A method and system for analyzing the carbon footprint of a crude oil product

By acquiring carbon footprint activity data in the crude oil production process, and using weighted analysis and random forest models to identify key links and factors, the problem of carbon footprint identification in crude oil production was solved, enabling the formulation of low-carbon emission plans and the green transformation of enterprises.

CN122175125APending Publication Date: 2026-06-09CHINA PETROLEUM & CHEMICAL CORP +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
CHINA PETROLEUM & CHEMICAL CORP
Filing Date
2024-12-09
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Existing technologies cannot accurately identify key links and influencing factors of carbon footprint in crude oil production, making it impossible to formulate effective low-carbon emission alternatives and hindering the achievement of low-carbon strategic goals for oil companies.

Method used

By acquiring carbon footprint activity data at each stage of crude oil production, a weighted analysis model is used to calculate the importance of key stages, and a random forest model is used to quantify the influence of influencing factors, thereby identifying key stages and influencing factors of the carbon footprint.

Benefits of technology

It provides data support for oil companies to formulate low-carbon emission alternatives, guides companies to achieve low-carbon transformation, promotes green and sustainable development, and achieves low-carbon strategic goals.

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Abstract

This application provides a method and system for analyzing the carbon footprint of crude oil products. The method includes the following steps: calculating the carbon footprint generated by each stage of the crude oil production process based on acquired activity data; calculating the importance score of each stage's contribution to the carbon footprint based on a pre-built weighted analysis model to determine the key stages of the carbon footprint; quantifying the influence of factors affecting the activity data based on a pre-trained random forest model to determine the key influencing factors of the carbon footprint; and conducting carbon footprint analysis on the crude oil product based on the determined key stages and key influencing factors. This provides strong data support for oil companies to formulate low-carbon emission alternatives and effectively guides companies to achieve low-carbon transformation, thereby achieving low-carbon strategic goals and promoting the green and sustainable development of enterprises.
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