Plausible Profile (Psqr) Corrosion Assessment Model: Refinement, Validation and Operationalization

2020 ◽  
Author(s):  
Shenwei Zhang ◽  
Jason Yan ◽  
Shahani Kariyawasam ◽  
Terry Huang ◽  
Mohammad Al-Amin
Keyword(s):  
Oil And Gas ◽  
Effective Area ◽  
Companion Paper ◽  
Assessment Model ◽  
Metal Loss ◽  
Model Refinement ◽  
Corrosion Morphology ◽  
Internal Review ◽  
Project Outcome ◽  
Corrosion Assessment

Abstract This paper presents the refinement, validation, and operationalization of Plausible Profiles (Psqr) corrosion assessment model that TC Energy (TCE) published in IPC 2018. Metal-loss corrosion continues to be a major integrity threat to oil and gas pipelines. Inline inspection (ILI) based corrosion management, where ILI measured anomalies are assessed and mitigated, has proven to be the best way to manage corrosion. The assessment model used to estimate the burst pressure of pipelines has the most significant impact on integrity decisions. These decisions include (1) which anomalies to excavate based on In-line inspection (ILI); (2) pressure reduction (i.e. derate) required to maintain safety until repairs are completed, and (3) repair decisions during the excavation. Consequently, TCE focused on improving the shape factor of the Modified B31G effective area technique and published an overview of the improvement in IPC 2018 paper titled “A More Accurate and Precise Method for Large Metal Loss Corrosion Assessment”. From 2018 TCE refined the model using further testing, validation, internal review, and external review. In 2019, the model was reviewed by eight industry experts through Pipeline Research Council International (PRCI) project “EC-2-9 Peer Review of the Plausible Profile (Psqr) Corrosion Assessment Model”. The project outcome recommended Psqr as an improved corrosion assessment model. The comments and recommendations provided by the reviewers will be reported in IPC 2020 in a companion paper. Validation results show the Psqr model is safe, and more accurate and precise than RSTRENG. The resulting magnitude of reduction in unnecessary activities depends on the corrosion morphology.

A More Accurate and Precise Method for Large Metal Loss Corrosion Assessment

2018 ◽  
Author(s):  
Shenwei Zhang ◽  
Jason Yan ◽  
Shahani Kariyawasam ◽  
Terry Huang ◽  
Mohammad Al-Amin
Keyword(s):  
Case Studies ◽  
Pressure Ratio ◽  
Field Measurements ◽  
Assessment Model ◽  
Metal Loss ◽  
Worst Case ◽  
Corrosion Morphology ◽  
Surgical Tool ◽  
Cutting Out

Pipeline integrity decisions are highly sensitive to the assessment model. A less accurate and less precise model can conservatively trigger many unnecessary actions such as excavations without providing additional safety. Therefore, a more accurate and precise model will reduce excavations and provide higher assurance of safety. This is akin to using a more precise surgical tool such as a laser for cutting out a brain tumor where you can cut closer to the edge and be assured of cutting out more of the tumor (safer) and yet cut less of the surrounding brain tissue (less conservative). This paper presents a novel model for assessing large metal-loss corrosion based on in-line inspection (ILI) or field measurement. The model described in this paper utilized an unconventional approach, namely multiple plausible profiles (P2), to idealize the shape of the corrosion, and therefore is referred to as P2 model. In contrast, all existing models use one single profile for characterizing corrosion profile, e.g. RSTRENG utilizes a single worst-case river bottom profile to characterize the shape of corrosion. The P2 model has been initially validated using fourteen (14) full scale specimen-based hydrostatic tests on pipes containing real large corrosion features. Validation results showed that the P2 model is safe, but less conservative and more precise than RSTRENG. The magnitude of reduction in conservatism depends on the corrosion morphology. On average, the P2 model achieves 15% reduction in model bias and 44% reduction in standard deviation of model error. Further validation was provided using the testing data published by PRCI and PETROBRAS. Another set of burst tests are being conducted by TransCanada as part of the continuous validation of P2 model. The effectiveness of the P2 model was demonstrated through two case studies (denoted by Case study 1 and 2). Case Study 1 included 170 external metal-loss corrosion features that were excavated from different pipeline sections, and have field-measurements using laser scan tool. Case Study 2 included 154 ILI-reported external metal-loss corrosion features with RSTRENG calculated rupture-pressure-ratio (RPR) of less than or equal to 1.25 (i.e. RPR ≤ 1.25); hence, these features were classified as immediate features. The Case Studies showed that the use of the P2 model resulted in 80% less number of ILI-reported features requiring immediate action (i.e., RPR ≤ 1.25) and 89% less number of excavated features requiring repair (e.g., sleeve or cut-out) compared to the respective number of features identified by RSTRENG-based assessment. The reduction in the number of features requiring excavation or repair is highly morphology-dependent with the highest reduction achievable for pipeline containing long and wide corrosion clusters (e.g., tape-coated pipeline). However, the P2 model is applicable to all clusters regardless of the number of individual corrosion anomalies associated with the cluster.

A Data Driven Validation of a Defect Assessment Model and its Safe Implementation

2020 ◽  
Author(s):  
Shahani Kariyawasam ◽  
Shenwei Zhang ◽  
Jason Yan
Keyword(s):  
Data Analytics ◽  
Model Performance ◽  
Management Program ◽  
Assessment Model ◽  
Metal Loss ◽  
Good Representation ◽  
Validation Data ◽  
Corrosion Morphology ◽  
Integrity Assessment ◽  
Defect Assessment

Abstract This paper presents data analytics that demonstrates the safe implementation of defect assessment models which use uncertain measurements of defect and material properties as inputs. Even though this validation is done for a corrosion assessment model implementation, it can be generalized for any defect assessment validation where the inputs have uncertainty (as they do in implementation). The questions arising from the validation of the Plausible Profiles (Psqr) model and related review led to a large amount of data analytics to demonstrate various aspects of safety in implementation. The data analytics demonstrates how the safety of model implementation can be verified using a well-designed set of data. The validation of Psqr model was conducted on a unique set of data consisting of metal-loss corrosion clusters with Inline Inspection (ILI) reported size, laser scan-measured dimension, and well monitored burst testing pressure. Therefore, this validation provided an unprecedented set of validation data that could represent many perspectives, such as model performance (with all uncertainties associated with other parameters removed), in-the-ditch decision scenario, and ILI-based decision scenario. Moreover, the morphologies of the 30 corrosion clusters tested is a good representation of large corrosion clusters that have failed historically in the pipeline industry. One of learnings from post-ILI failures due to corrosion in the industry is that corrosion morphology played a significant role. Previous model validations were mostly performed on simple single anomalies or simple clusters with few individual corrosion anomalies. It is important that a corrosion model is validated using real corrosion morphologies that are representative of in-service conditions. The analysis of this unprecedented and comprehensive set of data led to great learning and revealed how safety can be achieved optimally with good understanding of how uncertainties associated with ILI sizing error, material property, model error, and safety factors interact and play into integrity. It also revealed the role of common misunderstandings that are barriers to effective pipeline integrity assessment. Overcoming these misunderstandings have helped in developing a more effective ILI based corrosion management program that will avoid more failures and reduce unnecessary integrity actions.

Effective Improvements to Reliability Based Corrosion Management

2010 ◽  
Author(s):  
Shahani Kariyawasam ◽  
Warren Peterson
Keyword(s):  
Decision Making ◽  
Oil And Gas ◽  
Data Gathering ◽  
Effective Area ◽  
The Face ◽  
Integrity Management ◽  
Reliability Methods ◽  
Specific Growth Rates ◽  
Safe Condition ◽  
Effective Use

Reliability methods have being adopted by oil and gas operators for integrity management decisions. These methods explicitly account for all relevant uncertainties and are designed to provide consistent safety. Consequently, a risk or reliability based approach is a very appropriate basis for decision making in the face of uncertainties. However, as in the effective use of any powerful methodology the sensitivities of the method to assumptions and limitations of applicability need to be well understood. This paper presents how improvements were made to reliability based integrity program by understanding its limitations and sensitivities. First the inputs that have the highest impact on the results were identified. These inputs are the most appropriate areas for improvement and data gathering. It is also very important to understand how the results are to be used and for what purpose. The results of this particular inline inspection based reliability assessment are used to make better excavation and repair decisions. A defect-based and joint-based decision making process is essential for determining with sufficient confidence if each defect and joint is in a safe condition. Consequently, the improvements are focused on discriminating between the myriad of defects found during an inline inspection run. Distinct field characteristics of corrosion growth are also taken into account in these improvements. The paper presents the implementation of effective area methods for future integrity probabilistic evaluations. It also describes the benefit of applying defect-specific growth rates. Finally, case studies are presented to demonstrate the effectiveness of the changes.

Pipeline Bundle: A Smart Solution for Infield Transportation—Part 1: Overview and Engineering Design

2009 ◽  
Author(s):  
R. Song ◽  
Z. Kang ◽  
Yuanlong Qin ◽  
Chunrun Li
Keyword(s):  
Engineering Design ◽  
Thermal Performance ◽  
Oil And Gas ◽  
Cost Effective ◽  
Companion Paper ◽  
Transportation Engineering ◽  
Innovative Solution ◽  
Water Pipeline ◽  
Protection Potential ◽  
Offshore Oil And Gas

Pipeline bundle system consisting of carrier pipe, sleeve pipe and internal flowlines offers innovative solution for the infield transportation of oil and gas. Due to its features, pipeline bundle offers a couple of advantages over conventional pipeline in particular for cases where multi-flowlines and high thermal performance are of great interests. The main benefits and advantages of such system include excellent thermal performance to prevent wax formation and hydrates, multiple bundled flowlines, mechanical and corrosion protection, potential reuse, etc. With the developments of offshore oil and gas industries, more and more hydrocarbon resources are being explored and discovered from shallow to deep water. Pipeline bundle system can be a smart solution for certain applications, which can be safe and cost effective solution. The objective of this paper is to overview pipeline bundle technology, outline detailed engineering design issue and procedure. Focus is given to its potential application in offshore for infield transportation. Engineering design principles and procedures for pipeline bundle system has been highlighted. A companion paper addressed the details of the construction and installation of pipeline bundle system. An example is given at the end of this paper to demonstrate the pipeline bundle system concept and its application.

A Novel Corrosion Monitoring and Prediction System Utilizing Advanced Artificial Intelligence

2021 ◽  
Author(s):  
Klemens Katterbauer ◽  
Waleed Dokhon ◽  
Fahmi Aulia ◽  
Mohanad Fahmi
Keyword(s):  
Real Time ◽  
Oil And Gas ◽  
Salt Water ◽  
Oil And Gas Industry ◽  
Training Dataset ◽  
Metal Loss ◽  
Corrosion Monitoring ◽  
Prediction System ◽  
Well Performance ◽  
Data Driven Approach

Abstract Corrosion in pipes is a major challenge for the oil and gas industry as the metal loss of the pipe, as well as solid buildup in the pipe, may lead to an impediment of flow assurance or may lead to hindering well performance. Therefore, managing well integrity by stringent monitoring and predicting corrosion of the well is quintessential for maximizing the productive life of the wells and minimizing the risk of well control issues, which subsequently minimizing cost related to corrosion log allocation and workovers. We present a novel supervised learning method for a corrosion monitoring and prediction system in real time. The system analyzes in real time various parameters of major causes of corrosion such as salt water, hydrogen sulfide, CO2, well age, fluid rate, metal losses, and other parameters. The data are preprocessed with a filter to remove outliers and inconsistencies in the data. The filter cross-correlates the various parameters to determine the input weights for the deep learning classification techniques. The wells are classified in terms of their need for a workover, then by the framework based on the data, utilizing a two-dimensional segmentation approach for the severity as well as risk for each well. The framework was trialed on a probabilistically determined large dataset of a group of wells with an assumed metal loss. The framework was first trained on the training dataset, and then subsequently evaluated on a different test well set. The training results were robust with a strong ability to estimate metal losses and corrosion classification. Segmentation on the test wells outlined strong segmentation capabilities, while facing challenges in the segmentation when the quantified risk for a well is medium. The novel framework presents a data-driven approach to the fast and efficient characterization of wells as potential candidates for corrosion logs and workover. The framework can be easily expanded with new well data for improving classification.

Effectiveness of Ultraviolet Light For Mitigating Risk of Microbiologically Influenced Corrosion in Steel Pipeline

2015 ◽  
Vol 74 (4) ◽  
Author(s):  
M. K. F. M. Ali ◽  
N. Md. Noor ◽  
N. Yahaya ◽  
A. A. Bakar ◽  
M. Ismail
Keyword(s):  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Microbiologically Influenced Corrosion ◽  
Metal Loss ◽  
Desulfovibrio Vulgaris ◽  
Pipeline System ◽  
Long Distance ◽  
Gas Industry ◽  
Internal Corrosion ◽  
Steel Pipeline

Pipelines play an extremely important role in the transportation of gases and liquids over long distance throughout the world. Internal corrosion due to microbiologically influenced corrosion (MIC) is one of the major integrity problems in oil and gas industry and is responsible for most of the internal corrosion in transportation pipelines. The presence of microorganisms such as sulfate reducing bacteria (SRB) in pipeline system has raised deep concern within the oil and gas industry. Biocide treatment and cathodic protection are commonly used to control MIC. However, the solution is too expensive and may create environmental problems by being too corrosive. Recently, Ultraviolet (UV) as one of the benign techniques to enhance mitigation of MIC risk in pipeline system has gained interest among researchers. An amount of 100 ml of modified Baar’s medium and 5 ml of Desulfovibrio vulgaris (strain 7577) seeds was grown in 125 ml anaerobic vials with carbon steel grade API 5L-X70 coupons at the optimum temperature of 37°C and pH 9.5 for fifteen days. This was then followed by exposing the medium to UV for one hour. Results from present study showed that UV radiation has the ability to disinfect bacteria, hence minimizing the risk of metal loss due to corrosion in steel pipeline. 

Risk-Based Approach to Evaluate Casing Integrity in Upstream Wells

2018 ◽  
Vol 140 (12) ◽  
Author(s):  
Mohammed D. Al-Ajmi ◽  
Dhafer Al-Shehri ◽  
Mohamed Mahmoud ◽  
Nasser M. Al-Hajri
Keyword(s):  
Fresh Water ◽  
Oil And Gas ◽  
Probabilistic Approach ◽  
Metal Loss ◽  
Gas Wells ◽  
Wrong Decision ◽  
Average Value ◽  
Oil And Gas Wells ◽  
Proactive Measures ◽  
Casing Failure

Downhole casing leaks in oil and gas wells will highly impact the shallow water horizons and this will affect the environment and fresh water resources. Proactive measures and forecasting of this leak will help eliminate the consequences of downhole casing leaks and, in turn, will protect the environment. Additionally, downhole casing leaks may also cause seepage of toxic gases to the fresh water zones and to the surface through the casing annuli. In this paper, we introduced a risk-based methodology to predict the downhole casing leaks in oil and gas wells using advanced casing corrosion logs such as electromagnetic logs. Downhole casing corrosion was observed to assess the remaining well life. Electromagnetic (EM) corrosion logs are the current practice for monitoring the casing corrosion. The corrosion assessment from EM logs is insufficient because these logs cannot read in multiple casings in the well. EM tool gives average reading for the corrosion in the casing at a specific depth and it does not indicate the orientation of the corrosion. EM log does not assess the 360 deg corrosion profile in the casing and it only provides average value and this may lead to wrong decision. All of this makes EM logs uncertain tools to assess the corrosion in the downhole casing. A unified criterion to assess the corrosion in the casing and to decide workover operations or not has been identified to minimize the field challenges related to this issue. A new approach was introduced in this paper to enhance the EM logs to detect the downhole casing corrosion. Corrosion data were collected from different fields (around 500 data points) to build a probabilistic approach to assess the casing failure based on the average metal loss from the EM corrosion log. The failure model was used to set the ranges for the casing failure and the probability of casing failure for different casings. The prediction of probability of failure (PF) will act as proactive maintenance which will help prevent further or future casing leaks.

A Reconsideration of the Log-Secant Model for Failure Pressure Prediction of Surface-Breaking Axial Cracks in Pipelines

2020 ◽  
Vol 142 (4) ◽  
Author(s):  
Colin Scott
Keyword(s):  
Surface Crack ◽  
Driving Force ◽  
Crack Extension ◽  
Oil And Gas ◽  
Crack Depth ◽  
Metal Loss ◽  
Regulatory Requirements ◽  
Failure Pressure ◽  
Industry Standards ◽  
Preliminary Validation

Abstract In the late 1960s and early 1970s, the researchers of the NG-18 Committee at the Battelle Institute in Columbus Ohio completed a seminal study on the failure pressures of axial flaws in oil and gas pipelines. Key developments included the “ASME B31G” equations for assessment of blunt metal loss flaws, the log-secant model for sharp through-wall cracks, and the log-secant model for sharp surface-breaking cracks. These equations are well-established and feature in various industry standards, recommended practices, and federal regulatory requirements. This work is a reconsideration of the log-secant model for axial surface-breaking cracks. The original equations were derived based on a through-wall crack, for which the crack length is the driving force for crack extension. However, for a surface crack, the crack depth is the correct driving force for crack extension. This work rederives the log-secant model starting with an infinitely long surface crack, and then empirically corrects for a finite length. The result is a new failure pressure model of similar form to the original log-secant model, but with a few key differences. Preliminary validation work using the original NG-18 data shows promising results.

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