A Novel Pipeline Age Evaluation: Considering Overall Condition Index and Neural Network Based on Measured Data (original) (raw)
Related papers
Corrosion Assessment of some Buried Metal Pipes using Neural Network Algorithm
International Journal of Engineering and Manufacturing, 2017
The key aim of this assessment is to characterize the rate of corrosion of buried Nickel plated and non-plated AISI 1015 steel pipes using a Modified Artificial Neural Network on Matlab and taking the oil and gas area of Nigeria as a case study. Ten (10) metal specimens were used. Five (5) were nickel electroplated specimens buried differently in 5 plastic containers containing 5 different soil samples with the other 5 non-plated specimens also buried into the same 5 soil samples but different plastic containers. In carrying out the experiment, the data that was collected for 25 consecutive days were grouped into sets of input and output data. This was required so as to appropriately feed the modelling tool (Artificial Neural Network). The input data were; temperature of the soil sample, temperature of the immediate surroundings, and pH of the soil sample while the output data was weight loss. Conclusively, Modified Artificial Neural Network relationships between the varied selected input parameters that affects corrosion rate (soil sample temperature, immediate environment temperature and pH value) and the output parameter (Corrosion Penetration Rate) were derived. Also, soil sample temperature and the immediate surrounding temperature combined conditions had the strongest effect on corrosion penetration rate while the immediate surrounding temperature and the pH value combined conditions had the weakest effect on corrosion penetration rate.
2024
This study addresses the challenge of accurately predicting corrosion rates and estimating the remaining life of underground gas pipelines, which is complicated by the complex interaction of physical factors and environmental conditions. Traditional models are inadequate in capturing these variables, leading to less reliable predictions, which this study aims to address by developing a more accurate and optimized artificial neural network (ANN) model. This study focuses on predicting corrosion rates and estimating the remaining life of underground gas pipelines using ANNs implemented in MATLAB. It incorporates both physical factors, such as maximum corrosion depth and pipe thickness, and environmental variables such as moisture, soil resistivity, and chloride concentration. The analysis identified corrosion depth and wall thickness as significant contributors, influencing material integrity by 20% and 16%, respectively. The optimal ANN model, with a Levenberg-Marquardt structure and one hidden layer of 10 neurons, achieved superior accuracy, with an MSE of 0.038 and R² of 0.9998. The study addresses the challenge of accurately predicting corrosion rates and remaining life in underground gas pipelines by developing an optimised ANN model. Its contribution lies in creating a highly accurate prediction tool that outperforms traditional models and enables more informed decisions for pipeline maintenance and safety.
Spektra: Jurnal Fisika dan Aplikasinya
Research has been carried out to map and identify the potential for soil corrosion for the planning of cathodic gas pipeline protection systems. The research location is located in Cimanggis - Bitung, West Java, which is located at coordinates 6o19'00 "- 6o28'00" South Latitude and 106o43'00 "- 106o 55'30" East Longitude. Measurement of soil resistivity using the Wenner method that refers to ASTM G37, with variations in the distance of 0,75m, 1,50m, 2,50m and 6,00m with the number of measuring points as many as 185 points. Based on the results of data processing and soil resistivity interpretation seen that there are several locations that have low to extreme corrosion levels. Therefore, for these locations, technical planning and calculation for the protection of the pipeline to be installed is necessary.
A Neural Network Predictive Model of Pipeline Internal Corrosion Profile
2014
Internal corrosion is a crucial issue for the safe operation of oil&gas pipelines. This is a phenomenon due to interaction of different mechanisms. Water and electrochemistry, protective scales, flow velocity, steel composition and localized bacteria attacks are relevant. Despite the large number of models proposed in literature, the corrosion process is very complex and rarely reproduced by existing models. For this reason, an artificial neural network (ANN) based model is investigated, with the aim to correctly predict the presence of metal loss and corrosion rate along a pipeline. In this paper, a case study is considered, based on real field data. The model integrates the geometrical profile of a real pipeline, flow simulations and the most important deterministic corrosion models. It is shown that the ANN model outperforms the deterministic ones.
An Artificial Neural Network Modeling for Pipeline Corrosion Growth Prediction
Corrosion defect assessment becoming a forte issue in pipeline reliability assessment to accurately predict the severity of its condition. Due to the uncertainties inherit from the pipeline inspection at present, statistical model use to model the corrosion growth apply a correctional methods to reduce the gap (means and variation) between predicted values and the actual data. This study aims to develop a time dependent corrosion growth model for oil and gas pipeline using Artificial Neural Network (ANN) as an alternative to the current method and to evaluate its applicability without enforcing data correctional methods. This model is formulated based on parameters of defect extracted from in-line inspection data (ILI) and quantified by statistical analysis. The develop model gives the prediction of the corrosion depth and length of the defect that can be used to calculate the corrosion rate or growth. The results and outcome of the present study can help pipeline operators to predi...
Artificial neural network for the evaluation of CO2 corrosion in a pipeline steel
Journal of Solid State Electrochemistry, 2009
This paper presents a predictive model for the determination of different types of corrosion by using electrochemical impedance spectroscopy curves and artificial neural network. This proposed model obtains predictions for three different types of corrosion by using Nyquist impedance curves from four input variables: inhibitor concentration, time of exposure, and the real and imaginary experimental component of these curves. The model takes into account the variations of inhibitor concentration over steel to decrease the corrosion rate. For the network, the Levenberg-Marquardt learning algorithm, the hyperbolic tangent sigmoid transfer function and the linear transfer function were used. The best fitting training data set was obtained with five neurons in the hidden layer, which made possible to predict satisfactory efficiency (R>0.99). On the validation of the data set, simulations and theoretical data tests were in good agreement (R>0.9905). The developed model can be used for the determination of the type of curves related to the nature phenomena and rate of corrosion at the metal surface.
Corrosion monitoring in pipelines with a computerized system
Alexandria Engineering Journal, 2021
This study aims to combine the smart pigs as a non-destructive test (NDT) inspection technique with software developed for the assessment of pipeline corrosion defects to ensure fitness for the surface. The software uses decision support systems, connected through the correlated linkage technique, which is coded using Microsoft Access and Visual C#. This software measures general internal pipeline corrosion forms to identify locations with potential corrosion features and predict corrosion conditions in the future. Computer-aided corrosion management program (CACM) examined maximum corroded depth of internal corrosion, maximum allowable axial corrosion defect length, failure pressure, the corrosion rate, and the remaining pipeline life. This work introduces a wide-ranging review of computer-aided corrosion management programs. The proposed method of assisting and detecting corrosion internal defects and defects data should be available. This software is easy to use without complicated analysis. It helps to reduce unplanned shutdowns in the oil and gas production industry.
PIPELINE CORROSION CONTROL IN OIL AND GAS INDUSTRY: A CASE STUDY OF NNPC/PPMC SYSTEM 2A PIPELINE
Corrosion in pipelines is one of the major challenges faced by oil and gas industries all over the world. This has made corrosion control or management a major factor to consider before setting up any industry that will transport products via pipelines. In this study the types of corrosion found on system 2A pipeline were; pitting, microbial, sulfide-stress cracking, hydrogen-stress cracking and hydrogen-induced cracking and these were caused by poor maintenance of the pipeline system, severe mutilation of the pipeline coatings, substrates due to vandalization and coating failures. The data from cathodic protection control method from Nigeria National Petroleum Corporation (NNPC)/ Pipeline and Product Marketing Company (PPMC) for system 2A line was analyzed and it was deduced that about 10.3km of the pipeline was well protected and possibly fit for use and about 62.7km is experiencing under protection which means corrosion is predicted to take place in that segment in a short time and finally about 16km of the pipeline is experiencing corrosion. From the results obtained, it can be deduced that the use of cathodic protection technique as a method of controlling corrosion in oil and gas pipelines is effective and efficient when compared to other methods and thus constant monitoring is needed to achieve optimum efficiency.
Processes, 2020
Pipelines are like a lifeline that is vital to a nation's economic sustainability; as such, pipelines need to be monitored to optimize their performance as well as reduce the product losses incurred in the transportation of petroleum chemicals. A significant number of pipes would be underground; it is of immediate concern to identify and analyse the level of corrosion and assess the quality of a pipe. Therefore, this study intends to present the development of an intelligent model that predicts the condition of crude oil pipeline cantered on specific factors such as metal loss anomalies (over length, width and depth), wall thickness, weld anomalies and pressure flow. The model is developed using Feed-Forward Back Propagation Network (FFBPN) based on historical inspection data from oil and gas fields. The model was trained using the Levenberg-Marquardt algorithm by changing the number of hidden neurons to achieve promising results in terms of maximum Coefficient of determination (R 2) value and minimum Mean Squared Error (MSE). It was identified that a strong R 2 value depends on the number of hidden neurons. The model developed with 16 hidden neurons accurately predicted the Estimated Repair Factor (ERF) value with an R 2 value of 0.9998. The remaining useful life (RUL) of a pipeline is estimated based on the metal loss growth rate calculations. The deterioration profiles of considered factors are generated to identify the individual impact on pipeline condition. The proposed FFBPN was validated with other published models for its robustness and it was found that FFBPN performed better than the previous approaches. The deterioration curves were generated and it was found that pressure has major negative affect on pipeline condition and weld girth has a minor negative affect on pipeline condition. This study can help petroleum and natural gas industrial operators assess the life condition of existing pipelines and thus enhances their inspection and rehabilitation forecasting.
A Review of Field Corrosion Control and Monitoring Techniques of the Upstream Oil and Gas Pipelines
Nigerian Journal of Technological Development, 2018
All steel pipelines used in hydrocarbon transportation are susceptible to either electrolytic or galvanic corrosion attack which deteriorate with time leading to failure even before end of design life. Consequences of corrosion attack and eventual failure of pipelines within oil and gas industry has been classified into economic, health, safety and environmental impacts. The present study considers detailed review of practical field corrosion control and monitoring mechanisms necessary to preserve, extend service life of pipelines and reduce corrosion impacts. The corrosion controls are various preventive strategies considered during construction and prior to pipelines' commissioning which include design, material selection, protective coating, chemical treatment and cathodic protection system. But the corrosion monitoring strategy is aimed at establishing condition of pipelines and environmental variables that may accelerate corrosion process and this includes potential survey, bacteria count, corrosion coupons and intelligent pigging. The identified corrosion control and monitoring techniques are not governed by any industry code and standard but has been generally accepted as best practice within the oil and gas industry as ways of combating corrosion and evaluating pipelines condition. Therefore, effective implementation of the identified corrosion control and monitoring strategies would limit corrosion attack and guide pipelines' operators to make informed decision and timely respond to corrosion threat before failures.