WO2017024534A1

WO2017024534A1 - Method for determining critical state during operation of crude oil pipeline

Info

Publication number: WO2017024534A1
Application number: PCT/CN2015/086682
Authority: WO
Inventors: 赵龙
Original assignee: 深圳朝伟达科技有限公司
Priority date: 2015-08-11
Filing date: 2015-08-11
Publication date: 2017-02-16
Also published as: WO2017024699A1; WO2017025013A1

Abstract

A method for determining a critical state during the operation of a crude oil pipeline, comprising: acquiring pipeline parameters corresponding to crude oil pipeline transmission performance; according to the pipeline parameters, determining a probability distribution function of the pipeline parameters; according to a pre-set number of times, sampling the probability distribution function of the pipeline parameters; according to a sampling result, determining variables of the pipeline parameters; according to the variables, acquiring a pipeline condensation probability corresponding to each parameter in the pipeline parameters; according to the pipeline condensation probability corresponding to each parameter, determining a critical value corresponding to each parameter; and according to the critical value corresponding to each parameter, determining whether the crude oil pipeline is in a critical state.

Description

Method for judging critical state of crude oil pipeline during operation

Technical field

The invention relates to the field of crude oil pipelines, and particularly relates to a method for determining a critical state of a crude oil pipeline during operation.

Background technique

When the pipeline is running, due to changes in the environmental conditions of the pipeline and changes in the rheological characteristics of the pipeline oil (the change of some influencing factors is uncontrollable), the transmission may decrease and the friction may increase. With timely safeguards, pipeline transportation may be in an unstable state of operation, and it is highly probable that initial condensation or even condensation of the pipeline will occur. The turning point of the pipeline entering the unstable working area is the critical state, as shown in Figure 1. Accurately determining the critical state of pipeline operation can effectively prevent the pipeline from entering the unstable working area, thus ensuring the safety margin of pipeline operation is accurate, economical and reliable, thus avoiding the loss caused by the condensation tube, and adjusting the corresponding parameters according to the basis to improve the pipeline transportation. Economic benefits.

The current common practice for determining whether a pipeline is in a critical state is that the minimum transmission in accordance with the pipeline operation is lower than the critical throughput of the unstable working region. This method does not take into account the impact of the external environment on the operation of the pipeline. At the same time, because there is no standardized critical state determination method, no research has been carried out on the safety margin determination method.

Summary of the invention

The present invention is a critical state determination and safety margin determination method based on deterministic and uncertain methods for perfecting traditional methods.

The critical state determination and safety margin determination method of the crude oil pipeline proposed by the invention is shown in FIG. 2, and the main steps are as follows:

1) Automatic extraction and analysis of parameter reports;

2) Analyze the distribution law of each influencing factor and determine its probability distribution function;

3) Numerical simulation times setting and random sampling of parameters;

4) Set the initial value of the variable in turn for each influencing factor;

5) Calculate the probability of the condensation tube under the parameter values of each target factor by numerical simulation;

6) Find the critical value of each influencing factor variable;

7) Calculate the safety margin of each influencing factor;

8) Output pipe operation threshold and safety margin calculation results.

Specifically:

(1) Automatic extraction and analysis of parameter reports

Collect and analyze the output, inlet and outlet temperature, pressure, off-site temperature, air temperature, total heat transfer coefficient, pipeline depth, and soil parameter uncertainty data. Based on the actual production report, consider the uncertainty of these factors. The data under abnormal conditions are screened out; the automatic extraction analysis of the calculation parameters involved is performed by software, such as volume, pressure, temperature of entry and exit, etc., and the accuracy of these data is checked.

(2) Analyze the distribution law of influencing factors and determine the probability distribution function of influencing factors

According to ISO 16708, the distribution law of uncertainty of each influencing factor is analyzed, and its probability distribution function is established:

1) Select the probability distribution model

Select a probability distribution model based on experience from similar problems, physical reasoning or analysis results, or a good distribution of empirical data; when a large amount of data is available, use the correlation method to estimate the skewness and kurtosis coefficients to select a A suitable model of the class.

Some of the most commonly used distributions related to pipeline problems include:

a) describe the beta distribution of the distribution of bounded variables;

b) describing the lognormal distribution of the resistance variable when the information is limited;

c) describing a normal distribution of linear physical parameters and additional independent errors;

d) describing a uniform distribution of physical phenomena such as current directed distribution;

e) Weibull distribution describing long-term wave height and current values, where the famous index and Rayleigh distribution are special cases of Weibull distribution;

f) Gumbel distribution describes the extreme value of the population whose population distribution is of the exponential type;

2) Estimating the distribution parameters

Including chart method, least squares fitting method, maximum likelihood estimation technique, moment method and Bayes estimation method.

Corresponding to the linear or nonlinear optimization problem of the dependent distribution type, the least squares fitting method is generally used to obtain the distribution parameter by calculating the minimum value of ∑(x _i,obs -x _i,model ) ² , where x _i,obs is Observe the quantity, and x _{i, model} is the prediction result of the corresponding distribution model;

For complex pipeline operation problems, the moment method is often used. For samples containing n measured values (x ₁ ,..., x _n ), the mean (μ), standard deviation (σ), and skewness are calculated as follows. Four moment estimates of (δ) and kurtosis (κ):

3) Verify the distribution of the fit

The applicability of the fitted model can be pointed out by objective methods or subjective judgments.

The most commonly used objective methods are the Kolmogorov-Smirnov test and the χ ² test (χ square test);

When the available data is small, the objective method is prone to lack evidence to eliminate the candidate distribution. At this time, the engineering judgment based on the probability chart is usually the preferred method: draw the experience and the fitted distribution function on a quantile map or Zhang is constructed to fit the important part of the distribution (the left and right tails or the central part of the distribution) on the graph where the fitted model can appear in a straight line.

(3) Numerical simulation times setting and random sampling of parameters

First, set the number of simulations. According to statistics, the number of samplings has little effect on the sampling results between 15,000 and 25,000 times, so the number of sampling times is set to 15,000 times to 20,000 times.

Then, random sampling is generated from the corresponding probability distribution function of each influencing factor; considering the physical meaning of the production operation parameter value, in the absence of specific operating conditions, the upper and lower limits of each parameter need to be set; the upper and lower limits of the transmission are The mean value is increased or decreased by 3 times. The upper and lower limits of the temperature of the inlet and outlet are 0°C-90°C, the upper and lower limits of the ground temperature are -20°C-50°C, the upper and lower limits of the buried depth of the pipeline are 0m-2m, and the upper and lower limits of the total heat transfer coefficient are according to different pipelines. Set separately.

(4) Set the target influence factor variable value for each influencing factor

When analyzing the critical value of an influencing factor, use it as the target influencing factor, fix the parameter values of other influencing factors, traverse the parameter values of the adjustment target factors; and analyze other influencing factors separately by analogy;

Based on the pipeline operating conditions, an appropriate step size is given, the range of the target influencing factors is equally divided, and the probability of each boundary point below the critical mass is calculated. Comparing the calculation results, the probability is lower than the critical mass. The value range of the abrupt change, the step size is reduced, the interval is continued to be equally divided according to the step size, and the probability is lower than the critical volume; and so on, until the value interval that is less than the critical value of the critical volume is narrowed To one point.

(5) Calculate the probability of lower than critical output under the parameter values of each influencing factor by numerical simulation

The numerical simulation is used to calculate the probability of lower than the critical output under the parameter values of each influencing factor:

For a given limit state, the probability analysis includes a model of the generalized random load S and the generalized random resistance R; the corresponding limit state function can be expressed generally as follows:

g(x)=R-S (5)

Obviously, when g(x) < 0 marks failure;

Define the failure probability as follows:

Where x is a random variable vector; f _x (x) is a joint probability density function;

(6) Find the critical value of each influencing factor variable

Comparing the target parameter with different parameter values corresponding to the critical mass probability, when the calculated lower than the critical mass probability is greater than the production requirement lower than the critical mass probability, the system is determined to reach the limit operating state, and the corresponding target factor parameter The value is the critical value of the target factor;

As shown in Figure 1, Zone II is the unstable working area of the hot oil pipeline; if the hot oil pipeline runs in this zone, when some flow factors reduce the flow, the frictional resistance increases, thus further increasing the flow rate. Decrease, resulting in lower flow rate → increased friction resistance → lower pump displacement → further increase in friction resistance → pump displacement continues to decrease, eventually leading to pipeline stoppage; the boundary volume of II and III is critical output. It is also a minimum allowable volume of the hot oil pipeline in which the pipeline is normally operated; the state in which the pipeline operates when the critical flow corresponds is the critical state.

(7) Calculate the safety margin of each influencing factor

Sensitivity analysis was carried out for each influencing factor, and the change of each factor was compared by 10% and 20%, corresponding to the change range of the probability of lower than the critical mass, so as to determine the contribution value of each factor to the flow safety;

According to the difference between the current value of each influencing factor and its critical value, the safety margin is determined by the following formula.

(8) Output calculation results (pipeline operation threshold and safety margin)

According to the calculated operating threshold and safety margin, the safety of the parameters of the influencing factors of the crude oil pipeline is determined, and the parameter adjustment scheme and output result are proposed.

Through the method proposed by the present invention, in the calculation of the flow safety evaluation, for a certain influencing factor, the parameter values of other influencing factors are fixed, and the parameter values of the influencing factors are traversed until the system reaches the limit operating state, thereby obtaining the influencing factors. The critical value is finally calculated to obtain the safety margin of each influencing factor and guide the safe operation of the crude oil pipeline.

The invention can determine whether the pipeline is in a critical state during operation, thereby obtaining a safety margin of each influencing factor, and providing technical support for the fluidity safety evaluation. The invention can reliably and accurately determine the critical state of the crude oil pipeline during operation and determine the safety margin.

DRAWINGS

Figure 1 is a steady-state operating characteristic curve of the hot oil pipeline (the exit temperature is constant, u is the viscosity temperature index);

Figure 2 is a flow chart for determining the critical state and safety margin of the crude oil pipeline during operation;

Figure 3 is a graph showing the variation of the probability of the critical output below the exit temperature.

detailed description

The critical state determination and safety margin determination method of the crude oil pipeline during operation are shown in Fig. 2. Through the method proposed by the invention, a crude oil pipeline in China is evaluated below the critical mass probability, and the method is adapted to the on-site production. The conclusion.

(1) Automatic extraction and analysis of parameter reports

Collect and analyze the uncertainty of the output, inlet and outlet temperature, pressure, off-site temperature, total heat transfer coefficient, pipeline depth, and so on.

(2) Analyze the distribution law of each factor and determine the probability distribution function of the influencing factors

The results of the data analysis of a pipeline in March are as follows:

Table 1 Analysis results of data distribution law of a pipeline in March

The statistical results show that the parameters such as outbound temperature, output, and ground temperature are not always constant, but fluctuate slightly around a certain value, and basically obey the normal distribution law. Taking the outbound temperature as an example, the probability distribution function is

(3) Numerical simulation times setting and random sampling of parameters

First set the number of analog sampling to 20,000 times; set the upper and lower limits of the transmission to its mean value plus or minus 3 times variance, the upper and lower limits of the temperature of the inlet and outlet are 0 °C-90 °C, the upper and lower limits of the ground temperature are -10 °C-20 °C, the pipeline is buried The upper and lower limits are 0m-2.5m, and the upper and lower limits of the total heat transfer coefficient are 2W/(m ² □°C) _-3 W/(m ² □°C);

Then, random samples are generated from the corresponding values generated by the probability distribution functions of the influencing factors.

(4) Set variables in turn for each influencing factor

Based on the pipeline operating conditions, the average value of the fluctuation stop time, pipeline output, and outbound temperature is traversed within the upper and lower limits of the sampling according to the step size.

(5) Calculate the probability of subcritical transmission under the parameter value of each target by numerical simulation

Calculate the corresponding lower than the critical mass probability under different parameters such as stop time, pipeline output and outbound temperature, to the exit temperature For example, the calculation results of the lower than the critical mass probability corresponding to different outbound temperatures are shown in Table 2 and Figure 3.

Table 2 below the critical mass probability with the outbound temperature change calculation results

(6) Find the critical value of each influencing factor variable

It can be seen from the above prediction calculation results that the probability that the running output is lower than the critical mass output will show a monotonous decreasing trend with the increase of the outbound temperature. According to the safety production requirement that the probability of the critical mass is less than 0.01, the outbound temperature should not be lower than 77.65. °C, which is the critical value of the outbound temperature.

(7) Calculate the safety margin of each influencing factor

Sensitivity analysis was carried out for each influencing factor, and the change of each factor was compared by 10% and 20%, corresponding to the change range of the probability of lower than the critical mass, so as to determine the contribution of each factor to the flow safety. The results are shown in Table 3 and Table 4. .

Table 3 is the range of influence factors below the critical mass probability

Table 4 Sensitivity calculation results below the critical mass probability affected by various factors

The comparative analysis shows that the influence of outbound temperature change on the probability of lower than the critical mass is the most significant. The influence of the change of ground temperature on the probability of lower than the critical mass is second, and the influence of the change of wax thickness on the probability of lower than the critical mass. small;

According to the difference between the current value of each factor and its critical value, the safety margin of each influencing factor is determined by formula (7). The results are shown in Table 5.

Table 5 safety margin calculation results of each parameter

(8) Output calculation results (pipeline operation threshold and safety margin)

According to the calculated operating threshold and safety margin, the safety of each influencing parameter during the operation of the crude oil pipeline is determined, and the parameter adjustment scheme is proposed. The results are as follows:

The outbound temperature of the pipeline in March is 75 °C, the ground temperature is 4.3 °C, and the waxing thickness is 2.587 mm. From this, the probability that the operating loss is lower than the critical mass is predicted to be 0.0964, and the probability that the operating transmission is lower than the critical mass is less than 0.01. The safety production requirements require that the pipeline is in a low-load operation state, and the pipeline is likely to enter an unstable working area. It is recommended to take measures to adjust the current conveying process parameters.

Through experiments, the invention can determine whether the pipeline is in a critical state during operation, thereby obtaining a safety margin of each influencing factor, and providing technical support for the fluidity safety evaluation. It can reliably and accurately determine the critical state of crude oil pipeline operation and determine the safety margin.

The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments are obvious to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but the scope of the invention is to be accorded

Claims

A method for determining a critical state of a crude oil pipeline during operation, comprising:

Obtaining pipeline parameters corresponding to crude oil pipeline performance;

Determining a probability distribution function of the pipeline parameter according to the pipeline parameter;

Sampling the probability distribution function of the pipeline parameter according to a preset number of times;

Determining a variable of the pipeline parameter according to the sampling result;

Obtaining a condensing duct probability corresponding to each parameter in the pipeline parameter according to the variable;

Determining a critical value corresponding to each parameter according to the condensing duct probability corresponding to each parameter;

Whether the crude oil pipeline is in a critical state is determined according to a critical value corresponding to each parameter.
The determination method according to claim 1, wherein the pipeline parameters include a crude oil inlet and outlet temperature parameter, a pipeline pressure parameter, an off-site geothermal parameter, an air temperature parameter, a total heat transfer coefficient parameter, a pipeline buried depth parameter, and a soil. parameter.
The determining method according to claim 2, wherein the determining a probability distribution function of the pipeline parameter according to the pipeline parameter comprises:

Estimating the skewness and kurtosis coefficients according to the relevant method, and selecting a probability distribution model;

A probability distribution function of the pipeline parameter is determined based on the estimated distribution parameter and the probability distribution model.
The determination method according to claim 3, wherein the preset number of times is 5000 to 25,000.
The determining method according to claim 4, wherein the determining the variable of the pipeline parameter according to the sampling result comprises:

A variable of the pipeline parameter is determined based on the sampling result and a critical value of the pipeline parameter.