摘要
首先通过统计方法对D凹陷沙四段致密油储层中的油页岩、粉砂岩和泥质云岩3类岩性测井曲线敏感性进行分析,优选出声波时差、密度和自然伽马。其次基于敏感测井响应,分别建立了测井响应交会图岩性识别方法以及决策树和量子神经网络岩性识别模型。在测井响应交会图法中,首先利用密度-标准化自然伽马交会图区分油页岩与粉砂岩和泥质云岩,然后利用密度-声波时差交会图区分粉砂岩和泥质云岩;在决策树模型中,构建了3层岩性判别树状图,直观映射出4条分类规则;在量子神经网络模型中,构建了三层前馈量子神经网络模型,并优选出精度最高的样本构造方法。通过与实际取心结果对比分析发现,决策树和量子神经网络模型均能很好地识别致密油储层复杂岩性,而测井响应交会图法难以对致密储层复杂岩性进行有效识别。
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.
出处
《当代化工》
CAS
2015年第10期2341-2344,共4页
Contemporary Chemical Industry
基金
国家自然科学基金"骨架导电低阻油层人造岩样实验及导电规律与导电模型研究"
项目号:41274110
关键词
致密油储层
油页岩、粉砂岩和泥质云岩
岩性识别
量子神经网络
决策树
测井响应交会图
Tight oil reservoirs
Oil shale
siltstone and argillaceous dolomite
Lithology identification
Quantum neural network model
Decision tree model
Log response cross plot
