It is not clear how the distribution of retained fracturing fluids and its effect on the permeability and wettability in tight oil reservoirs interact. Especially, there are more qualitative studies and less quantitative studies on this issue. Under laboratory experimental conditions, this paper clarifies the distribution of retained fracturing fluids in the core and reveals the influence rule of retained fracturing fluids on tight reservoir permeability and wettability. It is found that the main retention space of retained fracturing fluids in a tight reservoir is a microporous interval, and the residual oil after oil displacement by retained fracturing fluids mainly exists in the core in the form of dots or porphyries. The smaller permeability and porosity of the core will lead to more retained fracturing fluids. The permeability of different cores after fracturing fluid retention has decreased to varying degrees compared with that before fracturing fluid retention. The wettability of core slices before and after fracturing fluid retention was tested, and the effect of retained fracturing fluids on reservoir wettability was not significant. This study has important significance for improving the recovery of tight oil reservoirs and enhancing the understanding of postfracturing fluid retention.

In the process of continuous production of natural gas wells, formation pressure and gas flow rate decrease continuously. The ability to carry liquid decreases continuously, thus gradually forming bottom hole liquid. Bottom hole liquid accumulation is an important reason for the decrease of production or shutdown of natural gas wells. How to diagnose whether there is liquid accumulation in natural gas wells and identify the degree of liquid accumulation, to adopt drainage gas recovery operation in time, is the research focus of efficient development of natural gas reservoirs. In this paper, a method for diagnosing bottom hole liquid accumulation combining production performance curve and modified Fernando inclined well critical liquid-carrying model is designed for a large scale double-branch horizontal well used in unconventional reservoirs. The method is applied to the Well X2 of He 8 Member in PCOC. The application results showed that there was no liquid accumulation in the horizontal and vertical sections of the Well X2. The liquid in the wellbore was generated at the bottom of the inclined section and the liquid accumulation is upward along the wellbore from the bottom of the inclined section, with the height of 3 m.

Post-fracturing shut-in, as an important means of improving the energy efficiency of fracturing fluid, has been widely used in the development process of unconventional reservoirs. The determination of the shut-in duration is key to the effectiveness of shut-in measures. However, the distribution characteristics of the fracturing fluid during the post-fracturing shut-in period in unconventional reservoirs, such as the Chang 7 reservoir, were not clear, and the shut-in duration needed further optimization. Therefore, this paper employed low-field nuclear magnetic resonance (NMR) technology to study the distribution characteristics of the fracturing fluid during the post-fracturing shut-in period in unconventional reservoirs and optimized the shut-in duration. The study showed that the Chang 7 reservoir had a complex pore structure and relatively low porosity and permeability. During the shut-in process, the filtrate was distributed in pore throats with radii ranging from 0.0012 μm to 0.025 μm. Pore throats with radii ranging from 0.003 μm to 0.07 μm acted as dynamic pore throats in the process of imbibition displacement. When the shut-in duration for the Chang 7 segment was 7 days, the growth rate of the retained volume of fracturing fluid filtrate was the highest. When the shut-in duration was 10 days, there was no oil displacement in the medium and large pores, and the retained volume of filtrate was lower than that at 7 days shut-in, indicating that an optimal shut-in duration would be 7 days. This study can provide theoretical and technical support for the development of unconventional reservoirs.

The complex seepage laws and high production costs in tight oil reservoirs have prompted scholars to apply machine learning methods to optimize construction plans and predict development results. The machine learning method uses artificial intelligence algorithm to make data "speak" to reveal the internal relationship and change rule among parameters in the process of system operation. In this paper, principal component analysis, k-means clustering, time series and other methods are used to predict the production capacity of tight oil reservoirs, reveal the development law of tight oil reservoirs, and guide the efficient and rapid development of unconventional resources in China.