CN113341723B - FPSO adds quantity control system with intelligent chemical agent - Google Patents

FPSO adds quantity control system with intelligent chemical agent Download PDF

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CN113341723B
CN113341723B CN202110676748.3A CN202110676748A CN113341723B CN 113341723 B CN113341723 B CN 113341723B CN 202110676748 A CN202110676748 A CN 202110676748A CN 113341723 B CN113341723 B CN 113341723B
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trend
oil content
data
time
addition amount
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CN113341723A (en
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王彬
孙恪成
邓欣
龚玉林
彭程
杨静
王鑫章
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CNOOC Energy Technology and Services Ltd
Oil Production Services Branch of CNOOC Energy Technology and Services Ltd
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Oil Production Services Branch of CNOOC Energy Technology and Services Ltd
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    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
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Abstract

The invention provides an intelligent chemical agent addition control system for a Floating Production Storage and Offloading (FPSO), which relates to the field of automatic control of chemical agents in the production water treatment process of a FPSO, and comprises a trend extraction training module based on real-time data of an oil content analyzer and an intelligent control module based on trend data, wherein under the condition of inaccurate existing measurement information, a set of intelligent algorithm is designed to realize online intelligent autonomous control of the chemical agent addition, and an oil content trend extraction algorithm model of production water is used for trend extraction and feedback control of the chemical agent addition, so that stable and qualified effluent quality indexes are realized; the waste of the medicament is reduced, and the operation cost is reduced; and the addition amount of the water clarifier is reasonably decided, and the performance of the FPSO platform medicament addition system is improved.

Description

FPSO is with intelligent chemical agent addition quantity control system
Technical Field
The invention relates to the field of automatic chemical agent control in a Floating Production Storage and Offloading (FPSO) production water treatment process, in particular to an intelligent chemical agent addition amount control method based on oil content analyzer measurement data.
Background
FPSO is the english abbreviation of floating production storage and offloading, chinese called "floating production storage offloading"; it has both production and oil storage functions, is a marine oil-gas processing plant, and has oil-gas treatment capacity of several thousand cubic meters or more(ii) a It is an oil storage wheel, and the oil storage capacity of the FPSO which is currently in service in the world reaches 35 multiplied by 104t; the water-saving device is strong in adaptability and can work in the water depth range of 20-1000 m; the external transportation submarine pipeline can be saved, and the shuttle tanker is used for transporting the commercial oil to the outside; the design has high reappearance period (100 years), strong wind and wave resistance, and can be moored for a long time and continuously work; the maximum advantage of FPSO exploitation oil field lies in that it can comparatively make full use of space and area, and strong adaptability to environmental conditions, the oil storage capacity is big, can dock outward with great oil ship, can reform transform reuse, reduces disposable input, improves the development benefit in oil field.
The offshore oil 111FPSO is located in the sea area between the PY4-2 oil field and the PY5-1 oil field, and is located at 261-degree 093.78 'in the east longitude and 2307-degree 333.52' in the north latitude; oil-gas water produced from a PY4-2/5-1 oil field is pressurized by an external delivery pump and is mixed and delivered to a single point through respective submarine pipelines, the oil-gas water enters an FPSO from the single point, oil, gas and water are separated in the FPSO, the oil-gas water separation process involves the addition of chemical agents, such as water cleaning agents and the like, and the addition of the chemical agents in industry is adjusted according to the flow data of external drainage water; the dehydrated and degassed qualified crude oil directly enters a cargo oil tank for storage, and then is periodically input into an oil extraction wheel through an external transportation metering system and is transported away by the oil extraction wheel; discharging the separated crude oil associated gas through a cold air defense system; the separated production water enters a production water treatment system for treatment, and is discharged directly after being treated to be qualified, namely the oil content is less than or equal to 50PPM, and the treatment process is shown in figure 1.
With the increasing emphasis on marine environmental protection, the FPSO platform water treatment process becomes more and more important for FPSO crude oil treatment. Most of the FPSO platform water treatment processes adopt the technologies of gravity settling separation of an oily sewage tank, centrifugal separation of a hydrocyclone, filtering separation, air-entrapping flotation separation and the like to treat crude oil basically; due to frequent and complex changes of working conditions and large fluctuation of the treatment process of production water, the quality index of discharged water greatly fluctuates, and a discharged water oil content analyzer commonly used on a current platform has low measurement precision under complex working conditions of high temperature, high pressure, sand carrying and the like, and inaccurate measurement data cannot support real-time feedback control of the addition amount of a chemical agent; limited by inaccurate and unreliable process measurements, the process generally does not achieve the goal of automatic control at present, and even does not achieve the goal of optimal control. Therefore, it is very necessary to develop an intelligent control algorithm to realize real-time feedback control of the addition amount of the chemical agent under the condition that the existing measurement information is inaccurate, so as to realize the goal of stable and qualified quality index of the discharged water.
Disclosure of Invention
The invention aims to solve the technical problem of overcoming the defects in the prior art and provides an intelligent chemical agent addition amount control system for an FPSO.
The invention is realized by the following technical scheme: an intelligent chemical agent addition amount control system for an FPSO comprises a trend extraction training module based on real-time data of an oil content analyzer and an intelligent control module based on trend data;
a trend extraction training module based on real-time data of an oil content analyzer is used for obtaining the oil content trend of the discharged water;
and the intelligent control module based on trend data is combined with the trend extraction training module based on real-time data of the oil content analyzer to calculate and obtain trend information of the oil content of the external drainage, feedback adjustment of the control quantity is carried out, and the control of the addition quantity of the chemical agent is intelligently controlled through interaction of the control parameters and a field DCS real-time database.
According to the above technical solution, preferably, the trend extraction training module based on real-time data of the oil content analyzer includes the following sub-steps:
step 1: selecting a language expressing the trend and determining a trend unit;
step 2: performing preliminary segmentation and fitting on the real-time data to obtain preliminary segmentation fitting data;
and step 3: and extracting the trend from the preliminary segmentation fitting data through the determined trend unit.
According to the above technical solution, preferably, step 1 includes the following sub-steps:
step 1.1: first, three primitives are defined, the primitives being defined as: { the primitives,
Figure BDA0003121360160000031
}
wherein y represents the signal value, t represents the sampling time, b represents the start time, e represents the end time, and i represents the serial number of the primitive;
step 1.2: seven trend units are composed of three primitives.
According to the above technical solution, preferably, step 2 includes the following sub-steps:
step 2.1: a polynomial segmentation fitting method is adopted to divide real-time data into a continuous interval form:
y(t)＝p(t-tb)+yb
y (t) represents the signal value, time tbDenotes the start of the interval, p is the slope, ybIs at tbThe ordinate of time;
step 2.2: let us assume at t1At a time, a set of characteristics of the linear approximation function, i.e. p, has been calculated1，yb1And tb1Known as t respectively1The slope, the ordinate and the start of the interval; after k (k ═ 1,2, 3 …) sample times, then at t1At the time of + k Δ t,
Figure BDA0003121360160000032
representing the predicted signal value by a model extrapolation algorithm
Figure BDA0003121360160000033
Step 2.3: the difference epsilon between the measured values and the model extrapolation algorithm is calculated as:
Figure BDA0003121360160000034
cumulative sum Cusum
Figure BDA0003121360160000035
Step 2.4: at each sampling instant t1+ k Δ t, the absolute value of the cumulative sum and a predetermined threshold th1A comparison is made.
According to the above technical solution, preferably, step 3 includes the following sub-steps:
step 3.1: in order to extract the trend from the preliminary segmentation fitting data obtained in the step 2, firstly converting the preliminary segmentation fitting data into a representation form of a graph;
step 3.2: fitting the preliminary segmentation fitting data by adopting a least square method to form a plurality of linear segments;
step 3.3: judging whether the fitted linear segment is acceptable or not by using accumulation and Cusum;
according to the above technical solution, preferably, the intelligent control module based on trend data includes the following steps:
step 401.1: judging the trend, if the trend is rising, indicating that the oil content is increasing, the addition amount of the water clarifier needs to be increased, wherein the increase amount is 2 tau, and tau is a change value of adding/reducing the medicament amount in an industrial field;
step 401.2: if the oil content is reduced, the addition amount of the water clarifier needs to be reduced, and the reduction amount is 2 tau;
step 401.3: if the oil content is unchanged, indicating that the oil content is unchanged, but if the oil content is still in a condition of exceeding the standard range, continuing to properly increase the addition amount of the water purifier at the moment, wherein the increase amount is tau;
step 401.4: if the content of the water clarifier is unchanged, the oil content is unchanged, and if the content of the water clarifier is in the standard range, the addition amount of the water clarifier is unchanged;
step 401.5: if the step is positive, the oil content is increased firstly and then is unchanged, the addition amount of the water clarifier is properly increased, and the increase is tau;
step 401.6: if the step is negative, the oil content is reduced firstly and then is unchanged, the addition amount of the water clarifier is properly reduced, and the reduction amount is tau;
step 401.7: if the oil content is up/down transient or down/up transient, indicating that the oil content is increased and then decreased or the oil content is decreased and then increased, the increase or decrease is tau, the oil content measured by the oil content analyzer is returned to the specified standard range.
The invention has the beneficial effects that:
(1) under the condition that the existing measurement information is inaccurate, a set of intelligent algorithm is designed to realize the online intelligent autonomous control of the addition amount of the chemical agent, and the stability and the qualification of the quality index of the discharged water are realized by using a production water oil-containing trend extraction algorithm model for trend extraction and the feedback control of the addition amount of the chemical agent;
(2) the waste of the medicament is reduced, and the operation cost is reduced;
(3) and the addition amount of the water clarifier is reasonably decided, and the performance of the FPSO platform medicament addition system is improved.
Drawings
FIG. 1 is a schematic illustration of a crude oil processing flow in an example of the present invention;
FIG. 2 is a diagram of primitives defined in an embodiment of the present invention;
FIG. 3 is a trend unit defined in an embodiment of the present invention;
FIG. 4 is a decision tree for trend recognition in an embodiment of the present invention;
fig. 5 is a block diagram of feedback control in an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it is obvious that the described embodiments are some, not all embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The inventor of the patent application finds that although the measurement accuracy of an external drainage oil content analyzer commonly used on the current platform is low under complex working conditions of high temperature, high pressure, sand carrying and the like, the fluctuation trend has a certain reference value and is higher in fluctuation accuracy, so that how to convert the data acquired by the oil content analyzer into more accurate fluctuation trend is actively contemplated to perform feedback control.
As shown in the figure, the invention provides an intelligent chemical agent addition amount control system for an FPSO, comprising:
a trend extraction training module based on real-time data of an oil content analyzer is used for obtaining the oil content trend of the discharged water;
the intelligent control module based on trend data is combined with the trend extraction training module based on real-time data of the oil content analyzer to calculate and obtain trend information of the oil content of the external drainage, feedback adjustment of the control amount is carried out, and control of the addition amount of the chemical agent is intelligently controlled through interaction of control parameters and a field DCS real-time database;
the trend extraction training module is based on real-time data of the oil content analyzer, fitting data are preliminarily divided by a polynomial division fitting method, the fitting data are further divided and fitted by a least square method, and whether the fitted data can be accepted or not is judged by accumulation and Cusum; if yes, continuing to fit; if not, re-fitting a group of linear approximation functions by using a least square method to obtain the oil content trend of the discharged water, wherein the method comprises the following steps:
step 1: selecting a language representing the trend;
step 1.1: there are many languages for representing the trend, and the language proposed by Sylvie charbonier et al is adopted in the embodiment of the invention to describe the trend; the language defines three primitives, shown in FIG. 2 as defined primitives:
one primitive is defined as follows: { the primitives,
Figure BDA0003121360160000061
}
wherein y represents the signal value, t represents the sampling time, b represents the start time, e represents the end time, and i represents the serial number of the primitive;
step 1.2: seven trend units are formed by three primitives, and a defined trend unit is shown in FIG. 3;
step 2: preliminary segmentation and fitting of real-time data;
step 2.1: a polynomial segmentation fitting method is adopted to divide real-time data into a continuous interval form:
y(t)＝p(t-tb)+yb
y (t) represents the signal value, time tbIndicates the start of the interval, p is its slope, ybIs at tbThe ordinate of time;
step 2.2: let us assume at t1At a time, a set of characteristics of the linear approximation function, i.e. p, has been calculated1，yb1And tb1Known as t respectively1The slope, the ordinate and the start of the interval; after k (k ═ 1,2, 3 …) sample times, then at t1At the time of + k Δ t,
Figure BDA0003121360160000062
representing the predicted signal value by a model extrapolation algorithm
Figure BDA0003121360160000071
Step 2.3: the difference epsilon between the measured values and the model extrapolation algorithm is calculated as:
Figure BDA0003121360160000072
cumulative sum Cusum
Figure BDA0003121360160000073
Step 2.4: at each sampling instant t1+ k Δ t, the absolute value of the cumulative sum and a predetermined threshold th1Comparing;
step 2.4.1: if | Cusum (t)1+kΔt)|＜th1Indicating that the current linear approximation function is acceptable and continuing to receive new data;
step 2.4.2: otherwise, the current linear approximation function is not acceptable, new data needs to be waited, the accumulated sum is immediately reset to 0, and the calculation of the accumulated sum is restarted;
and step 3: and (3) trend extraction:
step 3.1: in order to extract a trend from the preliminary segmentation fitting data obtained in the step 2, the preliminary segmentation fitting data is firstly converted into a representation form of a graph, and a least square method is adopted in the embodiment of the invention;
step 3.2: the following two methods are generally used to improve the approximation accuracy of the linear approximation function: one is to increase the power of the sequence terms and the other is to decrease the interval length; in the two methods, the second method is usually adopted, technicians firstly select a method for improving the power of the sequence term on the premise of considering the precision, but the improvement of the power of the sequence term can cause the increase of the calculation complexity to make the realization difficult, and the reduction of the length of the interval is easy to realize and can meet the requirement;
step 3.3: judging whether the fitted linear segment is acceptable or not by using accumulation and Cusum;
step 3.3.1: determining the minimum processing data length n;
step 3.3.2: from the current segment XicStarting point of (2)
Figure BDA0003121360160000081
Initially, data expansion is performed with each newly arrived linear segment;
step 3.3.3: if X isicThe number of the data in (1) reaches n, and the slope P of the linear segment is calculatedic；
Step 3.3.4: by using
Figure BDA0003121360160000082
(Current fragment X)icFrom the starting point of (c),
Figure BDA0003121360160000083
(fitting value of fragment), Pic(slope of segment),
Figure BDA0003121360160000084
(Current fragment X)icEndpoint) the fit at the end of the linear fragment was calculated:
Figure BDA0003121360160000085
step 3.3.5: calculating the cumulative sum Cusum;
step 3.3.5.1: if Id>htcAnd | Cusum | is less than htsThen the linear segment is acceptable and the fitting continues;
wherein h istc、htsIs a threshold value for trend recognition in the decision tree, which can be determined empirically;
step 3.3.5.2: otherwise, if the data is not acceptable, storing the data at the tail end of the last linear segment into the next segment, and restarting the step 3.3.2;
step 3.3.6: linear segments after the segmentation are defined, the total rising/falling of the linear segments is represented as I, and the rising/falling of discontinuous linear segments is represented as IdThe linear segment rising/falling of the same slope is represented as IsRespectively defined as follows:
Figure BDA0003121360160000086
Figure BDA0003121360160000087
Figure BDA0003121360160000088
y represents the signal value, t represents the sampling time, b represents the start time, e represents the end time, i represents the sequence number of the primitive, and p represents the slope;
step 3.3.7: substituting the preliminary segmentation fitting data into the step 3.3.6 to obtain I,IdAnd IsBy using the trend-identified decision tree shown in fig. 4, the corresponding trend can be identified, and the trend can be combined with the previously obtained trend to obtain a complete and continuous trend set, and the logic of the decision tree is as follows: if Id|＜htc、|I|>htsI.gtoreq.0 or Id≥htc、|Is|>hts、Id>0、sign(Id*Is)>0 is a rising segment; if Id|＜htc、|I|>htsI < 0 or Id|≥htc、|Is|>hts、Id＜0、sign(Id*Is)>0 is a descending segment; if Id|＜htc、I|＜htsThen it is an invariant fragment; if | Id≥htc、Is|≤hts、Id>0 is positive step; if Id≥htc、Is|≤hts、IdIf less than 0, the step is negative; if Id|≥htc、|Is|>hts、Id>0、sign(Id*Is) < 0 is up/down transient; if Id|≥htc、|Is|>hts、id＜0、sign(Id*Is) < 0 is a down/up transient.
An intelligent control module based on trend data, wherein fig. 5 is a diagram of the whole control system, a pump in the diagram is a water clarifier adding pump (valve), u represents the addition amount of a medicament, an oil content analyzer can detect the oil content of external drainage after a series of operations such as subsequent field operation, a controlled object is a whole production water treatment process, and the control of the medicament addition change amount of the intelligent control module based on the trend data is as described in step 4, wherein the step 4 comprises the following detailed steps:
step 401.1: judging the trend, if the trend is rising, indicating that the oil content is increasing, the addition amount of the water clarifier needs to be increased, wherein the increase amount is 2 tau, and tau is a change value of adding/reducing the medicament amount in an industrial field;
step 401.2: if the oil content is reduced, the addition amount of the water clarifier needs to be reduced, and the reduction amount is 2 tau;
step 401.3: if the content of the water clarifier is unchanged, the oil content is unchanged, but if the content of the water clarifier is beyond the standard range, the addition amount of the water clarifier is increased properly, and the increase is tau;
step 401.4: if the oil content is unchanged, and if the oil content is in the standard range, the addition amount of the water clarifier is unchanged;
step 401.5: if the step is positive, the oil content is increased firstly and then is unchanged, the addition amount of the water clarifier is properly increased, and the increase is tau;
step 401.6: if the step is negative, the oil content is reduced firstly and then is unchanged, the addition amount of the water clarifier is properly reduced, and the reduction amount is tau;
step 401.7: if the oil content is up/down transient or down/up transient, indicating that the oil content is increased and then decreased or the oil content is decreased and then increased, the increase or decrease is tau, the oil content measured by the oil content analyzer is returned to the specified standard range.
The beneficial effect of this embodiment is:
(1) under the condition that the existing measurement information is inaccurate, a set of intelligent algorithm is designed to realize the online intelligent autonomous control of the addition amount of the chemical agent, and the stability and the qualification of the quality index of the discharged water are realized by using a production water oil-containing trend extraction algorithm model for trend extraction and the feedback control of the addition amount of the chemical agent;
(2) the waste of the medicament is reduced, and the operation cost is reduced;
(3) and the addition amount of the water clarifier is reasonably decided, and the performance of the FPSO platform medicament addition system is improved.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the invention and simplifying the description, but do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the invention, it is to be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solution of the invention, and not for limiting the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (3)

1. An intelligent chemical agent addition amount control system for an FPSO (floating production storage and offloading) is characterized by comprising a trend extraction training module based on real-time data of an oil content analyzer and an intelligent control module based on trend data;
a trend extraction training module based on real-time data of an oil content analyzer is used for obtaining the oil content trend of the discharged water;
the intelligent control module based on trend data is combined with the trend extraction training module based on real-time data of the oil content analyzer to calculate and obtain trend information of the oil content of the external drainage, feedback adjustment of the control amount is carried out, and control of the addition amount of the chemical agent is intelligently controlled through interaction of control parameters and a field DCS real-time database;
the trend extraction training module based on real-time data of the oil content analyzer comprises the following sub-steps:
step 1: selecting a language expressing the trend and determining a trend unit;
the step 1 comprises the following sub-steps:
step 1.1: first, three primitives are defined, the primitives being defined as: { the primitives,
Figure FDA0003594740500000011
where y represents the signal value, t represents the sample time, b represents the start time, e represents the end time, and i represents the sequence number of the primitive;
step 1.2: seven trend units are formed by three primitives;
step 2: performing preliminary segmentation and fitting on the real-time data to obtain preliminary segmentation fitting data;
the step 2 comprises the following sub-steps:
step 2.1: a polynomial segmentation fitting method is adopted to divide real-time data into a continuous interval form:
y(t)＝p(t-tb)+yb
y (t) represents the signal value, time tbDenotes the start of the interval, p is the slope, ybIs at tbThe ordinate of time;
step 2.2: let us assume at t1At a time, a set of characteristics of the linear approximation function, i.e. p, has been calculated1，yb1And tb1Known as t respectively1The slope, the ordinate and the start of the interval; after k sample times, then at t1At the time of the + k deltat time,
Figure FDA0003594740500000021
representing the predicted signal value by a model extrapolation algorithm
Figure FDA0003594740500000022
Step 2.3: the difference epsilon between the measured values and the model extrapolation algorithm is calculated as:
Figure FDA0003594740500000023
cumulative sum Cusum
Figure FDA0003594740500000024
Step 2.4: at each sampling instant t1+ k Δ t, the absolute value of the cumulative sum and a predetermined threshold th1Carrying out comparison;
and step 3: and extracting the trend from the preliminary segmentation fitting data through the determined trend unit.
2. The system of claim 1, wherein step 3 comprises the following sub-steps:
step 3.1: in order to extract the trend from the preliminary segmentation fitting data obtained in the step 2, firstly converting the preliminary segmentation fitting data into a representation form of a graph;
step 3.2: fitting the preliminary segmentation fitting data by adopting a least square method to form a plurality of linear segments;
step 3.3: cumulative and Cusum were used to determine whether the fitted linear fragment was acceptable.
3. The intelligent chemical additive amount control system for the FPSO according to claim 2 wherein the intelligent control module based on the trend data comprises the steps of:
step 401.1: judging the trend, if the trend is rising, indicating that the oil content is increasing, the addition amount of the water clarifier needs to be increased, wherein the increase amount is 2 tau, and tau is a change value of adding/reducing the medicament amount in an industrial field;
step 401.2: if the oil content is reduced, the addition amount of the water clarifier needs to be reduced, and the reduction amount is 2 tau;
step 401.3: if the content of the water clarifier is unchanged, the oil content is unchanged, but if the content of the water clarifier is beyond the standard range, the addition amount of the water clarifier is increased properly, and the increase is tau;
step 401.4: if the content of the water clarifier is unchanged, the oil content is unchanged, and if the content of the water clarifier is in the standard range, the addition amount of the water clarifier is unchanged;
step 401.5: if the step is positive, the oil content is increased firstly and then is unchanged, the addition amount of the water clarifier is increased properly, and the increase amount is tau;
step 401.6: if the step is negative, the oil content is reduced firstly and then is unchanged, the addition amount of the water clarifier is properly reduced, and the reduction amount is tau;
step 401.7: if the oil content is up/down transient or down/up transient, indicating that the oil content is increased and then decreased or the oil content is decreased and then increased, the increase or decrease is tau, the oil content measured by the oil content analyzer is returned to the specified standard range.
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