D凹陷沙四段致密油儲層巖性識別方法研究

宋延杰 任一菱 唐曉敏 等

摘 要：首先通過統(tǒng)計方法對D凹陷沙四段致密油儲層中的油頁巖、粉砂巖和泥質(zhì)云巖3類巖性測井曲線敏感性進(jìn)行分析，優(yōu)選出聲波時差、密度和自然伽馬。其次基于敏感測井響應(yīng)，分別建立了測井響應(yīng)交會圖巖性識別方法以及決策樹和量子神經(jīng)網(wǎng)絡(luò)巖性識別模型。在測井響應(yīng)交會圖法中，首先利用密度-標(biāo)準(zhǔn)化自然伽馬交會圖區(qū)分油頁巖與粉砂巖和泥質(zhì)云巖，然后利用密度-聲波時差交會圖區(qū)分粉砂巖和泥質(zhì)云巖；在決策樹模型中，構(gòu)建了3層巖性判別樹狀圖，直觀映射出4條分類規(guī)則；在量子神經(jīng)網(wǎng)絡(luò)模型中，構(gòu)建了三層前饋量子神經(jīng)網(wǎng)絡(luò)模型，并優(yōu)選出精度最高的樣本構(gòu)造方法。通過與實際取心結(jié)果對比分析發(fā)現(xiàn)，決策樹和量子神經(jīng)網(wǎng)絡(luò)模型均能很好地識別致密油儲層復(fù)雜巖性，而測井響應(yīng)交會圖法難以對致密儲層復(fù)雜巖性進(jìn)行有效識別。

關(guān) 鍵 詞：致密油儲層；油頁巖、粉砂巖和泥質(zhì)云巖；巖性識別；量子神經(jīng)網(wǎng)絡(luò)；決策樹；測井響應(yīng)交會圖

中圖分類號：P 631.84 文獻(xiàn)標(biāo)識碼： A 文章編號： 1671-0460（2015）10-2341-04

Research on the Method of Lithology Identification of

Tight Oil Reservoirs in S4 Formation of D Sag

SONG Yan-jie 1，2，REN Yi-ling 1，3，TANG Xiao-min 1，2，DENG Xin 1，LIU Yue 1

（1. College of Geoscience， Northeast Petroleum University， Heilongjiang Daqing 163318，China；

2. State Key Laboratory Cultivation Base of Accumulation and Development of Unconventional Oil and Gas， Jointly-constructed by Heilongjiang Province and the Ministry of Science and Technology， Heilongjiang Daqing 163318，China；

3. Exploration and Development Research Institute of Liaohe Oilfield Company， PetroChina， Liaoning Panjin 124010，China）

Abstract： The lithologies of tight oil reservoirs in S4 Formation of D Sag can be divided into oil shale， siltstone and shaly dolomite. Based on statistical methods， the sensitivity of logging curves for lithology identification was analyzed， by which interval transit time， density and gamma ray were optimized. Log response cross plot， decision tree model and quantum neural network model were established to determine the lithology with selected sensitive log responses. In the process of lithology identification by the log response cross plot， oil shale was first identified by standardized gamma ray vs. interval transit time， after that， siltstone and shaly dolomite were distinguished with density vs. interval transit time. In the process of lithology identification by decision tree model， a dendrogram with three levels was built. The model mapped four rules intuitively. In the process of lithology identification by quantum neural network model， a three-layer feedforward quantum neural network was built， and the sample construction method with the highest accuracy was screened out. By comparing with the practical coring results， both the decision tree model and the quantum neural network model can determine the lithologies in tight oil reservoirs much better than the conventional log response cross plot， and they can be applied in lithology identification of tight oil reservoirs perfectly.

Key words： Tight oil reservoirs； Oil shale， siltstone and argillaceous dolomite； Lithology identification； Quantum neural network model； Decision tree model； Log response cross plot

D凹陷沙四段致密油儲層巖性復(fù)雜，測井響應(yīng)變化無規(guī)律，不同巖性測井響應(yīng)存在重疊。目前的巖性識別技術(shù)中，常規(guī)的取心分析識別巖性直觀準(zhǔn)確，但成本高、資料有限；巖屑錄井識別巖性存在滯后性，且依賴巖屑錄井資料質(zhì)量。1982年wollf[1]等人提出利用測井資料自動識別巖性，自此利用計算機(jī)自動識別巖性成為了常用的巖性識別技術(shù)。……