Forecasting the abnormal events at well drilling with machine learning

Gurina, Ekaterina; Klyuchnikov, Nikita; Antipova, Ksenia; Koroteev, Dmitry

doi:10.1007/s10489-021-03013-x

Cited by 11 publications

(11 citation statements)

References 31 publications

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“…More recently, Gurina et al [16] proposed an algorithm to forecast drilling accidents using a large multivariate timeseries mud telemetry dataset from Russia containing 6 drilling accidents.…”

Section: Review Of Related Studiesmentioning

confidence: 99%

Anomaly Detection Using Explainable Random Forest for the Prediction of Undesirable Events in Oil Wells

Aslam

Khan

Alansari

et al. 2022

Applied Computational Intelligence and Soft Computing

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The worldwide demand for oil has been rising rapidly for many decades, being the first indicator of economic development. Oil is extracted from underneath reservoirs found below land or ocean using oil wells. An offshore oil well is an oil well type where a wellbore is drilled underneath the ocean bed to obtain oil to the surface that demands more stability than other oil wells. The sensors of oil wells generate massive amounts of multivariate time-series data for surveillance engineers to analyze manually and have continuous insight into drilling operations. The manual analysis of data is challenging and time-consuming. Additionally, it can lead to several faulty events that could increase costs and production losses since the engineers tend to focus on the analysis rather than detecting the faulty events. Recently, machine learning (ML) techniques have significantly solved enormous real-time data anomaly problems by decreasing the data engineers’ interaction processes. Accordingly, this study aimed to utilize ML techniques to reduce the time spent manually to establish rules that detect abnormalities in oil wells, leading to rapid and more precise detection. Four ML algorithms were utilized, including random forest (RF), logistic regression (LR), k-nearest neighbor (K-NN), and decision tree (DT). The dataset used in this study suffers from the class imbalance issue; therefore, experiments were conducted using the original and sampled datasets. The empirical results demonstrated promising outcomes, where RF achieved the highest accuracy, recall, precision, F1-score, and AUC of 99.60%, 99.64%, 99.91%, 99.77%, and 1.00, respectively, using the sampled data, and 99.84%, 99.91%, 99.91%, 99.91%, and 1.00, respectively, using the original data. Besides, the study employed Explainable Artificial Intelligence (XAI) to enable surveillance engineers to interpret black box models to understand the causes of abnormalities. The proposed models can be used to successfully identify anomalous events in the oil wells.

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Section: Review Of Related Studiesmentioning

confidence: 99%

Anomaly Detection Using Explainable Random Forest for the Prediction of Undesirable Events in Oil Wells

Aslam

Khan

Alansari

et al. 2022

Applied Computational Intelligence and Soft Computing

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“…Since the current study is mostly aimed at studying the interpretability techniques for black-box machine learning models, in this section we describe methods that are used nowadays to interpret forecasting black-box models and assess the quality of obtained interpretability. The full literature review related to the forecasting of drilling accidents can be found in papers (Gurina et al, 2021(Gurina et al, , 2022b, while here we mostly focus on the review of the system that provides forecast and explanation why it was made.…”

Section: Related Workmentioning

confidence: 99%

“…This section provides a brief overview of the Bag-of-features model, used for forecasting drilling accidents in realtime and based on the Bag-of-features approach. As was mentioned in Section 1, the full description of the Bag-of-features model can be found in papers (Gurina et al, 2022b(Gurina et al, , 2021, while the general scheme of the model is presented in Figure 1.…”

Section: Overview Of Bag-of-features Model and Its Qualitymentioning

confidence: 99%

“…Figure 1: General scheme of Bag-of-features model, used for drilling accident forecasting problem solving (Gurina et al, 2022b(Gurina et al, , 2021.…”

Section: Classifiermentioning

confidence: 99%

“…In papers (Gurina et al, 2022b;Antipova et al, 2019) authors consider the problem of minimizing non-productive time (NPT) during the well construction process using AI techniques. At each time moment, the proposed ML model returns the probability of whether the corresponding time interval contains anomaly behavior that can lead to the specific type of accident or not.…”

Section: Introductionmentioning

confidence: 99%

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