Prediction of petroleum resource abundance based on artificial neural network method: a case study of third member of Paleogene Shahejie Formation in Wenliu area of Dongpu Sag, Bohai Bay Basin
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摘要: 油气资源丰度通常受多个因素控制，其相关参数信息种类繁杂、数据量庞大，应用传统的地质统计学方法定量预测准确度不高。为了快速预测油气资源量丰度并明确其主控因素，以渤海湾盆地东濮凹陷文留地区古近系沙河街组三段为例，采用基于多层感知器神经网络(MLP)方法对油气资源丰度进行定量预测，同时采用Boosting集成学习算法优化预测模型，分别对66组样本油气资源丰度数据进行预测。结果表明，训练集数据实测值与预测值相关系数分别达0.789和0.989，验证集数据实测值与预测值相关系数分别达0.618和0.825，测试数据中实测值和预测值相关系数分别达0.689和0.845；有效厚度、平均渗透率、有效孔隙度是影响油气资源丰度最主要的3个地质因素，重要性系数分别为33.93%、20.12%和19.53%，圈闭面积、地面原油密度、生烃中心贡献等参数为次要影响因素。采用Boosting集成学习算法优化之后的多层感知器模型预测准确性得到了很大的提升，能为有利目标优选及勘探开发方案调整提供可靠依据，为凹陷内其他区块油气资源评价提供较好的参考和借鉴。Abstract: The abundance of petroleum resource is influenced by various factors and involves complex parameters and extensive data. Consequently, traditional geostatistical methods often lack precision in quantitative prediction. To address this issue, this study focuses on the third member of Paleogene Shahejie Formation (member Es3) in the Wenliu area of the Dongpu Sag and utilizes a multi-layer perceptron neural network (MLP) for predicting petroleum resource abundance and employed the Boosting ensemble learning algorithm to optimize the prediction model. The MLP and MLP-Boosting algorithm models were test on 66 sample groups, yielding correlation coefficients of 0.789 and 0.989 for the training set, 0.618 and 0.825 for the validation set and 0.689 and 0.845 for the test set. The analysis identified effective thickness, average permeability and effective porosity are the most significant geological factors influencing petroleum resource abundance, with importance coefficients of 33.93%, 20.12% and 19.53%, respectively. Other factors such as trap area, surface crude oil density and sedimentary facies assignment were found to be less influential. Overall, the Boosting ensemble learning algorithm significantly enhanced the prediction accuracy of the multi-layer perceptron model, offering valuable insights for target optimization, exploration planning and petroleum resource evaluation in other blocks in the sag.
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Key words:
- machine learning /
- neural network /
- prediction model /
- resource abundance /
- Dongpu Sag /
- Bohai Bay Basin
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图 1 渤海湾盆地东濮凹陷研究区位置(a)、沙三段构造等值线(b)和典型油藏剖面(c)
据中国石化中原油田分公司。
Figure 1. Location of study area (a), isolines of third member of Shahejie Formation (b) and typical reservoir sections (c) in Dongpu Sag, Bohai Bay Basin
图 2 渤海湾盆地东濮凹陷新生界地层柱状图
Figure 2. Stratigraphic histogram of Cenozoic in Dongpu Sag, Bohai Bay Basin
图 3 渤海湾盆地东濮凹陷研究区排烃强度及烃源岩贡献赋值综合示意
据参考文献[25]修改。
Figure 3. Comprehensive schematic diagram of hydrocarbon expulsion intensity and contribution assignments of hydrocarbon source rocks in study area in Dongpu Sag, Bohai Bay Basin
图 4 渤海湾盆地东濮凹陷文留地区沙三段沉积相赋值综合示意
据中国石化中原油田分公司。
Figure 4. Comprehensive schematic diagram of sedimentary facies assignment in third member of Shahejie Formation in Wenliu area of Dongpu Sag, Bohai Bay Basin
图 5 多层感知器学习网络结构示意
Figure 5. Structure of multi-layer perceptron (MLP) learning network
图 8 基于多层感知器神经网络油气资源丰度建模结果
Figure 8. Results of petroleum resource abundance modeling based on MLP neural network
图 9 基于MLP-Boosting算法油气资源丰度建模结果
Figure 9. Results of petroleum resource abundance modeling based on MLP-Boosting algorithm modeling
图 10 MLP模型(a)和MLP-Boosting集成算法模型(b)检验数据相关性
Figure 10. Correlation coefficients of checking data of MLP model (a) and MLP-Boosting ensemble algorithm model (b)
图 11 MLP模型和MLP-Boosting集成算法模型可靠性分析
Figure 11. Reliability of MLP and MLP-Boosting ensemble algorithm models
图 12 渤海湾盆地东濮凹陷古近系沙河街组三段预选有利区分布
Figure 12. Distribution of preselected favorable areas of third member of Paleogene Shahejie Formation in Dongpu Sag, Bohai Bay Basin
图 13 渤海湾盆地东濮凹陷古近系沙河街组三段地质参数相关系数矩阵热图
Figure 13. Matrix heat map of correlation coefficients of geological parameters in third member of Paleogene Shahejie Formation, Dongpu Sag, Bohai Bay Basin
图 14 渤海湾盆地东濮凹陷古近系沙河街组三段地质参数数据
Figure 14. Geological parameters of third member of Paleogene Shahejie Formation, Dongpu Sag, Bohai Bay Basin
表 1 渤海湾盆地东濮凹陷文留地区沉积相赋值统计
Table 1. Statistics of sedimentary facies assignments of Wenliu area of Dongpu Sag, Bohai Bay Basin
参数 滨浅湖 滨浅湖—三角洲前缘 三角洲前缘 半深湖—深湖 分流河道 浅水三角洲 地质储量/104 t 319 118 1302 484.87 124 449 地质储量占比 0.11 0.04 0.47 0.17 0.04 0.16 沉积相赋值 0.25 0.09 1.00 0.37 0.10 0.34 
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表 2 基于多层感知器神经网络模型油气资源丰度预测结果
Table 2. Prediction results of petroleum resource abundance based on MLP neural network modeling
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表 3 基于MLP-Boosting算法模型油气资源丰度预测结果
Table 3. Prediction results of petroleum resource abundance based on MLP-Boosting algorithm model
参数 训练集 验证集 最小误差/(104 t/km2) -6.627 -21.478 最大误差/(104 t/km2) 5.963 44.258 平均误差/(104 t/km2) -0.205 1.754 平均绝对误差/(104 t/km2) 1.287 11.399 标准差 2.068 16.545 相关系数 0.989 0.825 样品数 47 19
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表 4 MLP模型和MLP-Boosting集成算法模型检验结果
Table 4. Test results of MLP and MLP-Boosting ensemble algorithm models
参数 检验数据 MLP MLP-Boosting 最小误差/(104 t/km2) -14.362 -11.288 最大误差/(104 t/km2) 38.262 36.32 平均误差/(104 t/km2) 7.6 3.507 平均绝对误差/(10 4t/km2) 11.408 8.936 相关系数 0.689 0.845 样品数 20 20
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表 5 渤海湾盆地东濮凹陷古近系沙河街组三段3个预选有利区带的资源丰度预测结果
Table 5. Resource abundance evaluation results of three pre-selected favorable zones in third member of Paleogene Shahejie Formation of Dongpu Sag, Bohai Bay Basin
预选有利区带 有利区带资源丰度/(104 t/km2) MLP- Boosting算法模型 MLP模型 最大值 最小值 平均值 最大值 最小值 平均值 区带1 75.79 47.11 61.37 64.84 33.20 59.13 区带2 65.27 36.28 56.16 65.43 35.03 55.23 区带3 65.32 65.12 65.21 65.43 38.56 63.54
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