【摘要】：在石油勘探的過程中,鉆井事故與復(fù)雜問題總是客觀存在的�？ㄣ@事故占整個(gè)鉆井事故的40%～50%,由卡鉆引起的資金耗費(fèi)占非生產(chǎn)耗費(fèi)的50%以上。在國家科技重大專項(xiàng)(合同號：2011ZX05021-006)“鉆井實(shí)時(shí)監(jiān)測與技術(shù)決策系統(tǒng)”中,設(shè)置了如何預(yù)警、識別各種井下風(fēng)險(xiǎn)事故的研究任務(wù),而卡鉆風(fēng)險(xiǎn)事故是最為復(fù)雜的鉆井風(fēng)險(xiǎn)事故之一。本文就基于實(shí)時(shí)數(shù)據(jù)對卡鉆事故的預(yù)警和識別展開了研究工作。 人工神經(jīng)網(wǎng)絡(luò)算法是一種非線性、強(qiáng)自適應(yīng)學(xué)習(xí)能力的數(shù)據(jù)信息處理方法,其極強(qiáng)的非線性逼近能力能真實(shí)表示出輸入變量與輸出變量之間的非線性關(guān)系。針對卡鉆事故中參數(shù)變化規(guī)律,將事故與數(shù)據(jù)變化之間的非線性關(guān)系通過人工神經(jīng)網(wǎng)絡(luò)對特征參數(shù)基于樣本進(jìn)行訓(xùn)練建立聯(lián)系,在實(shí)時(shí)數(shù)據(jù)傳輸?shù)幕A(chǔ)上,能夠?qū)⒅笇?dǎo)卡鉆事故的各個(gè)參數(shù)變化非線性映射成對卡鉆事故的識別。 本文根據(jù)已經(jīng)發(fā)生的卡鉆事故記錄數(shù)據(jù),深入分析和總結(jié)了卡鉆事故發(fā)生的類別和特點(diǎn)。對卡鉆事故發(fā)生過程進(jìn)行研究,并用對應(yīng)參數(shù)變化表示整個(gè)過程的發(fā)展,確定了各個(gè)卡鉆事故在整個(gè)過程中的特征參數(shù)。 卡鉆風(fēng)險(xiǎn)是卡鉆事故發(fā)生早期的異常反應(yīng),對卡鉆風(fēng)險(xiǎn)進(jìn)行識別能夠在卡鉆事故發(fā)生之前進(jìn)行預(yù)警。將井下鉆具活動方式分類為正常鉆進(jìn)狀態(tài)和鉆具的上下活動狀態(tài),配以鉆具靜止時(shí)間等實(shí)時(shí)計(jì)算參數(shù),針對卡鉆風(fēng)險(xiǎn)的發(fā)生能夠較好的區(qū)別各類卡鉆對應(yīng)發(fā)生狀態(tài)。根據(jù)各狀態(tài)下卡鉆風(fēng)險(xiǎn)征兆規(guī)律,運(yùn)用神經(jīng)網(wǎng)絡(luò)算法對井下發(fā)生異常進(jìn)行識別,并結(jié)合實(shí)時(shí)計(jì)算參數(shù)建立卡鉆風(fēng)險(xiǎn)預(yù)警模型。 根據(jù)卡鉆事故發(fā)生之后對應(yīng)的表征規(guī)律,卡鉆事故發(fā)生之后對各鉆井狀態(tài)下呈現(xiàn)相同規(guī)律,運(yùn)用神經(jīng)網(wǎng)絡(luò)算法實(shí)現(xiàn)對各狀態(tài)下發(fā)生了的卡鉆事故進(jìn)行識別,結(jié)合卡鉆事故發(fā)生之前對卡鉆風(fēng)險(xiǎn)預(yù)警的分類情況,確定卡鉆事故的類別,建立了卡鉆事故分類識別模型。 在卡鉆風(fēng)險(xiǎn)預(yù)警及卡鉆事故識別模型的基礎(chǔ)上設(shè)計(jì)了卡鉆風(fēng)險(xiǎn)預(yù)警與識別軟件。對有一定規(guī)律的卡鉆風(fēng)險(xiǎn),可進(jìn)行預(yù)警和識別,其結(jié)果部分與實(shí)際相符。通過實(shí)例進(jìn)一步完善之后,可以對現(xiàn)場提供卡鉆事故預(yù)警及識別參考。
[Abstract]:In the process of petroleum exploration, drilling accidents and complex problems always exist objectively. Drilling jam accounts for 40% of the total drilling accident, and the capital cost caused by drilling jam accounts for more than 50% of the non-production cost. In the National Science and Technology Major Project (contract number: 2011ZX05021-006), "drilling Real-time Monitoring and Technical decision system", the research task of how to early warning and identify various downhole risks and accidents has been set up. The drilling risk accident is one of the most complex drilling risk accidents. In this paper, the early warning and recognition of drilling jam accidents based on real-time data are studied. Artificial neural network (Ann) algorithm is a kind of data information processing method with nonlinear and strong adaptive learning ability. Its strong nonlinear approximation ability can truly express the nonlinear relationship between input variable and output variable. In view of the rule of parameter change in drill jam accident, the nonlinear relation between accident and data change is linked by artificial neural network to train characteristic parameters based on sample, and on the basis of real time data transmission, the relationship between the nonlinear relation between the accident and the change of data is established by means of artificial neural network. The nonlinear mapping of the parameters used to guide the drill jam accident can be used to identify the drill jam accident. Based on the recorded data of drilling accidents, this paper analyzes and summarizes the types and characteristics of drilling accidents. The development of the whole process is represented by the change of corresponding parameters, and the characteristic parameters of each drill jam accident in the whole process are determined. Drilling risk is the early abnormal reaction of drilling accident. The downhole drilling tools are classified as the normal drilling state and the upper and lower moving state of the drill tool and the real-time calculation parameters such as the drilling tool static time can be used to distinguish the corresponding occurrence states of various kinds of drill jammed well in view of the occurrence of drilling jam risk. According to the regularity of the risk symptom of drilling jam under different conditions, the neural network algorithm is used to identify the abnormal underground and the early warning model of the risk of drilling jam is established by combining the real-time calculation parameters. According to the corresponding representation law after the drill jam accident, the same rule is presented for each drilling state after the drill jam accident occurs, and the neural network algorithm is used to realize the recognition of the drill jam accident in each state. According to the classification of early warning of drilling risk before the occurrence of drill jam accident, the classification and recognition model of drill jam accident is established. Based on the model of early warning and identification of jam risk, the software of early warning and recognition of drilling risk is designed. Early warning and identification can be carried out for the drilling risk with certain regularity, and the results are in accordance with the actual situation. After further improvement through examples, it can provide early warning and identification reference for drilling jam accident in the field.
【學(xué)位授予單位】：西南石油大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TE28

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