How Oil & Gas and IoT Come Together for Operations

Summary

ARC Advisory Group attended the second IoT in Oil and Gas conference held in Houston last year.  The event drew over 250 registered attendees, more than double the inaugural event held in 2015.  Several companies presented interesting, informative, and thought-provoking presentations on how oil & gas and IoT come together for impacting oil & gas operations.  Others provided information on various solutions related to IoT, analytics, advanced communications, software, remote monitoring solutions, and more.  We sensed a real buzz in the air and heard lots of interesting discussions on why the current environment represents the “perfect storm” (i.e., opportunity) for the oil & gas industry to embrace “doing things differently” by using IIoT-enabled solutions to thrive in these challenging times. 

From ARC’s perspective, the event provided an informative and interesting lineup of content.  This included 18 detailed presentations, some informative and engaging panel discussions, and the opportunity to visit supplier exhibit tables and discuss their respective IIoT-enabled solutions and associated market dynamics. The event reinforced ARC’s belief that the benefits of digital transformation in the oilfield will have a lasting and positive impact for both suppliers and consumers of hydrocarbons.

 

IIoT in Oil and Gas - What Is It?

David Lafferty, President of Scientific Technical Services (as well as an ARC Associate), addressed the question: “IoT in Oil and Gas - What is It?” In his presentation,  Mr. Lafferty discussed his belief (shared by ARC), that the oil & gas industry is on the cusp of a major inflection point and that IIoT represents a whole new approach - moving away from control-centric systems to ubiquitous connected devices, feeding the enterprise systems of the future.  IIoT-enabled solutions will allow companies to acquire, communicate, store, analyze, and share data more effectively and turn it into actionable information for improved decision-making.

Lafferty explained that IIoT requires five key pieces:

• Sensors – with embedded intelligence, storage and processing power
• Networks – for sensors networks, mobility and backhaul
• Big Data – with data repositories and data aggregation
• Analytics – descriptive, diagnostic, predictive and prescriptive

• Visualization – data presentation, HMIs, smartphones, tablets

Some of the key oil & gas industry business drivers Lafferty identified for implementing IIoT include:

• Data have moved beyond the operators. Examples include integrity monitoring (e.g., wellhead monitoring, equipment health, corrosion monitoring) and integrity mitigation system monitoring (e.g., chemical treatment or cathodic protection)
• Users want results-oriented pricing (Service as a Product) such as paying for pounds of thrust, as opposed to buying a jet engine; number of readings provided per month; etc.
• Geographically dispersed areas of operation (unconventional shale fields, for example, are huge)
• Monitoring is different than control, typically with a much larger and diverse user base and lower, less latency-dependent sampling rates.  This requires lower price points, and more efficient M2M protocols such as MQTT
• OEMs need improved mean time to repair (MTTR) and products that provide incremental and recurring revenue streams
• Time-to-market - fast pace of unconventional wells cannot wait for infrastructure to be developed

• “Great crew change” – a new work force expects a connected world and applications that are as easy to use as an iPhone

Lafferty also highlighted some of the barriers to IIoT.  These include high entry costs when using conventional methods for additional monitoring, solutions implemented as silos, stranded data, non-uniform implementations, incomplete network coverage (viewed as an infrastructure problem), and the large amount of hype surrounding IIoT.

Some of the potential business value of IIoT that Lafferty cited include better visibility of operations resulting in: improved production, improved integrity and safety, reduced unplanned downtime, reduced labor, and reduced energy costs.

Some of the IIoT sweet spots in the oilfield include: annulus pressure monitoring, production chemicals optimization, corrosion monitoring, gas monitoring, and OEM equipment monitoring.

Other Noteworthy Presentations 

We also saw a number of other noteworthy presentations, summarized below.

Rewriting the Energy Industry

Nav Dhunay, CEO of Ambyint, discussed how companies can leverage machine learning, IIoT, and Big Data analytics to rewrite the energy industry. He mentioned the “perfect storm” in the industry in adoption of digital solutions and how the inversion of power in organizations via IIoT-enabled solutions will empower field technicians and frontline workers with actionable information, rather than just data.  Mr. Dhunay believes this will have as much, or more, impact on operations than at the C-level.

Mr. Dhunay talked about the difference between “innovation” and “disruption,” with innovation being doing the same things better, and disruption doing new things that make old things obsolete.  He said the real value in IoT lies in the new asset created (augmented intelligence) and the potential business transformations derived via augmented behaviors.  Dhunay highlighted four key areas of industry focus:

• Attract integration talent
• Simplify the application landscape, with a radical improvement of the user interface
• Deliver declining unit costs (“Let’s stop selling the $10,000 toilet seat”)

• Drive better standards

He highlighted the migration from operational efficiency and new products and services (today) to an outcome-based and autonomous pull economy in the future.  Dhunay talked about the importance of machine learning for data ingestion, advanced data analysis and closed-loop operations (i.e., autonomous) to deal with challenges of massive layoffs and the “Great Crew Change.”

Empowering the Oil & Gas Industry

Kadri Umay, CTO of Process Manufacturing and Resources at Microsoft presented highlights on the firm’s strategy for leveraging IIoT to empower the oil & gas industry to achieve more and help drive operational improvements.

Mr. Umay cited shocking data from a McKinsey study indicating that less than 1 percent of sensor data is being made available to decision makers. A Bain & Co. study showed better data analysis could help oil & gas companies boost production 6 to 8 percent and, according to Shell, a relatively small increase in recovery of just 1 percent globally would equate to three years’ production at current levels.

Umay discussed how Microsoft developed “systems of intelligence” powered by hyper-scale cloud capabilities (via 34 global Azure data centers) can be leveraged via PaaS and IaaS service solutions and virtual machines designed to help integrate the entire upstream value chain into a powerful “system of intelligence.”  Umay said digital transformation is more than IoT.  Overall business outcomes include optimizing operations, engaging customers, empowering employees, and transforming products into services to help stay connected with the customer. He highlighted the importance of ecosystems and partners and the power of standards in helping drive operational excellence.

Value of Open Source Software in Drilling Operations

Kenneth Smith, GM, Energy; and Wade Salazar, Solutions Engineer, at Hortonworks talked about the value of open source software in drilling applications.

Mr. Smith briefly introduced Hortonworks and how the company leverages Apache NiFi and Hadoop to provide connected data platforms that capture perishable insights from “data in motion” and enrich historical insights on “data at rest” to empower predictive analytics.

Mr. Salazar presented results from a drillship customer project that involved collecting and delivering 20,000 data points from critical equipment (such as blowout preventers and top drives) at 1 Hz into a platform that could service both BI and data science projects simultaneously using only satellite bandwidth at 64 kb/sec.  Hortonworks focused on increasing the value of the industrial control system data and “democratizing” the ICS data cost effectively using an open industrial connected data platform comprised of Hortonworks Data Flow and Data Platform products.  He discussed the challenges and opportunities dealing with OT and IT collaboration issues.

Leveraging an OT Object Data Model to Enable Strategic Business Value from IIoT and Advanced Analytics Securely

During a side meeting with Craig Harclerode, Global Business Development Director, Oil & Gas at OSIsoft, we learned about the strong presence of the OSIsoft PI system as the de facto standard for real-time enterprise infrastructure in oil & gas.  Mr. Harclerode described IIoT in terms of the hybrid environment of “big data pipes” and “small data pipes” of both real-time data and metadata, on-premise and cloud, and decentralized and decentralized analytics (fog).  Harclerode discussed the challenges of dealing with time-series and real-time data and how the PI system infrastructure is unique in its ability to deal with these challenges.

Many oil & gas companies develop hybrid OT data models (typically using PI AF) to extend the advanced data analytics capabilities to the IIoT.  In some cases, users are bypassing the SCADA system by directly taking IIoT sensor data into the PI infrastructure for cost-per-performance and security reasons.  He cited a common practice of users avoiding using their SCADA or DCS systems for data acquisition if they do not have to and using the PI system as the OT data object model to ingest IIoT data.  According to Mr. Harclerode, in these situations, the OSIsoft PI AF system provides the environment to aggregate, normalize, and optimize the value and usability of the data. Applications such as analytics and visualization are layered on top of the PI infrastructure OT data model, which is empowered further by the cloud.

Harclerode described the OT data model by using the concept of OT “chart of accounts” that acts as a framework/repository of all operational real-time data, metadata, calculations, and analytics to enable IT/OT and IIoT integration and business value.  The ability to aggregate across a large number of data sources, data standards, protocols, etc. via tag-based interfaces (>450) and smart asset-based connectors (12-15) enables the normalization of a variety of types of data after quality check or data validation.  Integrating historical time-series data and metadata enables more effective analytics, event frames, and alarm notifications.

The OT data model looks like a multi-dimensional linear and relational data store capable of visualization and analytics from SAP Lumira, Spotfire TIBCO, etc.  The ability to rationalize where and what analytics are done in each environment is a key differentiator of the OT data model via cloud or edge.  Adding geospatial real-time analytics (via ArcGIS, an ESRI product) because of increased use of mobile devices is another noteworthy functional capability of the PI infrastructure system.

The OT data object model is built on smart objects that provide configurable object-oriented functionality that allows the user to build templates, elements and attributes, and methods such that anyone using Excel can build and manage their own model. Harclerode provided three different use cases -- MOL, TransCanada, and Devon Energy -- in which the users benefitted from PI infrastructure and the OT data model and the integration and analytics capabilities provided.

Conclusions

It was exciting to participate in an event that is exhibiting strong growth (attendance more than doubled) due to the expanding interest and activity surrounding IIoT.  ARC observed some informative and compelling presentations, participated in lots of discussions during session breaks, and saw increased interest in how companies can improve the performance of their oil and gas operations by leveraging IIoT-enabled solutions.  We are looking forward to attending the upcoming third Annual IoT in Oil and Gas conference again in Houston in mid-September of this year.

ARC believes that owner-operators should seek out suppliers that are prepared to develop the appropriate IIoT-enabled and advanced analytic solutions that enable them to lower costs; improve operational performance; and provide greater operational visibility, flexibility and agility.  Users should continue to collaborate with and, if necessary, encourage suppliers to help them reduce their capex and operational costs, improve operational performance, increase recovery rates and production, and improve profitability.  Ultimately, this would benefit the suppliers as well.

 

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Keywords: IoT, IIoT, Analytics, Oil and Gas, Machine Learning, Optimization, Remote Monitoring, Advanced Communications, OSIsoft, Hortonworks, Microsoft, ARC Advisory Group.