本文選題：鉆井作業(yè) + 風(fēng)險(xiǎn)評(píng)價(jià)��； 參考：《西南石油大學(xué)》2015年碩士論文

【摘要】：在石油行業(yè),鉆井作業(yè)是一項(xiàng)投資大,風(fēng)險(xiǎn)高的工程。它的特點(diǎn)是多工種、多工序、連續(xù)作業(yè)、環(huán)節(jié)多、規(guī)模大、技術(shù)復(fù)雜。本文主要從鉆井作業(yè)現(xiàn)場(chǎng)人的不安全行為與物的不安全狀態(tài)兩方面來分析其中的不安全因素。鉆井作業(yè)是石油勘探與開發(fā)中的一個(gè)十分重要的環(huán)節(jié)與手段。近些年來石油勘探開發(fā)規(guī)模的不斷壯大,整個(gè)國家對(duì)石油的需求量增加,從而對(duì)鉆井作業(yè)的安全性要求逐漸提高。為了保證鉆井作業(yè)的安全,對(duì)其進(jìn)行風(fēng)險(xiǎn)評(píng)價(jià)勢(shì)在必行。 相比傳統(tǒng)的風(fēng)險(xiǎn)評(píng)價(jià)方法,貝葉斯網(wǎng)絡(luò)在堅(jiān)實(shí)的數(shù)學(xué)基礎(chǔ)上克服了多態(tài)性、非獨(dú)立性、時(shí)序性等問題,通過概率圖形的方式直觀的表示元素、節(jié)點(diǎn)之間的依賴關(guān)系,具有雙向推理的性能,從而可以進(jìn)行前向預(yù)測(cè)分析以及后向的診斷分析。因而,本文進(jìn)行了貝葉斯網(wǎng)絡(luò)在鉆井作業(yè)風(fēng)險(xiǎn)評(píng)價(jià)中的研究,主要包括以下幾個(gè)方面： (1)了解國內(nèi)外有關(guān)風(fēng)險(xiǎn)評(píng)價(jià)的研究現(xiàn)狀,對(duì)現(xiàn)有的風(fēng)險(xiǎn)評(píng)價(jià)方法進(jìn)行比較。 (2)識(shí)別鉆井作業(yè)的風(fēng)險(xiǎn)和確定風(fēng)險(xiǎn)評(píng)價(jià)標(biāo)準(zhǔn)。鉆井作業(yè)的風(fēng)險(xiǎn)包括人的不安全行為和物的不安全狀態(tài)等影響因素。并根據(jù)鉆井安全等相關(guān)研究確定了風(fēng)險(xiǎn)評(píng)價(jià)標(biāo)準(zhǔn)。 (3)采用貝葉斯網(wǎng)絡(luò)分析法對(duì)鉆井作業(yè)的風(fēng)險(xiǎn)隱患進(jìn)行分析；在鉆井作業(yè)風(fēng)險(xiǎn)評(píng)價(jià)模型的結(jié)構(gòu)學(xué)習(xí)基礎(chǔ)上,構(gòu)建出風(fēng)險(xiǎn)評(píng)價(jià)的貝葉斯網(wǎng)絡(luò)圖；在鉆井作業(yè)風(fēng)險(xiǎn)評(píng)價(jià)模型的概率預(yù)測(cè)部分,使用Matlab計(jì)算出某個(gè)時(shí)間段內(nèi)鉆井作業(yè)現(xiàn)場(chǎng)的風(fēng)險(xiǎn)概率,根據(jù)風(fēng)險(xiǎn)評(píng)價(jià)標(biāo)準(zhǔn)對(duì)這個(gè)時(shí)間段的鉆井作業(yè)進(jìn)行風(fēng)險(xiǎn)級(jí)別的歸類。 (4)綜合運(yùn)用貝葉斯網(wǎng)絡(luò)的結(jié)構(gòu)學(xué)習(xí)和概率預(yù)測(cè)設(shè)計(jì)了貝葉斯網(wǎng)絡(luò)鉆井作業(yè)風(fēng)險(xiǎn)評(píng)價(jià)模型。通過現(xiàn)有的安全評(píng)價(jià)系統(tǒng)中的有關(guān)數(shù)據(jù)對(duì)貝葉斯網(wǎng)絡(luò)風(fēng)險(xiǎn)評(píng)價(jià)模型進(jìn)行驗(yàn)證。 最后本文采用C/S結(jié)構(gòu)和.NET開發(fā)環(huán)境,在Windows環(huán)境下以Visual Studio2010為開發(fā)平臺(tái),使用C#語言和Sql Server數(shù)據(jù)庫,對(duì)鉆井作業(yè)風(fēng)險(xiǎn)評(píng)價(jià)模塊進(jìn)行設(shè)計(jì)與實(shí)現(xiàn)。
[Abstract]:In the petroleum industry, drilling operation is a project with high investment and high risk. Its characteristics are multi work, multi process, continuous operation, many links, large scale, and complex technology. This paper mainly analyzes the unsafe factors in the two aspects of the unsafe behavior of the people in the drilling operation and the unsafe state of the objects. In the development of a very important link and means. In recent years, the scale of oil exploration and development is growing, the demand for oil is increasing in the whole country, thus the requirements for the safety of drilling operations are increasing gradually. In order to ensure the safety of drilling operations, it is imperative to evaluate the risk of the drilling operation.
Compared with the traditional method of risk assessment, Bayesian networks overcome the problems of polymorphism, non independence, timing, etc. on the basis of solid mathematics, which are intuitively expressed by the pattern of probability, and the dependence between nodes has the performance of bidirectional reasoning, so that the forward prediction analysis and the backward diagnosis can be analyzed. In this paper, the research of Bayesian network in risk assessment of drilling operation is carried out, mainly including the following aspects:
(1) to understand the research status of risk assessment both at home and abroad, and compare the existing risk assessment methods.
(2) identifying the risk of drilling operations and determining the criteria for risk assessment. The risk of drilling operations includes factors such as unsafe behavior of human beings and the unsafe state of objects, and the risk assessment standards are determined according to the related research of drilling safety.
(3) the Bayes network analysis method is used to analyze the risk of drilling operation, and on the basis of the structure learning of the drilling operation risk assessment model, the Bayesian network diagram of risk evaluation is built. In the probability prediction part of the drilling operation risk assessment model, the wind in a certain time period is calculated by using Matlab. Risk probability is classified according to the risk assessment standard for drilling operations in this time period.
(4) the Bayesian network drilling operation risk evaluation model is designed by using Bayesian network structure learning and probability prediction, and the Bayesian network risk evaluation model is verified by the relevant data in the existing security evaluation system.
At last, this paper adopts C/S structure and.NET development environment, and uses Visual Studio2010 as the development platform under Windows environment, uses C# language and Sql Server database to design and implement the drilling operation risk evaluation module.

【學(xué)位授予單位】：西南石油大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TE28;TP18

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 李志瑤;宗芳;張屹山;;貝葉斯網(wǎng)絡(luò)推理分析的團(tuán)樹傳播算法——以停車行為分析為例[J];長春大學(xué)學(xué)報(bào);2012年05期

2 范偉;劉峰;徐世軍;邢茜;;多節(jié)點(diǎn)有向無環(huán)圖優(yōu)化算法[J];重慶理工大學(xué)學(xué)報(bào)(自然科學(xué));2011年12期

3 姚琴芬;;Matlab語言在物理實(shí)驗(yàn)數(shù)據(jù)處理中的應(yīng)用[J];大學(xué)物理實(shí)驗(yàn);2011年06期

4 魏延;李曉虹;鄔嘯;;后驗(yàn)概率加權(quán)的模糊隸屬度函數(shù)[J];重慶大學(xué)學(xué)報(bào);2012年08期

5 樊寧;;貝葉斯網(wǎng)絡(luò)推理算法研究[J];信息技術(shù);2011年02期

6 蔣光忠;;鉆井工程信息的綜合開發(fā)與利用[J];石油天然氣學(xué)報(bào)(江漢石油學(xué)院學(xué)報(bào));2006年02期

7 王煥寶;張佑生;;基于圖元的事件圖生成算法[J];計(jì)算機(jī)工程;2007年12期

8 劉樹凱;關(guān)杰;常亞勤;;針對(duì)流密碼K2算法的猜測(cè)決定攻擊[J];計(jì)算機(jī)工程;2011年07期

9 吳俊杰;;鉆井工程事故監(jiān)測(cè)和預(yù)警方法研究[J];錄井工程;2006年01期

10 黃會(huì)偉;袁印實(shí);史玉穎;;風(fēng)險(xiǎn)圖表法安全完整性等級(jí)評(píng)價(jià)方法[J];化工自動(dòng)化及儀表;2014年02期

相關(guān)博士學(xué)位論文 前4條

1 范敏;基于貝葉斯網(wǎng)絡(luò)的學(xué)習(xí)與決策方法研究及應(yīng)用[D];重慶大學(xué);2008年

2 陳勇明;基于統(tǒng)計(jì)視角的灰色系統(tǒng)的幾個(gè)基本問題研究[D];西南財(cái)經(jīng)大學(xué);2008年

3 孫巖;貝葉斯網(wǎng)絡(luò)結(jié)構(gòu)學(xué)習(xí)算法研究與應(yīng)用[D];大連理工大學(xué);2010年

4 張軍;先驗(yàn)信息整合的壓縮感知理論及其應(yīng)用研究[D];華南理工大學(xué);2012年

，

本文編號(hào)：1820164