Experimental Study on the Properties of Gas Diffusion in Various Rank Coals under Positive Pressure

Acidification technology is an important measure for enhancing the extraction of coalbed methane from seams with low permeability and abundant minerals, and the acidification scheme is the key to the success of acidification treatment. To determine the optimal acidification modification scheme, an improved AHP−TOPSIS method is proposed to decide on the optimal conditions for wettability modification. This method constructs an evaluation index system, taking the wettability of coal as the target layer and the pro/hydrophobic functional groups in coal as the index layer. Meanwhile, it innovatively takes the adsorption energy of each functional group when absorbing a single water molecule as the basis for assigning weights to the evaluation indexes. Then, nine acidification modification schemes are evaluated and selected by the improved AHP−TOPSIS method based on the test results of different schemes to get the optimal one. The optimal scheme selected by the AHP−TOPSIS method is validated by water adsorption tests and isothermal adsorption tests. The results showed that the significance of each evaluation index is ranked as follows: aromatic structures > hydroxyl groups > aliphatic functional groups > oxygen-containing functional groups. The optimal acidification modification scheme is selected by the AHP−TOPSIS method with a HF concentration of 4% and a reaction time of 6 h. The ranking of acidification modification schemes obtained by the AHP−TOPSIS method is in high agreement with the ranking of water adsorption tests. When compared with raw coal, the coal samples treated with the optimal scheme have lower adsorption capacity for gas, which indicates that the aforementioned method could be used to evaluate and select the optimal acidification modification scheme, and the selected optimal scheme has the potential to increase the output of coalbed methane.

Section: Steps Of the Ahp−topsis Method Ahp Methods Is A Mathematical...mentioning

confidence: 99%

Research on the Optimization for Acidification Modification Scheme Considering Coal’s Wettability Based on the AHP–TOPSIS Method

He,

Yuan,

et al. 2023

“…In matrix pores, gas flows mostly in the form of diffusion determined by the concentration gradient. In fractures, gas flows mainly in the form of seepage affected by the pressure gradient. − Diffusion can directly affect the equilibrium of gas pressure in the coal seam and has a certain influence on seepage. Therefore, in-depth exploration of diffusion law of gas from coal seam is to the fundamental task for revealing the migration mechanism of gas from coal seam, and has a great significance for calculating the gas content of coal and the amount of gas emission from fallen coal and designing gas extraction. , …”

Section: Introductionmentioning

confidence: 99%

Study on the Solution and Variation Law of Diffusion Coefficient Based on the Numerical Simulation Optimization Method

Shen,

Wang,

Ren

et al. 2024

Since the diffusion coefficient is a key parameter to characterize the diffusion rate of methane molecules, its measurement and solution have always been a research hotspot. The diffusion coefficient is normally solved through analytical solutions of theoretical models, which is complex and poorly applicable. In comparison, the numerical simulation optimization method can seek a solution easily and quickly, providing a clue for solving such problem. In this paper, first, gas desorption experiments were conducted on coal samples with different initial gas equilibrium pressures, coal particle sizes, and metamorphic degrees. Combined with existing theoretical models, the numerical simulation optimization method was adopted to solve the diffusion coefficient of the coal particle. Furthermore, the applicability and advantages of the numerical simulation optimization method were discussed. Finally, the variation law of the diffusion coefficients was analyzed. The results demonstrate that the numerical simulation optimization method can not only solve the diffusion coefficient easily and quickly but also reveal the law of diffusion concentration with time. The d values between the solution results and the experimental data under different conditions are all smaller than 0.2, which proves the effectiveness and accuracy of the simulation optimization method. The diffusion coefficient of gas from coal particles is unrelated to the initial gas equilibrium pressure, yet it has a Z-shaped relationship with the coal particle size and a V-shaped relationship with the metamorphic degree.

“…9−11 At present, the main research method for fracture conductivity is based on indoor testing methods. Xiao et al 12 took carbonate rock as the research object and experimentally studied the effects of acid erosion, proppant embedding depth, and different proppant sizes and acid injection on fracture conductivity during hydraulic fracturing and acid fracturing; Guo et al 13 conducted tests on steel plates and shale to understand the effects of flowback rate, fracturing fluid, and closure stress on proppant flowback and fracture conductivity; Sun et al 14 used ceramic and quartz sand proppant obtained from Changqing Oilfield in China. For example, the fracture conductivity under different proppant mixing ratios was evaluated through experiments; Tariq et al 15 studied the effects of different acid fracturing fluids, rock hardness, and surface roughness on the fracture conductivity of carbonate rocks through experiments.…”

Section: Introductionmentioning

confidence: 99%

Fracture Conductivity Prediction Based on Machine Learning

Wang,

Zhang,

et al. 2024

Hydraulic fracturing technology is the main method to develop low-permeability reservoirs. Fracture conductivity is not only the basis of fracture optimization design but also one of the key parameters to determine the effect of hydraulic fracturing. However, current methods of calculating fracture conductivity require a lot of time and labor cost. This research proposes a fracture conductivity prediction model based on machine learning. The main controlling factors of fracture conductivity are determined using the Pearson coefficient method and gray correlation analysis. Example application shows that the R 2 values of the BP neural network model based on a genetic algorithm for predicting the fracture conductivity of block A and block B are 0.981 and 0.975, respectively, indicating that the machine learning model can accurately predict fracture conductivity.