Sliding directional intelligent control method based on GRU

Abstract

The invention discloses a sliding directional intelligent control method based on GRU. The sliding directional intelligent control method comprises the steps: 1, onsite initial sample data in slidingdrilling are obtained, classified and integrated, and stored in a database; 2, initial sample data in the database are subjected to data cleaning, the initial sample data after cleaning is subjected to standardization processing, and standardized sample data are obtained after processing; 3, a GRU network model is constructed; and 4, real-time data of sliding drilling is collected and input into the GRU network model, automatic control parameters are generated, and the automatic control parameters are input into a torsional pendulum system to achieve intelligent control. According to the sliding directional intelligent control method, effective and high-quality data are obtained by acquiring, fusing and storing the onsite data and cleaning and standardizing the data, a data generator is designed, GRU model training is performed by utilizing the onsite data to form the GRU network model, the automatic control parameters of sliding directional operation are generated based on the GRU network model, and intelligent decision and control of the torsional pendulum system are realized.

Description

technical field [0001] The invention belongs to the technical field of oil and gas drilling (drilling) engineering, and in particular relates to a GRU-based sliding orientation intelligent control method, which is used for sliding orientation intelligent control, further improves the intelligent level of sliding drilling orientation operation, and realizes the intelligent decision-making of sliding orientation operation . Background technique [0002] With the rise of technologies such as big data and artificial intelligence, the concept of artificial intelligence in directional well drilling has been gradually proposed in the past two years. In order to improve single well production, oil and gas recovery, and development benefits, the scale of directional drilling operations such as horizontal wells, multi-lateral wells, and extended-reach wells continues to increase. And model optimization, simulating the decision-making behavior of directional experts, making directiona...

AI Technical Summary

Problems solved by technology

Although this method realizes the function of determining the control parameters of the torsional drill string through historical operation data, in the actual application process, this technology still has the following defects: the amount of sliding drilling data is huge, and the model training process is prone to gradient explosion and gradient disappearance. problems, which did not propose targeted solutions

The model learning data features is slow and inefficient, and may even fail to learn any data features. It is difficult to quickly and efficiently obtain a neural network model that can be used to guide drilling operations

Moreover, the accuracy and performance of the model are poor, and there is a safety risk in direct application to control the torsion system

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