CN112394163A - Crude oil water content analysis method and device - Google Patents
Crude oil water content analysis method and device Download PDFInfo
- Publication number
- CN112394163A CN112394163A CN202011474851.1A CN202011474851A CN112394163A CN 112394163 A CN112394163 A CN 112394163A CN 202011474851 A CN202011474851 A CN 202011474851A CN 112394163 A CN112394163 A CN 112394163A
- Authority
- CN
- China
- Prior art keywords
- water content
- crude oil
- real
- data
- predicted value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 239000010779 crude oil Substances 0.000 title claims abstract description 157
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 157
- 238000004458 analytical method Methods 0.000 title claims abstract description 47
- 238000000034 method Methods 0.000 claims abstract description 40
- 230000000087 stabilizing effect Effects 0.000 claims abstract description 21
- 239000003921 oil Substances 0.000 claims abstract description 17
- 238000012549 training Methods 0.000 claims description 16
- 238000004590 computer program Methods 0.000 claims description 13
- 230000015654 memory Effects 0.000 claims description 10
- 238000005457 optimization Methods 0.000 claims description 10
- 238000012216 screening Methods 0.000 claims description 9
- 238000003860 storage Methods 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 8
- 230000008569 process Effects 0.000 abstract description 2
- 238000001514 detection method Methods 0.000 description 10
- 239000003208 petroleum Substances 0.000 description 7
- 239000000243 solution Substances 0.000 description 7
- 239000003129 oil well Substances 0.000 description 4
- 239000003381 stabilizer Substances 0.000 description 4
- 238000004891 communication Methods 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 239000000523 sample Substances 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000006641 stabilisation Effects 0.000 description 2
- 238000011105 stabilization Methods 0.000 description 2
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 238000002347 injection Methods 0.000 description 1
- 239000007924 injection Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000003908 quality control method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012706 support-vector machine Methods 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
- 235000020681 well water Nutrition 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/26—Oils; viscous liquids; paints; inks
- G01N33/28—Oils, i.e. hydrocarbon liquids
- G01N33/2823—Oils, i.e. hydrocarbon liquids raw oil, drilling fluid or polyphasic mixtures
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/26—Oils; viscous liquids; paints; inks
- G01N33/28—Oils, i.e. hydrocarbon liquids
- G01N33/2835—Oils, i.e. hydrocarbon liquids specific substances contained in the oil or fuel
- G01N33/2847—Water in oil
Abstract
The application provides a method and a device for analyzing the water content of crude oil, wherein the method comprises the following steps: acquiring real-time data of equipment; the real-time data of the equipment comprises a stabilizing tower temperature, a stabilizing tower pressure, a stable front oil inlet heat exchanger pressure and an air cooler temperature; inputting the real-time data of the equipment into a crude oil water content analysis comprehensive model to obtain a real-time predicted value of the water content of the crude oil; the crude oil water content analysis comprehensive model is composed of a crude oil water content prediction regression model and a data judgment model. The method for analyzing the water content of the crude oil provided by the embodiment of the application analyzes the water content of the crude oil in real time through real-time process parameters of equipment, and the adopted comprehensive model for analyzing the water content of the crude oil comprises a data judgment model for judging data of input and output of the comprehensive model besides a crude oil water content prediction regression model.
Description
Technical Field
The application relates to the field of petroleum industry, in particular to a method and a device for analyzing water content of crude oil.
Background
At present, in the field of petroleum industry, the water content of crude oil is an important data of petroleum in exploitation, transportation and transaction, and the online detection of the water content of the crude oil has important significance on the construction of digital oil fields such as oil well water outlet, oil outlet horizon, crude oil yield estimation, oil well development life prediction, oil well yield quality control, oil well state detection, water injection operation and the like.
In the prior art, the crude oil water content online detection generally adopts a crude oil water content online analyzer, which mainly comprises a radio frequency method, a microwave method, an electromagnetic wave method, a capacitance method and the like, and the methods used by the current crude oil water content online analyzer are not strong in adaptability, for example, the radio frequency method, the microwave method and the capacitance method are only suitable for the case of low water content of crude oil, while the electromagnetic wave method is only suitable for the case of high water content, and because the oil has high viscosity and is easy to adhere to a probe, the result error of the online water content analyzer is large, and further the subsequent crude oil stabilization operation cannot respond to the fluctuation of the water content of the crude oil in real time, so that the product index is greatly.
Disclosure of Invention
In view of this, an object of the present application is to provide a method and an apparatus for analyzing a water content of crude oil, which are used to solve the problem of how to reduce an error of online detection of the water content of crude oil in the prior art.
In a first aspect, an embodiment of the present application provides a method for analyzing water content of crude oil, including:
acquiring real-time data of equipment; the real-time data of the equipment comprises a stabilizing tower temperature, a stabilizing tower pressure, a stable front oil inlet heat exchanger pressure and an air cooler temperature;
inputting the real-time data of the equipment into a crude oil water content analysis comprehensive model to obtain a real-time predicted value of the water content of the crude oil; the crude oil water content analysis comprehensive model is composed of a crude oil water content prediction regression model and a data judgment model.
In some embodiments, the inputting the real-time data of the equipment into the analysis and synthesis model of the water content of the crude oil to obtain the real-time predicted value of the water content of the crude oil comprises:
analyzing whether the real-time data of the equipment is available data or not according to a professional knowledge base through the data judgment model;
if the equipment real-time data are available data, inputting the equipment real-time data into the crude oil water content prediction regression model to obtain a predicted value to be detected;
analyzing whether the accuracy of the predicted value to be detected reaches a first preset threshold value or not according to the professional knowledge base through the data judgment model;
and if the accuracy of the predicted value to be detected reaches a first preset threshold value, confirming that the predicted value to be detected is the real-time predicted value of the water content of the crude oil.
In some embodiments, the method further comprises:
acquiring historical data from a decentralized control system; the historical data comprises the historical temperature of the stabilizing tower, the historical pressure of the steady-state oil inlet heat exchanger, the historical temperature of an air cooler and the historical water content of crude oil;
and carrying out regression model training according to the historical data to obtain a crude oil water content prediction regression model.
In some embodiments, the training of the regression model based on the historical data comprises:
screening the historical data according to a professional knowledge base to obtain historical data under normal working conditions;
and inputting the historical data under the normal working condition into a model to be trained so as to carry out regression model training.
In some embodiments, further comprising:
acquiring a real-time predicted value of the water content of the crude oil and real-time parameters of equipment in a preset time period;
screening according to the accurate value of the real-time predicted value of the water content of the crude oil to obtain a target predicted value with the accuracy exceeding a second preset threshold value;
and carrying out optimization training on the crude oil water content prediction regression model according to the target predicted value and the equipment real-time parameter corresponding to the target predicted value to obtain an optimized crude oil water content prediction regression model.
In a second aspect, embodiments of the present application provide an apparatus for analyzing a water content of crude oil, including:
the data module is used for acquiring real-time data of the equipment; the real-time data of the equipment comprises a stabilizing tower temperature, a stabilizing tower pressure, a stable front oil inlet heat exchanger pressure and an air cooler temperature;
the analysis module is used for inputting the real-time data of the equipment into a crude oil water content analysis comprehensive model to obtain a real-time predicted value of the water content of the crude oil; the crude oil water content analysis comprehensive model is composed of a crude oil water content prediction regression model and a data judgment model.
In some embodiments, an analysis module, comprising:
the first analysis unit is used for analyzing whether the real-time data of the equipment is available data or not according to a professional knowledge base through the data judgment model;
the calculation unit is used for inputting the real-time equipment data into the crude oil water content prediction regression model to obtain a predicted value to be detected if the real-time equipment data are available data;
the second analysis unit is used for analyzing whether the accuracy of the predicted value to be detected reaches a first preset threshold value or not according to the professional knowledge base through the data judgment model;
and the confirming unit is used for confirming that the predicted value to be detected is the real-time predicted value of the water content of the crude oil if the accuracy of the predicted value to be detected reaches a first preset threshold value.
In some embodiments, further comprising:
the optimization module is used for acquiring a real-time predicted value of the water content of the crude oil and real-time equipment parameters in a preset time period; screening according to the accurate value of the real-time predicted value of the water content of the crude oil to obtain a target predicted value with the accuracy exceeding a second preset threshold value; and carrying out optimization training on the crude oil water content prediction regression model according to the target predicted value and the equipment real-time parameter corresponding to the target predicted value to obtain an optimized crude oil water content prediction regression model.
In a third aspect, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of the method in any one of the above first aspects when executing the computer program.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, performs the steps of the method in any one of the above first aspects.
According to the method for analyzing the water content of the crude oil, the analysis and calculation of the water content of the crude oil are carried out through a crude oil water content analysis comprehensive model according to real-time data of equipment including the temperature of a stabilizer, the pressure of the stabilizer, the pressure of a steady front oil inlet heat exchanger, the temperature of an air cooler and the like. The method for analyzing the water content of the crude oil provided by the embodiment of the application also comprises the data judgment model besides the crude oil water content prediction regression model in the used crude oil water content analysis comprehensive model, so that the data judgment can be carried out on the data of the input and output comprehensive model, the error of the online detection of the water content of the crude oil can be effectively reduced, and the production efficiency of the petroleum industry is improved.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
FIG. 1 is a schematic flow chart of a method for analyzing water content of crude oil according to an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart of another method for analyzing water content of crude oil according to the present disclosure;
FIG. 3 is a schematic structural diagram of an analysis apparatus for water content in crude oil according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
The embodiment of the application provides a crude oil water content analysis method, as shown in fig. 1, comprising the following steps:
s101, acquiring real-time data of equipment; the real-time data of the equipment comprises the temperature of a stabilizing tower, the pressure of the stabilizing tower, the pressure of a steady front oil inlet heat exchanger and the temperature of an air cooler;
s102, inputting the real-time data of the equipment into a crude oil water content analysis comprehensive model to obtain a crude oil water content real-time predicted value; the crude oil water content analysis comprehensive model is composed of a crude oil water content prediction regression model and a data judgment model.
Specifically, real-time data of equipment acquired by sensors arranged on key equipment such as a heating furnace, a stabilizing tower and the like is acquired, and the real-time data of the equipment comprises the temperature of the stabilizing tower, the pressure of a steady front oil inlet heat exchanger (the pressure of crude oil inlet heat exchanger before stabilization) and the temperature of an air cooler.
And then inputting the acquired real-time equipment data into the crude oil water content analysis comprehensive model for data analysis to obtain a real-time predicted value of the water content of the crude oil.
In order to solve the problem of poor adaptability in the existing analysis method, the real-time data of the equipment adopts data which is not required to be acquired by using a probe sensor as far as possible. In addition, in order to reduce the error of the online detection of the water content of the crude oil, the comprehensive model used in the embodiment of the application comprises a data judgment model besides the crude oil water content prediction regression model used for calculating the predicted value of the water content of the crude oil, the data judgment model analyzes data of input comprehensive model and the predicted value of water content of crude oil to be output according to a professional knowledge base, the professional knowledge base is obtained by digitizing professional knowledge and experience of relevant professionals in the petroleum industry, the quantitative or qualitative rule of the real-time data change of the equipment is determined by the data judgment model, the input real-time data of the equipment is ensured to be available, the accuracy of the predicted value of the water content of the output crude oil is also ensured, the error of the online detection of the water content of the crude oil can be effectively reduced, so that the production efficiency of the petroleum industry is improved.
In some embodiments, the step S102 of inputting the real-time data of the equipment into the crude oil water content analysis comprehensive model to obtain the real-time predicted value of the crude oil water content, as shown in fig. 2, includes:
step S1021, analyzing whether the real-time data of the equipment is available data or not according to a professional knowledge base through the data judgment model;
step S1022, if the real-time data of the equipment is available data, inputting the real-time data of the equipment into the crude oil water content prediction regression model to obtain a predicted value to be detected;
step S1023, analyzing whether the accuracy of the predicted value to be detected reaches a first preset threshold value or not according to the professional knowledge base through the data judgment model;
and step S1024, if the accuracy of the predicted value to be detected reaches a first preset threshold value, determining that the predicted value to be detected is a real-time predicted value of the water content of the crude oil.
Specifically, after the real-time data of the equipment is input into the crude oil water content analysis comprehensive model, the data judgment model firstly judges whether the real-time data of the equipment meets the available standard according to the professional knowledge data corresponding to the data type contained in the real-time data of the equipment in the professional knowledge base, and if the real-time data of the equipment does not meet the available standard, the data analysis is directly stopped, and the real-time data of the equipment is deleted; and if the real-time data of the equipment are consistent, inputting the real-time data of the equipment into a crude oil water content prediction regression model to perform prediction calculation of the crude oil water content.
The method comprises the steps that a predicted value to be detected is obtained after prediction calculation is carried out through a crude oil water content prediction regression model, a calculation result of the crude oil water content prediction regression model has certain errors, in order to guarantee the accuracy of an output crude oil water content real-time predicted value, the accuracy of the predicted value to be detected needs to be judged through a data judgment model, the data judgment model calculates a theoretical value range of crude oil water content under current equipment real-time data according to data in a professional knowledge base, and if the predicted value to be detected exceeds the allowable error of the theoretical value, the predicted value to be detected cannot be used; if the predicted value to be detected does not exceed the allowable error of the theoretical value, namely the quasi-removing degree of the predicted value to be detected reaches a first preset threshold value, the predicted value to be detected can be used as the real-time predicted value of the water content of the crude oil at the moment.
In some embodiments, the above method further comprises:
step 201, acquiring historical data from a distributed control system; the historical data comprises the historical temperature of the stabilizing tower, the historical pressure of the steady-state oil inlet heat exchanger, the historical temperature of an air cooler and the historical water content of crude oil;
and step 202, carrying out regression model training according to the historical data to obtain a crude oil water content prediction regression model.
Specifically, in order to train the crude oil water content prediction regression model, historical data of the equipment needs to be acquired from a Distributed Control System (DCS), and the historical data includes relevant equipment operation process data acquired by non-probe sensors, such as a stabilizing tower historical temperature, a stabilizing tower historical pressure, a steady-state oil inlet heat exchanger historical pressure, an air cooler historical temperature, crude oil historical water content and the like.
And analyzing and training the historical data by adopting a machine learning algorithm of a support vector machine, and establishing a crude oil water content prediction regression model.
In some embodiments, in step 202, performing regression model training according to the historical data includes:
2021, screening the historical data according to a professional knowledge base to obtain historical data under normal working conditions;
2022, inputting the historical data under the normal working condition into a model to be trained to perform regression model training.
Specifically, the historical water content data of the crude oil directly extracted from the DCS system has the problems of large average error, lag and the like, because part of the data is obtained by calculation under an abnormal working condition, and direct use may cause poor accuracy of the established crude oil water content prediction regression model, so that the equipment historical data corresponding to the historical water content data of each crude oil needs to be analyzed according to a professional knowledge base, the historical data under the abnormal working condition is eliminated, the quality of modeling data for establishing the crude oil water content prediction regression model is improved, and the accuracy of the crude oil water content prediction regression model is improved.
In some embodiments, the method further comprises:
step 203, acquiring a real-time predicted value of the water content of the crude oil and real-time parameters of equipment in a preset time period;
204, screening according to the accurate value of the real-time predicted value of the water content of the crude oil to obtain a target predicted value with the accuracy exceeding a second preset threshold value;
and step 205, performing optimization training on the crude oil water content prediction regression model according to the target predicted value and the equipment real-time parameter corresponding to the target predicted value to obtain an optimized crude oil water content prediction regression model.
Specifically, in order to continuously optimize the accuracy of the crude oil water content prediction regression model, the crude oil water content real-time predicted value and the corresponding equipment real-time parameter obtained in the period are periodically obtained, data are analyzed by professional technicians, the crude oil water content real-time predicted value with the accuracy exceeding a second preset threshold value is screened out to serve as a target predicted value, the crude oil water content prediction regression model is retrained through the target predicted value and the corresponding equipment real-time parameter, iterative optimization is carried out on the crude oil water content prediction regression model, and the accuracy of the crude oil water content real-time prediction is improved.
The present application also provides a crude oil water content analysis device, as shown in fig. 3, the device includes:
a data module 30, configured to obtain device real-time data; the real-time data of the equipment comprises the temperature of a stabilizing tower, the pressure of the stabilizing tower, the pressure of a steady front oil inlet heat exchanger and the temperature of an air cooler;
the analysis module 31 is used for inputting the real-time data of the equipment into the crude oil water content analysis comprehensive model to obtain a real-time predicted value of the water content of the crude oil; the crude oil water content analysis comprehensive model is composed of a crude oil water content prediction regression model and a data judgment model.
In some embodiments, the analysis module 31 includes:
a first analyzing unit 311, configured to analyze whether the device real-time data is available data according to the professional knowledge base through the data determining model;
a calculating unit 312, configured to, if the real-time data of the equipment is available data, input the real-time data of the equipment into the crude oil water content prediction regression model to obtain a predicted value to be checked;
a second analysis unit 313, configured to analyze, by using the data determination model, according to the professional knowledge base, whether the accuracy of the predicted value to be tested reaches a first preset threshold;
and the confirming unit 314 is configured to confirm that the predicted value to be tested is the real-time predicted value of the water content of the crude oil if the accuracy of the predicted value to be tested reaches a first preset threshold.
In some embodiments, as shown in fig. 3, the apparatus further comprises:
the optimization module 32 is used for acquiring a real-time predicted value of the water content of the crude oil and real-time equipment parameters within a preset time period; screening according to the accurate value of the real-time predicted value of the water content of the crude oil to obtain a target predicted value with the accuracy exceeding a second preset threshold value; and performing optimization training on the crude oil water content prediction regression model according to the target predicted value and the equipment real-time parameters corresponding to the target predicted value to obtain an optimized crude oil water content prediction regression model.
Corresponding to a crude oil water content analysis method in fig. 1, the embodiment of the present application further provides a computer device 400, as shown in fig. 4, the device includes a memory 401, a processor 402, and a computer program stored on the memory 401 and executable on the processor 402, wherein the processor 402 implements the crude oil water content analysis method when executing the computer program.
Specifically, the memory 401 and the processor 402 can be general memories and processors, which are not limited in particular, and when the processor 402 runs a computer program stored in the memory 401, the crude oil water content analysis method can be executed, so that the problem of how to reduce the error of online detection of the water content of crude oil in the prior art is solved.
Corresponding to a crude oil water content analysis method in fig. 1, the present application further provides a computer readable storage medium, on which a computer program is stored, and the computer program is executed by a processor to perform the steps of the crude oil water content analysis method.
Specifically, the storage medium can be a general storage medium, such as a mobile disk, a hard disk, and the like, and when a computer program on the storage medium is executed, the method for analyzing the water content of crude oil can be executed, so that the problem of how to reduce the error of online detection of the water content of crude oil in the prior art is solved. According to the method for analyzing the water content of the crude oil, the analysis and calculation of the water content of the crude oil are carried out through a crude oil water content analysis comprehensive model according to real-time data of equipment including the temperature of a stabilizer, the pressure of the stabilizer, the pressure of a steady front oil inlet heat exchanger, the temperature of an air cooler and the like. The method for analyzing the water content of the crude oil provided by the embodiment of the application also comprises the data judgment model besides the crude oil water content prediction regression model in the used crude oil water content analysis comprehensive model, so that the data judgment can be carried out on the data of the input and output comprehensive model, the error of the online detection of the water content of the crude oil can be effectively reduced, and the production efficiency of the petroleum industry is improved.
In the embodiments provided in the present application, it should be understood that the disclosed method and apparatus may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus once an item is defined in one figure, it need not be further defined and explained in subsequent figures, and moreover, the terms "first", "second", "third", etc. are used merely to distinguish one description from another and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the present disclosure, which should be construed in light of the above teachings. Are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method for analyzing the water content of crude oil is characterized by comprising the following steps:
acquiring real-time data of equipment; the real-time data of the equipment comprises a stabilizing tower temperature, a stabilizing tower pressure, a stable front oil inlet heat exchanger pressure and an air cooler temperature;
inputting the real-time data of the equipment into a crude oil water content analysis comprehensive model to obtain a real-time predicted value of the water content of the crude oil; the crude oil water content analysis comprehensive model is composed of a crude oil water content prediction regression model and a data judgment model.
2. The method of claim 1, wherein inputting the real-time data of the equipment into the crude oil water content analysis comprehensive model to obtain a real-time predicted value of the water content of the crude oil comprises:
analyzing whether the real-time data of the equipment is available data or not according to a professional knowledge base through the data judgment model;
if the equipment real-time data are available data, inputting the equipment real-time data into the crude oil water content prediction regression model to obtain a predicted value to be detected;
analyzing whether the accuracy of the predicted value to be detected reaches a first preset threshold value or not according to the professional knowledge base through the data judgment model;
and if the accuracy of the predicted value to be detected reaches a first preset threshold value, confirming that the predicted value to be detected is the real-time predicted value of the water content of the crude oil.
3. The method of claim 1, wherein the method further comprises:
acquiring historical data from a decentralized control system; the historical data comprises the historical temperature of the stabilizing tower, the historical pressure of the steady-state oil inlet heat exchanger, the historical temperature of an air cooler and the historical water content of crude oil;
and carrying out regression model training according to the historical data to obtain a crude oil water content prediction regression model.
4. The method of claim 3, wherein the performing regression model training based on the historical data comprises:
screening the historical data according to a professional knowledge base to obtain historical data under normal working conditions;
and inputting the historical data under the normal working condition into a model to be trained so as to carry out regression model training.
5. The method of claim 1, further comprising:
acquiring a real-time predicted value of the water content of the crude oil and real-time parameters of equipment in a preset time period;
screening according to the accurate value of the real-time predicted value of the water content of the crude oil to obtain a target predicted value with the accuracy exceeding a second preset threshold value;
and carrying out optimization training on the crude oil water content prediction regression model according to the target predicted value and the equipment real-time parameter corresponding to the target predicted value to obtain an optimized crude oil water content prediction regression model.
6. An apparatus for analyzing water content of crude oil, comprising:
the data module is used for acquiring real-time data of the equipment; the real-time data of the equipment comprises a stabilizing tower temperature, a stabilizing tower pressure, a stable front oil inlet heat exchanger pressure and an air cooler temperature;
the analysis module is used for inputting the real-time data of the equipment into a crude oil water content analysis comprehensive model to obtain a real-time predicted value of the water content of the crude oil; the crude oil water content analysis comprehensive model is composed of a crude oil water content prediction regression model and a data judgment model.
7. The apparatus of claim 6, wherein the analysis module comprises:
the first analysis unit is used for analyzing whether the real-time data of the equipment is available data or not according to a professional knowledge base through the data judgment model;
the calculation unit is used for inputting the real-time equipment data into the crude oil water content prediction regression model to obtain a predicted value to be detected if the real-time equipment data are available data;
the second analysis unit is used for analyzing whether the accuracy of the predicted value to be detected reaches a first preset threshold value or not according to the professional knowledge base through the data judgment model;
and the confirming unit is used for confirming that the predicted value to be detected is the real-time predicted value of the water content of the crude oil if the accuracy of the predicted value to be detected reaches a first preset threshold value.
8. The apparatus of claim 6, further comprising:
the optimization module is used for acquiring a real-time predicted value of the water content of the crude oil and real-time equipment parameters in a preset time period; screening according to the accurate value of the real-time predicted value of the water content of the crude oil to obtain a target predicted value with the accuracy exceeding a second preset threshold value; and carrying out optimization training on the crude oil water content prediction regression model according to the target predicted value and the equipment real-time parameter corresponding to the target predicted value to obtain an optimized crude oil water content prediction regression model.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of the preceding claims 1-5 are implemented when the computer program is executed by the processor.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of the claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011474851.1A CN112394163A (en) | 2020-12-14 | 2020-12-14 | Crude oil water content analysis method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011474851.1A CN112394163A (en) | 2020-12-14 | 2020-12-14 | Crude oil water content analysis method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112394163A true CN112394163A (en) | 2021-02-23 |
Family
ID=74625241
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011474851.1A Pending CN112394163A (en) | 2020-12-14 | 2020-12-14 | Crude oil water content analysis method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112394163A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112905548A (en) * | 2021-03-25 | 2021-06-04 | 昆仑数智科技有限责任公司 | Safety audit system and method |
| WO2023017032A1 (en) * | 2021-08-13 | 2023-02-16 | Aktiebolaget Skf | Automatic detection method and device for detecting substance composition in viscous material |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006262850A (en) * | 2005-03-25 | 2006-10-05 | Snow Brand Milk Prod Co Ltd | Method for controlling butter moisture or method and apparatus for controlling number of revolution of churning cylinder |
| CN102095509A (en) * | 2009-12-11 | 2011-06-15 | 上海莫克电子技术有限公司 | Forecasting system for micro water content of oil in transformer |
| US20180245463A1 (en) * | 2016-07-21 | 2018-08-30 | Schlumbergr Technology Corporation | Method for determining a water cut of an oil-water mixture produced from an oil well |
| CN109359692A (en) * | 2018-10-23 | 2019-02-19 | 中国石油大学(北京) | A kind of crude oil water content on-line measurement model and its construction method, application |
| CN110487992A (en) * | 2018-05-14 | 2019-11-22 | 中国石油天然气股份有限公司 | The correcting device and method of crude oil water content |
| CN110939432A (en) * | 2019-12-06 | 2020-03-31 | 中海石油(中国)有限公司 | Method for predicting water content of oil reservoir in ultrahigh water-cut period |
-
2020
- 2020-12-14 CN CN202011474851.1A patent/CN112394163A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006262850A (en) * | 2005-03-25 | 2006-10-05 | Snow Brand Milk Prod Co Ltd | Method for controlling butter moisture or method and apparatus for controlling number of revolution of churning cylinder |
| CN102095509A (en) * | 2009-12-11 | 2011-06-15 | 上海莫克电子技术有限公司 | Forecasting system for micro water content of oil in transformer |
| US20180245463A1 (en) * | 2016-07-21 | 2018-08-30 | Schlumbergr Technology Corporation | Method for determining a water cut of an oil-water mixture produced from an oil well |
| CN110487992A (en) * | 2018-05-14 | 2019-11-22 | 中国石油天然气股份有限公司 | The correcting device and method of crude oil water content |
| CN109359692A (en) * | 2018-10-23 | 2019-02-19 | 中国石油大学(北京) | A kind of crude oil water content on-line measurement model and its construction method, application |
| CN110939432A (en) * | 2019-12-06 | 2020-03-31 | 中海石油(中国)有限公司 | Method for predicting water content of oil reservoir in ultrahigh water-cut period |
Non-Patent Citations (6)
| Title |
|---|
| 唐建峰 等: "油气水分离设备仿真与应用", 《流体机械》 * |
| 张京敏 编著: "《物流运作管理》", 30 June 2015, 中国财富出版社 * |
| 李志明 等: "基于SVM的软测量在原油含水率估算中的应用", 《燕山大学学报》 * |
| 范俊甫: "四种原油含水率预测模型的对比研究", 《中国优秀硕士学位论文全文数据库》 * |
| 韩国军: "原稳装置基于现场总线的模糊控制系统设计与实现", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
| 黄佳: "气流干燥过程水分软测量系统的研究与开发", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112905548A (en) * | 2021-03-25 | 2021-06-04 | 昆仑数智科技有限责任公司 | Safety audit system and method |
| CN112905548B (en) * | 2021-03-25 | 2023-12-08 | 昆仑数智科技有限责任公司 | Security audit system and method |
| WO2023017032A1 (en) * | 2021-08-13 | 2023-02-16 | Aktiebolaget Skf | Automatic detection method and device for detecting substance composition in viscous material |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2021179572A1 (en) | Operation and maintenance system anomaly index detection model optimization method and apparatus, and storage medium | |
| CN108780314B (en) | Model adaptation and online learning for unstable environments | |
| CN113805548B (en) | Machining intelligent control system, machining intelligent control method and computer readable medium | |
| CN112394163A (en) | Crude oil water content analysis method and device | |
| EP3795975B1 (en) | Abnormality sensing apparatus, abnormality sensing method, and abnormality sensing program | |
| EP1958034B1 (en) | Use of sequential clustering for instance selection in machine condition monitoring | |
| CN116432091B (en) | Equipment fault diagnosis method based on small sample, construction method and device of model | |
| CN110991739A (en) | Construction method and prediction method of industry electric quantity demand prediction model | |
| CN113837596B (en) | Fault determination method and device, electronic equipment and storage medium | |
| CN111277459A (en) | Equipment anomaly detection method and device and machine-readable storage medium | |
| CN116453438B (en) | Display screen parameter detection method, device, equipment and storage medium | |
| JP2019096227A (en) | Operation improvement effect calculation apparatus, operation improvement effect calculation method, operation improvement effect calculation program, and recording medium | |
| CN113723734A (en) | Method and device for monitoring abnormity of time series data, electronic equipment and storage medium | |
| CN113536066A (en) | Data anomaly detection algorithm determination method and device and computer equipment | |
| CN114202256A (en) | Architecture upgrading early warning method and device, intelligent terminal and readable storage medium | |
| CN111901156B (en) | Method and device for monitoring faults | |
| CN113110961A (en) | Equipment abnormality detection method and device, computer equipment and readable storage medium | |
| CN113128797A (en) | Method and device for monitoring abnormal business indexes | |
| CN115293735A (en) | Unmanned factory industrial internet platform monitoring management method and system | |
| CN112398706B (en) | Data evaluation standard determining method and device, storage medium and electronic equipment | |
| CN112990682A (en) | Chemical device control parameter optimization method and system based on big data | |
| WO2022032102A1 (en) | Apparatus and method for electronic determination of system data integrity | |
| CN113296836A (en) | Method for training model, testing method, device, electronic equipment and storage medium | |
| CN110688273B (en) | Classification model monitoring method and device, terminal and computer storage medium | |
| CN113610225A (en) | Quality evaluation model training method and device, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20220322 Address after: Room 1501, Gehua building, No.1 Qinglong Hutong, Dongcheng District, Beijing Applicant after: Kunlun Digital Technology Co.,Ltd. Applicant after: CHINA NATIONAL PETROLEUM Corp. Address before: 100010 room 1501, Gehua building, No.1 Qinglong Hutong, Dongcheng District, Beijing Applicant before: Kunlun Digital Technology Co.,Ltd. |
|
| TA01 | Transfer of patent application right | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210223 |
|
| RJ01 | Rejection of invention patent application after publication |