Accident Prediction Method Based on Smoothing and Network Model Machine Learning

A smoothing processing and network model technology, applied in the field of drilling early warning model, can solve the problems of low efficiency, high cost, heavy training workload, etc., and achieve the effect of stable data processing, improved efficiency, and smooth operation

Inactive Publication Date: 2021-03-16
SOUTHWEST PETROLEUM UNIV +1
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] The purpose of the present invention is to address the above-mentioned deficiencies in the prior art and provide an accident prediction method based on smoothing processing and network model machine learning to solve the problems of large training workload, high cost and low efficiency in the existing drilling early warning method

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  • Accident Prediction Method Based on Smoothing and Network Model Machine Learning
  • Accident Prediction Method Based on Smoothing and Network Model Machine Learning
  • Accident Prediction Method Based on Smoothing and Network Model Machine Learning

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Embodiment Construction

[0041] The specific embodiments of the present invention are described below so that those skilled in the art can understand the present invention, but it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes Within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are included in the protection list.

[0042] According to an embodiment of the present application, the accident prediction method based on smoothing processing and network model machine learning of this solution includes:

[0043] S1. Build a drilling early warning system, and mark the drilling accident anomalies based on smoothing.

[0044] Step S1 is described in detail below

[0045] Suppose the well is drilled at time t 0 -t N The measured data in the t...

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Abstract

The invention discloses an accident prediction method based on smoothing processing and network model machine learning, and the method comprises the steps: S1, building a well drilling early warning system, and carrying out the marking of an accident abnormity of a well drilling based on smoothing processing; S2, constructing a training well network based on a machine learning model of the networkmodel to form a similar model between multi-classification models corresponding to edge-connected drilling wells when an objective function is minimum; Using the measured data in the training well totrain an optimization model to obtain a parameter solution of the model; Searching for a neighbor drilling well similar to a test well in the network, estimating parameters of the test well model according to the neighbor drilling well model, and predicting the accident of the test well by using the estimated parameters. According to the method, data processing is stable, the workload and the cost of setting up systems between different drilled wells can be reduced through a machine learning method, building and construction of the drilling early warning model are more concise and efficient,operation is smooth, and efficiency is improved.

Description

technical field [0001] The invention belongs to the technical field of drilling early warning models, and in particular relates to an accident prediction method based on smoothing processing and network model machine learning. Background technique [0002] In the drilling construction process, the possibility of engineering accidents exists at any time, and the occurrence of accidents will cause huge losses of funds and huge waste of time. Giving a certain degree of warning before the occurrence of engineering accidents is of great significance for preventing the occurrence of accidents, controlling the development of accidents, and minimizing losses. For a long time, safe drilling and optimized drilling have been one of the important research topics of drilling engineering. According to the parameter changes in the drilling process, the drilling data is analyzed and processed, and the accident warning model is established to predict and diagnose the accident phenomenon, and...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06Q50/02G06N20/00
Inventor 李平陈雁代臻朱婷婷蒋裕强程超童兴格谢静付永红郑津蒋婵蒋增政钟学燕刘影
Owner SOUTHWEST PETROLEUM UNIV
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