【摘要】：我國現(xiàn)有的主力油田,基本都已經(jīng)進入了中后期的開發(fā)階段,剩余可采油氣藏往往是地層狀況復雜的非常規(guī)油氣藏,開采難度非常大。準確識別儲層巖性對儲層評價有著重要的意義,能夠加快勘探開發(fā)的進程,促進油氣穩(wěn)產(chǎn)工作。本課題利用采油廠累積的豐富的地質資料數(shù)據(jù),結合人工智能技術,提出新的儲層巖性識別模型,為油氣田的勘探與開發(fā)提供有力的支持。本文分析了目前儲層巖性識別常用技術的方法與原理。其中最準確的巖性識別方法是通過取心對巖心薄片進行鑒定,但是取心成本非常高,耗時耗力,對每口井都進行取心是不現(xiàn)實的,因此不能滿足實際工作需求。后來發(fā)展到聚類分析法與主成分分析法等,這些方法解決了不能取心的問題,也取得了較好的結果,但是巖性識別的精度還是不夠,針對這些問題,本文提出基于人工智能技術的智能識別方法。本文主要采用測井曲線來識別巖性,常規(guī)的智能模型輸入輸出都是幾何點式的,沒有考慮到測井曲線隨深度變化而變化,因此需要采用能夠直接處理過程信號的模型。實際工區(qū)部分儲層非均質性嚴重,導致巖性識別正確率還不夠高,本文提出多核核函數(shù)方法,用多個核函數(shù)更加精確的描述儲層巖性特征以提高識別率。本文將上述理論相結合提出了多核過程支持向量機與多尺度核徑向基過程神經(jīng)網(wǎng)絡兩種智能模型來做巖性識別工作。多核模型從機制上改善模型對復雜特征信號的表示能力,并運用智能算法對模型參數(shù)進行優(yōu)化,從而達到更高的識別精度。本課題基于人工智能技術,將理論研究構建成可實際應用的軟件原型系統(tǒng),并用實際數(shù)據(jù)在系統(tǒng)中進行了應用,且取得了良好的效果。本文研究成果對于油氣田的勘探開發(fā)工作是有實際使用意義的,能夠在一定程度上對實際工作起到幫助的作用,具有理論價值與實際應用價值。
[Abstract]:The main oil fields in our country have basically entered the middle and late stage of development, and the remaining recoverable reservoirs are often unconventional reservoirs with complex stratigraphic conditions, which is very difficult to exploit. Accurate identification of reservoir lithology is of great significance to reservoir evaluation, which can speed up the process of exploration and development and promote the stable production of oil and gas. Based on the abundant geological data accumulated in oil production plant and artificial intelligence technology, a new reservoir lithology identification model is proposed in this paper, which can provide strong support for exploration and development of oil and gas fields. In this paper, the methods and principles of common techniques for reservoir lithology identification are analyzed. The most accurate lithologic identification method is to identify the core slice by coring, but the cost of coring is very high, and it is time-consuming and labor-consuming, so it is not realistic to coring every well, so it can not meet the practical requirements. Later, cluster analysis and principal component analysis were developed. These methods solved the problem of uncoring and achieved good results, but the accuracy of lithology recognition was not enough. This paper presents an intelligent recognition method based on artificial intelligence technology. In this paper, logging curves are mainly used to identify lithology. The conventional intelligent model is geometric point type and does not take into account the change of logging curve with depth. Therefore, it is necessary to adopt a model that can process signals directly. Due to the serious heterogeneity of some reservoirs in practical working areas, the correct rate of lithology recognition is not high enough. In this paper, a multi-kernel function method is proposed to describe the lithologic characteristics of reservoirs more accurately in order to improve the recognition rate. In this paper, two intelligent models of multi-kernel process support vector machine and multi-scale radial basis function neural network are proposed to identify lithology. The multi-core model can improve the representation ability of the model to the complex feature signal from the mechanism, and use the intelligent algorithm to optimize the model parameters, so as to achieve higher recognition accuracy. Based on artificial intelligence technology, this paper constructs a practical software prototype system based on artificial intelligence technology, and uses the actual data in the system, and obtains good results. The research results in this paper are of practical significance to the exploration and development of oil and gas fields, and can help the actual work to a certain extent, and have theoretical value and practical application value.
【學位授予單位】：東北石油大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TE311

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