本文選題：煤層氣 + 井底流壓　； 參考：《西安科技大學(xué)》2017年碩士論文

【摘要】：隨著我國(guó)工業(yè)化發(fā)展水平越來(lái)越高以及進(jìn)程的越來(lái)越快,國(guó)家對(duì)新能源的需求也越來(lái)越大。終端能源需求正在逐步從傳統(tǒng)能源向優(yōu)質(zhì)高效潔凈能源轉(zhuǎn)化,作為新型環(huán)保能源的煤層氣在我國(guó)的能源結(jié)構(gòu)中占有極其重要的地位。然而,雖然我國(guó)煤層氣資源豐富,但有超過(guò)一半的煤層氣低產(chǎn)量井由于排采管理不善造成了產(chǎn)能束縛,所以急需新型的煤層氣排采技術(shù)及裝備來(lái)提高產(chǎn)能,實(shí)現(xiàn)合理的煤層氣排采管理及產(chǎn)能的提高。影響排采效果的主要因素包括:非連續(xù)性排采因素、井底流壓控制因素、排采強(qiáng)度因素等。本文在探討了煤層氣單井采氣的基本原理以及工藝流程的基礎(chǔ)上,深入分析了基于中國(guó)煤層氣特殊資源條件和氣井工程狀態(tài)的井底流壓預(yù)測(cè)方法,保證連續(xù)、平穩(wěn)、逐級(jí)降低井底流壓,控制排采強(qiáng)度,提高開(kāi)發(fā)井產(chǎn)能釋放成功率,增加氣井產(chǎn)氣年限,從根本上改變我國(guó)排采嚴(yán)重依賴個(gè)體經(jīng)驗(yàn)的局面。針對(duì)煤層氣單井采氣系統(tǒng)中井底流壓特征參數(shù)具有連續(xù)平穩(wěn)、逐級(jí)降壓的變化規(guī)律,本文研究了基于支持向量機(jī)的煤層氣井井底流壓預(yù)測(cè)模型,該方法可以有效地解決預(yù)測(cè)模型中支持向量機(jī)的參數(shù)尋優(yōu)問(wèn)題。針對(duì)支持向量機(jī)的參數(shù)選擇不同對(duì)預(yù)測(cè)性能的影響也不同的特點(diǎn),本文介紹了幾種常見(jiàn)的用于支持向量機(jī)參數(shù)尋優(yōu)的優(yōu)化方法,分別為交叉驗(yàn)證法、網(wǎng)格搜索算法、遺傳算法以及粒子群算法。通過(guò)方法對(duì)比,找到了適合本文對(duì)應(yīng)煤層氣采氣系統(tǒng)樣本數(shù)據(jù)的參數(shù)尋優(yōu)最佳方法,而且通過(guò)可視化的編程使結(jié)果以圖表的形式清晰的顯示出來(lái)。在此基礎(chǔ)上,分析研究了模糊信息粒化的方法,將信息�；椒ㄅc支持向量機(jī)方法相結(jié)合,建立模糊粒化模型,獲取目標(biāo)的近似解范圍。該模型采用最佳參數(shù)尋優(yōu)算法優(yōu)化模糊粒化模型參數(shù),并采用誤差評(píng)價(jià)模型的精確度。仿真結(jié)果表明,該方法具有較好的實(shí)用性,可以有效的預(yù)測(cè)井底流壓的變化趨勢(shì),具有良好的預(yù)測(cè)和分析效果。
[Abstract]:With the development of industrialization in China, the demand for new energy is increasing. The terminal energy demand is gradually changing from traditional energy to high-quality and efficient clean energy. As a new type of environmental protection energy, coal bed methane (CBM) occupies an extremely important position in the energy structure of our country. However, although our country is rich in coal bed methane resources, more than half of the coal bed methane low production wells are constrained by production capacity due to poor production management. Therefore, it is urgently needed to develop new coal-bed methane drainage technology and equipment to improve production capacity. To achieve reasonable coal bed methane production management and productivity improvement. The main factors affecting the drainage effect include: discontinuous production discharge factor, bottom hole flow pressure control factor, drainage intensity factor and so on. On the basis of discussing the basic principle and technological process of single well gas recovery of coalbed methane, this paper deeply analyzes the prediction method of bottom hole flow pressure based on the special resource condition of coal bed methane in China and the engineering state of gas well, so as to ensure continuity and stability. The downhole flow pressure is reduced step by step, the production intensity is controlled, the success rate of productivity release of development well is increased, and the gas production life of gas well is increased, which fundamentally changes the situation that the production of production in our country depends heavily on individual experience. In view of the fact that the characteristic parameters of bottom-hole flow pressure in a single well gas recovery system of coalbed methane have the regularity of continuous steady and stepwise pressure reduction, a prediction model of bottom-hole flow pressure of coalbed methane wells based on support vector machine is studied in this paper. This method can effectively solve the parameter optimization problem of support vector machine in prediction model. Aiming at the different influence of parameter selection of support vector machine on prediction performance, this paper introduces several common optimization methods for parameter optimization of support vector machine, which are cross-validation method and grid search algorithm, respectively. Genetic algorithm and particle swarm optimization. Through the comparison of methods, we find the best method for optimizing the parameters corresponding to the sample data of CBM production system in this paper, and through visual programming, the results can be clearly displayed in the form of charts. On this basis, the method of fuzzy information granulation is analyzed and studied. Combining the information granulation method with the support vector machine method, the fuzzy granulation model is established, and the approximate solution range of the target is obtained. The optimal parameter optimization algorithm is used to optimize the parameters of the fuzzy granulation model and the accuracy of the model is evaluated by error evaluation. The simulation results show that this method has good practicability and can effectively predict the change trend of bottom hole flow pressure and has good prediction and analysis effect.
【學(xué)位授予單位】：西安科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TE37;TP18

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 宋曉洋;劉立勛;;基于SVM回歸的連續(xù)血壓測(cè)量方法[J];吉林大學(xué)學(xué)報(bào)(信息科學(xué)版);2016年03期

2 霍靜;王永明;顧君忠;;感染性腹瀉周發(fā)病例數(shù)的PCA-SVM回歸預(yù)測(cè)研究[J];計(jì)算機(jī)應(yīng)用與軟件;2016年02期

3 楊玉勝;劉駿;;基于SVM多變量時(shí)間序列回歸預(yù)測(cè)工程造價(jià)指數(shù)[J];湖南交通科技;2015年04期

4 劉佳;施龍青;韓進(jìn);滕超;;基于Grid-Search_PSO優(yōu)化SVM回歸預(yù)測(cè)礦井涌水量[J];煤炭技術(shù);2015年08期

5 路世昌;趙博琦;畢建武;;基于模糊信息�；疭VM時(shí)序回歸CPI預(yù)測(cè)[J];統(tǒng)計(jì)與決策;2015年14期

6 張卓;石鑫;王興艷;張正濤;計(jì)玉冰;;井下節(jié)流氣井井底流壓計(jì)算方法研究[J];石油化工應(yīng)用;2015年06期

7 李東;周可法;孫衛(wèi)東;王金林;吳艷爽;;基于GA-SVM回歸的成礦有利度預(yù)測(cè)方法探討[J];新疆地質(zhì);2014年04期

8 吳財(cái)芳;姚帥;杜嚴(yán)飛;;基于時(shí)間序列BP神經(jīng)網(wǎng)絡(luò)的煤層氣井排采制度優(yōu)化[J];中國(guó)礦業(yè)大學(xué)學(xué)報(bào);2015年01期

9 柳成志;滕立惠;;利用支持向量機(jī)識(shí)別松遼盆地火山巖巖性[J];地質(zhì)與資源;2014年03期

10 宋建;;氣液兩相氣井井底流壓計(jì)算模型研究[J];能源與節(jié)能;2014年02期

相關(guān)碩士學(xué)位論文 前5條

1 胡安冉;基于SVM煤層氣單井采氣系統(tǒng)故障預(yù)報(bào)的研究[D];大連理工大學(xué);2014年

2 李紅梅;基于線性回歸和SVM的煙葉質(zhì)量分析及等級(jí)預(yù)測(cè)模型[D];昆明理工大學(xué);2013年

3 高俊杰;混沌時(shí)間序列預(yù)測(cè)研究及應(yīng)用[D];上海交通大學(xué);2013年

4 邢佳瑩;基于維度約簡(jiǎn)的木材含水率建模及回歸預(yù)測(cè)方法研究[D];東北林業(yè)大學(xué);2012年

5 尹俊祿;煤層氣井產(chǎn)能預(yù)測(cè)與增產(chǎn)技術(shù)研究[D];長(zhǎng)江大學(xué);2012年

，

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