主成分分析法在泥頁巖地層巖性識(shí)別中的應(yīng)用
發(fā)布時(shí)間:2018-09-17 19:35
【摘要】:泥頁巖地層巖性復(fù)雜,非均質(zhì)性強(qiáng),利用常規(guī)測(cè)井交會(huì)圖法識(shí)別巖性往往具有多解性和不確定性。依據(jù)主成分分析理論,建立多條測(cè)井曲線的主成分計(jì)算模型,主成分Y_1,Y_2,Y_3的累積方差貢獻(xiàn)率可達(dá)91.39%,能夠準(zhǔn)確反映原測(cè)井曲線的全部有效信息。研究結(jié)果表明,主成分分析法能夠有效識(shí)別泥頁巖地層的淺灰色泥巖、黑色泥巖、灰色粉砂巖及細(xì)砂巖等多種巖性,回判率達(dá)90.37%。與常規(guī)測(cè)井交會(huì)圖法相比,主成分分析法可靠性更高,在泥頁巖儲(chǔ)層研究領(lǐng)域具有較廣泛的應(yīng)用前景。
[Abstract]:The lithology of shale strata is complex and heterogeneity, and it is often characterized by multiple solutions and uncertainties to identify lithology by using conventional well logging cross plot method. According to the principal component analysis theory, the principal component calculation model of many log curves is established. The cumulative variance contribution rate of the principal component Y1D / Y2T / Y3 can reach 91.39, which can accurately reflect all the effective information of the original log curve. The results show that principal component analysis (PCA) can effectively identify shale-grayish mudstone, black mudstone, gray siltstone and fine sandstone, and the recovery rate is 90.37g. The principal component analysis (PCA) is more reliable than the conventional well cross plot method and has a broad application prospect in the field of shale reservoir research.
【作者單位】: 成都理工大學(xué)油氣藏地質(zhì)及開發(fā)國家重點(diǎn)實(shí)驗(yàn)室;
【基金】:國家自然科學(xué)基金項(xiàng)目“硬脆/塑性泥頁巖微裂縫產(chǎn)生的巖石物理學(xué)機(jī)理基礎(chǔ)研究”(41572130)
【分類號(hào)】:P618.13;P631.81
[Abstract]:The lithology of shale strata is complex and heterogeneity, and it is often characterized by multiple solutions and uncertainties to identify lithology by using conventional well logging cross plot method. According to the principal component analysis theory, the principal component calculation model of many log curves is established. The cumulative variance contribution rate of the principal component Y1D / Y2T / Y3 can reach 91.39, which can accurately reflect all the effective information of the original log curve. The results show that principal component analysis (PCA) can effectively identify shale-grayish mudstone, black mudstone, gray siltstone and fine sandstone, and the recovery rate is 90.37g. The principal component analysis (PCA) is more reliable than the conventional well cross plot method and has a broad application prospect in the field of shale reservoir research.
【作者單位】: 成都理工大學(xué)油氣藏地質(zhì)及開發(fā)國家重點(diǎn)實(shí)驗(yàn)室;
【基金】:國家自然科學(xué)基金項(xiàng)目“硬脆/塑性泥頁巖微裂縫產(chǎn)生的巖石物理學(xué)機(jī)理基礎(chǔ)研究”(41572130)
【分類號(hào)】:P618.13;P631.81
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