PIPELINE INTEGRITY MANAGEMENT: an operating model for the midstream industry
Owners of liquid hydrocarbon and natural gas pipelines must now conform to the demand of new regulations involving public safety and environmental concerns. The public scrutiny and regulatory approvals imposed on pipelines, combined with the negative consequences of an incident, make decisions regarding pipeline integrity management increasingly important. The safety and reliability of pipelines has become a corporate-level social accountability issue for every operator. In this article, Dale St. Denis, Khurram Farooqui and Eric Scheller discuss the need for a comprehensive approach to asset integrity management, what it takes to successfully implement an integrity management program and how to get started.
Pipelines containing and transporting hazardous products are subject to regular statutory inspections, verifications and re-certifications. Until recently, such requirements have been principle-based and, for instance, imposed pressure testing of oil pipelines every 10 years. However, in the US and in Europe a stricter approach is being implemented that requires pipeline operators to demonstrate and document that the integrity of their pipeline facilities is maintained at all times. Pipeline owners must assess the risks associated with the operation of their facilities and take all measures to mitigate the consequences of any failures.3
Operators are required to have a comprehensive integrated Pipeline Integrity Management program (PIM) in place. A PIM is much more than a set of tools or an inspection and maintenance program; it is an operating practice encompassing safety, engineering, operations, inspection, maintenance, compliance, environmental and corporate communication. Not only is the mechanical condition of the asset, its reliable operation and meeting its delivery obligations at stake, but, in the case of any incident affecting the public and the environment, so is the image, reputation and business of the operator and its stakeholders.
The concept of a risk assessment for asset integrity is not new to the oil and gas industry; it has been practiced in the refining and petrochemical sectors for many years. However, risk assessments for pipelines are still evolving to take advantage of new in-line inspection technologies, equipment degradation monitoring techniques, geographic information systems (GIS) and risk assessment models that enable documenting and categorizing pipeline risks. The goal is to ensure the safe and reliable operation of the pipeline and allow timely, economic decisions to be made based on pipeline inspection and risk mitigation.
The practice of risk analysis for pipeline integrity management involves pipeline data collection and integration to facilitate assessing the risk of identified hazards, such as internal corrosion, external corrosion, stress corrosion cracking, mechanical damage, overstressing/fatigue, construction/ manufacture, ground movement and natural hazards. The consequences of these hazards causing a rupture, leak or loss of serviceability are evaluated. These may include loss of life or injury, environmental damage, loss of revenue and damage to operator reputation.
The risk analysis evaluates the probability of failure due to a hazard and the consequence of that failure. These factors are multiplied together to provide a measure of the risk for each hazard. The risks for each hazard may then be combined to give an overall estimate of the risk level for each section of pipe. The calculated risk is compared against an acceptable or target risk level or benchmark value to determine the high-risk sections of a pipeline and to plan maintenance and risk mitigation and response activities. The risk assessment process is continuous and the results of inspection and maintenance activities drive repeat analysis to reassess risk. Pipelines in high-consequence areas are assigned more frequent inspection intervals and more stringent tolerances for triggering maintenance and risk mitigation activities. Figure 3 illustrates how the risk assessment program incorporates the risk ranking profiles of each pipeline segment and physical asset. Threats and risk exposures are identified for each segment.
Critical Success Factors for Conducting a Risk Assessment for Pipeline Integrity Management
To maximize the potential benefits of implementing a risk assessment model for pipeline integrity management, the following capabilities must be in place:
Any implementation will require the buy-in and active involvement from several parts of the organization, including safety, operations, engineering, integrity management, regulatory compliance, legal, commercial and public relations. The degree of organizational alignment can be assessed by asking the following questions:
- Is there a clear vision for integrity management 2-5 years in the future and what is the role that risk models and predictive maintenance play in that vision? Do all stakeholders understand that vision?
- Are the specific benefits of achieving the vision clearly articulated for each department or group?
- Is the call to action for each department clearly articulated, executable and achievable?
- Is each department willing to commit the resources necessary to achieve the vision?
As an example, one possible benefit of applying risk models for triggering predictive maintenance activities involves the commercial group. If the commercial group has advance notification of when an asset is being brought down for maintenance and can optimize its positions accordingly, there is a tremendous upside in additional profit. However, in many cases this is not clearly articulated or even recognized.
Data Governance and Management
Even the most sophisticated risk model and predictive maintenance solution is of no use unless the underlying data is complete, current and accurate. Achieving this is more difficult than it sounds, as the amount of data can be very large, is stored in different systems and is controlled by different parts of the organization. A well-thought-out strategy for maintaining this data is crucial and can be implemented by putting a data governance organization in place. Addressing training and organizational change management issues is critical for data governance to be successful.
Figure 4 illustrates the various roles within a data governance organization, which include Data Content Owners, Data Stewards and Analysts, Data Quality Leads and a Data Governance Board.
- The Data Content Owner is typically a functional manager with accountability for the organization’s data asset. Data Content Owners are assigned at the information entity level for supervisory control and data acquisition (SCADA), geographic information system (GIS) and engineering specification and designs.
- Data Stewards and Analysts are the go-to people for their given information elements with responsibility for compiling and validating the requisite data. Data Stewards manage one or more information elements for a given information entity and ensure that data elements are unambiguously defined and do not conflict with other elements in the registry.
- The Data Quality Lead works with Data Content Owners, Stewards and Analysts across all entities that make up the entire data landscape in the organization. The overarching organization control of data quality resides with the Data Quality Lead.
- Finally, the Governance Board, comprised of the Data Content Owners and the IT & SCADA systems managers, is led by the Data Quality Lead to manage initiatives for improving data quality throughout the organization.
Although predictive maintenance solutions can be put in place on a small scale in isolated parts of the organization, the full benefit of a risk assessment model applied to predictive maintenance can only be realized if the systems and sources of information are integrated into a collaboration platform. Figure 5 shows a high-level view of the systems landscape for the pipeline integrity management ecosystem.
The risk assessment maintenance engine at the heart of the solution brings its maximum value as a knowledge management system when it is up-to-date and accurate information is integrated at the enterprise level. In such an advanced system, commercial data, such as commodity prices, trade positions and customer product schedules, could also be integrated so that the assembled structured data coupled with the aforementioned quantitative methodology could drive work prioritization in line with actual commercial and operational needs. This type of data centric, integrated approach could allow commercial and operational managers to adapt insights gained from data analysis to grow top-line revenues through increased throughput from reduced slack down time and grow bottom-line results by eliminating duplicative activities and stacking opportunistic events to achieve remediation plans.
Figure 6 outlines the sequence in which a predictive maintenance solution can be rolled out to the enterprise. The first step (establishing the vision and the roadmap) is the most critical. This is a long journey for any organization and a clear direction and path are needed against which progress can be measured. Without them, progress is likely to be slow and inefficient and may stall altogether.
A key foundational step is to decide upon the appropriate maintenance model. Data requirements must be defined given the risk model and the vision for the initiative, and the appropriate data governance organization needs to be put in place. The data governance needs to be a joint undertaking by both the business and IT, and a well-designed and wellmanaged governance process will save a significant amount of clean-up effort later. Additionally, based on high-level requirements, an enterprise architecture needs to be established (or modified).
The solution needs to be tested by one or more proofs of concept. This could, for example, be done for a specific asset and with some initial integration between systems. The risk assessment model parameters should be tested and adjusted based on a comparison of predicted maintenance recommendations against actual inspections.
Finally, the overall roadmap needs to be refined and a phased rollout plan needs to be created with discrete projects to implement pieces of the predictive maintenance solution.
As a result of increased regulatory scrutiny, operators of midstream pipelines have a mandate to implement a pipeline integrity management program. Risk assessment models allow the pipeline operator to optimize inspection, predictive maintenance and mitigation dollars by planning maintenance and risk mitigation activities on pipeline sections that pose the highest risks to the organization.
is a Director with Sapient Global Markets’ Commodities and Midstream Practices. Khurram has worked on all aspects of the midstream and downstream value chain. He has built pipeline systems, worked on field force management systems, built custom trading and risk management systems and led large engagements of crude and products trading packages.
Dale St. Denis