CN110390439A - Oil field Early-warning Model system based on big data rough set theory - Google Patents
Oil field Early-warning Model system based on big data rough set theory Download PDFInfo
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- CN110390439A CN110390439A CN201910690069.4A CN201910690069A CN110390439A CN 110390439 A CN110390439 A CN 110390439A CN 201910690069 A CN201910690069 A CN 201910690069A CN 110390439 A CN110390439 A CN 110390439A
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- 239000003921 oil Substances 0.000 claims abstract description 79
- 238000004519 manufacturing process Methods 0.000 claims abstract description 43
- 238000004458 analytical method Methods 0.000 claims abstract description 20
- 238000012544 monitoring process Methods 0.000 claims abstract description 10
- 239000010779 crude oil Substances 0.000 claims abstract description 3
- 238000000034 method Methods 0.000 claims description 8
- 238000004891 communication Methods 0.000 claims description 6
- 230000000505 pernicious effect Effects 0.000 claims description 2
- 239000007789 gas Substances 0.000 description 9
- 230000005540 biological transmission Effects 0.000 description 3
- RWSOTUBLDIXVET-UHFFFAOYSA-N Dihydrogen sulfide Chemical compound S RWSOTUBLDIXVET-UHFFFAOYSA-N 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 229910000037 hydrogen sulfide Inorganic materials 0.000 description 2
- 241000406668 Loxodonta cyclotis Species 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000008021 deposition Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 239000000295 fuel oil Substances 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000009671 shengli Substances 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Mining
Abstract
The oil field Early-warning Model system based on big data rough set theory that the present invention relates to a kind of, belongs to oil field automation production technical field, comprising: big data obtains module, for obtaining every monitoring data in the production of oil field；Early-warning Model constructs module, for the oil field Early-warning Model based on big data rough set theory；Early warning analysis module determines the warning grade of emergency case in crude oil production for obtaining the oil field Early-warning Model that every monitoring data and Early-warning Model building module are established in the oil field production that module obtains according to the big data；The present invention, which can be realized, carries out early warning analysis to the emergency case occurred in the production of oil field, comprehensively can carry out early warning to various types of risks, is conducive to scientific and reasonable management production, promotes working efficiency, while ensureing that oil field produces clean and safe.
Description
Technical field
The present invention relates to oil field automation production technical fields, and in particular to a kind of oil based on big data rough set theory
Field Early-warning Model system.
Background technique
In recent years, each elephant carries out information-based promotion one after another, and guides production by the monitoring of big data mode.But in oil
The acquisition and transmission of data are absorbed in field informationization lifting process, often all the time to meet the real-time prison produced to front
Control and assurance, using and analyzing after but ignoring big data acquisition.Simultaneously because it is sudden to go wrong in the production of oil field
And diversity, lack to control means effective in production process and a kind of effective alarm mode, existing alarm mode
The data information for analyzing collection in worksite transmission mostly or by technical staff, then relies on the statistical law of historical data information
Problem conclusion is obtained after being analyzed.
And influence factor is more in the production of oil field, again by manually calculating analysis after acquisition, this makes in the analysis process
There are certain subjectivities, and experience otherness, situations such as judging by accident of failing to judge, and the big data analysis existing in oil field occur often
The drawbacks of system is not perfect, and the result drawn has not objectivity and lacks scientific basis.Simple relying on this simultaneously is united
The alarm mode of meter rule and micro-judgment, does not account for the factor of actual influence data information, and operational process is artificial
It participates in, the error rate for not only resulting in early warning judgement is higher, and efficiency is lower.
Summary of the invention
The oil field Early-warning Model system based on big data rough set theory that the purpose of the present invention is to provide a kind of, to solve
The problems mentioned above in the background art.
To achieve the above object, the invention provides the following technical scheme:
A kind of oil field Early-warning Model system based on big data rough set theory, comprising:
Big data obtains module, for obtaining every monitoring data in the production of oil field；
Early-warning Model constructs module, for the oil field Early-warning Model based on big data rough set theory；
Early warning analysis module, for obtaining every monitoring data and institute in the oil field production that module obtains according to the big data
The oil field Early-warning Model that Early-warning Model building module is established is stated, determines the warning grade of emergency case in crude oil production.
As further technical solution of the present invention: the big data obtains module, is specifically used for obtaining oil field production in fact
When data.
As further technical solution of the invention: the oil field product practice include: function figure, load, temperature,
Pressure, power consumption, flow and harmful gas concentration.
As further technical solution of the invention: the Early-warning Model constructs module, flat specifically for establishing decision
Platform uses four-tuple below to be described:
DF=(U, A ∪ B, Y, f (x))；
Wherein, U is domain, and U={ L1, L2 ..., Ln }, n are the regular number in decision-making platform, and A is conditional attribute collection, and B is decision
Property set, Y are the codomains of information function f (x), and f (x) is the information function of analysis decision platform.
As further technical solution of the invention: using oil field real-time production data as conditional attribute collection A, A={ function
Figure, load, temperature, pressure, power consumption, flow, harmful gas concentration }；Using warning grade as decision kind set B, B={ peace
Entirely, exception, danger }.
As further technical solution of the invention: the early warning analysis module, specifically for utilizing the oil field obtained
The decision-making platform of real-time production data and foundation of taking this as a foundation, makes inferences decision rule using the method for forward reasoning,
Obtain the warning grade of emergency case in the production of oil field.
As further technical solution of the invention: the oil field Early-warning Model system based on big data rough set theory
System further includes communication module.
Compared with prior art, the beneficial effects of the present invention are: obtaining module by big data first obtains oil field production
Real time data is then based on rough set theory building Early-warning Model, then according to the product practice of acquisition and building
Early-warning Model determines the warning grade of emergency case in the production of oil field；It can be realized to the emergency case occurred in the production of oil field
Early warning analysis is carried out, early warning comprehensively can be carried out to various types of risks, is conducive to scientific and reasonable management production, is promoted
Working efficiency, while ensureing that oil field produces clean and safe.
Detailed description of the invention
Fig. 1 is the structural block diagram of the oil field Early-warning Model system based on big data rough set theory；
Fig. 2 is another structural block diagram of the oil field Early-warning Model system based on big data rough set theory；
Fig. 3 is the flow chart of the oil field Early-warning Model system early warning method based on big data rough set theory.
In figure: 1- big data obtains module, 2- Early-warning Model constructs module, 3- early warning analysis module, 4- communication module.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description.
Embodiment 1
Oil field Early-warning Model system based on big data rough set theory as shown in Figure 1, comprising:
Big data obtains module 1, Early-warning Model building module 2 and early warning analysis module 3, in which:
Big data obtains module 1, for obtaining oil field production real-time parameter big data；
Early-warning Model constructs module 2, for constructing oil field Early-warning Model based on big data rough set theory；
Early warning analysis module 3, for obtaining oil field production real-time parameter big data and the Early-warning Model according to the module 1
The reason of constructing the oil field Early-warning Model that module 2 is established, analyzing oil field production emergency case and warning grade.
The acquisition and transmission that oil field produces real-time big data are carried out by automated system first, then according to the oil field of acquisition
The building that big data carries out Early-warning Model based on rough set theory is produced, finally according to the oil field Early-warning Model of building, determines oil
Field production in emergency case the reason of and warning grade.
Preferably, the big data obtains module 1, is specifically used for:
It obtains oil field and produces real-time parameter big data, wherein big data includes: function figure, load, temperature, pressure, power consumption, stream
Amount and harmful gas concentration.
Emergency case has complexity and variability in the production of oil field, is often produced by many factors collective effect,
These factors mainly include internal factor (such as feed flow is insufficient, bar is disconnected, wax deposition and harmful gas concentration are excessively high) and it is external because
In terms of plain (such as meteorologic factor, pipeline factor, human factor) two.Since emergency case has environment and the person in the production of oil field
Harm is big, uncertain high, discovery the features such as there are hysteresis qualitys, it is desirable that Early-warning Model system should have real-time, accuracy and
Therefore intelligence chooses the factor of the representative several factors as oil field production early warning, comprising: function figure, load, temperature
Degree, pressure, power consumption and flow.
Further, oil field production monitoring data can also be further obtained, for example, harmful gas concentration, pernicious gas
Concentration can be used as danger early warning index, and the warning grade of current production environment can be directly determined according to the size of hydrogen sulfide,
It is accordingly evacuated and is handled with Field Force.
Preferably, the Early-warning Model constructs module 2, is specifically used for:
Decision-making platform is established, four-tuple below is used to be described:
DF=(U, A ∪ B, Y, f (x))；
Wherein, U is domain, and U={ L1, L2 ..., Ln }, n are the regular number in decision-making platform, and A is conditional attribute collection, and B is decision
Property set, Y are the codomains of information function f (x), and f (x) is the information function of analysis decision platform；
Using oil field real-time production data as conditional attribute collection A, A=, { function figure, temperature, pressure, power consumption, flow, has load
Evil gas concentration }, d (t) is the information function of conditional attribute collection A；
It is the information function of decision kind set B using warning grade as decision kind set B, b (t),
B={ safety, exception, danger }.
In the present embodiment, flat according to Shengli Oil Field block heavy oil block productive prospecting and parameter building initial decision
Platform, for example, constructing the decision table of comparisons according to the data that the following table 1 is shown.
Table 1
| Parameter value | Safety | It is abnormal | It is dangerous |
| Temperature (DEG C) | 20-50 | < 20 | >50 |
| Pressure (MPa) | 0-1.6 | 1.6-4 | >4 |
| Power consumption (Kw.h) | 32-50 | < 32 | > 50 |
| Flow (t) | 0-35 | 35-45 | >45 |
| Concentration of hydrogen sulfide (ppm) | 0-4 | 4-6.5 | >6.5 |
For example, warning grade is safety when pipeline back pressure is 0~1.6MPa；When pipeline back pressure is 1.6~4MPa Shi Weiyi
Often；It is dangerous when pipeline back pressure is > 4MPa.Other parameters similar can be arranged, no longer illustrated one by one here.
Due to rough set do not need the decision table of comparisons that any additional or extra condition data are constituted can directly into
Row reason, therefore, is described the decision table of comparisons with a four-tuple:
DT=(U, A ∪ B, Y, f (x))；
Wherein, U is domain, and A is conditional attribute collection, and B is decision kind set, and Y is the codomain of information function f (x), and f (x) is analysis
The information function of decision-making platform；By the foundation and equivalent partition introducing four that carry out equivalence class to monitoring class set and warning grade collection
Tuple DT can obtain, U={ L1, L2 ..., Ln }, n are the regular number in decision-making platform, A=function figure, load, temperature, pressure,
Power consumption, flow, harmful gas concentration }, B={ safety, exception, danger } creates the rough set of oil field early warning system.
Preferably, the early warning analysis module 3, is specifically used for:
For producing the oil field early warning mould that real-time parameter big data and Early-warning Model building module are established according to acquired oil field
The reason of type, analysis oil field production emergency case and warning grade.
In the oil field Early-warning Model building research based on big data rough set theory, the early warning analysis module 3 is specific
For production big data input Early-warning Model in oil field to be carried out forward reasoning, verification algorithm, direct algorithms reasoning process is as follows: defeated
Enter oil field production real-time parameter to carry out Fuzzy processing and generate production big data；Carry out early warning operation and by operation result and oil
Rule base multilevel iudge in the database of field specifies under parameter whether have the rule accordingly described, if so, then carrying out analytic operation
And export early warning result；If nothing performs the next step.It can be selected from currently available rule base according to Strategy of Conflict Resolution simultaneously
A rule makes inferences out, and the new rule of release is added in rule base.It uses during the early warning system can produce with oil field
Emergency case carries out real-time early warning and reduces environmental pollution so that scientific arrangement is carried out the work, it is ensured that personal safety increases warp
Ji benefit benefit.
Embodiment 2
It is described based on big data rough set theory as shown in Fig. 2, the present embodiment advanced optimizes on the basis of embodiment 1
Oil field Early-warning Model system further includes communication module 4, issue warning grade by communication module 4, as shown in figure 3, working as
When harmful gas concentration is greater than preset value, early warning analysis module 3 determines warning grade and directly transmits alarm by communication module 4
Information needs to analyze and located in time to host computer to remind the current production environment of monitoring personnel serious emergency case occur
Reason.
It is obvious to a person skilled in the art that invention is not limited to the details of the above exemplary embodiments, Er Qie
In the case where without departing substantially from spirit or essential attributes of the invention, the present invention can be realized in other specific forms.Therefore, no matter
From the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and the scope of the present invention is by appended power
Benefit requires rather than above description limits, it is intended that all by what is fallen within the meaning and scope of the equivalent elements of the claims
Variation is included within the present invention.Any reference signs in the claims should not be construed as limiting the involved claims.
In addition, it should be understood that although this specification is described in terms of embodiments, but not each embodiment is only wrapped
Containing an independent technical solution, this description of the specification is merely for the sake of clarity, and those skilled in the art should
It considers the specification as a whole, the technical solutions in the various embodiments may also be suitably combined, forms those skilled in the art
The other embodiments being understood that.
Claims (8)
1. a kind of oil field Early-warning Model system based on big data rough set theory characterized by comprising
Big data obtains module (1), for obtaining every monitoring data in the production of oil field；
Early-warning Model constructs module (2), for the oil field Early-warning Model based on big data rough set theory；
Early warning analysis module (3), for according to the big data obtain in the oil field production that module obtains every monitoring data with
And the oil field Early-warning Model that the Early-warning Model building module is established, determine the warning grade of emergency case in crude oil production.
2. the oil field Early-warning Model system according to claim 1 based on big data rough set theory, it is characterised in that: institute
It states big data and obtains module (1), be specifically used for obtaining oil field product practice.
3. the oil field Early-warning Model system according to claim 2 based on big data rough set theory, it is characterised in that: institute
Stating oil field product practice includes: function figure, load, temperature, pressure, power consumption, flow and harmful gas concentration.
4. the oil field Early-warning Model system according to claim 1 based on big data rough set theory, it is characterised in that: institute
Early-warning Model building module (2) is stated, specifically for establishing decision-making platform, four-tuple below is used to be described:
DF=(U, A ∪ B, Y, f (x))；
Wherein, U is domain, and U={ L1, L2 ..., Ln }, n are the regular number in decision-making platform, and A is conditional attribute collection, and B is decision
Property set, Y are the codomains of information function f (x), and f (x) is the information function of analysis decision platform.
5. the oil field Early-warning Model system according to claim 4 based on big data rough set theory, it is characterised in that: will
Oil field real-time production data is as conditional attribute collection A, A={ function figure, load, temperature, pressure, power consumption, flow, pernicious gas
Concentration }.
6. the oil field Early-warning Model system according to claim 4 based on big data rough set theory, it is characterised in that: will
Warning grade is as decision kind set B, B={ safety, exception, danger }.
7. the oil field Early-warning Model system according to claim 1 based on big data rough set theory, it is characterised in that: institute
Early warning analysis module (3) are stated, it is flat specifically for the oil field real-time production data using acquisition and the decision for foundation of taking this as a foundation
Platform makes inferences decision rule using the method for forward reasoning, obtains the warning grade of emergency case in the production of oil field.
8. the oil field Early-warning Model system according to claim 1 based on big data rough set theory, it is characterised in that: institute
Stating the oil field Early-warning Model system based on big data rough set theory further includes communication module (4).
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112270138A (en) * | 2020-09-02 | 2021-01-26 | 中海石油(中国)有限公司深圳分公司 | Complex oil field group flow guarantee and capacity release determination method |
| CN112270473A (en) * | 2020-10-27 | 2021-01-26 | 山东鼎滏软件科技有限公司 | Early warning method and device for oil and gas field time sequence data |
-
2019
- 2019-07-29 CN CN201910690069.4A patent/CN110390439A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112270138A (en) * | 2020-09-02 | 2021-01-26 | 中海石油(中国)有限公司深圳分公司 | Complex oil field group flow guarantee and capacity release determination method |
| CN112270138B (en) * | 2020-09-02 | 2023-08-29 | 中海石油(中国)有限公司深圳分公司 | Complex oilfield group flow guarantee and productivity release determination method |
| CN112270473A (en) * | 2020-10-27 | 2021-01-26 | 山东鼎滏软件科技有限公司 | Early warning method and device for oil and gas field time sequence data |
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Application publication date: 20191029 |