A Bigru Network Drilling Condition Recognition Method Combined with Attention Mechanism

Abstract

The invention discloses a BiGRU network drilling condition identification method combined with an attention mechanism. The method uses a large amount of actual data generated during drilling as a basis, and carries out a series of data enhancement methods to expand the number of samples to the greatest extent. less problems. And in order to reduce the black box effect during machine learning, a decision tree recognition model guided by semi-empirical formulas combined with actual data is designed to assist manual data annotation. For the time series drilling information, a bidirectional gate control unit network capable of extracting sequence features is used to train the model, and the parameters are strictly adjusted. Finally, the data of a single well that did not participate in the training was used to test the generalization ability of the model without transfer learning and pre-training; comparison experiments proved that this method can ensure a high accuracy in the time series prediction of drilling conditions The feasibility and versatility under the conditions have practical application value.

Description

technical field [0001] The invention relates to the technical field of oil and gas field exploitation, in particular to a method for identifying drilling conditions with a bi-directional gate control unit BiGRU network combined with an attention mechanism. Background technique [0002] In the context of the times, China has paid more and more attention to oil exploration, and the demand for oil has continued to rise. Drilling engineering serves the development process of oil fields. The rapid operation of drilling construction provides economic benefits and high timeliness. Monitoring and management during drilling operations pose enormous challenges. Restricted by traditional manual management methods, in the drilling construction stage, the management efficiency, decision-making accuracy and response speed are still at a low level, and the drilling supervision ability cannot keep up with the rapid development of drilling technology. Fast and accurate decision-making respo...

AI Technical Summary

Problems solved by technology

[0004] All in all, the following problems have not been fully resolved in the rare research on the identification of drilling conditions: 1. The number of samples for training is small, and the obtained model has a good effect on paper, but the actual effect has not been rigorously demonstrated, so it is difficult to apply it to practice Production

2. The network model is very simple, no further parameter adjustment work has been done, and the upper limit of the neural network has not been reached

3. There is no generalization ability test, such as whether the same experimental results as during training can be obtained under different data characteristics is still unknown

4. There is no targeted design for data with very time-series characteristics such as drilling information

5. There is no restriction on the black box effect during machine learning training, and the interpretability of the model is poor

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