The energy sector is one of the world's most complex business environments. From resource extraction to product refinement and distribution, numerous environmental, engineering, operational, and economic challenges test even the most experienced managers.
Given such management complexity, the energy sector is often a laboratory for advanced management techniques and decision-making models later adopted by other industries and businesses seeking to improve their performance.
Making the Complex More Manageable
Energy companies typically have a management dashboard that provides a snapshot of the business at any moment. While invaluable for day-to-day management, a dashboard may be less useful when it comes to making complex, longer-term, or large-scale strategic decisions. More data is needed to make the right determination regarding, for example, a multi-million/billion-dollar investment, merger, or spin-off.
Instead, it is decision support models which offer the powerful analytic engines that help decision-makers capture and analyze large volumes of data to see critical trends, dynamics, and other factors influencing their final decision. ‘What-if’ scenario analysis capabilities enable senior decision-makers to understand the various options in play and take the best actions for the business.
While decision support models are typically the preserve of board members and senior managers, there is value in having middle managers use similar tools to manage their projects.
Integrating Pipeline Management into Decision Support Frameworks
For energy companies, decision support models will likely include data from across the business, such as production, extraction, and exchange rates; future oil and gas prices; ongoing political developments — the list can be lengthy.
While companies in other industries may take a 5-year view of an investment project, it is not unusual for energy companies to take up to a 30-year perspective, given the complexity of accessing, refining, and distributing reserves. The metrics used to make decisions in such situations would include the operational metrics cited above. More strategic issues would also, such as expected GDP growth rates, emerging energy types, global demographic patterns, technology changes, and changing geopolitical environments.
There can, however, be a blind spot in a business’ decision support framework: its pipeline infrastructure. In terms of strategic and operational decision-making, a business’ pipeline infrastructure either works to the business requirements or its performance is below expected standards due to routine maintenance work or emergency fixes.
Getting More Value from PipelineRegimes
Pipeline inspection regimes typically pass through pipeline sectors on a 5-year cycle to find issues that can impact the network's effectiveness, safety, and business value. This process is disruptive to the business, as pipeline pressure drops when a platform is inserted into the network, slowing the transmission rate of product.
From a decision support perspective, this 5-year cycle can limit the volume of data and the insight it provides, meaning pipelineissues may be underweighted in the overall process.
Given that much of the global pipeline infrastructure was built between the 1950s and 1970s, age-related issues will become more critical in the future and need to be factored into the strategic decision-making picture.
Furthermore, pipeline networks currently geared up to handle oil and gas will likely have to accommodate the demands of other energy carriers, such as.
Using New Technology for Richer Pipeline Insights
A range of technological developments in the last five years has put pipeline managers on par with their peers in the rest of the business in terms of presenting a data-rich picture to the company. Pipeline teams can now model data to align their pipeline management strategy with the business’ 30-year investment perspective.
platforms have become more sophisticated to diagnose smaller pipeline issues involving , , deformities, cracks, pipeline movement, and more. They can now defects at the sub-millimetric level to diagnose the extent, severity, progress, and associated with a full range of current and potential threats. Sophisticated diagnostic platforms, such as NDT Global’s high-tech tool fleet, can capture immense volumes of data—up to petabytes—in a single run.
All this data is extremely valuable but requires a mix of expertise from highly qualified engineers and analysts to be immediately applicable and useful to yourmanagement team.
Leveraging Data Analytics to Deliver Pipeline Integrity Management at Scale
Enter advanced data analytics.
The enormous volume of data collected requires leveraging automation and artificial intelligence (AI) technologies to consolidate, review, analyze, and manage it to surface critical insights.
AI and machine learning (ML) capabilities allow computer applications to identify and assess the scale and scope of pipeline threats and anomalies. This rich insight is complemented by the expertise provided by geologists, engineers, and other top-notch industry professionals to deliver a comprehensive perspective of theof a pipeline network. Data management processes also allow for specific issues to be escalated to suitable professionals if an application detects a significant problem or if the results are ambiguous.
The results can be captured, consolidated, and enriched with other data sources, such as data from previous runs or snapshots of similar issues in different pipelines.
NDT Global is constantly pushing the boundaries of technology and innovation to help pipeline operators access deeper data-driven insights into the condition of their pipelines. We deliver the Power of Clarity, so analysts, engineers, and senior managers can make the right business decisions at the right time.