Oil And Gas Pipeline Erosion Prediction Based On Machine Learning

The process of erosion is a multifaceted occurrence in which solid particulates,propelled by a flowing medium,abrade material from substrate surfaces.Within the context of oil and gas production and transportation,fluid may contain various solid inclusions,such as sand and scale.When they impact with the pipe wall at high flow rates,it can result in erosion and subsequent wall thinning.This can pose significant risks to the integrity of the pipeline and associated equipment.However,the existing erosion research approaches and models are mostly based on empirical formulas,theoretical analysis or simulation,which are only applicable to specific working conditions and present certain limitations.As such,exploring the potential for utilizing machine learning-based theories to predict oil and gas pipeline erosion from a data-driven perspective is of paramount importance for ensuring the safe and efficient transportation of oil and gas.In this work,the potential for predicting pipe elbow erosion was first investigated.Using simulation data acquired under gas-solid two-phase flow conditions and incorporating machine learning algorithms,four prediction models were developed.A comparative analysis was performed to ascertain the optimal model.The results reveal that the KELM model is the optimal model with the lowest error and the highest fit in both simulation and experimental data.It was observed that the predicted and actual values exhibit a similar trend in response to variations in the apparent gas flow rate,under diverse conditions of particle diameter and pipe diameter.This demonstrates that the model effectively considers the impact of input parameters on erosion and confirms the reliability of the model.Subsequently,the optimization and improvement of the KELM model was achieved through the integration of hybrid kernel functions and swarm intelligence algorithms.A novel WOA-HKELM elbow erosion prediction model is proposed for gas-liquid-solid three-phase flow conditions.The WOA was found to possess significant advantages over other swarm intelligence algorithms in terms of optimization performance.The WOA-HKELM model optimized the hyperparameters and adopted a hybrid kernel function for implicit mapping,resulting in a significant improvement in prediction accuracy.Furthermore,the WOA-HKELM model accurately reflected the trend of variation in erosion rate with respect to VSG/VSL in milky flow and Remix in annular flow,thereby demonstrating a high degree of reliability in the proposed prediction model.Eventually,a novel prediction model for oil and gas pipeline erosion was proposed,utilizing the ISMA-SSL-DHKELM framework.,which incorporates deep learning methodologies to effectively extract data features.A semi-supervised learning method,based on point-by-point flow regularization,was also employed to reduce dependence on data volume,and a multi-strategy improved swarm intelligence algorithm is added to enhance the efficiency of model parameter search.The application of the Gold Point Set method,parameter nonlinear variation,golden sine algorithm,and adaptive t-distribution perturbation strategy helped to improve the SMA and validate its effectiveness in the standard test function.The results show that compared to the HKELM model and DHKELM model,the ISMA-SSL-DHKELM model with multi-layer ELM-AE structure can better extract data features,and the semi-supervised mechanism can maintain prediction stability even with low sample size.Additionally,the improved SMA-optimized model demonstrated higher prediction accuracy than the SSL-DHKELM erosion prediction model optimized by other algorithms.Meanwhile,the proposed model captures the impacts of particle concentration,pipe diameter,particle diameter,and viscosity on the critical flow rate with accuracy,displaying high robustness.In this study,a generalizable oil and gas pipeline erosion model is presented that is based on the principles of machine learning.The proposed model holds the potential to promote the progress of pipeline erosion prediction methods and enrich the study of complex fluid multiphase flow in petroleum engineering.