Emission prediction of oil-type gas out of the coal seam floor based on integration of advanced electrical resistivity exploration techniques with the GA-BP

Abstract Oil-type gas disasters are a recurrent concern in coal-oil-gas coexistence mines. To proactively anticipate the volume of oil-type gas emissions from floor rocks, this study introduces an investigative methodology to forecast the geological conditions of floor rocks ahead of the roadway face, leveraging the Direct Current (DC) method. The assessment of electrical resistance in rock formations, widely employed for identifying geological characteristics, serves as the basis for proposing a geological anomaly index derived from rock resistivity. This index effectively characterizes the stability of rock strata, providing an indirect assessment of fracture development. Serving as a real-time geological detection index for floor rocks located 100 meters ahead of the roadway face, it enhances predictive capabilities. Moreover, when amalgamated with parameters such as floor rock thickness and permeability, the paper presents simulations of oil-type gas emissions under varying geological conditions. Subsequently, an adaptive optimization of the Back Propagation (BP) neural network is achieved through the Genetic Algorithm Back Propagation Neural Network (GA-BP) model to evaluate the quantity of oil-type gas emissions in roadways. This advanced real-time prediction method is applied in Huangling coal mining to forecast oil-type gas emissions from the floor rocks in the excavation roadway area. Results demonstrate a congruence with field monitoring outcomes, affirming the accuracy of the predictive model. In conclusion, this advanced real-time prediction technique enables continuous monitoring and real-time forecasting of oil-type gas emissions ahead of roadways. This capability facilitates the implementation of specific measures for pre-extraction in gas disaster prevention and control, thereby ensuring the safety of coal mine production. Furthermore, the versatility of this advanced real-time prediction method extends to early warnings of rock mass instability-related disasters. Through a comprehensive understanding of subsurface conditions, continuous monitoring of changes, and the application of predictive models, timely actions can be undertaken to mitigate risks and uphold safety standards.