Study on Gas Migration Through Cement Microstructure under Multi-Field Effects During Offshore Gas Storage Well Construction
CONSTRUCTION AND BUILDING MATERIALS（2025）
China Univ Petr East China | CNPC Bohai Drilling Engn Co Ltd | Southwest Petr Univ
Abstract
Studies show that gas migration is one of the most challenging issues in reliable operation of offshore gas storage wells. The key reason of this phenomenon is the spatiotemporal distribution of cement microstructures under effects of alternating temperature and complicated cement hydration process. Aiming at addressing this challenge, a cement slurry hydration kinetics model under different curing temperature is proposed. Moreover, a cement slurry hydration kinetics-based wellbore temperature prediction model is proposed considering the complex environmental conditions in offshore area. Combining these models, a fractal theory-based prediction model is proposed for cement slurry microstructure under multi-field effects. Experimental verification by considering the multi-field effects demonstrates that the relative simulation error of the proposed model is within 10 %. The models are employed to simulate a case study of offshore gas storage well construction. The spatiotemporal distribution of cement hydration microstructures, hydration process, and temperature in offshore gas storage wellbore are explored. The present study provides a theoretical foundation for mitigating gas migration during the construction of offshore gas storage wells.
MoreTranslated text
Key words
Gas storage well,Offshore,Cement,Temperature coupling,Mircostructure
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined