Integrating the root cause analysis to machine learning interpretation for predicting future failure

Abstract

The research proposes a new model for evaluating offshore pipelines due to corrosion. The existing inspection method has an inherent limitation in reusing the primary root cause analysis data to forecast the potential loss and corrosion mitigation, particularly in the scope of data utilization. The study implements Artificial Intelligence to transfer the knowledge of failure analysis as a consideration for conducting the inspection and lowering the risk of failure. This work combines experimental and modelling methodologies to assert the actual and feasible inspection method. The elemental composition, hardness, and tensile tests are utilized to unveil the types of corrosion products and metallic properties. Scanning Electronic Microscope and Energy Dispersive X-Ray (SEM-EDX) and X-Ray Diffractometer (XRD) was utilized to assess the corrosion product and their corresponding morphology to reveal the corrosion mechanism. The Gaussian Mixture Model (GMM), aided by the Pearson Multicollinear Matrix, shows the typical risk and predicts the damage mechanism of the spool to suggest the types of mitigation scenarios for the pipeline's longevity. According to the laboratory result, the wide and shallow pit corrosion and channelling are evident. The result of the tensile and hardness test confirms the types of the API 5 L X42 PSL 1 standard material. The SEM-EDX and XRD provide a piece of clear evidence into the corrosion product are primarily due to CO₂ corrosion. The silhouette score agrees well with the results of the Bayesian information criterion of GMM to show three different risk levels low, medium, and high-risk profiles. The combination of injection of chemicals such as parasol, biocide and cleaning pigging are a few solutions to address CO₂ corrosion. This work can be used as a guideline for assessing and clustering the risk based on the risk-based inspection.

1. Introduction

Subsea pipelines, called ‘the artery of energy business', are essential tools to deliver oil and gas (hydrocarbon) to meet the nation's energy demands from the offshore production site. Despite its critical role in ensuring the availability of the source energy, the industry remains challenged with high rates of materials degradation due to corrosion. A recent study [1] shows that minor corrosion may lead to severe catastrophic failure while retaining the corrosion business lost around $1.4 billion and another $ 0.6 billion related to pipeline maintenance [2]. The National Association of Corrosion Engineers (NACE) shows the average of business losses due to corrosion is approximately 5% of the country's gross national product [3]. Accordingly, their report the approximate global funding reaches USD 2.5 trillion to cover corrosion mitigation, excluding safety and environmental issues [4]. In essence, the cost of corrosion in marine pipelines is expensive. It promotes intensive corrosion mitigation and monitoring to ensure the integrity of oil and gas production facilities is fit for service.

It is possible to note that offshore pipeline's inherent factors which induce corrosion, including the uncontrolled amount of sulphate-reducing bacteria [5], carbon dioxide [6], hydrogen sulfide gas [7], sand, and the deterioration of coating due to contamination with seawater [8]. Most importantly, CO₂ (carbon dioxide) corrosion has been considered a common issue in the industry and is propagated in pipelines due to condensation of the water vapour, allowing H₂S (hydrogen sulfide) and CO₂ to dissolve. The process results in a thinner inner wall as more CO₂ and H₂S accumulate when the temperature and pressure decrease. Moreover, combining the CO₂–H₂O–H₂S creates an electrochemical reaction at lower pH, damaging the pipelines as longer service time is applicable [9]. Hence, accurate prediction related to the occurrence of CO₂ corrosion and proper monitoring is critical to overcoming the inability to control the degradation of materials.

Root-cause failure analysis (RCFA) is an element of technical method that contributes to unveiling the primary failure of a component fails in advance and acquiring its corresponding damage mechanism. It links to the limitation of material to cope with multiple geological conditions and the threat from the environment where the pipeline is laying, especially in offshore environments [10]. Few studies have focused on the benefit of RCFA to determine the source of failure and a series of practical solutions correlated to improvement methods to hinder repetitive CO₂ corrosion [[11], [12], [13]]. In this work, the spool sample from oil and gas pipeline material has been sought to fail. In addition to conducting the details characterization, a screening assessment is required to identify the possible cause of failure that considerably impacts future prevention.

One tool in internal CO₂ corrosion assessment and mitigation is the utilization of the inline inspection (ILI). ILI has been extensively used to determine the inner wall loss condition using magnetic flux leakage (MFL) and to evaluate the degradation mode due to the non-uniformity of corrosion threat. At the same time, regular pigging before corrosion inhibitor [[14], [15], [16]] injection is more effective in maintaining the integrity of pipelines. Recently, a study [17] showed that the tools are effective in acquiring the condition of internal corrosion due to the lack of a scrubber of the dehydration system. However, the relationship between the features of ILI inspection and the effect of CO₂ in the gas pipelines was ignored. Then the study of [18] improves the model by noting a few features of ILI parameters such as pipeline distance, upstream welding, width stress corrosion, and defect verification. A further improvement was comprehensively elaborated in the publication of [19] through implementing several machine learning models coupled with discussion related to screening and assessment of RBI. Nevertheless, the above model might be insufficient to describe the correlation between the dead leg of CO₂ gas and how the result of ILI identifies the presence of water and other aggressive chemical species to stimulate corrosion. In addition, ILI inspection result generates massive data, which may have inherent uncertainties and noise based on various material responses.

This work originally proposes unsupervised machine learning to apply the transferring knowledge of RCFA and ILI inspection to provide acceptable and practical corrosion mitigation and prevention in the ageing facilities of the main gas line. Several common machine learning studies have been successfully implemented for various purposes. For instance, the work of [20] argues that the combination of the Artificial neural network (ANN) and Support vector regression (SVR) is effective the analyze and predicting atmospheric corrosion. In their work, the effect of relative humidity, rainfall, and air composition serves as input data to estimate the corrosion process. In comparison, the other publication [21] describes the corrosion behaviour of several alloys to forecast the curves of polarization. The research of [22] implements multiple supervised machine learning, such as a k-nearest neighbour, Decision tree, Gradient boosting decision tree, Random forest, and Support vector machine, to simulate the electrochemical reaction related to sulphide and chloride ions.

Of all success, the utilization of machine learning, a broader requirement for clustering data from ILI inspection datasets for interpreting the root cause of the corroding main gas line, is not fully explored. The novelty of this paper aims to mechanize knowledge acquisition based on actual failure analysis and the result from ILI inspection. A few benefits of this paper are related to data construction, speed up the data analysis, and engineering soundness. Foremost, the data construction can be automized based on the data's nearness or similarity, which may project homogenous corrosion threats and damage mechanisms to the pipelines [23]. Secondly, the clustering method could be a solution to quickly process the generated data from ILI to ensure the datasets resemble the same corrosion damage from the inspected pipelines' according to their severity defect condition. Engineering soundness may be inherent human subjectivity in measuring the risk. Hence, clustering the data features helps to justify the decision with less time consumption and highly accurate prediction. In this case, the clustering method only recommends inspection and mitigation when the most severe pipelines are detected. This method allows for estimating a company's financial spending by utilizing data mining as a basis for exploiting regularities of data without the oversight of the importance of regular inspection [24]. Therefore, the research for the effective utilization of data mining of ILI becomes a priority in the oil and gas company to protect pipelines' inner walls from CO₂ corrosion.

It is also critical to note the limitation of the research includes selecting corrosion mitigation tools such as magnetic flow leakage of pigging. The chosen intelligent system is beneficial to gain numerous datasets corresponding to the response on the preferred prevention scenario to protect the metals. Furthermore, using Unsupervised Machine Learning of the Gaussian Mixture Model provides a greater perspective in clustering the risk of corrosion to gain the pattern and mapping a new recommendation for forthcoming inspection.

This work is arranged in a systematic way as follows. Section 2 gives a background of the research completed by the research method in Section 3. Section 4 provides the result and discussion of a practical industrial case to show the connection between the experimental and modelling setting. Section 5 concludes the study.

2. Background of research

Corrosion occurs differently along the pipeline segmentation. RCFA becomes vital in helping the reoccurrence of similar anomalies and excessive business loss. This study elaborates on the damage mechanism of the spool specimen from the subsea pipelines to evaluate the material's primary root cause of failure using several laboratory assessments such as visual examination, chemical composition, hardness, and tensile test. In this case, retrieving and examining the failed pipelines' condition is critical as the initial step to allow a more detailed assessment and focus on higher-risk pipeline segmentation. Based on initial observation (Fig. 1), it is predicted that the primary cause of the corrosion is due to CO₂ corrosion.

Fig. 1.

Visual inspection of the ex-spool.

However, the existing knowledge restricts the possibility of transferring and reusing the information provided by the result of RCFA as a reference for performing the best decision to protect the pipeline from similar failure and leak. The Machine Learning algorithms help to discover the relationship between the ILI inspection data and match the prediction with the experimental data.

2.1. A. experimental research

In this research, a case study uses machine learning to provide the model of integration and development of RCFA and ILI from the main gas lines from the subsea tie-in spool pipe. The ex-spool of the pipelines delivers gas to support the gas lift activities. The operating pressure was 690 psig with a design temperature of 100 °F. The dope and wrap of 5/32 were applied to coat the outer side of the pipeline. The installed pipeline has been in service for nearly 31 years, although the design lifetime is 25 years for 1.8 km of gas delivery. A historical record reports the as-received spool specimen leaked for two consecutive years, and their severity risk level was high. Based on the expert evaluation, it was deduced that there is no indication of external corrosion due to a lack of coating and low cathodic protection. However, corrosion monitoring sampling shows that low pH of five coupled with high content of H₂S and carbon dioxide of 40 ppm and 10%.

Despite the inactive platform, the subsea tie-in spool remains active to deliver gas under dead-leg conditions. The pressure remains low without a subsea valve between the nearest lines and the subsea tie-in spool. Fig. 1 shows the visual inspection of the spool given to the laboratory for further damage mechanism evaluation.

The outer diameter of the spool covered with polymeric coating, suspected as polyethene coating, without evidence of external damage on the outer diameter of the spool. Furthermore, the scale and corrosion products appear on the spool's internal surface diameter (see Fig. 2).

Fig. 2.

The internal surface of the ex-spool.

According to Fig. 2, the scale resembles the color of limestone, while the corrosion products were brown-red and observed on the areas identified as shallow pits and channelling. The shallow pit also appears in the inner layer of the spool of the low-carbon steel, which is typical for gas delivery applications. Furthermore, the corrosion monitoring of the pipeline excludes the sulphate-reducing bacteria monitoring system due to the delivery of dry gas only.

2.2. A. Machine learning model

Artificial intelligence (A.I.) has recently shifted how root cause analysis works. In this study, a further prediction of machine learning of the carbon dioxide model was proposed, coupling with their electrochemical reaction due to internal corrosion. Several works have introduced the potential identification of the damage mechanism involved in the failure of subsea pipelines, as previously elaborated by Ref. [25]. While the study of [26]argues that using Machine Learning is beneficial for measuring and modelling risk. The result shows the issue related to the involvement of the third party can be measured using the Bayesian network. The network provides a deeper understanding of the statistical model to estimate the contribution of external corrosion defects recently used by Ref. [27]. In this work, the elaboration to harness the potential of clustering is extensively used to group the sequence impact of corrosion in the main gas line, including their service condition based on the result of ILI.

As in the case of corrosion failures, the Gaussian Mixture Model (GMM) helps to classify the similarity of the datasets that occurs in the gas pipelines, enabling the specific root causes of failures. Considering the massive variance/covariance of the data, the model appears effective in transforming the non-circular shape data. Another essential aspect of modelling is the overlap data may mistakenly be classified. This circumstance shows that the corrosion may inherit a similar root cause of failure [28].

It is also imperative to note that the GMM collects the multidimensional Gaussian probability of distribution. In the case of a pipeline's failure, the likelihood of any input dataset, such as log distance, types of corrosion (internal or external), length of corrosion, etc., can be simulated. Classification aims to explore the possibility of clustered datasets without labelling the input and output data. Hence, this methodology allows the estimation of the minimum training datasets from the ILI inspection database without requiring physical corrosion data inspection acquisition and might be adopted for other corrosion mitigation scenarios.

3. Methods

3.1. Experimental

3.1.1. Material collection and preparation

The length of the specimen of material was 10 m and retrieved from one of the oil and gas company platforms during the shut-in period. The pipelines were prepared to unveil the inner condition related to corrosion. The specimen preparation includes different sections of the carbon steel pipe with multiple wall thicknesses. The detected positions of each section were at 3 o'clock, 6 o'clock, 9 o'clock, and 12 o'clock. The corresponding retrieved pipe section was dissected and opened from 12 o'clock (see Fig. 3 a and b).

Fig. 3.

(a) The cutting direction of carbon steel (b) Multiple wall thickness at various clock positions.

In this study, the position of the corrosion product is uniformly scattered in the inner location of the pipe. It drew grids at approximately 1.5 cm along the axial and radial directions. Eventually, the morphology of corrosion from the inner walls of the main gas line pipe section was observed while characteristics were photographed.

3.1.2. Physical and chemical performance test

A set of laboratory testing and characterization was conducted to reveal the primary root cause of the failed pipelines. The chemical test was conducted using an Optical Emission Spectrometer (OES) of WAS Lab Foundry-Master Oxford Instrument according to ASTM A751 [29] and ASTM E415 [30] standards for ferrous materials. The failed material was characterized using a spark to allow electron excitation and obtain the optical emission spectrum. The characterization was critical to get the elemental composition of Carbon (C), Manganese (Mn), Phosphorous (P), Sulfur (S), and Iron (Fe). The test was intended to unveil the type of failed material and evaluate its corrosion resistance.

Moreover, the as-received spool was then visually examined to document any features on the sample and determine the location related to the damage mechanism. The dimensional gauge of the width specimen is 12.53 mm, with the thickness and length of the tensile specimen of 10.86 mm and 50 mm. The Shimadzu Servopulser EHP-EB20186838 series tested the properties of tensile carbon steel pipe according to the ASTM E8 standard [31]. The uniaxial dog bone specimen was tensioned to obtain the fracture on the specimen gauge. Based on the ASTM E18 [32], the Rockwell B hardness of the cross-section carbon steel pipe was assessed using QualiRock Digital Hardness Tester, as illustrated in Fig. 4.

Fig. 4.

A schematic diagram of the Rockwell hardness test indentation position.

The sample was cut for a flat surface before being prepared for indentation using Rockwell B indenters. The specimen was loaded with 10 kg of load and was set to fit the indenter before a continuous loading of 100 kg to provide permanent indentation.

3.1.3. Analysis of surface morphology

The prepared specimen was subjected to surface studies to identify the internal diameter of the surface from the failed spool. The Scanning Electronic Microscope (SEM) of FEI Inspect F50 was used to obtain the surface morphological image of the corroded material. Meanwhile, the corrosion product microanalysis was identified using Energy Dispersive X-Ray (EDX). The equivalent between the X-ray intensity and the corrosion product element concentration was used to measure the corroded material's effect on seawater exposure. The specimen for the Analysis was taken from the scales found on the internal surface of the spool. The primary sample was examined to obtain types of corrosion products and to assert the corrosion product compound identified using EDX.

3.2. Machine learning studies

3.2.1. Dataset descriptions and processing

This work integrated the experimental result into machine-learning studies to fill the missing gap between the laboratory on the inspected pipeline and the integrity pipeline inspection using inline inspection. This paper examines the aging pipelines of more than three decades upon commissioning. It is common practice to use magnetic flux leakage to inspect the pipelines and generate defects. From herein, the defects serve as inspection datasets and transfer the knowledge to suggest and evaluate the sweet corrosion mitigation to hinder unplanned operation shutdown. The types and interpretations of the defect are depicted in Table 1.

Table 1.

The description of the datasets.

Types	Interpretation
LD	Log distance: The distance the pig launcher
Iden	Identification: The types of corrosion (external or internal)
Length	The length of the corroded area
Width	The width of the corroded area
Feature Class	The anomaly class of the inspection pipelines
ERF	Estimated Risk Factor

The data dimension is 7 × 496, with total instances (dataset) is 3276, and the ILI inspection details are given in Table 2.

Table 2.

The shortlist of the instance of ILI inspection.

log dis	Iden	Length (mm)	Width (mm)	Depth (mm)	Feature Class	ERF
0.203	0	21	30	28	0	0.21
0.231	0	26	75	27	1	0.21
0.638	0	11	16	14	0	0.21
0.646	1	6	8	10	2	0.2
1.23	1	6	16	27	3	0.2

The above list identifies the unclean and unstructured dataset, which requires further cleaning and feature removal. The pre-processing method was used to clean the dataset, lower the standard deviation, and reduce the complexity in computation time. The command of feature_selection was executed to perform the above objective. In the above list, the “iden” (identification) of 0 and 1 correspond to internal and external corrosion.

Fig. 5 shows the modelling methodology using the Gaussian mixture model and Pearson multicollinear matrix. In this work, the cluster number was set to three to consider the severity level of the pipelines (low, medium, and high risk). The probability of each dataset was calculated using multiple numbers of Gaussian as illustrated in Equation (1) [33],

$$p\left(x\right)=\sum _{k=1}^K{\pi }_{k}N\left(x_i|{\mu }_k,{\sigma }_k\right)$$ (1)

In the above equation, p(x) is the likelihood of data points from their corresponding Gaussian (principal component), K is the total number of components, π_k is coefficient of mixing, and $N\left(x_i|{\mu }_k,{\sigma }_k\right)$ corresponds to the model optimization component. All the datasets then process to find the balance between the model that has been created and the capability model to map the pattern using the Bayesian information criterion (BIC) [34]. This statistical model was used to confirm whether the dataset was properly assigned in three different clusters. Equation (2) shows the calculation of the BIC to achieve the high accuracy of the model and decreasing overfitting data [35],

$${\gamma }_{BIC}=M\ln {\sigma }^2+\kappa \ln M$$ (2)

In this equation, ${\gamma }_{BIC}$ is the BIC factor, κ elicits the number of variable parameters, M is the sample size. In essence, the objective of clustering two and three-dimensional features of GMM is to acquire the most critical pipelines that affect the pipeline's severity level. The maintenance execution should prioritize the most severe cluster's high-risk equipment.

Fig. 5.

The Machine Learning Analysis method and flow chart.

In this work, we attempt to model the root-cause analysis using the dataset generated from ILI inspection. As part of the model evaluation, expectation maximization (EM) was measured to verify the separation of datasets amongst the cluster. The evaluation model of clustering is measured by the silhouette score and calculated using Equation (3) [36],

$$S\left(i\right)=\frac{b\left(i\right)-a\left(i\right)}{\max \left\{a\left(i\right),b\left(i\right)\right\}}$$ (3)

Where the b(i) is the mean distance of the dataset of the inspected pipelines, and the a(i) is the point entails in the cluster. The expectation step assigns each dataset of “x_n” to its respective cluster “C_n".

They show that the datasets are perfectly separated, the little distance, or are incorrectly clustered. The goodness of fit for the GMM model shows a range of measurement intervals, giving scores of 1, 0, and −1. In this case, the nearness of datasets with a silhouette score of 1 indicates a perfect separation amongst the cluster. In contrast, the negative value shows the dataset in between two or more clusters. The measurement can be used to validate the model of clustering using GMM.

The novelty of this work is to harness the data mining to achieve the alignment between unsupervised machine learning and pairwise features (see Table 1). This is imperative since the estimation method can be further detailed by acquiring the most prominent features that affect the degradation of failed material. Identification was made by pairing the features of ILI. According to the literature [37], pairwise will be significant when the score was greater than 0.5. As such, the matrix score provides the strength of dependence on the features to assert the result of GMM.

4. Results and discussion

4.1. Experimental result

4.1.1. Visual examination

Fig. 6 shows the inner layer of the failed material possessing a shallow pit suggesting slight irregularities due to variation in material composition (see Table 3).

Fig. 6.

A more detailed visual inspection.

Table 3.

The chemical composition result.

Sample Code	C (%)	Mn (%)	P (%)	S (%)	Fe (%)
Spool Ex Specimen	0.17	0.69	0.018	0.006	Bal.
Standard
API 5 L X42 PSL 1	0.28 max	1.30 max	0.03 max	0.03 max	Bal.
API 5 L X52 PSL 1	0.28 max	1.40 max	0.03 max	0.03 max	Bal.

Based on the cross-sectional of the specimen, it is possible to observe extensive non-uniform corrosion and the thickness of the corrosion layer depicts incremental changes (Fig. 6). Nevertheless, the appearance of a good continuity combination between the smooth and non-smooth surface of the failed material's inner layer is obvious. The mechanism of CO₂ corrosion in each location may vary depending on the influence of operating conditions, liquid characteristics, and materials design [38].

In addition, two distinct shallow pits appear in the form of wide and narrow shallow pits. Wide shallow pit forms when the pit's depth changes remarkably from the upper section to the lower bottom pit. Likewise, the depth of narrow-deep pit corrosion changes quickly [39]. Based on Fig. 6, it concludes that the wide shallow pit appears to be more observable in the spool, indicating that non-uniform corrosion coupled with pitting corrosion occurs. This fact agrees well with the prediction of different degradation materials under various conditions in the inner layer of the specimen.

On the other hand, shallow pit corrosion and channelling share the common space within the inner layer of the specimen. The scales' color is typical limestone, while the corrosion product is brown-red. These findings indicate that the spool's environment contributed to the precipitation of scale and corrosion process. However, in the gaseous system, corrosion can only occur when free liquid water is present and promotes the dissolution of corrodents such as carbon dioxide (CO₂) gas or hydrogen sulfide (H₂S) gas. The corrosion layer is predicted to correspond to the presence of iron carbonate of FeCO₃ [40]. Furthermore, the channelling may occur in situ due to uncontrollable interaction between the metal and CO₂ gas, which elongates the corrosion product's nucleation and growth.

4.1.2. Physical and chemical performance result

The physical test of the failed material intends to unveil the composition of the element towards corrosion, while the chemical test evaluates the requirement of standards. Table 3 shows the result of the chemical composition test of the failed materials using OES.

The type of ex-spool specimen is under the API 5 L X42 PSL 1 category (Tabel 3). The presented element's content is generally lower than the standard limit of API 5 L X42 PSL. In this case, the content of Mn is moderately less than the standard minimum limit of 0.69% to indicate the lower corrosion resistance of failed material [41]. On the other hand, the presence of phosphorous can decrease intergranular corrosion, which may be affected by the segregation of phosphorous across the grain boundary [42]. The P value decreases almost half of the standard value at 0.018% attributed to the material's incapability of resisting corrosion in an aggressive environment [43].

Understanding the composition provides a valid interpretation of the corrosion degradation mechanism. It is a point of interest to note that the presence of sulfur (S) of 0.006% becomes the first indication of the failed material possibility of exhibiting impurities from the natural contamination of H₂S. The study of [44] and the water chemistry sampling on the inspected area where the ex-spool was taken provides a shred of lucid evidence. It shows the trace of H₂S as impurities at a considerable amount (0.015 psi) causes the degradation of carbon steel. Equations (4), (5)) provide the possible corrosion assisted by H₂S and sulphate-reducing bacteria (SRB) [45],

$$\begin{array}{l}{H}_{2}S\rightleftarrows H^{+}+H{S}^{-}\\ H{S}^{-}\rightleftarrows H^{+}+S^{2-}\end{array}$$ (4)

In this equation, it is possible to note the partial dissolution of hydrogen sulfide results in decreasing the pH of the environment. Furthermore, the interaction between iron and H₂S dismisses the electron and increases the corrosion rate [46],

$$Fe+H_{2}S\rightleftarrows Fe{S}_{1-y}+xH{S}^{-}+\left(2-y\right)H^{+}+2e^{-}$$ (5)

The result of the ex-spool actual inspection in the gaseous system agrees well with this fact, as stated in Section 2. This value is greater than the standard value of NACE MR0175/ISO15156 [47] at 0.005 psi, while the effect of the Fe_xS_y corrosion product decreases the corrosion protection. The result aligns with the visual inspection finding in which the corrosion pits at lower pH grow and developed on the front of the horizontal surface entailing an extensive concentration of FeCO₃ and FeS [48].

It should be considered the effect of tensile strength towards the loss of material to resist corrosion. Table 4 shows the result of the tensile test of the specimen.

Table 4.

The result of the specimen tensile test.

Sample Code	Tensile Strength MPa (psi)	Yield Strength MPa (psi)	Elongation (%)
Spool Ex ECOMM-EWY	485 (70,343)	318 (46,122)	33.13
Standard
API 5 L X42 PSL 1	Min 415 (60,200)	Min 290 (42,100)	–
API 5 L X52 PSL 1	Min 460 (66,700)	Min 360 (52,200)	–

Based on the tensile test result, the value of tensile properties was acceptable and typical for low-carbon steel and complied with API 5 L X42 PSL (Table 4). Comparing the standard material, the tensile strength of the ex-spool remains capable after being exposed to CO₂ corrosion, indicating that corrosion had no apparent effect on the tensile strength.

Table 5 shows the hardness test result with their corresponding capability to withhold the load.

Table 5.

A hardness test result of material of Ex Spool Specimen.

Sample Code	Hardness
Spool Ex ECOMM-EWY	71 HRB
Standard
API 5 L X42 PSL 1	n/a
API 5 L X52 PSL 1	n/a

Based on the hardness test result, the hardness value was acceptable, and there was no sign of high hardness. Under the API 5 L standard, the hardness value was not required in the document.

4.1.3. Surface morphology

The result of the corrosion product from the internal diameter of the spool using SEM-EDX and their corresponding elemental result is illustrated in Fig. 7a (locations 1) and Fig. 7b (location 2). Meanwhile, the detail of location has been shown in Fig. 6.

Fig. 7.

Surface analysis of ex-spool failed material (a) location 1 (b) location 2.

The analysis of the internal diameter of the spool using SEM-EDX reveals common noticeable chemical elements such as Fe, C, O, S, Mn, and Cl. It is possible to note that the higher content of iron (75.08%), carbon (0.46%), oxygen (15.46%), and sulfur (0.37%) is more significant in location 1 compares to location 2. Hence, the formation of FeCO₃ might be related to higher content of iron, carbon and oxygen. The EDX microanalysis shows major content of iron and oxygen. However, this iron oxide could occur when oxygen gas is involved in the electrochemical reaction during corrosion and it is possible when the spool was exposed to atmospheric conditions.

Therefore, the iron oxides in the corrosion products were formed during the warehouse storage of the spool material after being taken out from the subsea. The presence of chloride in the EDX results confirmed the seawater environment.

Meanwhile, the element sulfur (S) detected in the EDX microanalysis gives another indication of the corrosion mechanism which might be contributed by the presence of hydrogen sulphide (H₂S) gas or microbiologically induced corrosion (MIC). These two corrosion mechanisms have a fingerprint on the corrosion products. Usually in a form of corrosion appears in the iron sulphide (Fe_xS_y) compound when attacking carbon steel (see Equation (5)).

It confirms the results of the visual inspection to show remarkably non-uniform corrosion attack of wide shallow pits at the face-up surface direction. At the same location, it is noted that the irregular flaky-shaped scale/corrosion product is evident (Fig. 7a). It presumes that the carbon steel was exposed to a high content of water for a long period of duration while chloride accumulation occurs. The result aligns with the observation made in Ref. [49]. In contrast, the flowery shape (needle shape) appears in location 2 (Fig. 7b) which indicates the presence of the hexagonal of feroxyhyte or δ-FeOOH [50].

4.1.4. XRD results

The analysis of compounds in the corrosion products was performed using X-Ray Diffraction (XRD) and their result is depicted in two distinct areas; area 1 (Fig. 8a) and area 2 (Fig. 8b).

Fig. 8.

The result X-ray difractogram of ex-spool sampel

The characteristic of the deposits on the internal surface of the spool is differentiated into two characters which are hardly-removed deposit (Area 1) and crumbling-and-easily-removed deposit (Area 2), as illustrated in Fig. 8c. Furthermore, Table 6 compares the possible corrosion product in two distinctive regions 1 and 2.

Table 6.

The identification of corrosion product.

Compound	Chemical formula	Area 1	Area 2
Siderite	FeCO3	22	29
Iron phosphate	FePO4	7	1
Iron Oxide	FeO	9	–
Iron Sulfide	FeS	7	–

In addition, the result shows that siderite appears in both locations with a remarkable increase in Location Area 2. This high probability confirms the hypothesis of the work and assumption given by the expert at the initial stage of the analysis test. It indicates that a higher chance of carbon dioxide exists in the corrosion product. Although a trace of iron carbonate is extensively found in the corrosion product, the remaining chemical compound such as iron phosphate (FePO₄), iron oxide (FeO), and iron sulphide (FeS) provide proof for the interaction of phosphorous, oxygen, and sulphide on the carbon steel surface as rust. It is assumed that FePO₄ was a weak interaction corrosion inhibitor product with pipelines. According to Refs. [51,52], phosphorous improves the protection of pipelines due to their adsorption of highly electron donor atoms on the surface of metals. Moreover, the presence of iron oxide concludes the formation of the ex-spool specimen into the atmosphere upon retrieval from subsea facilities. Compared to FeO, the presence of FeS is highly linked with the reaction of carbon steel with dissolved hydrogen sulphide (H₂S) in free liquid water. Nevertheless, this hypothesis needs to be confirmed with the supporting data and tests related to the treatment based on the water chemistry of the fluid in the spool.

4.2. Machine learning studies

Despite the success of the present experimental method, a set of data utilization can be maximized to identify the anomaly and group the defect location and types of treatment since it influences maintenance decision-making. This method proposes a possible improvement in future research since the integration between the experimental and modelling technique is critical to add knowledge in monitoring gas pipelines while mitigating human subjectivity in determining dissimilar corrosion. Accordingly, it is possible to reuse and transfer the knowledge of root cause analysis and establish integration with the inline inspection. Specifically in this work, carbon dioxide corrosion becomes a primary factor to degrade the inner wall of pipelines.

On the contrary, the role of MFL pigging is accountable for cleaning the internal pipeline surface mechanically. Due to the complexity of the data generated from ILI, the datasets of defects can be utilized to provide a better strategy for maintenance.

Fig. 9a–d shows the clustering model to search for the ideal balance composition related to the similarity of corrosion defects. The clustering intends to classify the inhomogeneity of the corrosion, which may vary across the cluster. The clusters of 0, 1, and 2 correspond to the low, medium, and high risks of CO₂ corrosion.

Fig. 9.

The calculation of (a) silhouette scores (b) The evaluation of cluster ideal number (c) Pigging clustering model of the ex-spool specimen (d) Pearson Matrix Modelling.

It is possible to conclude that the nearness of clusters 0 and 2 is roughly equal to clusters 0 and 1, as depicted in Fig. 9c. This closeness indicates the reversibility of the dataset between the cluster. In this case without a proper planning pigging activity, all datasets in cluster 0 vulnerable shifted to clusters 1 and 2. The result in Table 7 is consistent with the detail of the predicted cluster based on the ILI and modelling technique (Fig. 9c).

Table 7.

The result of ILI and Modelling.

log dis (m)	Iden	Length (mm)	Width (mm)	Depth (mm)	Feature Class	ERF	Predicted Cluster
0.638	1	11	16	14	0	0.21	0
0.646	1	6	8	10	2	0.2	0
182.992	1	9	10	21	2	0.2	1
207.991	0	57	17	21	3	0.2	1
1568.016	1	10	14	52	0	0.21	2
1801.274	0	64	22	31	3	0.2	2

Overall, it is possible to note that the corrosion effect due to CO₂ extensively occurs almost at the end of pipelines with a maximum depth of corrosion of 52 mm (Cluster 2). The result agrees well with the high probability to discover siderite (FeCO₃) in both locations using the XRD test (see Table 6). In contrast, a minor effect of corrosion was found at the initial pig launcher with a small depth of corrosion of 14 mm (Cluster 0). Based on Table 7, the corrosion degradation mechanism can be illustrated as follows,

$$\begin{array}{ccc}\mathrm{A}\mathrm{n}\mathrm{o}\mathrm{d}\mathrm{e}\text{:}& \mathrm{F}\mathrm{e}\to {\mathrm{F}\mathrm{e}}^{2+}+2\mathrm{e}-& \left(\mathrm{o}\mathrm{x}\mathrm{i}\mathrm{d}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\right)\end{array}$$

$$\begin{array}{ccc}\mathrm{C}\mathrm{a}\mathrm{t}\mathrm{h}\mathrm{o}\mathrm{d}\mathrm{e}\text{:}& {2\mathrm{H}}^{+}+{2\mathrm{e}}^{-}\to {\mathrm{H}}_2& \left(\mathrm{r}\mathrm{e}\mathrm{d}\mathrm{u}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\right)\end{array}$$

Meanwhile, the dissolved CO₂ gas provides the following reaction.

$$\begin{array}{cc}{\mathrm{C}\mathrm{O}}_2{+\mathrm{H}}_2\mathrm{O}\to {\mathrm{H}}_2{\mathrm{C}\mathrm{O}}_3& \left(\mathrm{a}\mathrm{c}\mathrm{i}\mathrm{d}\mathrm{f}\mathrm{o}\mathrm{r}\mathrm{m}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\right)\end{array}$$

The carbonic acid is known to partially dissociate according to the following reaction steps,

H₂CO₃ → H⁺ + HCO₃⁻

HCO₃⁻ → H⁺ + CO₃^2-

Therefore, following these reactions, the hydrogen ions were reduced at the cathode site as the steel is corroded to give the electron. On the other side, the product of iron oxidation which is ferro ions (Fe²⁺) is captured by the carbonate ions (CO₃²⁻) to form iron carbonate (FeCO₃). The form of metal loss caused by sweet corrosion could be shallow pitting and/or channelling due to a mesa-type attack.

Consequently, the level of risk may increase in the inspected pipeline segment. An alternative strategy to prevent the shifting between the cluster is by conducting a typical combination of chemical injection and cleaning pigging activity. It is noteworthy to mention that chemical treatment such as injection of gas corrosion inhibitors and biocides comes before mechanically cleaning the pipeline [53]. The ideal corrosion protection in cluster 0 is by injecting the parasol chemical to dissolve the deposit inside the pipelines. Moreover, the formation of concentrated chemical film between two consecutive pipeline pigs allows the chemical at minimum volume to progressively protect the pipelines and increases the flow assurance.

An alternative method to overcome the issue of corrosion in cluster 1 is utilizing the mechanical cleaning pigging. In this circumstance, the mechanical force improves the flow assurance, disrupts the growth of sulphate-reducing bacteria and colonies, and optimizes the chemical injection performance. Controlling the growth of bacteria minimizes the probability to lower the formation of Fe_xS_y and confirms equations (4), (5)). In essence, the corrosion defect in cluster 2 can be mitigated by implementing the biocide injection and continuous corrosion inhibitor injection and cleaning pigging. In this instance, biocide injection eliminates the SRB bacteria while corrosion inhibitors develop an internal pipeline film to protect from impurities such as CO₂ and H₂S. Cleaning pigging is cost-effective considering the corrosion threat when corrosion monitoring related to SRB monitoring fails to achieve success.

The model evaluation is critical to ensure the modelling represents the pipeline's condition before administrating the maintenance recommendation. The assessment includes the determination of the silhouette score (S·S.). According to the literature [54], the closer the value to 1, the more related the pairwise. Fig. 9a shows that the highest S.S. value is nearly 0.70 to show that the predicted risk types align with the actual condition of the inspected pipelines and agrees well with the lower value of the Bayesian Inference Criterion (BIC) (see Fig. 9b).

Based on the modelling result, the lowest BIC value is in cluster 3 despite an average score of around 300. It should remember that the result of BIC is consistent with the outcome of S.S. and the clustering data set, which gives the idea that the selected method to approach root-cause Analysis fits the primary root-cause factor of the failed materials. It is also possible to deduce that the lower BIC model should reveal that it is better than another model. However, the minimum amount of BIC shows that clustering methodology is appropriate in predicting the root-cause Analysis based on machine learning.

In addition, the Pearson Multicollinear Matrix (PMM) composes the relationship between the pairwise factor that affects the possible failed materials. The matrix helps to determine the most influential factor to affect the primary root cause according to the datasets given in the inline inspection. In this instance, the strongest correlation between the distance of pipelines and the length and depth of the corroded area is evident (see Fig. 9d). It is possible to deduce that the most influential pairwise affects the failed material log distance vs depth (0.27) and log distance vs length (0.13). The higher the score, the more related the pairwise. According to the above matrix, it can be predicted that the corrodent deposition induces CO₂ corrosion. The result of XRD and SEM-EDX agrees that the PMM score of logarithm distance versus corrosion depth is greater than the other influencing factors.

Despite the success of the above experimental and modelling result, the research remains unclear in identifying the root-cause factor related to the affected phases in their corresponding metallography. It is valuable to note that the changes in the microstructure phase retain essential information to understand the corrosion model. On the other hand, networking modelling helps the corrosion interdependence between the parameters to assert the GMM clustering modelling, especially the appearance of multiple overlapping data points. It gives a lucid insight into finding the centrality of the data and the most influential parameter to affect the corrosion.

5. Conclusion

The material has been tested and investigated in the laboratory for further analysis and investigation of its damage mechanism. The physical and chemical test show that the material is categorized as API 5 L X42 PSL 1 with an elongation of 33.3% and comprises carbon, manganese, Phosphorous, Sulfur, and Iron. A trace value of sulfur confirms the presence of Fe_xS_y as a corrosion product which may reflect the presence of H₂S and the activity of sulphate-reducing bacteria. Further, internal analysis indicates the presence of narrow and wide shallow pitting corrosion which corresponds to the FeCO₃. The result of SEM-EDX and XRD confirms the trace of carbon dioxide and sulfur as primary corrosion products. Hence, it can be concluded that CO₂ is the dominant factor to cause the failure of the ex-spool of materials. The modelling of GMM shows the likelihood of all datasets given from ILI inspection to cluster in three groups. Based on the model, it is possible to note that the data points are vulnerable to shifting from one cluster to another without proper and strict corrosion mitigation. According to the operational condition of the pipeline, injecting chemicals such as parasol, gas corrosion inhibitors, and cleaning pigging are selected to reduce the effect of corrosion in the entire main gas pipeline. However, the limitation of this work relates to the incapability to predict whether the pipelines require immediate inspection or retain the pipelines is in service. A pairwise Pearson multicollinear matrix merely suggests the most influential parameters of depth vs length of corrosion and log distance vs depth of corrosion without noting the criticality of the parameter to influence the corrosion. This can be addressed by utilization of a preferred classifier of supervised machine learning due to robust performance and implementing ensemble machine learning techniques to reinforce the ILI data and evaluate the performance model. Combining the existing knowledge with the recent source of machine learning offers a novel way to collect, analyze, and execute the protection gas pipeline without ignoring the objectivity of the corrosion threat based on ILI inspection.

Author contribution statement

Taufik Aditiyawarman: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Wrote the paper.

Johny Wahyuadi Soedarsono: Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data.

Agus Paul Setiawan Kaban: Analyzed and interpreted the data; Wrote the paper.

Suryadi, Haryo Rahmadani and Rini Riastuti: Analyzed and interpreted the data.

Data availability statement

The data that has been used is confidential.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper