse data sources on O&G operations.Also,these analyses depend on flaring and methane leakage datasets,which should ideally be updated in near real-time,challenging to integrate effectively to process models.To tackle these challenges,this study proposes a Geographic Information System(GIS)-based data platform called Pyxis for integrating and managing data input associated with GHG emissions estimates in the O&G sector.The Pyxis architecture includes a scalable geo-database for source management and an automated data pipeline for data management using spatial indexing.This greatly reduces the manual labor traditionally needed for data matching and merging.In addition,top-down remote sensing data can be seamlessly associated with bottom-up field operations data through Pyxis,which improves data recency and spatiotemporal coverage.Here,we apply Pyxis to the O&G fields of Brazil as a case study to show how it can help generating accurate estimates of Carbon Intensity(CI)with data management among disparate and inconsistent data sources.This work highlights the potential of scaling up Pyxis globally via integrating artificial intelligence models for data extraction and ultimately becoming a valuable tool for GHG emissions monitoring and policymaking in the O&G industry.展开更多
基金the Aramco Services Company and Natural Gas Initiatives at Stanford University。
文摘The oil and gas industry strives to improve environmental stewardship and reduce its carbon footprint,but lacks comprehensive global operational data for accurate environmental assessment and decision-making.This challenge is compounded by dispersed information sources and the high costs of accessing proprietary databases.This paper presents an innovative framework using Large Language Models(LLMs)–specifically GPT-4 and GPT-4o–to extract critical oil and gas asset information from diverse literature sources.Our framework employs iterative comparisons between GPT-4’s output and a dataset of 129 ground truth documents labeled by domain experts.Through 11 training and testing iterations,we fine-tuned prompts to optimize information extraction.The evaluation process assessed performance using true positive rate,precision,and F1 score metrics.The framework achieved strong results,with a true positive rate of 83.74%and an F1 score of 78.16%on the testing dataset.The system demonstrated remarkable efficiency,processing 32 documents in 61.41 min with GPT-4o,averaging 7.09 s per extraction-a substantial improvement over the manual method.Cost-effectiveness was also achieved,with GPT-4o reducing extraction costs by a factor of 10 compared to GPT-4.This research has significant implications for the oil and gas industry.By creating an organized,transparent,and accessible database,we aim to democratize access to critical information.The framework supports more accurate climate modeling efforts,enhances decision-making processes for operations and investments,and contributes to the sector’s ability to meet environmental commitments.These improvements particularly impact emissions reduction and energy transition strategies,potentially transforming how data is extracted and utilized in this field and beyond.
基金funded under Aramco Services Company Agreement CW34768.
文摘Consistent estimation and monitoring of greenhouse gas(GHG)emissions in the Oil and Gas(O&G)industry is challenging due to inaccessible,fragmented,and unstandardized datasets.Earlier efforts in estimating such emissions required extensive manual analysis to harmonize diverse data sources on O&G operations.Also,these analyses depend on flaring and methane leakage datasets,which should ideally be updated in near real-time,challenging to integrate effectively to process models.To tackle these challenges,this study proposes a Geographic Information System(GIS)-based data platform called Pyxis for integrating and managing data input associated with GHG emissions estimates in the O&G sector.The Pyxis architecture includes a scalable geo-database for source management and an automated data pipeline for data management using spatial indexing.This greatly reduces the manual labor traditionally needed for data matching and merging.In addition,top-down remote sensing data can be seamlessly associated with bottom-up field operations data through Pyxis,which improves data recency and spatiotemporal coverage.Here,we apply Pyxis to the O&G fields of Brazil as a case study to show how it can help generating accurate estimates of Carbon Intensity(CI)with data management among disparate and inconsistent data sources.This work highlights the potential of scaling up Pyxis globally via integrating artificial intelligence models for data extraction and ultimately becoming a valuable tool for GHG emissions monitoring and policymaking in the O&G industry.
