The Dbriefs Oil & Gas series presents: The Internet of Things A new analytical framework for addressing industry challenges John England, Vice Chairman, US Oil and Gas Leader, Deloitte LLP Gregory Bean, Director, Deloitte Consulting LLP Andrew Slaughter, Executive Director, Deloitte Services LP August 20, 2015
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The Dbriefs Oil & Gas series presents:
The Internet of ThingsA new analytical framework for addressing industry challenges
John England, Vice Chairman, US Oil and Gas Leader, Deloitte LLPGregory Bean, Director, Deloitte Consulting LLP Andrew Slaughter, Executive Director, Deloitte Services LP
IoT: Creating differentiated value from information• It is not merely the features of a product
or service, information about that product or service creates differentiated value for companies.
• Creating information is enabled by a suite of technologies that basically integrates sensing, communications, and analytics capabilities, typically referred as the “Internet of Things.”*
• Companies that control the flow of information, and complete the Information Value Loop for modifying future action, enjoy competitive advantage.
Information value loop
*The Internet of Things (IoT) is a suite of technologies and associated business processes that allows us to track and count, observe and identify, and evaluate and act in circumstances heretofore effectively invisible and beyond reach.
Source: Michael E. Raynor and Mark J. Cotteleer, The more things change: Value creation, value capture, and the Internet of Things, Deloitte Review 17, Deloitte University Press
Linking IoT with business prioritiesIoT deployments should address specific business priorities, which can be classified into three: improving reliability, optimizing operations, and creating new value.
• Lower oil prices drive upstream firms to place higher business priority on optimization where IoT applications are relatively immature.
• Increase in business complexity drives midstream players to advance commercial opportunities, along with improving pipeline safety and reliability.
• Slowing demand growth, rising competition, and volatile feedstock market pressure downstream players to explore new areas of optimization and value creation.
• Companies developing subsea systems that operate at 20,000 psi and withstand temperatures of up to 350F.
• Firms increasing downhole intensity and above-ground activity in shales, and moving to hostile/remote locations.
Bulk data generated by smart sensors capturing
this complexity
• The growing scale and frequency of hydrocarbon reservoirs data has led to data explosion.
• Internal data generated by large integrated firms now exceeds 1.5 terabytes a day.1
Lack of standards choking
information flow
• The need to expand the scope of data (data independent of scientific principles and cross-disciplinary) is restricted by industries’ weak data-management capabilities.
• Lack of open standards to integrate diverse data create the bottleneck at the aggregation stage of the value loop.
1. Abdelkader Baaziz and Luc Quoniam, “How to use Big Data technologies to optimize operations in Upstream Petroleum Industry,” 21st World Petroleum Congress, June 19, 2014.
Upstream: Creating new valueIf common data standards are able to integrate diverse data sets, companies can gain insights into previously disconnected aspects of operations and adjust how they make decisions.
Case study: Apache, in collaboration with an analytics software firm, not only improved the performance of its electrical submersible pumps (ESPs) but also predicted a field’s production capacity in three steps1:
The pump failure data generated in Step 1 was used to prescribe right pump configuration for the next well.
The companies used multi-disciplinary data about pumps, production, completion, and subsurface characteristics to predict the pump failure with prescriptions to avoid future failures.
The ESP performance and configuration data generated in Step 2 was used to evaluate field’s potential production capacity before acquiring them.
Step 1: Predict pump failure
Step 2: Prescribe optimal pump configuration
Step 3: Predict
production capacity
1. Ayata, “Customer Profile: Apache Corporation,” September 05, 2013
• US midstream has seen a shift from a simple business model of transporting limited grades of products to a complex and dynamic model of transporting variable volumes/grades from multiple locations.
• Rising business complexity, combined with aging pipeline network and legacy control devices, creates reliability issues. Annual losses touch $10 billion due to fuel leaks and thefts.1
• What is needed is a shift towards building a data-enabled infrastructure, in other words, getting started on the informational value loop by investing in sensors that create data.
1. Penn Energy, “The Role of satellites in oil and gas pipeline monitoring for leak & theft detection,” May 30, 2014
Midstream: Sensorizing the networkSelect players are addressing this bottleneck by creating data through advanced sensors that accurately identifies and measures dents, cracks, spills, corrosion, etc.
Case study: TransCanada and Enbridge are testing four technologies that essentially see, feel, smell, and hear various aspects of their oil pipelines.
1. Vapor-sensing tubes that “see” bitumen spilled by shooting air down a tube.
2. A fiber-optic distributed temperature sensing system that “feels” fluctuations in temperature caused by bitumen leaking into ambient soil.
3. Hydrocarbon sensing cables that sends electric signals to “smell” hydrocarbons.
4. Fiber-optic distributed acoustic sensing system that “hears” sound variations and can indicate a pipeline leak.
Sensor cables that TransCanada is testing in an industry partnership with Enbridge and the Governments of Alberta and Canada
Hydrocarbon sensing cable’s electrical impedance will change when it gets in contact with oil.
Vapor sensing tube can report volatile organic compound (VOC) to terminal hydrocarbon vapor sensor.
Fiber optic cable used for either distributed temperature sensing (DTS) or distributed acoustic sensing (DAS).
Midstream: Analyzing data all along the network A company would likely accrue a larger competitive and commercial advantage if it analyzes product and flow data more comprehensively all along its network.
Indicative examples:
• Leveraging data across the company’s network to help shippers find the best paths to market, charging them differently for having route optionality in contracts.
• Forecasting algorithms on historic volumes transported can reveal ways in which the company might use pricing incentives that induce producers and end users to smooth volumes.
• Similarly, a real-time analysis of changing volumes across the company’s network of shale plays can alert it to new price differentials.
Which oil and gas segment has traditionally had the most sophisticated data infrastructure with more established data processes and a longer history of automation and optimization?
Downstream: Taking intelligence beyond asset level• Unscheduled shutdowns and
ineffective maintenance practices remain a big concern for refiners—US alone witnessed 2,200 unplanned shutdowns between 2009 and 2013.1
• Condition-based predictive maintenance solutions at an asset or plant level have already started to make inroads. What is needed is new areas of competitive differentiation and revenue generation outside the refinery limits.
• This calls for analysis of data across the system (including pre- and post- links in logistics & distribution) and, moreover, across the ecosystem (adding external variables such as consumer profile and behavior, etc.)
1. Hydrocarbon Publishing Company, “Refinery Power Outage Mitigations,” 2014
Indicative example: Refiners can target new age customers by using innovation in smart handheld devices and advanced telematics system in vehicles, like the one developed by Toyota with SAP and Verifone to simplify drivers fueling experience. [1]
Refiners send customized offers via telematics to customers and add more appeal to their traditional loyalty and reward programs.
Refiners enroll in such connected-car prototypes which direct customers to the nearest enrolled fuel retail outlet by analyzing distance and fuel levels.
Refiners gain more visibility into consumer behaviors by mashing up existing petro-cards data with the data collected by cloud-enabled emerging telematics solutions across vendors.
[1] SAP, “SAP, Toyota InfoTechnology Center and VeriFone Connect Cars and Provide Drivers With Simplified Fueling,” July 01, 2014
Summing upCreating and capturing value from IoT requires identifying primary business objectives, ascertaining new sources of information, and clearing bottlenecks that limit the flow of information.
Oil and gas segment
Top business objective
Dominant value drivers Bottleneck Potential
solution
Upstream Optimization Scope and latency Aggregate Standards
Midstream Reliability Scale, accuracy, and timeliness Create Sensors
Downstream New value creation
Scope, timeliness, and security Act Ecosystem
management
Table 1. Analysis of IoT value by oil and gas segment
For more information about IoT in the oil and gas industry, see our latest report Connected barrels: Transforming oil and gas strategies with the Internet of Things.
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