發(fā)布時(shí)間：2018-05-17 05:31

  本文選題：鉆井液 + 融合推理��； 參考：《西南石油大學(xué)》2015年碩士論文

【摘要】：鉆井液設(shè)計(jì)作為鉆井工程設(shè)計(jì)的重要內(nèi)容,也是鉆井液現(xiàn)場(chǎng)施工的重要理論依據(jù)。就目前調(diào)研看來,我國(guó)大多數(shù)油田的鉆井液設(shè)計(jì)主要還是依靠專業(yè)設(shè)計(jì)人員通過對(duì)油井相關(guān)數(shù)據(jù)的分析,在進(jìn)行大量實(shí)驗(yàn)的基礎(chǔ)上,綜合可參考的歷史資料以及設(shè)計(jì)者積累的經(jīng)驗(yàn)來完成的。這種傳統(tǒng)的設(shè)計(jì)方式存在設(shè)計(jì)結(jié)果因人而異,設(shè)計(jì)書格式不夠統(tǒng)一,難以與國(guó)際接軌等諸多缺點(diǎn)。因此,為了提高鉆井液設(shè)計(jì)質(zhì)量,利用計(jì)算機(jī)對(duì)設(shè)計(jì)進(jìn)行輔助,將人工智能系統(tǒng)引入到設(shè)計(jì)中去是解決傳統(tǒng)鉆井液設(shè)計(jì)方式上這些不足的較為普遍方法。同時(shí)隨著油氣勘探開發(fā)技術(shù)的飛速發(fā)展和需求量的不斷攀升,現(xiàn)代鉆井技術(shù)對(duì)鉆井液提出了更新更高的要求,各種新型鉆井液技術(shù)也不斷得到應(yīng)用和發(fā)展,在追求高效低成本的今天,智能化的鉆井液設(shè)計(jì)及管理技術(shù)也得到了更多的關(guān)注,因而開發(fā)更加實(shí)用于現(xiàn)代鉆井液設(shè)計(jì)及鉆井液數(shù)據(jù)管理的軟件已十分必要。 基于以上認(rèn)識(shí),本文研究了基于專家規(guī)則推理(Rule-Based Resoning, RBR)技術(shù)、基于范例推理(Case-Based Resoning, CBR)技術(shù)以及支持向量機(jī)(Support Vector Machine,SVM)技術(shù)在鉆井液設(shè)計(jì)中的應(yīng)用方法,并建立了這三種推理技術(shù)的融合推理模型,以此模型開發(fā)了鉆井液優(yōu)化設(shè)計(jì)系統(tǒng),用于對(duì)鉆井液的設(shè)計(jì)過程加以輔助,提高鉆井液設(shè)計(jì)效率和設(shè)計(jì)質(zhì)量。本系統(tǒng)將專家規(guī)則推理與范例推理以及支持向量機(jī)的回歸機(jī)融合在一次鉆井液設(shè)計(jì)推理過程中,避免了單一推理模型由于推理過程過于簡(jiǎn)單,不符合專家思維以及推理結(jié)果不準(zhǔn)確而引起的系統(tǒng)實(shí)用性差等問題。本文完成的主要研究工作和取得的成果如下： (1)調(diào)研了傳統(tǒng)鉆井液設(shè)計(jì)的一般過程及工藝原理,總結(jié)了一套類似于專家思維的鉆井液計(jì)算機(jī)設(shè)計(jì)邏輯思路。 (2)收集了多條鉆井液專家經(jīng)驗(yàn),并按一定規(guī)則建立了規(guī)則庫(kù)；收集了各大油田鉆井液技術(shù)資料,并按一定原則建立了鉆井液范例庫(kù)；通過收集的鉆井液配方,以室內(nèi)實(shí)驗(yàn)數(shù)據(jù)為基礎(chǔ),結(jié)合支持向量機(jī)建立了預(yù)測(cè)鉆井液配方的模型庫(kù)。 (3)研究了基于規(guī)則推理技術(shù)、基于范例推理技術(shù)和支持向量機(jī)的基本原理和它們?cè)阢@井液設(shè)計(jì)中的應(yīng)用方法,以及三種技術(shù)的融合應(yīng)用方法。 (4)根據(jù)鉆井液設(shè)計(jì)的工藝特點(diǎn),以Visual Studio2010為開發(fā)平臺(tái),vb.net和c++為設(shè)計(jì)語言開發(fā)了鉆井液優(yōu)化設(shè)計(jì)系統(tǒng)。將系統(tǒng)的設(shè)計(jì)結(jié)果與現(xiàn)場(chǎng)5口井的應(yīng)用實(shí)例進(jìn)行對(duì)比,結(jié)果發(fā)現(xiàn)該系統(tǒng)較好的完成了鉆井液的體系選擇和性能參數(shù)設(shè)計(jì)。
[Abstract]:Drilling fluid design, as an important content of drilling engineering design, is also an important theoretical basis for drilling fluid field construction. According to the current investigation, the drilling fluid design of most oilfields in China mainly depends on the professional designers through the analysis of the relevant data of the wells, on the basis of a large number of experiments. Comprehensive reference of historical materials and designers accumulated experience to complete. The traditional design method has many shortcomings, such as different design results, not uniform design format, difficult to connect with the international standards, and so on. Therefore, in order to improve the design quality of drilling fluid, it is a common method to solve these problems in the traditional drilling fluid design mode by using computer to assist the design and to introduce artificial intelligence system into the design. At the same time, with the rapid development of oil and gas exploration and development technology and the increasing demand, modern drilling technology has put forward higher requirements for drilling fluid, and various new drilling fluid technologies have been continuously applied and developed. Nowadays, with the pursuit of high efficiency and low cost, more attention has been paid to the intelligent drilling fluid design and management technology, so it is necessary to develop more practical software for modern drilling fluid design and drilling fluid data management. Based on the above understanding, this paper studies the application methods of Rule-Based Resoning-based (RBR-based), Case-Based Resoning-based (CBR-based) and support Vector Machine (SVM) in drilling fluid design based on expert rule reasoning (RBR), Case-Based reasoning (CBR) and support Vector Machine (SVM) in drilling fluid design. The fusion reasoning model of these three reasoning technologies is established, and a drilling fluid optimization design system is developed based on this model, which is used to assist the drilling fluid design process and improve the design efficiency and design quality of drilling fluid. In this system, expert rule reasoning, case reasoning and regression machine of support vector machine are combined in the primary drilling fluid design reasoning process, and the single inference model is avoided because the reasoning process is too simple. Some problems such as poor system practicability caused by inaccuracy of reasoning results and so on are not in accordance with expert thinking. The main research work and results achieved in this paper are as follows: 1) the general process and process principle of traditional drilling fluid design are investigated, and a set of logical thinking of computer design of drilling fluid similar to expert thinking is summarized. (2) the expert experience of several drilling fluids has been collected, and the rule base has been established according to certain rules; the technical data of drilling fluid in major oilfields have been collected, and the drilling fluid sample bank has been established according to certain principles. Based on laboratory experimental data and support vector machine (SVM), a model base for predicting drilling fluid formulation was established. (3) the basic principles of rule-based reasoning, case-based reasoning and support vector machine, their application in drilling fluid design, and the fusion of the three techniques are studied. According to the technological characteristics of drilling fluid design, a drilling fluid optimization design system is developed with Visual Studio2010 as the development platform and c as the design language. The design results of the system are compared with the application examples of 5 wells in the field. The results show that the system selection and performance parameter design of the drilling fluid are well completed.
【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)】：TE254

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