The detection of anomalies and the prediction of corrosion defects in oil and gas pipelines constitute critical tasks for ensuring industrial safety and improving operational reliability. This study addresses the problem of regression-based prediction of corrosion defect levels (CR—corrosion defect) using operational process parameters. Machine learning methods, including Decision Tree, Random Forest, LightGBM, and CatBoost, were employed to develop predictive models. Data preprocessing was performed, including feature standardization and hyperparameter tuning using KFold cross-validation. Model performance was primarily evaluated using the Root Mean Square Error (RMSE) on both training and test datasets, as this metric is more sensitive to large prediction errors, which is particularly important in the context of corrosion defect analysis. Additionally, Mean Squared Error (MSE), Mean Absolute Error (MAE), and the coefficient of determination (R²) were used to provide a comprehensive assessment of model accuracy and robustness. Experimental results demonstrate that the CatBoost model achieved the best performance, yielding the lowest RMSE on the test dataset (0.02040) with a close value on the training dataset (0.01682), indicating strong generalization capability. Furthermore, this model outperformed the others in terms of MSE, MAE, and R² on the test dataset (MSE = 0.000418, MAE = 0.006319, R² = 0.695544). The obtained results confirm the effectiveness of ensemble methods and gradient boosting algorithms for regression modeling of corrosion defect development processes in pipeline systems.

Pipeline systems for oil and gas transportation are complex distributed infrastructure facilities whose efficient and safe operation largely depends on the application of modern information and communication technologies. In the context of industrial digital transformation, intelligent monitoring systems capable of continuous acquisition, transmission, and analysis of telemetry data for the early detection of emergency conditions have become particularly relevant. One of the most critical challenges is the timely detection and accurate localization of leaks, which can result in significant economic losses, environmental damage, and threats to public safety. The objective of this study is to develop an approach for determining the coordinates of a pipeline leak based on intelligent processing of measurement data using machine learning methods. The proposed solution is intended for integration into information and communication systems for supervisory control and digital monitoring of pipeline transport. A two-layer multilayer perceptron implemented in the MATLAB environment is employed as the data analysis tool, enabling the development of a computationally efficient algorithm suitable for practical use in decision-support systems operating in near-real-time conditions. The neural network was trained on experimental datasets generated for various leak locations and flow rate values of the transported medium and was tested on independent datasets. The influence of the number of neurons in the hidden layer on leak localization accuracy was investigated. Maximum and root mean square localization errors were used as performance metrics. The results demonstrate that increasing model complexity by raising the number of neurons beyond 3–4 does not lead to a significant improvement in accuracy and may be accompanied by overfitting, thereby reducing the reliability of the algorithm when processing new data. It was found that the optimal neural network architecture comprises three neurons in the hidden layer, providing a root mean square error of approximately 2 km and a maximum error not exceeding 5.5 km. The obtained results confirm the effectiveness of neural network methods for intelligent analysis of telemetry information and demonstrate the feasibility of developing scalable information and communication systems for early leak detection. The practical significance of this work lies in improving the accuracy of accident localization and reducing pipeline operation risks through the implementation of intelligent data processing algorithms within digital industrial monitoring platforms.

Pipelines are widely used for the transportation of water, oil, gas, and other liquid and gaseous media. The occurrence of leaks in pipeline systems leads to significant losses of natural resources and may pose a threat to the environment and public safety. In this regard, the problem of timely leak detection and localization remains one of the most important challenges in the oil and gas industry. This paper addresses the problem of determining the leak location in a pipeline based on pressure measurement data using artificial intelligence methods. An approach to leak localization is proposed based on approximating the relationship between pressure drop, leak flow rate, and the distance to the leak location using a radial basis function neural network implemented in the MATLAB environment. Experimental data were obtained during testing of a leak detection system under simulated leak conditions with different operating modes. The simulation results demonstrate that the developed model provides an effective approximation of the studied relationship and allows the leak location to be determined with a root-mean-square error of approximately 1.9 km for medium and large leaks, confirming the potential of neural network-based methods for pipeline monitoring applications.

This study presents a comparative analysis of regression models for predicting a synthetic corrosion defect severity (CR) indicator in pipeline systems. The investigated dataset contains 10,292 observations, eight input features, and a synthetic target variable. Fifteen models and configurations were compared: Dummy Regressor (Dummy), Linear Regression (LR), Ridge Regression (Ridge), Lasso Regression (Lasso), Elastic Net (EN), second- and third-degree polynomial regression (Poly2 and Poly3), k-Nearest Neighbors (KNN), Support Vector Regression (SVR), Extra Trees Regressor (ETR), Gradient Boosting Regressor (GBR), Histogram-based Gradient Boosting Regressor (HGBR), AdaBoost Regressor (ABR), Multilayer Perceptron (MLP), and CatBoost Regressor (CBR). The data were split into an 80% development set and a prespecified 20% held-out internal test set. Model selection and hyperparameter tuning were performed without accessing the test set using repeated nested cross-validation, with five outer folds repeated five times and five inner folds. The lowest mean root mean square error (RMSE) in the outer cross-validation was achieved by second-degree polynomial regression, with an RMSE of 0.019557 and a standard deviation of 0.001796; the mean absolute error (MAE) was 0.003762, and the coefficient of determination (R²) was 0.721530. However, its advantage over SVR was not statistically significant after Holm correction (adjusted p = 0.0662). On the prespecified held-out internal test set, Poly2 achieved an RMSE of 0.021454, an MAE of 0.003967, and an R² of 0.679304. In the highest CR decile, the error increased to an RMSE of 0.050622, the bias was −0.017468, and R² decreased to −1.372106, indicating systematic underestimation of high CR values. Permutation importance analysis identified carbon dioxide (CO₂) content, gas production rate, pressure, water production rate, basic sediment and water (BSW), and oil production rate as the most influential features. These findings are limited to a computational experiment conducted on synthetic data and require external validation using actual corrosion measurements.

Бұл мақалада анықталмағандық жағдайында жұмыс жасайтын магистральдық мұнай құбырындағы технологиялық процестердің моделін құру қарастырылады. Біріншіден, технологиялық режимдерді өлшеу барысында магистральдық мұнай құбыры тағы құрылатын құрылымдық жүйесі мен ауысатын өлшемдермен жасақталған күрделі сипаттағы жүйе ретінде ұсынылған. Айдау процесін бастау және тоқтату, бұрмаларды қосу және ажырату, бекіту және реттеу арматурасының процесі барысында, сондай – ақ әртүрлі бұзылулар ен апаттар-құбырдың сынуы мен толып қалу уақытында пайда болатын мұнай құбырындағы ауыспалы процестердің математикалық сипаттамасы келтірілген. Мұнай ағынының ауысатын параметрлерінің ішінде құбырдағы қысым мен ағынның ауысу сипаттамалары зерттеледі. Құбырдың бастапқы жай-күйі оның негізгі үлестірілген параметрлері жағынан және стационарлық емес жұмыс режимі жағдайында MATLAB/SimHydraulics бағдарламалық қамтамасыз ету тұрғысында есептеледі. Содан соң ұсынылған әдіс магистральдық мұнай құбыры аймағында орнатылмаған процестерді модельдеу есебін шешу мақсатында қолданылады. Осылайша, құбырдағы стационарлық емес (динамикалық) модельдерді құру нәтижесінде алынған тәжірибені магистралдық мұнай құбырын қолдану кезінде алдын ала пайымдау және басқару жүйелерінің өнімділігін көбейту үшін қолдануға болады.