發(fā)布時(shí)間：2018-11-01 20:14

【摘要】：油藏動(dòng)態(tài)分析是油田開(kāi)發(fā)中一項(xiàng)重要工作,傳統(tǒng)動(dòng)態(tài)分析輔助軟件通常將分析規(guī)則以“硬編碼”的方式嵌入系統(tǒng),具有可擴(kuò)展性和可維護(hù)性差的固有缺點(diǎn),同時(shí)無(wú)法保存油田開(kāi)發(fā)中的重要知識(shí)。當(dāng)動(dòng)態(tài)分析的指標(biāo)發(fā)生變化時(shí),需要重新修改程序代碼,這樣不僅工作量大而且容易出錯(cuò),軟件維護(hù)成本高。另外,油藏動(dòng)態(tài)分析涉及業(yè)務(wù)范圍廣泛,針對(duì)每項(xiàng)分析內(nèi)容,需要重復(fù)編寫(xiě)低水平代碼,軟件復(fù)用性差。規(guī)則引擎作為人工智能領(lǐng)域的一項(xiàng)重要研究成果,是一個(gè)可嵌入系統(tǒng)任意位置具有智能推理的小組件,能夠復(fù)用到不同系統(tǒng)中,降低了軟件的開(kāi)發(fā)成本。同時(shí),將分析邏輯采用規(guī)則的形式單獨(dú)存放,便于系統(tǒng)維護(hù),也有利于總結(jié)和推廣油田開(kāi)采過(guò)程中積累的經(jīng)驗(yàn)知識(shí)。本文以油藏動(dòng)態(tài)分析和規(guī)則引擎為研究對(duì)象,詳細(xì)探討了適用于油藏動(dòng)態(tài)分析的規(guī)則引擎的實(shí)現(xiàn)及應(yīng)用。首先,從業(yè)務(wù)層面和系統(tǒng)層面研究油藏動(dòng)態(tài)分析知識(shí)的特點(diǎn),提出了“事實(shí)模板-條件元素-推理規(guī)則”三層的知識(shí)表示模型,為基于規(guī)則的推理和知識(shí)的存儲(chǔ)管理提供模型基礎(chǔ)。其次,詳細(xì)介紹了RETE算法的原理,針對(duì)RETE算法本身網(wǎng)絡(luò)結(jié)構(gòu),通過(guò)對(duì)Beta節(jié)點(diǎn)左右存儲(chǔ)區(qū)分別建立索引的方式提高模式匹配的效率;基于實(shí)際應(yīng)用中動(dòng)態(tài)分析具有過(guò)程性的特點(diǎn),通過(guò)建立虛擬工作內(nèi)存區(qū)并逆向加載事實(shí)的方式降低匹配過(guò)程中內(nèi)存使用率。然后,基于開(kāi)源的規(guī)則引擎CLIPS6.3版本,通過(guò)增加功能和提高性能兩方面對(duì)其進(jìn)行改造得到適用于油藏動(dòng)態(tài)分析的規(guī)則引擎。在功能上,通過(guò)添加可視化知識(shí)管理模塊,彌補(bǔ)了CLIPS知識(shí)管理方面的不足;在性能上,通過(guò)改進(jìn)的RETE算法解決了油田大數(shù)據(jù)量推理效率不高的問(wèn)題。最后,將實(shí)現(xiàn)的規(guī)則引擎應(yīng)用于某采油廠(chǎng)實(shí)際項(xiàng)目中,證明了實(shí)現(xiàn)的規(guī)則引擎的可行性和有效性;通過(guò)與傳統(tǒng)動(dòng)態(tài)分析方案的比較可知基于規(guī)則引擎的新方案以少許性能損失獲得了更好的可擴(kuò)展性和可維護(hù)性。
[Abstract]:Reservoir performance analysis is an important work in oilfield development. The traditional software for dynamic analysis usually embed the analysis rules into the system in the way of "hard coding", which has the inherent shortcomings of poor scalability and maintainability. At the same time, the important knowledge of oilfield development can not be preserved. When the index of dynamic analysis changes, it is necessary to modify the program code again, which is not only heavy workload but also error-prone, and the cost of software maintenance is high. In addition, reservoir performance analysis involves a wide range of business, for each analysis content, it is necessary to repeatedly write low-level code, software reuse is poor. Rule engine, as an important research result in artificial intelligence field, is a small component with intelligent reasoning in any location of embedded system. It can be reused to different systems and reduce the cost of software development. At the same time, the analytical logic is stored separately in the form of rules, which is convenient for the maintenance of the system, and also conducive to summing up and popularizing the experiential knowledge accumulated in the course of oilfield production. Taking reservoir dynamic analysis and rule engine as research objects, this paper discusses in detail the realization and application of rule engine suitable for reservoir dynamic analysis. Firstly, the characteristics of reservoir dynamic analysis knowledge are studied at the operational and system levels, and the knowledge representation model of "fact template, conditional element and inference rule" is proposed. It provides a model basis for rule-based reasoning and knowledge storage management. Secondly, the principle of RETE algorithm is introduced in detail. Aiming at the network structure of RETE algorithm, the efficiency of pattern matching is improved by indexing the left and right storage areas of Beta nodes separately. Based on the process characteristic of dynamic analysis in practical application, the memory utilization rate in matching process is reduced by establishing virtual working memory area and loading the facts in reverse. Then, based on the CLIPS6.3 version of the open source rule engine, the rule engine for reservoir performance analysis is obtained by improving the performance and function of the engine. In function, the lack of CLIPS knowledge management is made up by adding visual knowledge management module, and the problem of low reasoning efficiency of large amount of data in oil field is solved by improved RETE algorithm in performance. Finally, the realized rule engine is applied to the actual project of a certain oil production plant, which proves the feasibility and effectiveness of the realized rule engine. Compared with the traditional dynamic analysis scheme, the new scheme based on rule engine achieves better scalability and maintainability with little performance loss.
【學(xué)位授予單位】：東北石油大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：TE331;TP18

【參考文獻(xiàn)】

相關(guān)期刊論文 前2條

1 史忠植,楊至成,方健梅;知識(shí)工程[J];計(jì)算機(jī)學(xué)報(bào);1986年04期

2 汪成亮;周亞鑫;陳娟娟;;基于工業(yè)環(huán)境的改進(jìn)Rete算法[J];計(jì)算機(jī)工程與設(shè)計(jì);2009年19期

，

本文編號(hào)：2304974