Remaining useful life prediction of crude oil pipeline by means of deterioration curves

Shaik, Nagoor Basha; Pedapati, Srinivasa Rao; Dzubir, Faizul Azly Abd

doi:10.1002/prs.12112

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…Meanwhile, ERF denotes the ratio of the pipeline design pressure to the safe maximum pressure calculated as per the ASME B31G analytical criterion. According to pipeline integrity standards, ERF is used to evaluate the severity of pipeline life issues, and the ERF equation is provided by ( 1 ) 38 . In this relationship, the assessment pressure is the operator's pressure, and the safe operating pressure is the estimated defect assessment method's safe operation pressure.…”

Section: Methodsmentioning

confidence: 99%

A novel neural network-based framework to estimate oil and gas pipelines life with missing input parameters

Shaik,

Jongkittinarukorn,

Benjapolakul

et al. 2024

Sci Rep

Self Cite

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Dry gas pipelines can encounter various operational, technical, and environmental issues, such as corrosion, leaks, spills, restrictions, and cyber threats. To address these difficulties, proactive maintenance and management and a new technological strategy are needed to increase safety, reliability, and efficiency. A novel neural network model for forecasting the life of a dry gas pipeline system and detecting the metal loss dimension class that is exposed to a harsh environment is presented in this study to handle the missing data. The proposed strategy blends the strength of deep learning techniques with industry-specific expertise. The main advantage of this study is to predict the pipeline life with a significant advantage of predicting the dimension classification of metal loss simultaneously employing a Bayesian regularization-based neural network framework when there are missing inputs in the datasets. The proposed intelligent model, trained on four pipeline datasets of a dry gas pipeline system, can predict the health condition of pipelines with high accuracy, even if there are missing parameters in the dataset. The proposed model using neural network technology generated satisfactory results in terms of numerical performance, with MSE and R2 values closer to 0 and 1, respectively. A few cases with missing input data are carried out, and the missing data is forecasted for each case. Then, a model is developed to predict the life condition of pipelines with the predicted missing input variables. The findings reveal that the model has the potential for real-world applications in the oil and gas sector for estimating the health condition of pipelines, even if there are missing input parameters. Additionally, multi-model comparative analysis and sensitivity analysis are incorporated, offering an extensive comprehension of multi-model prediction abilities and beneficial insights into the impact of various input variables on model outputs, thereby improving the interpretability and reliability of our results. The proposed framework could help business plans by lowering the chance of severe accidents and environmental harm with better safety and reliability.

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Section: Methodsmentioning

confidence: 99%

A novel neural network-based framework to estimate oil and gas pipelines life with missing input parameters

Shaik,

Jongkittinarukorn,

Benjapolakul

et al. 2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…This method does not need to consider various mechanisms and complicated influencing factors affecting wax deposition, but only needs to establish enough experimental data samples, then combine artificial intelligence methods to learn the experimental data samples, and then a trained artificial intelligence wax deposition prediction model can be constructed. This method can be used to predict the wax deposition in other operating conditions [18,19].…”

Section: ) Artificial Intelligence Prediction Modelmentioning

confidence: 99%

Prediction Model of Wax Deposition Rate in Waxy Crude Oil Pipelines by Elman Neural Network Based on Improved Reptile Search Algorithm

Chen,

Wang,

Jin

et al. 2024

ENERGY

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A hard problem that hinders the movement of waxy crude oil is wax deposition in oil pipelines. To ensure the safe operation of crude oil pipelines, an accurate model must be developed to predict the rate of wax deposition in crude oil pipelines. Aiming at the shortcomings of the ENN prediction model, which easily falls into the local minimum value and weak generalization ability in the implementation process, an optimized ENN prediction model based on the IRSA is proposed. The validity of the new model was confirmed by the accurate prediction of two sets of experimental data on wax deposition in crude oil pipelines. The two groups of crude oil wax deposition rate case prediction results showed that the average absolute percentage errors of IRSA-ENN prediction models is 0.5476% and 0.7831%, respectively. Additionally, it shows a higher prediction accuracy compared to the ENN prediction model. In fact, the new model established by using the IRSA to optimize ENN can optimize the initial weights and thresholds in the prediction process, which can overcome the shortcomings of the ENN prediction model, such as weak generalization ability and tendency to fall into the local minimum value, so that it has the advantages of strong implementation and high prediction accuracy.

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“…In reality, despite increasing water cuts in the crude oil pipelines, due to aging fields, and which will remain a recurring challenge as the pipelines also get much older, more than 40 percent of the hydrocarbon pipelines worldwide are statistically more than 30 years old [4,5], as depicted in Fig. 4, despite operating under adverse conditions.…”

Section: Introductionmentioning

confidence: 99%

Valuating hydrocarbon pipeline facility service beyond 20–25 year economic life: Accounting for residual value

Alaneme¹,

Al-Lajam²,

Al-Jaafari³

et al. 2021

Nig. J. Tech.

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The 20–25 years economic life for hydrocarbon pipelines in the investment decision model is at wide variance with historical statistical records of more than 90-percent world-wide. Opinions diverge, from service type to the product quality, and materials resilience as basis for this premise. While, financial experts consider time to fully depreciate a capital investment, irrespective of the rate of returns, engineers consider operational availability and reliability duration. The risk is that actual residue values of pipelines worldwide are erroneously omitted in every project’s economics Cash-flow computation, thus eroding the investment decision quality. Statistics showed that more than 60-percent of pipelines worldwide have already exceeded the 25 years economic life, while more than 40-percent have operated more than 30-years and above. This theoretical appraisal identified a gap in the economic model in handling multi-criteria risk management uncertainties like hedging, weighting, etc., and highlighted the exigency to craft and assign numeric residue values for pipelines in the investment Cash-flow models.

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Remaining useful life prediction of crude oil pipeline by means of deterioration curves

Cited by 6 publications

References 12 publications

A novel neural network-based framework to estimate oil and gas pipelines life with missing input parameters

A novel neural network-based framework to estimate oil and gas pipelines life with missing input parameters

Prediction Model of Wax Deposition Rate in Waxy Crude Oil Pipelines by Elman Neural Network Based on Improved Reptile Search Algorithm

Valuating hydrocarbon pipeline facility service beyond 20–25 year economic life: Accounting for residual value

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