Assessment and forecasting of eco-environmental early-warning system for shale gas production in a pressure-state-response framework

ABSTRACT

Shale gas plays a significant strategic role in improving energy security and optimizing the energy mix for China. However, the potential environmental impacts are becoming an extremely serious problem with the rapid development of shale gas in China. This paper presents how to select and optimize a set of tailored eco-environmental early-warning indicators coupled with subjective and objective methods in a pressure-state-response (PSR) framework. An early-warning methodology is proposed based on the composite index method and gray prediction model to assess and predict eco-environmental problems of shale gas production. Meanwhile, the early-warning signals are designed based on the status quo of Changning shale gas play, one of the most favorable plays in China, which could send the different signals timely once the early-warning tipping points are broken. We find that Changning shale gas play had a severe warning with yellow signals from 2018 to 2019 and red signals from 2020 to 2023, and the projects have to be stopped if there are no more powerful and effective actions since 2020. The positive effects at the response level cannot offset the increasing negative influences at the pressure level, and methane emissions might be the potent greenhouse gas when extracting shale gas. The results show that the workers or managers on site could take more proactive actions and make better decisions to curb environment deterioration since the early-warning system is more practical and easier to understand and use for non-experts in practice.

KEYWORDS:

• Shale gas
• eco-environmental risks
• early-warning system
• changning shale gas play
• PSR framework

Declaration of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under [Grant No. 71801223] and the Fundamental Research Funds for the Central Universities of China under[Grant No. 19CX04009B].

Notes on contributors

Jinfeng Sun

Dr. Jinfeng Sun, an associate professor of management science and engineering, mainly focues on the research of energy economics and management, especaily conventional and uncoventioal oil and gas resources.

Xiaoping Zhu

Miss Xiaoping Zhu’s reasearch focuses on the forecasting and optimization based on decision science and operation reasearch.

Xiaoli Sun

Miss Xiaoli Sun is good at applying management science theories in economics evaluation and prediction for oil companies.