Why oil and gas businesses need to build their data layer foundation first – Intelligent CIO Middle East

Many oil and gas companies are rushing to layer on new digital technologies—cloud, machine learning, predictive analytics—before they have a data and analytics foundation. Pipeline companies need to put their data management and analytics strategies in place first to lay the groundwork for future technology success, says Stuart Parker of AVEVA.

The global pipeline market picture is more nuanced and unpredictable than at any time in history. The industry faces complex new challenges as well as vast opportunities.

A smart pipeline strategy enables companies to create new capabilities, new business models, and stay ahead of the competition. By leveraging knowledge management systems, powerful analytics, automation of workflows, and driving behavioral change in the workforce, oil and gas companies can evolve and transform how work is done to create a sustainable, profitable organization.

Companies should seek to reduce operational costs and carbon footprints across complex value chains, including a growing ecosystem of business partners.

While SCADA and production measurement software offer features to identify measurement-related issues, there are also opportunities to perform analytics on the measurement and raw data to provide earlier indications of problems.

Forward-thinking oil and gas companies are building intelligent pipeline frameworks to transform vast amounts of data into wisdom that generates business value. By leveraging existing operational data as well as new data sources, companies can take a model-driven approach that leads them to operational excellence.

Data with business context

Oil and gas pipeline companies were collecting vast amounts of operational data long before the term Industrial Internet of Things, or IIoT, was coined. However, it is often difficult to transform the vast amounts of raw data from SCADA, pipeline applications, ERP systems, and more into contextualized information around equipment and processes. Contextualizing this data ultimately enables operational improvement.

Not only does a wealth of raw data devoid of context, structure, or quality rarely pay dividends, but those tasked with using that data often find it difficult and cumbersome to extract insights. If users are too slow to develop and implement sustainable solutions, the company will accumulate significant lost opportunity costs.

When unstructured operational data accumulates in data lakes, traditional IT technologies can create more problems than they solve because businesses are forced to spend more time dealing with data than using it to deliver business value.

Unfortunately, many companies rush to layer on new technologies and solutions like cloud, machine learning, edge, IIoT, and predictive analytics before they have the right data and analytics foundations in place.

Adopting these new solutions can potentially provide new and valuable insights, but pipeline companies must first implement robust data management and analytics strategies. Deploying an enterprise-grade, real-time data management platform is the foundation for future technology success.

Generating value from data

To produce actionable intelligence, data must be structured and best used by subject matter experts who have the knowledge and experience to turn data insights into action.

The success of digital transformation depends on having a single source of truth. Operations data must first be standardized and contextualized so that it can be analyzed and visualized. Comprehensive data management systems can form the basis for the integration, validation, and analytics of operations data.

A centralized operations data management platform uses standardized and templated tag naming conventions and assets are cataloged in a flexible hierarchy. This platform becomes an operational system of record that provides the foundation for democratizing insights across any pipeline business model.

Using the data model, companies can accelerate digital transformation by merging operational data into a digital replica of physical assets. This can be achieved by developing a digital twin of the entire system using information such as drawings, 3D models, materials, engineering analysis, dimensional analysis, real-time pipeline data, and operational history.

Throughout the operational lifecycle, the digital twin is automatically updated in real time with up-to-date data, work records, and engineering information to optimize maintenance and operational activities. Engineers and operators can easily search asset tags to access critical up-to-date engineering and work information to diagnose the health of a specific asset.

Previously, such tasks required significant time and effort, and issues were often overlooked, such as failures or pipeline outages. With a digital twin, operational and asset issues are flagged and addressed early, and workflow becomes proactive rather than reactive. Pipeline companies can easily benchmark operational performance, such as pipeline throughput and energy consumption, to uncover gaps and improve pipeline efficiency.

Use cases for pipeline data

Advanced simulation and analysis tools can be used to model and predict fluid flows in the pipeline. This not only provides product and batch tracking and line packaging, but also helps uncover throughput improvements in existing assets. This enhanced visibility into operations allows pipeline operators to optimize throughput. Furthermore, they can plan for future infrastructure expansion to increase efficiency and yield to improve competitive advantage.

Advanced simulation tools can be used to model gas flow behavior and forecast loads for current and future gas days in near real time. These insights enable pipeline operators to better balance supply and demand, optimize capacity, and better comply with gas contracts.

Identifying pipeline leaks quickly is crucial to minimizing risks and preventing major spills, and many leaks cannot be detected with a single solution. There is no one-size-fits-all solution, and multiple detection technologies are often needed to detect different types of leaks that exhibit different flow patterns. Analytics can simulate the flow of liquids and gases and detect any subtle changes that may indicate a pipeline leak.

Training controllers is critical to ensuring operational safety and integrity while ensuring all operations adhere to a pipeline operators safety and compliance program. A pipeline training simulator is an operator training system that allows pipeline controllers to train on normal and abnormal operating scenarios in a safe and realistic environment.

Operators can walk through real pipeline operations and become certified before assuming the role and responsibilities as a controller in a live operating environment. Large oil and gas operators have significantly reduced training costs and time to qualification by using simulators as part of their organization-wide training programs.

Accurate measurement of volumes is critical to ensuring accurate and timely accounting both internally and at casing transfer points, and pipeline companies should use available data to improve accuracy.

Thanks to analytics, expert systems and artificial intelligence (AI), future operational systems will become even more automated, from planning to product delivery. This level of autonomous control will be provided with better data for operator decision-making, followed by recommendations and what-if scenarios, gradually resulting in automatic control during normal and eventually abnormal operations.

Pipeline companies are beginning to leverage action sequences that allow for chained sets of ready-made commands with pre- and post-action validation to ensure commands are executed safely and correctly. Using pipeline data analytics to guide actions using a rules-based approach can enable pipeline companies to improve outcomes when performing operations where the timing of actions is critical.

The oil and gas pipeline industry is under tremendous pressure to comply with ever-changing regulatory requirements, increase agility to succeed in a rapidly changing business environment, maintain cybersecurity concerns, and meet global sustainability goals.

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