粒子群寻优支持向量机在储层类型预测中的应用
Application of Particle Swarm Optimization Support Vector Machine in Reservoir Prediction

DOI: 10.12677/AG.2020.101003, PP. 18-26

Keywords: 储层预测，支持向量机，粒子群寻优，测井解释
Support Vector Classification (SVC), Particle Swarm Optimization (PSO), Reservoir Classification, Log Interpretation

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Abstract:

针对珠一坳陷恩平组致密砂岩储层难以预测的问题，本文在使用基于支持向量机分类模型的储层类型预测方法的基础上，结合粒子群寻优法对模型进行校正与完善，提高了模型的正确率。通过典型井测试与试采分析法对恩平组储层的样本进行了储层类型分类，在每种类型的储层样本中选取80%的样本作为建模数据，并在每种储层类型内进行乱序交叉验证，得到模型的交叉验证分数，然后用粒子寻优的方法提高模型交叉验证分数，得到最佳预测模型，再用未参与建模的样本对预测模型进行检验。由此模型作出测井解释图，即可以直观的看出有效储层的所在井段的位置，也能方便地计算出有效储层的厚度。
In view of the problem that it is difficult to predict reservoirs in tight sandstone reservoirs of Enping formation in area of Zhu I depression, SVC is used to predict porosity of reservoirs by logging interpretation. The correctness of the model is improved by combining SVC with particle swarm optimization. The reservoir types of Enping formation are classified by typical well test and production test analysis. 80% of the samples of each type of reservoir are selected as modeling data, and random cross validation is carried out in each type of reservoir to get the cross-validation score of the model. Then the particle optimization method is used to improve the cross-validation score of the model to get the best prediction model. Then the prediction model is tested with the samples that are not involved in the modeling. From this model, the well log interpretation map can be made, that is, the location of the well section where the effective reservoir is located can be directly seen and the thickness of effective reservoir can also be calculated conveniently.

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