Well deviation angle prediction method based on ensemble learning algorithm

Abstract

The invention discloses a well deviation angle prediction method based on an ensemble learning algorithm. According to the method, an SVR learner model, a neural network regression algorithm learner model, a random forest regression algorithm learner model and a Gaussian regression algorithm learner model in machine learning are employed to learn a learning sample, consisting of drilled borehole trajectory data, a drilling mode, a bottom drilling tool structure parameter and the like, of a certain well, the above four learner models are trained so as to separately predict the well deviation angle of a blind area at the bottom of the well, and then linear regression is carried out on a training result and a target value to obtain a final prediction result. Verification results of actual drilling data show that the method is high in prediction precision, effectively reduces the error of predicting the well bottom well deviation through a traditional constant curvature extrapolation method, and improves the accuracy of predicting the well deviation angle.

Description

technical field [0001] The invention belongs to the technical field of oil and natural gas drilling equipment, in particular to a well deviation angle prediction method based on an integrated learning algorithm applied in the technical field of rotary steering systems. Background technique [0002] In recent years, due to the difficulty and high cost of downhole measurement while drilling near the drill bit, it is basically monopolized by the three major oil companies. At present, MWD and LWD wireless while drilling instruments are mainly used in China, and the measurement point is 8-20m away from the drill bit. Therefore, the measurement point The data cannot correctly reflect the actual wellbore conditions at the position of the drill bit, which brings certain difficulties to the prediction and control of the wellbore trajectory. In the traditional wellbore trajectory prediction, there are a large number of influencing parameters related to drilling construction, and it is...

AI Technical Summary

Problems solved by technology

However, with the advancement of drilling technology and the rise of directional and horizontal drilling technologies, the traditional wellbore trajectory prediction method is increasingly unsuitable for modern drilling monitoring. Therefore, it is urgent to develop a new bottomhole inclination angle prediction method. to overcome the above shortcomings

Images