不同分类机器学习技术的应用,用于预测钻井时的形成顶部和石质学
Ahmed Farid Ibrahim1,2, Ashraf Ahmed1, Salaheldin Elkatatny1,2
1College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia.
ACS omega
|November 29, 2023
概括
机器学习模型使用钻探参数,如比特重量和透率,准确地预测形成顶部和石质学. 这种具有成本效益的方法提高了碳化合物操作中的实时估计.
科学领域:
- 地质科学和石油工程 石油科学和石油工程
- 人工智能和机器学习
背景情况:
- 形成顶部和石质识别的传统方法昂贵且耗时.
- 限制包括高成本,较低的准确性和时间滞后,阻碍碳化合物操作中的实时估计.
研究的目的:
- 利用机器学习模型,利用可访问的钻井参数预测形成顶部和石质.
- 通过开发具有成本效益和准确的实时估计方法来克服传统技术的局限性.
主要方法:
- 收集了来自中东两个油井的实地钻井数据,包括透率 (ROP),比特重量 (WOB) 和扭矩.
- 在井数据上训练并测试了高斯素朴贝叶斯 (GNB),逻辑回归 (LR) 和线性差异分析 (LDA) 模型.
- 对来自单独井的未见数据验证模型性能,以评估概括能力.
主要成果:
- 高斯的天真贝叶斯 (GNB) 展示了卓越的性能,实现了高准确度,精度,回忆和F1评分,用于石质学预测.
- 确定了对比特的重量 (WOB) 和透率 (ROP) 作为影响石质学识别的关键参数.
- 模型的验证准确度为GNB约为0.96,LR为0.95和LDA为0.92.
结论:
- 机器学习模型有效地使用易于获得的钻探参数实时预测形成石质和顶部.
- 开发的方法为优化钻探过程和减轻碳化合物勘探中的风险提供了高度准确和具有成本效益的解决方案.
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