Minxuan Li

Quasi-dry CO₂ fracturing technology is a new CO₂ fracturing technology that combines liquid CO₂ fracturing (dry CO₂ fracturing) and water-based fracturing. It uses a liquid CO₂ system containing a small amount of water-based fracturing fluid to carry sand, and it is characterized by sand blending at normal pressure, convenient preparation, the integrated application of resistance reduction and sand carrying, and no dedicated closed sand blender requirement. We developed a self-crosslinking emulsion-type water-based fracturing fluid (ZJL-1), which contained ionic bonds, hydrogen bonds, van der Waals forces, and hydrophobic associations, for quasi-dry CO₂ fracturing, and the comprehensive properties of the ZJL-1 fracturing fluid were evaluated. The results showed that the ZJL-1 fracturing fluid had obvious viscoelastic characteristics, a heat loss rate of less than 10% at 200 °C, a good thermal stability, sufficient rheology under high temperature and high shear conditions, and a good thermal stability. The resistance reduction rate reached 70%, which demonstrates a good resistance reduction performance. Compared with conventional guar fracturing fluid, ZJL-1 can carry more sand and has a lower core damage rate. The on-site use of quasi-dry fracturing showed that optimizing the mixing ratio of liquid CO₂ fracturing fluid and ZJL-1 fracturing fluid effectively enhanced oil and gas recovery. This can be used to optimize quasi-dry fracturing and can be used as a reference.

With the rapid development of computer science and technology, the Chinese petroleum industry has ushered in the era of big data. In this study, by collecting fracturing data from 303 horizontal wells in the Fuling Shale Gas Demonstration Area in China, a series of big data analysis studies was conducted using Pearson’s correlation coefficient, the unweighted pair group with arithmetic means method, and the graphical plate method to determine which is best. The fracturing parameters were determined through a series of big data analysis studies. The big data analysis process is divided into three main steps. The first is data preprocessing to screen out eligible, high-yielding wells. The second is a fracturing parameter correlation clustering analysis to determine the reasonableness of the parameters. The third is a big data panel method analysis of specific fracturing construction parameters to determine the optimal parameter range. The analyses revealed that the current amount of 100 mesh sand in the Fuling area is unreasonable; further, there are different preferred areas for different fracturing construction parameters. We have combined different fracturing parameter schemes by preferring areas. This analysis process is expected to provide new ideas regarding fracturing scheme design for engineers working on the frontline.