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Oil field early warning model system based on big data rough set theory

A rough set theory and early warning model technology, applied in the field of oilfield early warning model system, can solve the problems of lack of scientific basis, lack of effective control methods, omission of misjudgment and misjudgment due to differences in experience, etc., to ensure clean and safe, scientific and rational management of production, The effect of improving work efficiency

Pending Publication Date: 2019-10-29
山东圣哲石油装备有限公司
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in the process of improving oilfield informatization, we have always focused on data collection and transmission to meet the real-time monitoring and grasp of front-line production, but neglected the use and analysis of big data collection
At the same time, due to the suddenness and diversity of problems in oilfield production, there is a lack of effective control means and an effective early warning method in the production process. Most of the existing early warning methods are based on the analysis of data information collected and transmitted on site by technicians. , and then rely on the statistical law of historical data information to analyze and get the problem conclusion
[0003] However, there are many influencing factors in oilfield production, and manual calculation and analysis are performed after collection, which leads to a certain degree of subjectivity in the analysis process. Experience differences, missed judgments, and misjudgments often occur. Moreover, the current big data analysis in the oilfield The system is not perfect, and the results obtained are not objective and lack scientific basis.
At the same time, this early warning method that relies solely on statistical laws and empirical judgments does not consider the factors that actually affect the data information, and the manual participation in the operation process not only leads to a high error rate in early warning judgments, but also low efficiency

Method used

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  • Oil field early warning model system based on big data rough set theory
  • Oil field early warning model system based on big data rough set theory
  • Oil field early warning model system based on big data rough set theory

Examples

Experimental program
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Effect test

Embodiment 1

[0025] Such as figure 1 The shown oilfield early warning model system based on big data rough set theory includes:

[0026] Big data acquisition module 1, early warning model building module 2 and early warning analysis module 3, in which:

[0027] Big data acquisition module 1, used to acquire real-time parameter big data of oilfield production;

[0028] Early warning model building block 2, used to build an oilfield early warning model based on big data rough set theory;

[0029] The early warning analysis module 3 is used to obtain the real-time parameter big data of oilfield production according to the module 1 and the oilfield early warning model established by the early warning model building module 2, and analyze the reasons and early warning levels of oilfield production emergencies.

[0030] First, the automation system collects and transmits the real-time big data of oilfield production, and then constructs the early warning model based on the rough set theory base...

Embodiment 2

[0052] Such as figure 2 As shown, this embodiment is further optimized on the basis of Embodiment 1. The oilfield early warning model system based on big data rough set theory also includes a communication module 4, and the early warning level is issued through the communication module 4, such as image 3 As shown, when the harmful gas concentration is greater than the preset value, the early warning analysis module 3 determines the early warning level and directly sends an alarm message to the host computer through the communication module 4 to remind the monitoring personnel that there is a serious emergency in the current production environment, which needs to be analyzed in time and to process.

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Abstract

The invention relates to an oil field early warning model system based on a big data rough set theory, and belongs to the technical field of oil field automatic production. The system comprises a bigdata obtaining module used for obtaining various monitoring data in oil field production; an early warning model construction module used for an oil field early warning model based on a big data roughset theory; an early warning analysis module used for determining an early warning level of an emergency in crude oil production according to the monitoring data in the oil field production acquiredby the big data acquisition module and the oil field early warning model established by the early warning model construction module. According to the invention, early warning analysis can be carried out on emergencies occurring in oil field production, early warnings can be carried out on various types of risks comprehensively, scientific and reasonable production management is facilitated, the working efficiency is improved, and meanwhile, the cleanness and safety of oil field production are guaranteed.

Description

technical field [0001] The invention relates to the technical field of oilfield automatic production, in particular to an oilfield early warning model system based on big data rough set theory. Background technique [0002] In recent years, major oil fields have carried out informatization upgrades one after another, monitoring and guiding production through big data models. However, in the process of improving oilfield informatization, it has always focused on data collection and transmission to meet the real-time monitoring and grasp of front-line production, but neglected the use and analysis of big data collection. At the same time, due to the suddenness and diversity of problems in oilfield production, there is a lack of effective control means and an effective early warning method in the production process. Most of the existing early warning methods are based on the analysis of data information collected and transmitted on site by technicians. , and then rely on the s...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q10/06G06Q50/02
CPCG06Q10/04G06Q10/0637G06Q10/0635G06Q50/02
Inventor 潘力杰曾冠鑫尤鲲
Owner 山东圣哲石油装备有限公司
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