By Vista Projects
What does a cloud computing platform have to do with the oil and gas industry?
The answer: mass data aggregation and accessibility.
Digital Oilfield Outlook
The Digital Oilfield Outlook Report 2020 explores the state of the oil and gas sector’s digital transformation. In what is perhaps the biggest indication of how the energy industry is trending, the Outlook Report is sponsored by Amazon Web Services (AWS), a cloud computing platform.
AWS likely sees the oil and gas industry as a relevant sponsorship opportunity because the Fourth Industrial Revolution – or Industry 4.0 – is in full swing, with digital transformation initiatives predicted to reach nearly $7.4 trillion in spending by 2023.
More than ever before, energy companies are looking to streamline their digital transformation. With so many new tech startups and fancy buzzwords floating around, knowing where to gain the greatest return on investment can be tricky.
Data has an important role to play.
The Outlook Report includes the results of a survey of oil and gas professionals. The survey analyzed key challenges and opportunities for digital technology in oil and gas across 11 key applications.
Without going into each application, they all have to do with data, automation, and accessibility – core functions of a modern cloud computing platform.
Field productivity was one application in the survey that respondents ranked near the top in terms of
- technological maturity
- organizational readiness
- overall attractiveness
The Outlook Report defined field productivity as
“Maximizing worker efficiency by providing wireless mobility that enables on-demand access to field data, engineering drawings and inventory and communications with centralized operations experts (e.g., field tablets and digital twin, augmented reality).”
This sounds great in theory and the technology exists to make it happen. After all, if field staff can stream a classic movie on their tablets at the site, it stands to reason that the datasheet, operating manual, and maintenance record for a pump installed years ago could be equally as accessible.
Few oil and gas companies are currently achieving this level of field productivity.
A field operations staff member looks at the Plant Summary dashboard on a tablet from the site.
In many cases, the data systems weren’t configured for operations; they were only designed to meet construction needs. But achieving the field productivity described above requires a data system that supports the long-term needs of operations and maintenance teams.
The reality is that even new facilities are designed in database silos. The handover process is often still paper or PDF-based, and the datasets lack the interconnectivity required to maximize field productivity throughout the asset’s lifecycle.
What’s missing is a single-source-of-truth environment that:
- enables collaboration and transparency through the project execution phase
- serves as the foundation for ongoing changes and maintenance
- provides information access to a multitude of stakeholders for the facility’s full lifecycle
A single data-centric system that connects all stakeholders via a cloud-based platform not only improves field productivity, it also lowers the asset’s total cost of ownership (TCO).
Biggest Impact for Technology
When asked which technology trend will have the greatest impact on the oil and gas industry over the next year, the top choices identified by survey respondents were:
- Machine Learning
- The Internet of Things (IoT)
All three technologies can work together to help asset owners efficiently access and process data to reduce operating costs.
Chart ranking which technologies will have the most impact on digital oilfield applications. Source: Digital Oilfield Outlook Report 2020
The Industrial IoT
The industrial IoT is about connecting individual digital components and datapoints to a shared cloud-hosted database. When properly implemented, these connected databases form a single source of truth (SSOT) about an asset.
The SSOT provides consistent and verifiable data that can be accessed from any internet-enabled device. At Vista Projects, staff members can access the technical data portal from a desktop, a laptop, a Microsoft Surface Pro, and more.
The 3D model and all associated 1D and 2D assets are available in a few simple mouse clicks.
Vista’s technical data portal showing every piece of 1D, 2D and 3D data associated with a module in a single, intuitive user interface.
With all asset data organized and easily accessible, there are a variety of ways to optimize operations and improve field productivity, including automation and machine learning.
Automation is about automating processes to improve data consistency and quality. This is especially true when generating reports or completing other repeatable tasks. However, opportunities for industrial automation require an active SSOT.
At Vista, we implement an SSOT from the onset of engineering. The automated data aggregation eliminates manual reports and creates a real-time view of variables like piping or steel quantities as the design develops. This automation also frees up staff to focus on higher value tasks.
The Plant Summary dashboard view of Vista’s technical data portal shows real-time information about the asset.
The dynamic view of the digital asset carries forward through construction, commissioning, and into operations.
The potential to improve field productivity is huge.
Imagine a management of change (MOC) program for an upstream brownfield facility where field staff have instant access to the asset’s entire historical database.
Likewise, the engineering team back at head office can pull up everything it needs to know about the current state of the asset’s field operations to ensure a high-quality modification design.
Data automation centralized in an SSOT gives team members on-demand access to current and verifiable information. This increases transparency, collaboration, and quality, resulting in a more productive team making decisions based on real-time data.
Machine learning (ML) is about computer algorithms studying the interconnectivity of datasets to improve automatically through experience. In the oil and gas sector, ML use cases include:
- predictive maintenance
- clash detection and accurate modelling
- data extraction
- attribute and descriptor identification
- inventory management
- optimizing drilling operations
The list could go on. But again, ML can only function if the datasets that the algorithm is programmed to study are consistently aggregated and connected within an SSOT.
Digital Transformation Challenges
The digital transformation of the oil and gas industry won’t happen overnight, especially not when instability in global commodity markets is straining operator cash flow.
When asked about the challenges in implementing a digital strategy, the comments from survey respondents comprised a few key themes.
- Costs: the main challenge relates to the capital expenditure required to implement technology and train staff.
- Data Processes: these challenges revolve around trusting the quality of the data and being able to easily access it. One commenter described the challenge as not “Being able to collect data from many different sources and then being able to display in one user-friendly interface that is easy for decisionmakers to interpret.”
- People: the challenge with people is that we are often resistant to change and this can create a broader cultural resistance throughout the organization.
Experienced Solution Provider
When it comes to digital transformation, there is a lot on the line and few organizations can afford to get it wrong.
A fit-for-purpose implementation could easily lead to millions of dollars in cost reductions. But investing in the wrong technology or failing to get proper buy-in from staff can be a fast-track to failure.
Vista is an integrated industrial engineering consultancy with over 35 years of experience in the EPCM sector. For the last 7 years, we’ve been on our own digital transformation journey, helping clients integrate cloud-based systems, refine data architecture strategies, and navigate the complexities of Industry 4.0.
Along the way, we’ve automated our own processes, built customized software, and trained our workforce to make the most of technology.
Digital Transformation for Industrial Assets | Intro to Digitally Enabled & Data-Centric Engineering
Example Case Studies
- Helping a downstream startup who needed flexible collaboration
Vista implemented a data-centric engineering system enabled to modify the facility size and configuration as required with unprecedented efficiency and accuracy.
- Assisting with an owner-controlled digital execution environment
Vista’s Systems Integration Team served as a bridge between the owner’s project team, their corporate IT department, and the project’s third-party engineering contractors to implement the software, work processes, and educate all the project participants.
- Providing early guidance to owner on a design-one, build-many approach
Vista implemented a comprehensive technical data portal for a client looking to implement a design-one, build-many approach for a long-term asset development program.
Our truth-based approach is a proven project execution model that simplifies the digital transformation of industrial assets by centralizing data in a single-source-of-truth environment. With on-demand access to authoritative data, your operations and project teams can improve field productivity by:
- making more informed decisions
- shortening schedules
- increasing efficiency
- avoiding rework
- enhancing quality tasks
At the core of our approach is a technical data portal that provides a SSOT to all project stakeholders through a user-friendly interface.