Electric submersible pump system control and related problems analysis

Electric submersible pump is one of the more oil recovery equipment in the field. In fact, the ESP is a multistage centrifugal pump working in the well, put into the well together with the tubing, the ground power through the transformer, control panel and electric submersible pump dedicated cable to the underground electric submersible pump motor, the motor driven Multi-stage centrifugal pump rotation, the electrical energy into mechanical energy, the liquid wells up to the ground. Electric submersible pump ESP There are two main application problems, (1) energy saving, (2) control the electric submersible pump to work in the best condition. As the electric submersible pump is below the ground 2Km bottom of the work, the working environment is very harsh (high temperature, strong temperature, etc.), the general use of traditional power supply, that work under the full power frequency, and thus frequent failures, high operating costs. On the one hand, when the electric submersible pump starts at power frequency, the starting current is large and the voltage drop of the motor cable is large, so that the reverse voltage of the motor cable in the starting process is higher and the insulation performance of the cable is reduced. Each time the power is turned on, life. Electric submersible pump repair costs only as much as a project as much as 50,000 yuan, the value of 100,000 yuan on average put on the cable to be replaced 5 times to be replaced, the average electric submersible pump to repair once every 10 months, the maintenance cost to be 80,000 yuan, so that operating costs increase. Electric submersible pump on the other hand, under normal work, the prevalence of the motor load rate than teach low case, "big horse car" phenomenon is serious, resulting in a huge waste of electricity. In addition, the electric submersible pump to reduce power factor, power consumption, work frequency, ESP always work at rated speed, if the amount of fluid in short supply, easily lead to "dead wells" in the event of dead wells, the heavy losses. The correct solution is that the ESP should be able to adjust the pumping capacity according to the geological conditions to balance supply with demand. However, the traditional adjustment method is to change the nozzle to adjust the output, which not only results in the waste of energy but also can not be precisely controlled. Sometimes makes the motor and pump running at high pressure for a long time; sometimes make the well out of the sand serious, shortening the life of the equipment, so the field of electric submersible pump system control related technical issues a brief discussion is necessary. This article mainly discusses the characteristics of the controlled object, the selection of control strategy, the control algorithm and the application of VVVF technology. Electric submersible pump system oil production is to control the field as a special controlled object, we must first understand some of the properties shown in the oil field mining process. One of the most important is time-variability, due to the complex and changeable geological conditions, to sum up the following features. For the ESP system, from a macro perspective, the characteristics of the controlled object mainly in the following aspects: a. System parameters unknown, time-varying, randomness and dispersion; b. The unknown system lag and Time-varying; c. Serious non-linearity of the system; d. Relevance of variables in the system; and e. Unknown, diversity and randomness of environmental disturbances. These features bring many problems to system modeling and control. Traditional control is powerless due to its control over complex objects (or processes) that are subject to uncertainty. (1) Traditional control (such as PID) is based on the control of mathematical models, that is, The model of control, object and disturbance is known or can be obtained by identification. However, many of the control problems in oilfield systems are uncertain and often even abruptly change. For the "unknown", uncertain or little-known control problems, it is difficult to model with the traditional methods and therefore can not achieve effective control. (2) In the traditional control theory, for the highly nonlinear control objects, Some non-linear methods are available, but on the whole, the nonlinear theory is far less mature than the linear theory because the method is too complicated to be applied. There are a lot of nonlinear problems in the oilfield system. (3) The traditional control theory mainly uses the differential equation, the equation of state and various mathematical transformations as the research tool. The essence of the traditional control theory is a numerical calculation method, which belongs to the quantitative control category, The problem is highly structured and easy to describe or model using quantitative mathematical methods. However, the most concern and need for support in the oilfield system is sometimes the semi-structured and unstructured issues. (4) According to the system engineering, the generalized object should include the operating object and environment in the usual sense. However, the relationship among subsystems in the oilfield system is complicated. The coupling and mutual restraint of each element are highly complicated and sometimes unpredictable. Traditional control lacks an effective solution. (5) The conventional mathematical model-based control problem tends to be an interdependent one. Although the system based on this method often has the contradiction between robustness and sensitivity, The reliability of the control is not prominent. For oilfield systems, if the above method is used, the entire control system may collapse due to the change of conditions. Thus, the traditional method can not be effective for oilfield system control, we must explore more effective control strategies and methods. Submersible Pump Workflow Oilfield systems are characterized by classical mathematics that have not been considered. Although probability theory deals with uncertainty, it has its own underlying assumptions. These basic assumptions limit its use in expert systems and of course limit its use in other areas. It can only deal with problems that contain randomness, neither do I know nor blur. In fact, the four kinds of uncertainty information people find at present are gray information and unidentified information besides random information and fuzzy information. These four kinds of uncertain information are often presented at the same time in an oilfield system or simultaneously. At the same time, they affect people's understanding of system features and functions and affect people's research, management and control of oilfield systems. Moreover, no matter from the connotation of the concept or the axiomatic system and the theory of set theory, the four kinds of uncertainty information have the necessary connection. Therefore, to establish the basic control model describing the oilfield system and realize the comprehensive treatment of the oilfield information in the control system is a difficult and urgent problem to be solved urgently. Control is the need to model, but the control model is not the same mathematical model described by strict mathematical expressions. The essence of the model is to describe the nature of things, the description can be described in strict mathematical way, called the mathematical model; also can be described in language, called the language model; as well as framework models, logical models, etc., according to the object The complexity of the decision whether to choose which description more reflects the nature of the object. In complex oilfield systems, methods of combining qualitative and quantitative methods are often used. Such models have the following characteristics: (1) The integrity of the system information, the known information and the unknown information live together, all kinds of uncertainty information co-exist; the deterministic information and the uncertainty information live together. They are interrelated, mutually influential, mutually restrictive, and under certain conditions, mutual transformation, but the total quantity will not change. (2) The dynamism of system development, just like ordinary things, the system of uncertainty and its factors are all functions of time. They all change over time, develop, decay, and transform. (3) The observability of system information, the process of human cognition of things is not only the process of obtaining information, but also the measure of various factors in the system through the use of objective standards, scales (which may be collectively referred to as scales) formed in practice by humans process. Because the generation of uncertainty information is the inevitable result of material movement, it must be followed by law, observable, and recognizable. (4) The hierarchy of system information, the system can be divided into different levels. In the macroscopic view, it is uncertain information, and at the micro level, relative certainty information can be separated. With the deepening of the hierarchy, people have a deeper understanding of the system. (5) The gray and uncertainty information of the system information is observable, and the observability can be improved as the hierarchy deepens. But "uncertain information is inevitable." Therefore, the uncertainty information is not completely known, only partially known, partially unknown, although the unknown part can be deepened and narrowed with the measurement level; and there is also information loss in the known information. This part of the known (white), part of the unknown (black) phase relationship called gray sex. Modern control theory developed in the 1950s, whether it is state space law or black box method based on I / O description, accurate mathematical description is the basis of its analysis and design system. If the mathematical model of the object (or process) is not known, then it must first be modeled mathematically, but whether it is optimal control or adaptive control, the premise of the discussion is to require an accurate mathematical model and obviously not for complex systems in the field The above conditions, for the author's discussion of the control system, should not be chosen as a control strategy. (1) originated in the 1940s. It reflects the basic features of the human brain from some aspects, but it is not the true description of the human brain, but only its abstraction, simplification and simulation. The information processing of the network consists of neurons To achieve the interaction. The key to neural network control is to select a suitable neural network model and train and learn it until it meets the requirements, that is to find the optimal neural network structure and weights. However, neural network learning requires a certain amount of experimental samples, but it also needs to be run thousands of times to obtain the best structure. Sometimes obtained is a local optimal solution, rather than the global optimal solution, due to the limitations of the method, it is also difficult to achieve effective control of the oilfield object discussed in this article; (2) It is based on knowledge, at A specialized set of computer programs designed to simulate the behavior of people is capable of handling a wide range of qualitative, quantitative, precise and vague information so that different descriptions can be drawn up based on experience and knowledge gained from the controlled process Form, in order to reflect more characteristic of the object, offer control tactics and control modality for the control. After the dynamic information of the controlled process is extracted and processed and the pattern recognition is performed on the feature information, it is sent to the reasoning institution on the one hand and useful information is added to the knowledge base on the other hand. Reasoning agencies based on feature information and knowledge provided by the knowledge base to judge, reason, and the results sent to the control agencies, so as to give the appropriate control output, the controlled process implementation of control. However, because of the collection of feature information, the expression of feature information and the establishment of a complete knowledge base, it is difficult to implement. Therefore, the expert system is not necessarily a good choice. In the actual project, a very skilled operator, with his rich practical experience, can obtain more satisfactory control effects by judging the various phenomena in the field. If the experience of the measures taken into the corresponding control rules, and the development of a controller instead of these rules, which can achieve the control of complex industrial processes. Practice has proved that fuzzy controller based on fuzzy control theory (FC) can accomplish this task. Man's control experience is summarized and described in human language. The language is the shell of thinking, it has a great ambiguity. For example, to maintain the water level in a water tower, the water level can be stabilized at a fixed point by adjusting the water pump valve opening. According to human experience can have the following control rules: If the water level is higher than the fixed point, the drainage, if the difference is greater, then the drain valve open large, the faster the drain, if the difference is smaller, the drain valve open small drain If the water level is lower than the fixed point, then the water supply, if the difference is bigger, then the water supply valve opens the bigger, the water supply is quicker, if the difference is smaller, the water supply valve opens small, the water supply is slower. In the above description of the operating experience of the language, "higher than", "below", "open big", "open small" and so the words with a certain degree of ambiguity, it must be fuzzy set of fuzzy mathematics to describe These vague language, and use IF condition THEN action statement to be realized. Its core is to establish a mathematical model of linguistic analysis of complex systems or processes so that the natural language in daily life can be directly translated into the algorithmic language accepted by the computer. It provides a powerful tool for dealing with ambiguities that already exist in the objective world. The application of fuzzy control technology in China has achieved remarkable results. Although it is still in the stage of continuous improvement and development, its control quality and effect are still satisfactory. It is an alternative strategy for oilfield systems. Due to the complexity and uncertainty of the accused, it is traditional