Application in Drilling Tool Combination Aided Design Based on Data Intelligence

Abstract Drilling, as a direct and effective method of opening oil and gas layers, has been widely used. A reasonable combination of drilling tools plays a key role in increasing the rate of mechanical drilling, reducing drilling costs, and reducing downhole accidents. Conventional drilling relies on years of experience of on-site workers and reference to the operation of drilling wells, making use of drilling tools and lacking scientific basis. However, the reservoir situation is erratic, the unknown factors are very numerous, unpredictable, and the difficulty of drilling is increased. Drilling into unknown reservoirs, especially high-temperature and high-temperature risk wells, poses a huge threat to the lives of workers on site. Conventional drilling of known reservoirs will also encounter unknown problems such as drilling distance growth, stuck drilling, drilling tools falling, increased inclination, and deviation from the intended target position, which seriously reduces drilling efficiency, increases operating time, risk and drilling difficulty affected by the reasonable use of the drilling tool combination. With the development and application of computational intelligence, through the accumulation of massive geological property data, reservoir structure data, drilling tool parameters, construction data, drilling fluid parameters and other drilling data, intelligent drilling is used to predict unknown drilling information which can reduce the risk of drilling and improve drilling efficiency. In this paper, the work mode of "data running first, operation post" is used to further strengthen the application of drilling tools combination to improve the rate of mechanical drilling and reduce downhole problems.