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
Natural gas is an emerging and reliable energy source in transition to a low-carbon economy.The natural gas transportation pipeline network systems are crucial when transporting natural gas from the production endpoin...Natural gas is an emerging and reliable energy source in transition to a low-carbon economy.The natural gas transportation pipeline network systems are crucial when transporting natural gas from the production endpoints to processing or consuming endpoints.Optimizing the operational efficiency of compressor stations within pipeline networks is an effective way to reduce energy consumption and carbon emissions during transportation.This paper proposes an optimization framework for natural gas transportation pipeline networks based on deep reinforcement learning(DRL).The mathematical simulation model is derived from mass balance,hydrodynamics principles of gas flow,and compressor characteristics.The optimization control problem in steady state is formulated into a one-step Markov decision process(MDP)and solved by DRL.The decision variables are selected as the discharge ratio of each compressor.By the comprehensive comparison with dynamic programming(DP)and genetic algorithm(GA)in three typical element topologies(a linear topology with gun-barrel structure,a linear topology with branch structure,and a tree topology),the proposed method can obtain 4.60%lower power consumption than GA,and the time consumption is reduced by 97.5%compared with DP.The proposed framework could be further utilized for future large-scale network optimization practices.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financial support to conduct this work,and acknowledges Aramco Americas for their support under grant ASC AGREEMENT NO.CW57093.
文摘Natural gas is an emerging and reliable energy source in transition to a low-carbon economy.The natural gas transportation pipeline network systems are crucial when transporting natural gas from the production endpoints to processing or consuming endpoints.Optimizing the operational efficiency of compressor stations within pipeline networks is an effective way to reduce energy consumption and carbon emissions during transportation.This paper proposes an optimization framework for natural gas transportation pipeline networks based on deep reinforcement learning(DRL).The mathematical simulation model is derived from mass balance,hydrodynamics principles of gas flow,and compressor characteristics.The optimization control problem in steady state is formulated into a one-step Markov decision process(MDP)and solved by DRL.The decision variables are selected as the discharge ratio of each compressor.By the comprehensive comparison with dynamic programming(DP)and genetic algorithm(GA)in three typical element topologies(a linear topology with gun-barrel structure,a linear topology with branch structure,and a tree topology),the proposed method can obtain 4.60%lower power consumption than GA,and the time consumption is reduced by 97.5%compared with DP.The proposed framework could be further utilized for future large-scale network optimization practices.
