Coal and gas burst risk parameter drilling and predicting integrated method

Abstract

The invention discloses a coal and gas burst risk parameter drilling and predicting integrated method. In a process of constructing and drilling a coal bed by using a drilling machine, a guide pipe is arranged in a hole opening of a drilled hole, the drilled hole is connected with a slag collector and a gas drainage pipe, a gas concentration sensor and a flow speed sensor are arranged in the gas drainage pipe, a weight sensor is arranged at the bottom of the slag collector, a counter and a torque sensor are arranged on the drilling machine; during drilling, coal dust enters into the slag collector through the guide pipe, coal dust weight information is transmitted to a multi-information sensor through the weight sensor; gas information is transmitted to the multi-information processor through the gas concentration sensor and the flow speed sensor; the torque of the drilling machine and the quantity of drilling stems are transmitted to the multi-information processor through the torque sensor and the counter on the drilling machine; and the burst risk of coal and gas in a region is predicted. The coal and gas burst risk parameter drilling and predicting integrated method provides multi-information quantity for the prediction of the burst risk of the coal and the gas, so that the accuracy of the burst risk of the coal and the gas is improved.

Description

technical field [0001] The invention relates to an integrated drilling and measurement method for coal and gas outburst risk parameters, in particular to a rapid collection method for coal and gas outburst risk information, which automatically collects the amount of cuttings and gas at different positions of the borehole during the drilling construction process. Emission volume, rig torque and other parameters provide data for accurate prediction of coal and gas outburst risks. Background technique [0002] Coal and gas outburst is one of the most serious mine disasters, and the accurate prediction of the risk of coal and gas outburst is the most important part of the prevention and control of coal and gas outburst. At present, the coal and gas outburst risk prediction technology adopted by mines is too single in terms of prediction indicators, mainly using the single index and composite index provided in the "Regulations on the Prevention and Control of Coal and Gas Outburs...

AI Technical Summary

Problems solved by technology

At present, the coal and gas outburst risk prediction technology adopted by mines is too single in terms of prediction indicators, mainly using the single index and composite index provided in the "Regulations on the Prevention and Control of Coal and Gas Outburst" Although the technologies developed by some universities and research institutes, such as electromagnetic radiation, microseismic, and acoustic emission technologies, have enriched the amount of prediction information to a certain extent, these technologies are still not mature enough, and they are also aimed at the impact of coal on rock mass. A certain aspect of information is used to predict the prominent risk, and the collected prediction information is insufficient

Since coal and gas outbursts are affected by many factors such as ground stress, gas pressure, coal body strength, and mining process, no current prediction technology can capture enough information for accurate prediction

In the normal drilling process of the mine, there will be a variety of information, such as the real-time emission of drilling gas, the amount of cuttings, and the torque of the drilling rig, which can reflect the risk information of coal and gas outburst. However, due to the difficulty of real-time collection of these information are too large to be discarded, resulting in a significant waste of information for accurately predicting the risk of coal and gas outburst

At present, the coal and gas outburst risk prediction is only carried out through several predicted drilling holes, and the information during the drilling construction process is not collected. The amount of captured information is limited, and the outburst risk prediction is not accurate.

Images