中国安全生产科学技术
中國安全生產科學技術
중국안전생산과학기술
JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY
2015年
8期
157-163
,共7页
李凤%易俊%王文和%左代荣
李鳳%易俊%王文和%左代榮
리봉%역준%왕문화%좌대영
CO2 驱油%输油管道%脆弱性%指标体系%SVM
CO2 驅油%輸油管道%脆弱性%指標體繫%SVM
CO2 구유%수유관도%취약성%지표체계%SVM
CO2 flooding%oil pipeline%vulnerability%index system%SVM
CO2驱油在全国范围内的广泛开展导致内外扰动对输油管道的威胁大大增加,为指导企业发现输油管道的薄弱点从而预防事故发生,提出CO2驱油藏输油管道脆弱性概念及研究思路。将脆弱性分为5个等级并确定各级脆弱性的取值范围。深入分析脆弱性要素,从致灾因子、承灾体和灾害响应3个方面建立脆弱性评价指标体系,并确定各等级脆弱性对应的指标范围。利用MATLAB R2013 a的SVM回归方法,构建脆弱性评价模型并进行实例应用。结果表明:模型训练的输出与期望输出拟合较好,均方误差为9.98052×10-7;训练好的SVM模型具有较强的泛化能力和较高的准确性,其对检验样本脆弱性进行预测的最大相对误差为0.027。利用模型得到研究区域某输油管道的脆弱性值为0.381,其脆弱性程度为不太脆弱。
CO2驅油在全國範圍內的廣汎開展導緻內外擾動對輸油管道的威脅大大增加,為指導企業髮現輸油管道的薄弱點從而預防事故髮生,提齣CO2驅油藏輸油管道脆弱性概唸及研究思路。將脆弱性分為5箇等級併確定各級脆弱性的取值範圍。深入分析脆弱性要素,從緻災因子、承災體和災害響應3箇方麵建立脆弱性評價指標體繫,併確定各等級脆弱性對應的指標範圍。利用MATLAB R2013 a的SVM迴歸方法,構建脆弱性評價模型併進行實例應用。結果錶明:模型訓練的輸齣與期望輸齣擬閤較好,均方誤差為9.98052×10-7;訓練好的SVM模型具有較彊的汎化能力和較高的準確性,其對檢驗樣本脆弱性進行預測的最大相對誤差為0.027。利用模型得到研究區域某輸油管道的脆弱性值為0.381,其脆弱性程度為不太脆弱。
CO2구유재전국범위내적엄범개전도치내외우동대수유관도적위협대대증가,위지도기업발현수유관도적박약점종이예방사고발생,제출CO2구유장수유관도취약성개념급연구사로。장취약성분위5개등급병학정각급취약성적취치범위。심입분석취약성요소,종치재인자、승재체화재해향응3개방면건립취약성평개지표체계,병학정각등급취약성대응적지표범위。이용MATLAB R2013 a적SVM회귀방법,구건취약성평개모형병진행실례응용。결과표명:모형훈련적수출여기망수출의합교호,균방오차위9.98052×10-7;훈련호적SVM모형구유교강적범화능력화교고적준학성,기대검험양본취약성진행예측적최대상대오차위0.027。이용모형득도연구구역모수유관도적취약성치위0.381,기취약성정도위불태취약。
The extensive CO 2 flooding projects in the country brings increasing threat of internal and external per-turbations to oil pipeline .In order to guide enterprises to find the weak points of the pipeline and take appropriate measures to prevent accidents , the concept of oil pipeline vulnerability for CO 2 flooding reservoir and the research thoughts were proposed .The vulnerability was divided to five grades , and the value range of each grade was deter-mined.By analyzing the vulnerability factors in depth , the index system of vulnerability evaluation was established from three respects including the hazard factors , hazard bearing body and hazard response , and the span of every index corresponding to each vulnerability grade was determined .By using SVM regression method in MATLAB R2013a, the vulnerability evaluation model was built , and the case application was conducted .The results showed that the output of the model and the expected output fitted well , and the mean square error was 9.98052 ×10 -7 . The trained SVM model had strong generalization ability and high accuracy , the maximum relative error between the model-evaluated value and the expected output in the confirmatory experiment was only 0.027.By using the trained SVM model, the vulnerability of a certain oil pipeline was obtained as 0.381, and the vulnerability level was not too vulnerable .