公开钻井压力与溢流数据集，助力智能钻井算法研究
DataDRILL: Formation Pressure Prediction and Kick Detection for Drilling Rigs
- 构建含28个变量的钻井数据集，支持压力预测与溢流检测
- 主成分回归模型压力预测R²达0.78，残差预测偏差为0.922
- 填补领域数据空白，适合油气钻井智能化研究者使用
实时准确预测地层压力并检测溢流对钻井作业至关重要，能显著提升决策效率与成本效益。数据驱动模型在自动化钻井中日益流行，用于预测地层压力和检测溢流。然而，当前文献缺乏公开的支撑数据集，阻碍了该领域的技术进步。本文引入两个新数据集，支持研究人员开发智能算法以推进油气井钻井研究。数据集包含形成压力预测和溢流检测的数据样本，涵盖28个钻井变量，共超过2000条样本。采用主成分回归（PCR）进行地层压力预测，主成分分析（PCA）用于溢流识别的技术验证。值得注意的是，主成分回归的R²值为0.78，残差预测偏差（Residual Predictive Deviation）为0.922。
原文摘要 · Abstract (English)
Accurate real-time prediction of formation pressure and kick detection is crucial for drilling operations, as it can significantly improve decision-making and the cost-effectiveness of the process. Data-driven models have gained popularity for automating drilling operations by predicting formation pressure and detecting kicks. However, the current literature does not make supporting datasets publicly available to advance research in the field of drilling rigs, thus impeding technological progress in this domain. This paper introduces two new datasets to support researchers in developing intelligent algorithms to enhance oil/gas well drilling research. The datasets include data samples for formation pressure prediction and kick detection with 28 drilling variables and more than 2000 data samples. Principal component regression is employed to forecast formation pressure, while principal component analysis is utilized to identify kicks for the dataset's technical validation. Notably, the R2 and Residual Predictive Deviation scores for principal component regression are 0.78 and 0.922, respectively.
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