The oil and gas (O&G) industry is one of the first verticals to embrace Internet of Things (IoT) technologies in digital transformation.
With aging equipment and legacy infrastructure that were constructed decades ago with non-regular updates, potential breakdowns and major spills have become a real worry in the industry. Thus, while the integration of modern monitoring and advanced IoT technologies is not easy and cheap given the legacy environment, the O&G industry is biting the bullet.
“From an IoT perspective, the O&G industry’s approach to maintenance is slowly moving from reactive to preventive by leveraging a proactive approach to predictive analytics,” said Kateryna Dubrova, M2M, IoT & IoE analyst at ABI Research.
She noted that unpredictable oil prices and geopolitical conditions are causing greater adoption of monitoring and maintenance technologies. The priority is clear: cut operating costs and optimise maintenance to reduce expenses.
“The preventive maintenance approach that requires inspection and maintenance at regular intervals is considered practical. However, the ultimate question is whether the multibillion contract with IoT service providers can exist in the real world. So far, the technology has failed to ‘prevent’ complete machinery breakdowns,” Dubrova said.
The reality is that in 2019 alone, there were approximately 2,000 oil spill incidents and that since the Deepwater Horizon event in April 2010, statistics indicate more than 610,488 tons of fuel have been spilled globally.
Industrial challenges for business intelligence in O&G
Considering the current industry dynamic and rapid IoT transformation for top O&G players, ABI Research looked at the business intelligence and analytics challenges. Why do they seem to only promise Returns on Investment (ROIs)?
Challenge 1. Big Data: The O&G industry is a continuous processing industry consistently producing commodities—meaning that it generates a vast amount of information every second. The industry is transitioning its flow of information from sensors, detecting such elements as temperature, pressure, fluid viscosity, the presence of foreign substances, and seismic activity. First and foremost, technological advancement in the big data domain is still in its incumbent stage—and specifically in the IoT domain. Second, giant oil suppliers seem to turn to big data solutions to look for “all the answers” while big data solutions at the current historical conjunction are still predominantly concerned with ingestion and storage capabilities over advanced analytics.
Challenge 2. Advanced Analytics and Artificial Intelligence: Integration of Artificial Intelligence (AI) and advanced-analytics automating functions is still far in a horizon—this is just the reality. It is possible to apply AI-generated actionable insight into batch (non–real time) data processing and produce valuable analytics. However, a closer look at the predictive maintenance and O&G type of infrastructure reveals that real-time AI application does not exist yet. While top oil players market themselves as pro-tech, with predictive analytics being the key to their investment, consulting firms and the hiring of a few experts is not making the technology work and subsequently not making a difference in preventive measures.
Challenge 3. Capturing and Localizing Leaks: The current technologies for leak detection and prediction of such incidents is based on the known locations of reporting sensor nodes. The scalability of the pipeline leakage detection sensor network can constitute an utterly separate challenge since it would include a full-scale coverage of the pipeline network, which is enormous. In this case, the leak detection mechanism tool sets intertwined with various localization techniques can address the concerns, if only they were not transmitted as batches of data. In this regard, localization techniques with satisfactory performance will be a welcome addition to the leak detection mechanism toolbox. Conventional sensor-based monitoring is not able to deal with these types of problems, while a streaming analytics engine can.
Challenge 4. Expectations and Capabilities Gap: Following the previously acknowledged challenges, ABI Research has concluded that one of the biggest downfalls and challenges of IoT analytics in O&G is the c-suite expectations and capabilities gap. There is a trend for managers to not see a quick ROI from IoT analytics or to not see any business value at all since they are looking for a “saving on reduced downtime” rather than an investment in precaution. Across the IoT industry there is a misconception of what analytics entails and how the c-suite would use the insights coming from connected devices. The reality of the market is that the automating processes of data readability, filtering, cleansing, enrichment, and so on is at the early stages. There is arguably a need for technology advancement and democratization since teaching an operator complex data science is not always possible and is unrealistic. Hence, the expansion of IoT analytics efficiency is available, while the capabilities to accomplish it is not.
Challenge 5. Auditing of Data-Driven Decision Making: Concerns grow over the auditing of the already-made decisions, which are the subject of Machine Learning (ML) and sophisticated algorithms. Building AI and ML algorithms is a complex process, and currently there is no auditing trail for decision making or scrutiny over the parameters that are driving ML tool sets. There is a need for auditing and due-diligence procedures to construct and understand the AI decision outcome, as this is currently lacking. The debate centres on whether the oil company or operating or transporting company is responsible for leakage and whether this also extends toward technology providers. Table 1 shows that major oil companies are using technology vendors for predictive analytics and other IoT solutions. However, when it comes to accidents, those vendors are not included in the scrutiny and the conversation about responsibility.
A look into the future
Looking 10 years ahead, Dubrova predicted that the O&G industry can expect a long patch of digital transformation, tightening standards, and embracement of corporate social and environmental responsibility.
“The reality is that predictive maintenance is not entirely addressing preventable accidents and mistakes,” she said. “Therefore, it needs a new perspective and evaluation of future capital expenditure to address aging facilities, digital modernization, and the analytics for oil wells. ABI Research believes that the future uptake in the industry will be IoT streaming analytics technologies.”
She noted that the modernisation and rapid upgrade in cloud-based, real-time-analytics IoT technologies alongside the rise of operational technology such as digital twin is raising the possibility of improving preventive maintenance and increasing reliability of monitoring tools and techniques.
The following types of solutions, therefore, have the potential for enabling better capabilities to predictive and prescriptive analytics before equipment failure:
- Real-time ingestion and analytics from the sensor alongside cross-reference and comparison with newly generated technical data
- Comprehensive-time windowing libraries
- Ingestion, processing, and storage of real-time and historical sensor data at the edge, in the fog, and in the cloud
- Pattern recognition of normal and errant behaviour across various types of equipment in the real-time for provision of warning systems.
The third generation of streaming technologies from the IoT domain will allow remote monitoring to reduce the risk of human mistake (and loss of lives) and provide the ability to analyse continuous streams of events producing analytics of high speed, real-time sensor data streams that can handle over 100 million data points per second.
