Volume 40 Issue 6
Dec.  2023
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HUA Lulu, CAO Xiaochun, WANG Jincao, et al.Study on prediction model for drilling fluid classification based on XGBoost[J]. Drilling Fluid & Completion Fluid,2023, 40(6):765-770 doi: 10.12358/j.issn.1001-5620.2023.06.010
Citation: HUA Lulu, CAO Xiaochun, WANG Jincao, et al.Study on prediction model for drilling fluid classification based on XGBoost[J]. Drilling Fluid & Completion Fluid,2023, 40(6):765-770 doi: 10.12358/j.issn.1001-5620.2023.06.010

Study on Prediction Model for Drilling Fluid Classification Based on XGBoost

doi: 10.12358/j.issn.1001-5620.2023.06.010
  • Received Date: 2023-05-10
  • Rev Recd Date: 2023-08-15
  • Publish Date: 2023-12-30
  • 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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