地质−工程驱动的邻井辅助同井钻速预测与优化方法

樊永东; 金衍; 林伯韬; 韩雪银; 吴事难; 张家豪

doi:10.11911/syztjs.2024110

地质−工程驱动的邻井辅助同井钻速预测与优化方法

1.
中国石油大学（北京）人工智能学院，北京 102249
2.
中国石油大学（北京），北京 102249
3.
中海油能源发展股份有限公司工程技术分公司，天津 300450

基金项目: 国家自然科学基金企业创新发展联合基金项目“海相深层高温高压钻完井工程基础理论及控制方法”（编号：U19B6003–05）和中海油能源技术发展有限公司重大科研计划项目“地质工程一体化及工程多维数据先导性研究服务”（编号：ZX2022ZCGCF3289）资助。

详细信息

作者简介:
樊永东（1996—），男，陕西靖边人，2021年毕业于中国石油大学（北京）智能科学与技术专业，在读博士研究生，主要从事油气井工程信息化与智能化方面研究。E-mail：fanyongdong0912@163.com

通讯作者:
金衍，jiny@cup.edu.cn

中图分类号: TE21
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出版历程
- 收稿日期: 2023-02-06
- 修回日期: 2024-10-30
- 网络出版日期: 2024-11-15
- 刊出日期: 2025-02-27

Prediction and Optimization of ROP Assisted by Adjacent Well Data Based on Geological and Engineering Driving

1.
College of Artificial Intelligence, China University of Petroleum(Beijing), Beijing, 102249, China
2.
China University of Petroleum(Beijing), Beijing, 102249, China
3.
Engineering Technology Branch, CNOOC EnerTech-Drilling & Production Co., Tianjin, 300450, China

摘要

摘要:
渤海中部沙河街组储层主要为泥岩和深色砂泥岩，在钻遇该储层时机械钻速偏低，严重影响钻井周期与钻井成本。为解决上述问题，建立了基于地质−工程一体化的钻速预测与优化模型。该模型包括钻速预测与钻速优化2部分，基于地质与工程融合数据建立了邻井辅助同井钻速预测模型；在完成钻速预测后，定义了特征贡献度系数，以量化不同特征参数对最终结果的影响程度，既可以基于特征贡献度系数对预测结果进行解释，也可以得到对钻速影响较大且可人为可控的参数。针对显著影响钻速且可人为可控的参数，钻速优化模型通过网格搜索优化算法寻找最优参数组合，从而实现钻井提速。基于该模型对钻速优化可知，测试井的钻速平均提高了6.34%，对预测结果贡献最大的3组参数分别是伽马值、钻压和钻头钻井时长。该模型综合考虑了地质与工程因素，实现了高精度钻速预测与钻速大幅度提高，在实际应用的2口开发井中为钻速提高提供了有效的指导。
- 钻速预测 /
- 钻速优化 /
- 机器学习 /
- 地质−工程一体化 /
- 可解释性
Abstract:
The main lithology of the Shahejie Formation reservoir in the central Bohai Sea is mudstone and dark sandy mudstone, and the rate of penetration (ROP) is generally low when drilling through this reservoir, significantly influencing the drilling cycle and costs. To address this issue, a ROP prediction and optimization model integrating geology and engineering was proposed. This model consisted of two parts: ROP prediction and ROP optimization. The ROP prediction leveraged geological and engineering data to establish an ROP prediction model of the drilled well assisted by adjacent well data. After completing the ROP prediction, a feature contribution coefficient was defined to quantify the influence of different feature parameters on the final result. This feature contribution coefficient allowed for both an interpretation of the predicted results and the identification of controllable parameters that significantly affect ROP. For these controllable parameters, the ROP optimization model used a grid search optimization algorithm to explore the optimal parameter combination, thereby improving ROP. The ROP optimization results based on this model show that the ROP of the test well increases by 6.34% on average, with the three parameters contributing most to the prediction results being gamma values, weight on bit, and bit drilling time. This model effectively integrates geological and engineering parameters, achieving high-accuracy ROP predictions and substantial ROP improvements and providing valuable guidance for ROP enhancement in two development wells where it has been applied.
- prediction of penetration rate /
- optimization of penetration rate /
- machine learning /
- geological and engineering integration /
- interpretability

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图 1 机械钻速预测与优化模型工作流程

Figure 1. Process of ROP prediction and optimization

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图 2 研究区域沙河街组机械钻速的分布

Figure 2. ROP distribution in Shahejie Formation in the study area

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图 3 III号井部分录井参数去噪前后对比

Figure 3. Comparison of some logging parameters of well III before and after denoising

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图 4 特征筛选示意

Figure 4. Feature selection

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图 5 同井预测与邻井预测示意

Figure 5. Drilled well prediction and adjacent well prediction

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图 6 不同M与N下的预测结果

Figure 6. Prediction results of different M and N

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图 7 不同模型的预测结果与预测误差分析

Figure 7. Prediction results and prediction error analysis of different models

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图 8 测试井钻速优化结果

Figure 8. ROP optimization results of the test well

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图 9 开发井I和开发井II钻速预测与优化结果

Figure 9. Prediction and optimization results of the ROP of development well I and development well II

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图 10 开发井I和开发井II钻速分布与预测误差、钻速提升程度之间的关系

Figure 10. Relationship among ROP distribution, prediction error, and ROP improvement of development well I and development well II

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图 11 特征贡献度分析

Figure 11. Feature contribution analysis

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表 1 GBDT模型与LSTM模型的主要超参数

Table 1 Main hyper-parameters of GBDT and LSTM

模型	优化方法	主要超参数
GBDT	TPE	学习率：0.01；损失函数：均方差损失；弱学习器迭代次数：900；最大深度：55
LSTM	PSO	学习率：0.01；隐藏层个数：2；隐藏层神经元个数：128和64；迭代次数：1 800

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表 2 各评价指标随M取值的变化情况

Table 2 Variation of different evaluation indexes with M

$M$ 取值	E_MAP，%	P_ROPT	算法运行时长/ms
40	44.28	0.42	878
60	30.02	0.53	1 097
80	19.15	0.81	1 302
100	11.28	0.88	1 389
120	9.37	0.92	1 394
140	9.30	0.93	1 402

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表 3 待优化参数的取值范围

Table 3 Range of optimization parameters

优化参数	钻压/kN	转速/（r·min⁻¹）
允许最小值	0	0
允许最大值	882.6	165.0
采样间隔	9.8	1.0

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表 4 开发井I和开发井II的基本参数

Table 4 Basic information of development well I and development well II

井名	沙河街组测深/m	沙河街组垂深/m	机械钻速/（m·h⁻¹）
井名	沙河街组测深/m	沙河街组垂深/m	最小	最大	平均
开发井I	4 949～5 566	4 276～4 763	0.17	14.32	5.77
开发井II	5 046～5 560	4 374～4 733	2.09	15.31	7.05

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