Study on Prediction Model for Drilling Fluid Classification Based on XGBoost
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摘要: 根据钻井液体系设计的原则，结合实际钻井液设计资料，应用一种新的机器学习方法建立了钻井液体系分类预测模型。钻井液体系分类数据经过独热编码（one-hot）之后，通过灰色关联度分析方法，选择出钻井液体系分类预测的20个特征参数，其中压力的关联度最大，为0.8233。将选择的地质设计参数和工程设计参数，基于一种极端梯度增强算法（XGBoost）针对4种钻井液体系进行分类预测。结果显示，基于XGBoost的钻井液体系分类预测模型4类钻井液体系训练集的准确率都为100%，测试集的平均准确率为99.89%，精确率为99.97%，召回率为98.89%，F1值为0.98。将该模型应用于胜利油田M区块，分类结果符合实际钻井要求，能够辅助选择钻井液体系，为实现钻井液智能化设计提供了帮助。Abstract: A model for predicting the type of a drilling fluid system was established using a new machine learning method based on the principles of mud system design and by referencing the actual drilling fluid designs. By one-hot coding of the data concerning the classification of drilling fluid systems, twenty parameters for predicting the type of a drilling fluid were selected through grey relation analysis. Of these parameters pressure has the highest correlation degree, which is 0.8233. The selected geological parameters and engineering design parameters were used based on an extreme gradient boost (XGBoost) algorithm to predict the types of 4 drilling fluids. The results show that the accuracy of the training sets of the 4 drilling fluids are all 100%, the average percent accuracy of the test sets is 99.89%, the precision 99.97%, the recall rate 98.89%, and the F1 value 0.98. Applying this model to the M block in the Shengli Oilfield, the classification results met the drilling requirements, and was of help in selecting the suitable drilling fluids. This study has provided a help to the intelligent design of drilling fluid.
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Key words:
- Design of drilling fluid system /
- XGBoost /
- Machine learning /
- Grey relation analysis
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表 1 部分one-hot编码后的数据
井别 井斜/
（°）井深/
mT/
℃P/
MPa复杂情况 储层损害 主要岩性 pH 井漏 井塌 卡钻 酸敏 碱敏 盐敏 水敏 泥岩 砂岩 碳酸盐岩 盐膏层 生产井 2.00 530 28 5.50 0 1 0 0 0 0 1 1 0 0 0 7 2.00 557 28 6.00 0 1 0 0 0 0 1 1 0 0 0 7 2.50 578 30 6.50 0 1 0 0 0 0 1 1 0 0 0 7 定向井 23.45 2230 97 23.81 0 0 1 0 0 0 1 0 1 0 0 9 23.45 2240 98 24.54 0 0 1 0 0 0 1 0 1 0 0 9 24.31 2250 98 24.90 0 0 1 0 0 0 1 0 1 0 0 9 预探井 47.80 4410 65 112.00 1 0 0 1 0 0 0 0 0 1 0 5 47.80 4415 68 111.00 1 0 0 1 0 0 0 0 0 1 0 6 47.80 4420 70 112.50 1 0 0 1 0 0 0 0 0 1 0 6 超深井 36.50 8072 180 142.00 0 1 0 0 0 1 0 0 0 0 1 7 36.50 8109 180 138.00 0 1 0 0 0 1 0 0 0 0 1 7 39.50 8309 185 139.00 0 1 0 0 1 0 0 0 0 0 1 7 39.56 8414 190 141.00 0 0 1 0 1 0 0 0 0 0 1 7 39.56 8445 190 141.00 0 0 1 0 1 0 0 0 0 0 1 7 
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表 4 钻井液体系分类预测模型测试集性能评价
钻井液
体系类别准确率/
%精确率/
%召回率/
%F1值 1 0.99 1.00 0.97 0.98 2 0.99 0.98 0.98 0.98 3 0.99 0.99 0.99 0.99 4 1.00 1.00 1.00 1.00 平均值 0.99 0.99 0.98 0.98
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