本文選題：機(jī)采過程 + 無跡粒子濾波��； 參考：《西安石油大學(xué)》2016年碩士論文

【摘要】：油田是產(chǎn)能大戶,也是耗能大戶。機(jī)械采油是油田主要耗能方式,但其效率普遍不足30%,若每臺(tái)采油設(shè)備節(jié)省一點(diǎn)能耗,則效益驚人。如何進(jìn)一步提升抽油機(jī)井采油技術(shù)和管理水平成為油田普遍關(guān)心和亟待解決的關(guān)鍵問題。數(shù)字化油田的發(fā)展使井上井下布置了大量檢測(cè)裝置,記錄了豐富詳實(shí)的工況參數(shù)與產(chǎn)量、能耗數(shù)據(jù),這意味可由數(shù)據(jù)挖掘技術(shù),從大量生產(chǎn)數(shù)據(jù)中挖掘采油工藝潛在規(guī)律,并用數(shù)學(xué)模型描述;再通過智能優(yōu)化技術(shù)從獲取的工藝規(guī)律中尋找最佳的生產(chǎn)參數(shù),以使得機(jī)采系統(tǒng)實(shí)時(shí)保持最佳運(yùn)行狀態(tài),實(shí)現(xiàn)節(jié)能增效。為此,本文針對(duì)油田機(jī)采系統(tǒng)數(shù)據(jù)挖掘技術(shù)和生產(chǎn)參數(shù)智能優(yōu)化技術(shù)的關(guān)鍵科學(xué)問題展開深入研究,以油田機(jī)采系統(tǒng)為研究對(duì)象,通過理論研究、仿真實(shí)驗(yàn)及軟件開發(fā)促進(jìn)油田機(jī)采系統(tǒng)實(shí)現(xiàn)自主建模、智能優(yōu)化和自動(dòng)決策,具體包括以下內(nèi)容:(1)提出基于無跡粒子濾波神經(jīng)網(wǎng)絡(luò)(UPFNN)的油田機(jī)采系統(tǒng)動(dòng)態(tài)演化建模方法。建立精準(zhǔn)的機(jī)采工藝模型是實(shí)現(xiàn)生產(chǎn)優(yōu)化的前提。由于機(jī)采系統(tǒng)受機(jī)械、地層、人為等不確定因素影響,難以準(zhǔn)確掌握其生產(chǎn)參數(shù)、環(huán)境變量與系統(tǒng)性能之間的變化關(guān)系,為此本文提出利用無跡粒子濾波實(shí)時(shí)更新神經(jīng)網(wǎng)絡(luò)的權(quán)值和閾值,建立基于無跡粒子濾波神經(jīng)網(wǎng)絡(luò)子空間逼近的機(jī)采系統(tǒng)非線性動(dòng)態(tài)演化模型。該方法利用無跡卡爾曼濾波對(duì)粒子進(jìn)行估計(jì),產(chǎn)生重要性密度,并更新粒子,以提高提高粒子濾波精度,從而改善神經(jīng)網(wǎng)絡(luò)建模精度。(2)提出基于偏好多目標(biāo)優(yōu)化的油田機(jī)采過程生產(chǎn)參數(shù)優(yōu)化方法。油田為實(shí)現(xiàn)油藏的科學(xué)、合理化開采,通常需要根據(jù)油藏分布從全局上設(shè)計(jì)出各采油區(qū)在一段時(shí)間內(nèi)的開采量(即給定生產(chǎn)制度)。因此,機(jī)采系統(tǒng)優(yōu)化不能以采油量最大和用能最低為目標(biāo),而應(yīng)該以采油量接近某一給定值和用能最低作為優(yōu)化目標(biāo)。此外,油田機(jī)采系統(tǒng)生產(chǎn)優(yōu)化是在各種約束條件下求取目標(biāo)函數(shù)的最優(yōu)值,屬于復(fù)雜的非線性優(yōu)化問題,應(yīng)用傳統(tǒng)優(yōu)化理論往往遇到困難。帶精英策略的非支配排序遺傳算法通過計(jì)算個(gè)體之間的擁擠度來回避共享參數(shù)指定問題,并采用精英策略保存父代種群的優(yōu)秀個(gè)體,可實(shí)現(xiàn)多目標(biāo)并行優(yōu)化。這使得其在處理工業(yè)過程問題復(fù)雜、高維、難以解析得到的優(yōu)化模型時(shí)比傳統(tǒng)優(yōu)化方法更具優(yōu)勢(shì)。為此,本文首先結(jié)合無跡粒子濾波神經(jīng)網(wǎng)絡(luò)建立的機(jī)采過程模型和面向生產(chǎn)制度的偏好函數(shù),構(gòu)建偏好多目標(biāo)優(yōu)化模型,然后采用帶精英策略的非支配排序遺傳算法求解生產(chǎn)參數(shù)的Pareto解集,再通過有序加權(quán)獲得Pareto解集上每個(gè)方案的綜合評(píng)價(jià),最終獲得最佳方案。(3)開發(fā)數(shù)字化油田抽油機(jī)群調(diào)度優(yōu)化決策支撐系統(tǒng)。為實(shí)現(xiàn)理論指導(dǎo)實(shí)際生產(chǎn),本文將上述理論研究通過C#與MATLAB混合編程方式開發(fā)出一套數(shù)字化油田抽油機(jī)群調(diào)度優(yōu)化決策支撐系統(tǒng),并植入油田機(jī)采生產(chǎn)管理平臺(tái),實(shí)現(xiàn)了機(jī)采系統(tǒng)的自主建模、智能優(yōu)化和自主決策。
[Abstract]:Oil field is a large capacity and a big energy consumer. Mechanical oil production is the main energy consumption mode of oil field, but its efficiency is generally less than 30%. If each oil production equipment saves a little energy consumption, the benefit is astonishing. How to further improve the oil extraction technology and management level of pumping well becomes the key problem of common concern and urgent solution in the oilfield. A large number of detection devices are arranged in well on the well, and abundant and detailed working condition parameters and output and energy consumption data are recorded. This means that data mining technology can be used to excavate the potential law of oil production process from a large number of production data and describe it with mathematical model, and then the best production is found from the process rules obtained by intelligent optimization technology. In order to keep the optimal operating state of the mechanical production system in real time and achieve energy efficiency and increase efficiency, this paper studies the key scientific problems of the data mining technology of oil field mining system and the intelligent optimization technology of production parameters, and takes the oil field mining system as the research object, and promotes oil through theoretical research, simulation experiment and software development. The field machine mining system realizes independent modeling, intelligent optimization and automatic decision making, which includes the following contents: (1) a dynamic evolution modeling method based on the Untraced particle filter neural network (UPFNN) is proposed for the dynamic evolution of the oil field production system. The establishment of a precise process model is the prerequisite for the production optimization. With the influence of certain factors, it is difficult to accurately grasp the relation between the production parameters and the changes of the environment variables and the system performance. Therefore, this paper proposes to use the non trace particle filter to update the weights and thresholds of the neural network in real time and establish the nonlinear dynamic evolution model of the machine mining system based on the subspace approximation of the Untraced particle filter neural network. The non trace Calman filter is used to estimate the particle, generate the importance density, and update the particle to improve the precision of the particle filtering and improve the precision of the neural network modeling. (2) the optimization method of the production parameters of the oil field production process based on a lot of target optimization is proposed. The distribution of oil reservoirs is designed for a period of time (the given production system) in a period of time. Therefore, the optimization of the production system can not be aimed at the maximum oil production and the lowest energy use, but the production capacity should be close to a given value and the lowest energy use as the optimization target. The optimal value of the objective function under the constraint condition is a complex nonlinear optimization problem. It is difficult to apply the traditional optimization theory. The non dominated sorting genetic algorithm with the elite strategy avoids the shared parameter assignment problem by calculating the crowding degree among individuals, and uses the elite strategy to preserve the outstanding individuals of the parent population. Multi objective parallel optimization is realized. This makes it more advantageous than the traditional optimization method when dealing with the complicated industrial process problem, high dimension and difficult to parse. For this reason, this paper first combines the process model of the non trace particle filter neural network and the preference function facing the production system, and constructs a lot of objective optimization. The model, then using the non dominated sorting genetic algorithm with elite strategy to solve the Pareto solution set of the production parameters, and then through the ordered weighting to obtain the comprehensive evaluation of each scheme on the Pareto solution set, and finally get the best scheme. (3) developing the optimization decision support system for the scheduling optimization of the digital oilfield pumping unit. In this paper, a set of digital oilfield pumping unit scheduling optimization decision support system is developed through the mixed programming of C# and MATLAB, and the production management platform of oil field production is implanted. The autonomous modeling, intelligent optimization and independent decision of the machine production system are realized.

【學(xué)位授予單位】：西安石油大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TE35

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