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Petroleum drilling big data processing method and device based on machine learning

A technology of big data processing and machine learning, which is applied in the field of petroleum engineering, can solve problems such as the inability to give the best response plan for risks, poor real-time performance, and low accuracy of risk identification, so as to achieve real-time effective identification and early warning, improve accuracy, The effect of reducing false positive rate and false negative rate

Pending Publication Date: 2022-06-10
YANGTZE UNIVERSITY
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The identification and control of various complex situations and risk accidents during drilling operations has always been the focus of industry research. Since risk identification and control need to comprehensively consider the influence of factors such as geological links, drilling tool assemblies, and engineering parameters, the existing key parameter-based Fuzzy evaluation methods such as mathematical calculations, sensor data symptom discrimination, or Bayesian networks have low accuracy and poor real-time performance for risk identification, and cannot provide the best response to the identified risks.

Method used

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  • Petroleum drilling big data processing method and device based on machine learning
  • Petroleum drilling big data processing method and device based on machine learning

Examples

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Effect test

Embodiment 1

[0031] like figure 1 As shown, the present invention provides a method for processing oil drilling big data based on machine learning, comprising the following steps:

[0032] Step S1, acquiring real-time logging data and drilling risk record data of historical wells;

[0033] Step S2, according to the real-time logging data of historical wells and the drilling risk record data, using a machine learning algorithm to construct and train to obtain multiple drilling risk identification and early warning models;

[0034] Step S3: Input the real-time logging data of the target well into each drilling risk identification and early warning model according to the input parameter requirements of different drilling risk identification and early warning models, so as to perform real-time identification and early warning processing of the drilling risk of the target well.

[0035] As a preferred way of this embodiment, in step S1, according to the real-time logging data of historical wel...

Embodiment 2

[0050] like figure 2 As shown, the present invention provides a large data processing device for oil drilling based on machine learning, including:

[0051] The acquisition module is used to acquire the real-time logging data and drilling risk record data of historical wells;

[0052] The training module is used to construct and train multiple drilling risk identification and early warning models using machine learning algorithms based on the real-time logging data and drilling risk record data of historical wells;

[0053] The identification module is used to input the real-time logging data of the target well into each drilling risk identification and early warning model according to the input parameter requirements of different drilling risk identification and early warning models, so as to perform real-time identification and early warning processing of the drilling risk of the target well.

[0054] As a preferred way of this embodiment, the acquisition module is further...

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Abstract

The invention discloses a petroleum drilling big data processing method and device based on machine learning. The method comprises the steps that historical well real-time logging data and drilling risk record data are acquired; according to the historical well real-time logging data and the drilling risk record data, constructing and training by using a machine learning algorithm to obtain a plurality of drilling risk identification early warning models; and inputting the real-time logging data of the target well into each drilling risk identification and early warning model according to the input parameter requirements of different drilling risk identification and early warning models so as to execute real-time identification and early warning processing of the drilling risk of the target well. By adopting the technical scheme of the invention, the drilling risk can be effectively identified and early warned in real time, and the purpose of improving the risk early warning accuracy is achieved.

Description

technical field [0001] The invention belongs to the technical field of petroleum engineering, and in particular relates to a method and device for processing big data of oil drilling based on machine learning. Background technique [0002] With the deepening of exploration and development, the difficulty of oil and gas exploration and development is getting higher and higher. At this stage, the geological conditions faced by oil and gas exploration are becoming more and more complex, the depth of reservoirs is increasing, the drilling engineering is facing more and more complex risks, and drilling problems are becoming more and more serious, which leads to more and more costs for dealing with drilling risks and accidents. higher. Especially in the exploration of exploratory wells, due to unknown geological conditions, drilling risks are frequent, and drilling costs remain high. [0003] Achieving safe and efficient drilling is the primary goal of the drilling industry. Th...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06Q50/02G06N20/00G06K9/62
CPCG06Q10/0635G06Q50/02G06N20/00G06F18/24323
Inventor 白凯董阿兵徐用高
Owner YANGTZE UNIVERSITY
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