Abstract

Matrix acidizing is an essential strategy to maintain or increase the productivity or injectivity of hydrocarbon wells. However, for sandstone reservoirs, the heterogeneous flow reaction mechanism of acid–rock in porous media is very complex because of their complex mineral and chemical compositions. It is often difficult to match real formation conditions by experimental simulation. Also, traditional numerical simulation methods have the disadvantages of complex boundary processing and low computational efficiency. In this study, the lattice Boltzmann method (LBM) was used to establish the heterogeneous flow reaction model of acid–rock from a new perspective, which was solved by MATLAB to obtain the distribution of temperature, concentration of various substances, porosity, and permeability. The simulation results indicate that with increases in injection time and injection speed, the temperature and mass transfer distance of the acid will also increase. Changing the injection time had a more obvious influence on the transfer of temperature and mass than did changing the injection speed. The increasing rates of porosity and permeability in the middle of the flow channel were the highest. The fast-reaction mineral content, hydrofluoric acid injection concentration, and acid injection time had a great influence on the acidizing effect, whereas the slow-reaction mineral content, acid injection temperature, and injection speed had little influence on the acidizing effect. The results suggest that to improve the acidizing effect, priority should be given to improve the HF concentration and acid dose. It will be important for further guiding the optimization of acidizing process design parameters.

Highlights

  • Acidizing is an important procedure for stable and increased production of oil and gas wells and for facilitating water injection in wells

  • Labrid studied the heterogeneous flow reaction between a mud acid system and aluminosilicate at room temperature [4]. They discovered that the products generated by the heterogeneous flow reaction will react with hydrofluoric acid (HF) again, which will form a series of complex ions of aluminum fluoride and silicon fluoride

  • Because the reaction mechanism of an acid–rock heterogeneous flow in porous media is very complex, it is often difficult to match real formation conditions by experimental methods, and traditional numerical simulation methods have the disadvantages of complex boundary treatments and inefficient calculations

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Summary

Introduction

Acidizing is an important procedure for stable and increased production of oil and gas wells and for facilitating water injection in wells. Due to the complexity of the heterogeneous flow reaction of acid–rock in porous media, blind chemical stimulation greatly reduces the success rate of sandstone acidizing and seriously hinders the stimulation effect [3]. Labrid studied the heterogeneous flow reaction between a mud acid system and aluminosilicate at room temperature [4] They discovered that the products generated by the heterogeneous flow reaction will react with hydrofluoric acid (HF) again, which will form a series of complex ions of aluminum fluoride and silicon fluoride. Rodoplu et al established a model of the mechanism of wormhole formation in the process of acid–rock heterogeneous flow reactions based on laboratory experiments [6]. Our study focused on the development trend of “explaining macroscopic problems with mesoscopic and microcosmic models” and used the LBM as a special method to establish the corresponding heterogeneous flow reaction model of acid–rock. We believe this study will be important for further guiding the optimization of acidizing process design parameters

Establishing the Evolution Equation
Transformation from Lattice Space to Physical Space
Mathematical Model Based on the LBM
Establishment of Heterogeneous Flow Reaction Model for Acid–Rock
Double Distribution Function Model Simulates the Heat Transfer Process
Convection Dispersion Equation Model Simulates the Mass Transfer Process
Porosity and Permeability Distribution Model
Model Solution and Analysis
Temperature Distribution
Concentration Distribution
Porosity and Permeability Distribution
Conclusions

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