The Role of Machine Learning in Drilling Operations; A Review

Noshi, C. I.; Schubert, Jerome

doi:10.2118/191823-18erm-ms

Cited by 67 publications

(17 citation statements)

References 36 publications

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“…This subject is shared between geothermal and oil and gas industries where drilling operations are remarkably similar. There are myriad studies where machine learning techniques have successfully addressed the mentioned issues and provided reliable solutions to optimize the drilling stage (Barbosa et al 2019;Hegde et al 2020;Gray 2017, 2018;Noshi and Schubert 2018). Recently, the Department of Energy has funded a project with the theme of application of deep machine learning to optimize drilling operations (specifically for geothermal wells) which was awarded to Oregon State University with collaboration with one more US university, one DOE National Laboratory, in addition to four geothermal and oil and gas companies from Iceland, US and Norway (DOE, 2019).…”

Section: Drilling Stagementioning

confidence: 99%

Exploratory analysis of machine learning methods in predicting subsurface temperature and geothermal gradient of Northeastern United States

et al. 2021

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Geothermal scientists have used bottom-hole temperature data from extensive oil and gas well datasets to generate heat flow and temperature-at-depth maps to locate potential geothermally active regions. Considering that there are some uncertainties and simplifying assumptions associated with the current state of physics-based models, in this study, the applicability of several machine learning models is evaluated for predicting temperature-at-depth and geothermal gradient parameters. Through our exploratory analysis, it is found that XGBoost and Random Forest result in the highest accuracy for subsurface temperature prediction. Furthermore, we apply our model to regions around the sites to provide 2D continuous temperature maps at three different depths using XGBoost model, which can be used to locate prospective geothermally active regions. We also validate the proposed XGBoost and DNN models using an extra dataset containing measured temperature data along the depth for 58 wells in the state of West Virginia. Accuracy measures show that machine learning models are highly comparable to the physics-based model and can even outperform the thermal conductivity model. Also, a geothermal gradient map is derived for the whole region by fitting linear regression to the XGBoost-predicted temperatures along the depth. Finally, through our analysis, the most favorable geological locations are suggested for potential future geothermal developments.

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Section: Drilling Stagementioning

confidence: 99%

Exploratory analysis of machine learning methods in predicting subsurface temperature and geothermal gradient of Northeastern United States

et al. 2021

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“…Predictive modeling and big data analytics for time series drilling data have driven huge interest by the scientific community. The Researchers have successfully implemented machine learning and artificial intelligence methods with a major focus on reducing various parameters that are substantial for groundwater drilling such as the non-productive and invisible lost time during drilling [21]. For efficient groundwater acquisition, there is a need for an optimized drilling process such as rate of penetration.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Optimization-Based Voting Classifier and Chained Multi-Objective Regressor for Effective Groundwater Resource Management

Rizwan¹,

Khan

Iqbal

et al. 2021

IEEE Access

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Water is an essential source of life for every living thing, and drilling is the only source to gain water from underground. Different advanced technologies have been used to minimize the time factor and labor force. Along with technology to be used, some other factors are equally essential to be considered, like water level, the hardness level of the land, and the number of days spent on the whole process. The study proposed a weighted voting classifier based on Differential Evaluation (DE) to classify the regions with different soil colors and land layers. The weights are assigned to the candidate classifiers based on their performance for each class. For the assignment of the optimal weight, the DE optimization algorithm is used. Moreover, the study presents a chained multi-objective regression model to simultaneously predict the water level and total depth on different locations. The proposed work facilitates the drilling industry to increase the rate of penetration (ROP) by selecting the region with soft soil and land layer. The prediction of depth and water level allows the industry to estimate water levels in different areas at different depths. The dataset is provided by the research organization, which contains information of different drilling points. The results of the proposed weighted voting classifier are compared with the traditional machine learning models (kernel Naive Bayes, Gaussian SVM, Quadratic SVM, and Bilayered Neural network) and state of the art voting classifier in terms of precision, recall, and accuracy. Moreover, the proposed regression model is evaluated by well-known evaluation metrics, including Mean Absolute Error, Mean Square Error, and R2 score. Finally, the comparison verifies the effectiveness of the enhanced optimization-based classifier and multi-objective regressor.

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“…The use of advanced data analytics aids in improving safety, cost-effectiveness, and quality of drilling operations. These problems can be analyzed and solved by applying different Machine Learning (ML) techniques [4]- [6]. The ML models have already penetrated critical decision-making processes such as predicting the timespan required to complete a drilling process at some specific location.…”

Section: A Backgroundmentioning

confidence: 99%

Toward Effective Pattern Recognition Based on Enhanced Weighted K-Mean Clustering Algorithm for Groundwater Resource Planning in Point Cloud

Rizwan¹,

Iqbal

Khan

et al. 2021

IEEE Access

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Recently groundwater scarcity has accelerated drilling operations worldwide as drilled boreholes are substantial for replenishing the needs of safe drinking water and achieving long-term sustainable development goals. However, the quest for achieving optimal drilling efficiency is ever continued. This paper aims to provide valuable insights into borehole drilling data utilizing the potential of advanced analytics by employing several enhanced cluster analysis techniques to propel drilling efficiency optimization and knowledge discovery. The study proposed an L2-weighted K-mean clustering algorithm in which the mean is computed from transformed weighted feature space. To verify the effectiveness of our proposed L2-weighted K-mean algorithm, we performed a comparative analysis of the proposed work with traditional clustering algorithms to estimate the digging time and depth for different soil materials and land layers. The proposed clustering scheme is evaluated widely used evaluation metrics such as Dunn Index, Davies-Bouldin index (DBI), Silhouette coefficient (SC), and Calinski-Harabaz Index (CHI). The study results highlight the significance of the proposed clustering algorithm as it achieved better clustering results than conventional clustering approaches. Moreover, for facilitation of subsequent learning, achievement of reliable classification, and generalization, we performed feature extraction based on the time interval of the drilling process according to soil material and land layer. We formulated the solution by grouping the extracted features into six different blocks to achieve our desired objective. Each block corresponds to various characteristics of soil materials and land layers. Extracted features are examined and visualized in point cloud space to analyze the water level patterns, depth, and days required to complete the drilling operations.INDEX TERMS data analysis; features extraction; unsupervised learning; machine learning; strategic planning and management;

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The Role of Machine Learning in Drilling Operations; A Review

Cited by 67 publications

References 36 publications

Exploratory analysis of machine learning methods in predicting subsurface temperature and geothermal gradient of Northeastern United States

Exploratory analysis of machine learning methods in predicting subsurface temperature and geothermal gradient of Northeastern United States

Enhanced Optimization-Based Voting Classifier and Chained Multi-Objective Regressor for Effective Groundwater Resource Management

Toward Effective Pattern Recognition Based on Enhanced Weighted K-Mean Clustering Algorithm for Groundwater Resource Planning in Point Cloud

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