Two-phase flow behavior in fractured carbonate reservoirs was investigated due to the importance for geothermal and petroleum resource recovery, such as in this study the phase change of volatile oil. This paper presents a semi-analytical method for accurately modeling two-phase flow behavior and quickly predicting the production performance. The fractured carbonate reservoir was modeled with a dual-porosity model, and the phase change and two-phase flow were modeled using the black oil model. The production of the oil phase was obtained through linearizing and solving the mathematical model. The gas phase production was forecast using the producing gas-oil ratio (GOR), calculated using flowing material balance equations. By comparing the semi-analytical solution to the solution of the commercial numerical simulator and applying it to a field case, the accuracy and practicability of the proposed semi-analytical method could be validated. Based on the semi-analytical model, the influences of several critical parameters on production performance were also analyzed. The proposed model was shown to be efficient in evaluating two-phase production performance of horizontal volatile oil wells. Furthermore, the new technique is able to serve as a useful tool for analyzing two-phase production data and making forecasts for volatile oil wells in fractured carbonate reservoirs.

When the thermal maturity of the Longmaxi Formation in the southern Sichuan Basin is too high, the pore structure of shale becomes poor. Therefore, to investigate the effect of organic matter thermal maturity on shale pore structure, a study was conducted. Using the Longmaxi Formation shale in the southern Sichuan Basin as an example, the intrinsic relationship between shale porosity, pore structure parameters, organic matter laser Raman maturity, and organic matter graphitization degree was examined using X-ray photoelectron spectroscopy, particle helium porosity measurement, organic matter micro-laser Raman spectroscopy, and gas adsorption experiments. The results indicate that thermal maturity is the macroscopic manifestation of the graphitization degree of organic matter, and the correlation coefficient between the two is 0.85. A thermal maturity of 3.5% (with a corresponding organic matter graphitization degree of 17%) aligns with the highest values of shale porosity, pore volume, and pore-specific surface area across all pore size conditions. The evolution model of shale pore structure can be divided into two stages. The first stage is characterized by a thermal maturity between 2.0% and 3.5% (with a corresponding degree of graphitization of organic matter between 0% and 17%). During this stage, the number and connectivity of micro-macropores increase with increasing thermal maturity. The second stage is marked by a thermal maturity between 3.5% and 4.3% (with a corresponding degree of graphitization of organic matter between 17% and 47.32%). Basement faults are present, leading to abnormally high thermal maturity, poor preservation conditions, continuous generation of micropores, better connectivity, and a reduced number of pores. Medium macropores with good connectivity suffer from gas loss in the fracture network, leading to the collapse and disappearance of pores. The results mentioned in the statement have an important guiding role in the efficient exploration of shale gas in the Longmaxi Formation in the southern Sichuan Basin.

The process of mining produces a large amount of heavy metals and high-sulfate mine water, which is the main factor leading to environmental degradation in the mining area, and the removal of heavy metals and the recovery of sulfate from mine water is a difficult problem faced by mines today. Currently, sulfate is treated as a hazardous substance and is not recycled. In this paper, the precipitation–adsorption bipolar membrane electrodialysis (BMED) multi-technology-coupled recovery of sulfate method was used to treat mine water. The results showed that the coupling technology could remove heavy metals and sulfate in water better, while the sulfate desalination rate was about 96.8%, current efficiency was 54.2%, energy consumption was 0.823 kWh/kg, and the acid production concentration was 0.168 at an electrolyte concentration of 0.1 mol/L, an operating voltage of 12 V, an initial salt concentration of 30 g/L, and a flow rate of 3.5 mL/min. Mechanistic results showed that the precipitation–adsorption method could realize the removal of heavy metals from mine water. The BMED process realized the removal of sulfate and also the recovery of acid. The multi-technology coupling of precipitation–adsorption and bipolar membrane electrodialysis explored in this paper provides a direction for the in-depth treatment of mine water.

Low-resistivity shales are widely developed in the Sichuan Basin. The production of low-resistivity shale gas reservoirs ranges from high to low to none. The existing methods for gas-content prediction cannot accurately predict the gas content of low-resistivity shale. This increases the risk of shale-gas exploration. To prove that the random forest algorithm has apparent advantages in predicting the gas content of low-resistivity shale and reducing the risks associated with shale-gas exploration and development, three prediction methods were selected in this paper to compare their effects. The first method is known as the grey-correlation multiple linear regression method. Low-resistivity shale-gas content logging series were optimized using the grey-correlation approach, and then the low-resistivity shale-gas-content prediction model was established using the multiple linear regression method. The second method we selected was the resistivity method. The improved water-saturation model was used to predict the water saturation of low-resistivity shale, and then the gas content of low-resistivity shale was predicted based on the free-gas content and the adsorbed-gas-content model. The random forest algorithm was the third method we selected. Fourteen logging series were used as input data and the measured gas content was used as supervised data to train the model and to apply the trained model to the gas-content prediction. The findings demonstrated that the grey-correlation multiple regression method had poor accuracy in predicting gas content in low-resistivity shale; The resistivity method accurately predicted water saturation, and the predicted gas content was higher than the actual gas content. Because the random forest algorithm accurately predicted low-resistivity shale-gas content, its use in the Sichuan Basin was advantageous. The selection of a low-resistivity shale-gas-content prediction model was guided by the research findings.