【摘要】：鉆井作業(yè)是石油和天然氣勘探開發(fā)活動(dòng)中最主要的事故頻發(fā)區(qū)之一。鉆井作業(yè)現(xiàn)場的隱患數(shù)量和人員違章數(shù)量常常居高不下,大量的隱患和人員違章極易誘使安全事故的發(fā)生。安全事故一旦發(fā)生,便會(huì)造成人員損失、設(shè)備損壞和壞境污染,也會(huì)對(duì)經(jīng)濟(jì)效益和社會(huì)效益產(chǎn)生巨大影響。如何保證鉆井作業(yè)的安全進(jìn)行,預(yù)防事故的發(fā)生始終是鉆井作業(yè)行業(yè)需要重點(diǎn)關(guān)注的問題。 因而,全面辨識(shí)和分析油氣鉆井作業(yè)系統(tǒng)中的危險(xiǎn)源,了解鉆井作業(yè)現(xiàn)場的安全狀態(tài)是必要的。建立一套適合油氣鉆井作業(yè)安全評(píng)價(jià)模型是當(dāng)前油氣鉆井作業(yè)所急需解決的問題。本文的研究旨在為鉆井作業(yè)的安全評(píng)價(jià)提供有效的評(píng)價(jià)方法,為鉆井作業(yè)的安全監(jiān)管人員提供實(shí)時(shí)、客觀的決策依據(jù)。本文的研究是鉆井作業(yè)安全管理走向科學(xué)化、信息化的一種全新探索,對(duì)提升鉆井公司的安全管理水平有重大的意義。 油氣鉆井作業(yè)是一個(gè)復(fù)雜的系統(tǒng)工程,該系統(tǒng)的最大特點(diǎn)是動(dòng)態(tài)性、隨機(jī)性和模糊性。影響鉆井作業(yè)安全的因素眾多,各因素之間相互制約。鉆井作業(yè)進(jìn)行安全評(píng)價(jià)是一種非線性問題�？紤]到BP神經(jīng)網(wǎng)絡(luò)具有很好的非線性映射能力,粗糙集對(duì)不完備和不確定信息的強(qiáng)大分析能力,本文采用粗糙集和神經(jīng)網(wǎng)絡(luò)來構(gòu)建鉆井作業(yè)安全評(píng)價(jià)模型。本文主要開展一些幾個(gè)方面的研究：(1)了解安全評(píng)價(jià)和鉆井作業(yè)安全評(píng)價(jià)的國內(nèi)外研究現(xiàn)狀；(2)綜合辨識(shí)鉆井作業(yè)過程中的危險(xiǎn)源,從人的不安全行為和物的不安全狀態(tài)兩個(gè)方面出發(fā)對(duì)其進(jìn)行分析,建立鉆井作業(yè)安全評(píng)價(jià)指標(biāo)體系；(3)使用粗糙集和神經(jīng)網(wǎng)絡(luò)的松耦合模型做鉆井作業(yè)安全的定性評(píng)價(jià)和使用神經(jīng)網(wǎng)絡(luò)做鉆井作業(yè)安全的定量評(píng)價(jià)。在做定性安全評(píng)價(jià)時(shí),本文首先使用粗糙集對(duì)樣本數(shù)據(jù)做屬性約簡。然后,基于最小條件屬性集選取神經(jīng)網(wǎng)絡(luò)的訓(xùn)練樣本和測試樣本。最后,構(gòu)建神經(jīng)網(wǎng)絡(luò)模型,使用訓(xùn)練樣本對(duì)其訓(xùn)練,使用測試樣本進(jìn)行預(yù)測。之后,本文使用神經(jīng)網(wǎng)絡(luò)做了鉆井作業(yè)安全的定量評(píng)價(jià),分別使用訓(xùn)練樣本和測試樣本對(duì)網(wǎng)絡(luò)進(jìn)行了訓(xùn)練和測試；(4)鉆井作業(yè)安全評(píng)價(jià)模型的設(shè)計(jì),主要包括：系統(tǒng)的總體設(shè)計(jì),粗糙集模塊的程序設(shè)計(jì)和神經(jīng)網(wǎng)絡(luò)模塊的程序設(shè)計(jì)。
[Abstract]:Drilling is one of the most important accident-prone areas in oil and gas exploration and development activities. The number of hidden troubles and the number of personnel violating regulations are always high in drilling operation, and a large number of hidden dangers and personnel violations are easy to induce the occurrence of safety accidents. Once a safety accident occurs, it will cause loss of personnel, equipment damage and environmental pollution. It will also have a great impact on economic and social benefits. How to ensure the safety of drilling operation and how to prevent accidents are always the key issues for the drilling industry to pay attention to. Therefore, it is necessary to identify and analyze the hazard sources in the oil and gas drilling system and to understand the safety state of the drilling site. It is an urgent problem to establish a set of safety evaluation model for oil and gas drilling operation. The purpose of this paper is to provide an effective evaluation method for the safety evaluation of drilling operations, and to provide real-time and objective decision basis for the safety supervisors of drilling operations. The research of this paper is a new exploration of drilling operation safety management, which is scientific and information, and has great significance to improve the safety management level of drilling companies. Oil and gas drilling is a complex system engineering, the system is characterized by dynamic, randomness and fuzziness. There are many factors influencing the safety of drilling operation, and each factor restricts each other. Drilling safety evaluation is a nonlinear problem. Considering that BP neural network has good nonlinear mapping ability, rough set has strong ability to analyze incomplete and uncertain information, this paper uses rough set and neural network to construct safety evaluation model of drilling operation. This paper mainly carries out some research in several aspects: (1) to understand the current situation of safety assessment and safety evaluation of drilling operations at home and abroad; (2) identify the dangerous sources in drilling operation synthetically, analyze the unsafe behavior of human and the unsafe state of objects, and establish the evaluation index system of drilling operation safety; (3) the loosely coupled model of rough set and neural network is used to evaluate the safety of drilling operation qualitatively and quantitatively. In the qualitative security evaluation, the rough set is first used for attribute reduction of sample data. Then, the training samples and test samples of neural network are selected based on the minimum conditional attribute set. Finally, the neural network model is constructed, the training sample is used to train it, and the test sample is used to predict it. After that, the neural network is used to evaluate the safety of drilling operation quantitatively, and the network is trained and tested using training samples and test samples respectively. (4) the design of the safety evaluation model of drilling operation mainly includes: the overall design of the system, the programming of the rough set module and the program design of the neural network 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

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 沈春明;栗興華;軒大洋;;BP神經(jīng)網(wǎng)絡(luò)在安全評(píng)價(jià)中的應(yīng)用[J];工業(yè)安全與環(huán)保;2008年11期

2 伍穎;王穎;李俊;徐文輝;;基于安全系統(tǒng)的石油鉆井工程危險(xiǎn)辨識(shí)[J];工業(yè)安全與環(huán)保;2010年03期

3 譚露;王兵;劉昆;湯成斌;;基于灰色聚類方法的鉆井作業(yè)安全性綜合評(píng)價(jià)[J];重慶科技學(xué)院學(xué)報(bào)(自然科學(xué)版);2014年02期

4 梁娟;馬文勝;楊宗安;;鉆井作業(yè)安全風(fēng)險(xiǎn)與管理措施[J];中國石油和化工標(biāo)準(zhǔn)與質(zhì)量;2012年S1期

5 劉菊梅;龍政軍;王宏圖;李潔;;陸上油氣鉆井作業(yè)安全評(píng)價(jià)模型探索[J];石油天然氣學(xué)報(bào);2010年02期

6 沈永紅;王發(fā)興;;基于信息熵的粗糙集屬性離散化方法及應(yīng)用[J];計(jì)算機(jī)工程與應(yīng)用;2008年05期

7 趙強(qiáng);李景群;田建軍;龔忠利;;工作安全分析在鉆井作業(yè)現(xiàn)場的應(yīng)用[J];安全與環(huán)境工程;2010年01期

8 趙婧余;;基于粗糙集理論的鉆井風(fēng)險(xiǎn)模糊綜合評(píng)價(jià)研究[J];科技與管理;2010年02期

9 湯淳;重慶市開縣“12.23”特別重大天然氣井噴失控事故調(diào)查[J];勞動(dòng)保護(hù);2004年06期

10 練章富;鄧昌松;韓松;林鐵軍;丁亮亮;郭衍茹;;石油鉆井作業(yè)的安全葡萄圖管理技術(shù)[J];中國安全生產(chǎn)科學(xué)技術(shù);2012年05期

，

本文編號(hào)：2373042