Hydraulic fracturing technology is an important means to efficiently exploit unconventional oil and gas reservoirs. As the development of oil and gas fields continues at a high rate, the life cycle of oil and gas wells has been significantly shortened. Fracture sealing is often used to transform oil and gas reservoirs, maintaining long-term economic development benefits. Multiple high-conductivity channels were created between the borehole and the reservoir through temporary sealing of fractures near the contaminated zone. This extended the recovery range and further improved the recovery of oil and gas. A mathematical model was developed to predict the distribution of stress around the artificial fracture prior to the rupture of the seal. Finite element software was used to model the stress distribution around a reservoir containing natural and artificial fractures. We discuss the mechanical conditions for the initiation of a new fracture and the optimal timing for fracture sealing. The prediction of the propagation and propagation trajectories of the new fracture is revealed, and the behavior rules for the initiation and steering propagation of the new fracture are clarified. These results can facilitate theoretical studies and on-site technical optimization of fracture sealing.

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Bridging plugging is the most used method of plugging in unconventional oil reservoirs, and many factors affect the effect of bridging and plugging. Since the laboratory cannot simulate the actual leakage size of the lost formation and the corresponding leakage plugging process at the drilling site, the laboratory experiment results cannot reflect the actual leakage plugging construction effect. Aiming at the problem of frequent fracture leakage during drilling in Chepaizi block, Xinjiang, China, this paper proposes a set of machine learning methods based on a neural network. Three types of factors and 14 parameters with a strong correlation with the leakage control effect were screened out. Three categories of factors include construction parameters, choice of plugging material, and fluid properties of the carrier fluid. The training was carried out based on the collected field data, the appropriate activation function was set, and the deep well network structure was optimized. By improving the field plugging measures in the later period, the model was verified by these actual cases, and the results showed that the established model produced the highest R 2 of 0.974, has a good fit, and predicts well.

The method of plugging while drilling has been one of the commonly used methods to control formation loss during drilling. The damage to materials for plugging while drilling to MWD has become a complex problem. For many years, field engineers had insufficient knowledge of the passing performance of materials for plugging while drilling in measurement while drilling (MWD). In the existing research, the blocking mechanism of materials for plugging while drilling to mud screen during the flow process is still unclear. In this study, we use computational fluid dynamics coupled with discrete element method (CFD–DEM) to investigate materials’ plugging mechanism while drilling. The results show that the migration process of lost circulation materials (LCMs) in the mud screen can be divided into three stages, displacement, retention, and accumulation of LCMs. The blocking mechanism of LCMs on the mud screen comes from two aspects. One is from the bridging of LCMs with larger particle size in the holes of the mud screen. Another source is the difference between the entry speed and the overflow speed of LCMs. The particle size and mass fraction of LCMs and the viscosity and displacement of the fluid affect the flow properties of LCMs from these two factors, respectively.