A novel data-driven model for real-time prediction of static Young's modulus applying mud-logging data

IF 2.7 4区 地球科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Shadfar Davoodi, Mohammad Mehrad, David A. Wood, Mohammed Al-Shargabi, Grachik Eremyan, Tamara Shulgina
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引用次数: 0

Abstract

Effective drilling planning relies on understanding the rock mechanical properties, typically estimated from petrophysical data. Real-time estimation of these properties, especially static Young's modulus (\({E}_{sta}\)), is crucial for geomechanical modeling, wellbore stability, and cost-effective decision-making. In this study, predictive models of \({E}_{sta}\) were developed using mudlogging data from two vertically drilled wells (A and B) in the same field. \({E}_{sta}\) was estimated from petrophysical data across the studied depth range in both wells using a field-specific equation. Outlier data were identified and removed by evaluating the cross plot of mechanical specific energy and drilling rate for Well A. The data from Well A were then randomly divided into training and testing sets. The algorithms, multi-layer perceptron neural networks, random forests, Gaussian process regression (GPR), and support vector regression, were adjusted and applied to the training data. The resulting models were evaluated on the test data. The GPR model demonstrated the lowest RMSE values in both the training (0.0075 GPa) and testing (0.4577 GPa) phases, indicating superior performance. To further assess the models, the overfitting index and scoring techniques were employed, revealing that the GPR model exhibited the lowest overfitting value and outperformed the other models. Consequently, the GPR model was selected as the best-performing model and was analyzed using Shapley additive explanation to evaluate the influence of each input feature on the output. This analysis indicated that depth had the greatest effect, while rotation speed had the least impact on the model's output. The application of the GPR model to predict \({E}_{sta}\) in Well B demonstrated its high generalization capability. Therefore, it can be confidently stated that with additional data, this model could be effectively applied to similar depth ranges in other wells within the field. The study introduces innovations by applying GPR to predict \({E}_{sta}\) from mudlogging data, addressing outlier impact on predictions, and developing a real-time \({E}_{sta}\) prediction model for drilling.

Abstract Image

应用泥浆记录数据实时预测静态杨氏模量的新型数据驱动模型
有效的钻井规划有赖于对岩石力学性质的了解,这些性质通常是通过岩石物理数据估算出来的。实时估算这些属性,尤其是静态杨氏模量(\({E}_{sta}\)),对于地质力学建模、井筒稳定性和成本效益决策至关重要。本研究利用同一油田两口垂直钻井(A 井和 B 井)的泥浆记录数据建立了 \({E}_{sta}\) 的预测模型。\({E}_{sta}\) 是使用油田特定方程从两口井的岩石物理数据中估算出来的。通过评估 A 井的机械比能量和钻井速率的交叉图,识别并剔除离群数据,然后将 A 井的数据随机分为训练集和测试集。调整多层感知器神经网络、随机森林、高斯过程回归(GPR)和支持向量回归等算法,并将其应用于训练数据。结果模型在测试数据上进行了评估。GPR 模型在训练阶段(0.0075 GPa)和测试阶段(0.4577 GPa)的 RMSE 值都最低,表明其性能优越。为了进一步评估模型,采用了过拟合指数和评分技术,结果显示 GPR 模型的过拟合值最低,性能优于其他模型。因此,GPR 模型被选为表现最佳的模型,并使用 Shapley 加法解释进行分析,以评估每个输入特征对输出的影响。分析表明,深度对模型输出的影响最大,而旋转速度对模型输出的影响最小。应用 GPR 模型预测 B 井中的\({E}_{sta}\) 证明了该模型具有很高的泛化能力。因此,可以肯定地说,如果有更多的数据,该模型可以有效地应用于油田内其他井的类似深度范围。该研究通过应用 GPR 从泥浆记录数据中预测 \({E}_{sta}\)、解决离群值对预测的影响以及开发钻井实时 \({E}_{sta}\)预测模型进行了创新。
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来源期刊
Earth Science Informatics
Earth Science Informatics COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
4.60
自引率
3.60%
发文量
157
审稿时长
4.3 months
期刊介绍: The Earth Science Informatics [ESIN] journal aims at rapid publication of high-quality, current, cutting-edge, and provocative scientific work in the area of Earth Science Informatics as it relates to Earth systems science and space science. This includes articles on the application of formal and computational methods, computational Earth science, spatial and temporal analyses, and all aspects of computer applications to the acquisition, storage, processing, interchange, and visualization of data and information about the materials, properties, processes, features, and phenomena that occur at all scales and locations in the Earth system’s five components (atmosphere, hydrosphere, geosphere, biosphere, cryosphere) and in space (see "About this journal" for more detail). The quarterly journal publishes research, methodology, and software articles, as well as editorials, comments, and book and software reviews. Review articles of relevant findings, topics, and methodologies are also considered.
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