Predicting filter cake thickness in drilling fluids using machine learning techniques

IF 4.1 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY
Minghui Ou , Mohammed Al-Bahrani , Raman Kumar , Ashutosh Pattanaik , Hrushikesh Sarangi , Deepak Gupta , V. Naga Bhushana Rao , Mamurakhon Toshpulatova , Vikasdeep Singh Mann , Heyder Mhohamdi , Usama S. Altimari , Aseel Smerat , Samim Sherzod
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引用次数: 0

Abstract

Predicting filter cake thickness in drilling fluids is critical for improving drilling progressions and minimizing operational subjects such as pipe sticking and reduced permeability. This study investigates the performance of several machine learning models, including Decision Tree, Random Forest, AdaBoost, MLP-ANN, and Ensemble Learning, for accurately modeling filter cake thickness. A dataset of 354 experimental samples, derived from peer-reviewed studies, was employed to assess the relationships between input parameters such as nanoparticle type, nanoparticle concentration, salinity, temperature, and polymer characteristics. Model evaluation was performed using metrics such as Mean Squared Error (MSE), Coefficient of Determination (R2), and Average Absolute Relative Error Percentage (AARE%). Results indicate that the MLP-ANN model outperformed other algorithms, achieving an R2 of 0.9269 and an MSE of 0.0741 during testing. Cross-validation was implemented to ensure robust model training and evaluation, reducing overfitting observed in models like Decision Tree and AdaBoost. Additionally, SHAP investigation recognized nanoparticle concentration and type as the most influential factors impacting filter cake thickness, revealing their negative correlation with the target variable. These discoveries highlight the potential of advanced machine learning procedures to enhance drilling fluid design by identifying key parameters and optimizing formulations to reduce filter cake thickness.
利用机器学习技术预测钻井液滤饼厚度
预测钻井液中的滤饼厚度对于改善钻井进度、减少卡钻和降低渗透率等作业问题至关重要。本研究探讨了几种机器学习模型的性能,包括决策树、随机森林、AdaBoost、MLP-ANN和集成学习,用于准确建模滤饼厚度。来自同行评议研究的354个实验样本数据集用于评估输入参数(如纳米颗粒类型、纳米颗粒浓度、盐度、温度和聚合物特性)之间的关系。采用均方误差(MSE)、决定系数(R2)和平均绝对相对误差百分比(AARE%)等指标对模型进行评估。结果表明,MLP-ANN模型优于其他算法,在测试中达到R2为0.9269,MSE为0.0741。交叉验证是为了确保稳健的模型训练和评估,减少在Decision Tree和AdaBoost等模型中观察到的过拟合。此外,SHAP调查发现,纳米颗粒浓度和类型是影响滤饼厚度的最重要因素,与目标变量呈负相关。这些发现突出了先进机器学习程序的潜力,通过识别关键参数和优化配方来减少滤饼厚度,从而提高钻井液设计。
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来源期刊
Physics and Chemistry of the Earth
Physics and Chemistry of the Earth 地学-地球科学综合
CiteScore
5.40
自引率
2.70%
发文量
176
审稿时长
31.6 weeks
期刊介绍: Physics and Chemistry of the Earth is an international interdisciplinary journal for the rapid publication of collections of refereed communications in separate thematic issues, either stemming from scientific meetings, or, especially compiled for the occasion. There is no restriction on the length of articles published in the journal. Physics and Chemistry of the Earth incorporates the separate Parts A, B and C which existed until the end of 2001. Please note: the Editors are unable to consider submissions that are not invited or linked to a thematic issue. Please do not submit unsolicited papers. The journal covers the following subject areas: -Solid Earth and Geodesy: (geology, geochemistry, tectonophysics, seismology, volcanology, palaeomagnetism and rock magnetism, electromagnetism and potential fields, marine and environmental geosciences as well as geodesy). -Hydrology, Oceans and Atmosphere: (hydrology and water resources research, engineering and management, oceanography and oceanic chemistry, shelf, sea, lake and river sciences, meteorology and atmospheric sciences incl. chemistry as well as climatology and glaciology). -Solar-Terrestrial and Planetary Science: (solar, heliospheric and solar-planetary sciences, geology, geophysics and atmospheric sciences of planets, satellites and small bodies as well as cosmochemistry and exobiology).
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