The oil and gas industry is a vast and complex sector playing a pivotal role in powering economies worldwide. However, according to a McKinsey report, a typical offshore platform runs at nearly 77% of its peak production capacity. This deficit results in roughly 10 million barrels per day or $200 billion in annual revenue across the industry.
To help maximize lost opportunities like the above, predictive analytics steps in. Oil and gas enterprises today are generating vast amounts of data every day. This data can be leveraged to gain insights that can transform business operations and drive growth.
In this blog, we will explore how predictive analytics on the cloud for oil and gas are driving industry-wide transformation for the oil and gas sector, starting with its applications and use cases.
Applications of Predictive Analytics in the Oil and Gas Industry
Predictive analytics finds employment across all stages of oil and gas operations, including:
Unexplored sources of nontraditional data
Advancements in pattern recognition, cognitive analytics, and computer vision have helped gas companies explore unchartered sources of non-traditional data. This includes machine and sensor information produced by the Internet of Things (IoT) and raw data present in the deep web. Insights from them can result in better and more accurate decision-making in drilling, exploration, production, and development optimization. To top it off, deploying machine intelligence enables algorithmic capabilities to enhance employee performance, improve oil and gas analytics, streamline highly complex workloads, and create cognitive agents to emulate human reasoning and higher computing.
Diverse customer profiling
Sales professionals need to be at the top of the game when it comes to dealing with challenges in the oil and gas industry. This includes staying updated with regulations, legislation, trends, and best practices. For instance, the sales team can customize lubricant recommendations based on their previous buying behavior. With predictive analytics, customer profiling shifts from a mere static methodology to a dynamic procedure based on a set of highly advanced, pre-defined calculations.
Leveraging condition-based monitoring and variable analysis simulates real-life scenarios to forecast any potential maintenance issues and likewise enable maintenance to prevent equipment damage. This helps minimize the costs of extremely reactive maintenance problems, downtime, and timelines of mammoth-sized maintenance projects.
Geospatial analytics and visualization
Enabling geospatial visualization and analytics guides oil and gas companies in assessing pipeline risks based on two categories. These include a) types of vulnerabilities mainly related to inspection frequency, disaster-prone areas, and incident records, and b) impacts such as population density, proximity to water resources, and environmentally sensitive areas. Through inspections and emergency response plans, predictive modeling ensures safety and reduces time from risk identification to remediation to foster effective decision-making.
Establishing digital twins
Through predictive analytics, the creation of digital twins can contribute to different types of plant operating models for quicker decision-making. This is done by enabling soft sensors that assist in the making of these digital twins, specifically tailored for cyber-physical systems.
Predictive modeling can build soft sensors to assess components like polymerization, conversion rates, and melt index. These soft sensors are mainly deployed during polyethylene and polypropylene production processes to boost profitability and efficiency. For instance, the company can build a soft sensor to manage the polymerization reactor and its properties.
Oil companies enable continuous emission monitoring systems (CEMS) to identify, assess, and monitor emission levels. However, establishing CEMS is considered expensive in terms of maintenance and compliance. This is why companies are employing predictive analytics to create Predictive emission monitoring systems (PEMS) to assess greenhouse emissions. Generally, four stages are involved in the development of a predictive emissions model. These include a) data pre-treatment, b) variable selection, c) sensor validation with missing sensor detection, and model building. A special kind of sensor such as a self-validating soft sensor is utilized in advancing PEMS that helps in identifying inaccurate data, restructures it, and predicts the carbon emission levels in the industrial heaters.
The use of data-driven models helps determine catalyst performance. Usually, a learning-based soft sensor is enabled to assess the activity of catalysts in polymerization reactors. However, researchers have invented an advanced adaptive algorithm called the Incremental Local Learning Soft Sensing Algorithm (ILLSA). This helped in giving better predictions of the catalyst performance in processes and operations. On the other hand, some use predictive analytics to create a gray-box model that predicts the deactivation of zeolite catalysts like toluene, C8 and C9 aromatics in fixed-bed reactors. Machine Learning assists in defining the levels of catalyst saturation in a Fluid Catalytic Cracking (FCC) Unit.
Prediction of Distillation Products
Enforcing soft sensors helps in predicting parameters such as distillate and residue purity by comparing them with variables by hard sensors. Soft sensors employ algorithms like Principal Component Regression (PCR) or Partial Least Squares (PLS) to assess the components of a distillation component at different temperatures.
Revamping IT/OT Processes
By combining the power of IT and OT, companies can gain insights to resolve their operational issues. IOT devices can identify areas of concern that result in quicker response times. Through an operational data platform (ODP), companies can tap into hidden business operations for O&G enterprises. As a value key-value store, ODP makes the best use of data streams for streamlining and creating advanced control mechanisms to control their product operations. In addition, IT/OT convergence can make oil and gas companies more sustainable with connected systems helping in justified consumption of energy. In this way, leading oil and gas companies can minimize their energy expenses and carbon footprints. by workers. When it comes to workplace safety, safety devices can be integrated into a unified communication system that triggers responses when a hazard takes place.
Powering Analytics for Oil and Gas Firms on Cloud: ITOps specific use cases
By using cloud computing to modernize data analytics techniques, oil and gas companies prepare strategies to upgrade their production processes. Cloud enables cloud analytics and automated data-streaming techniques to speed up the process of assessing and managing greenhouse gas emissions. Insights generated by flare gas meters will help in determining processes that produce more flaring amounts; resulting in modernizing the procedures.
Predictive maintenance can reduce maintenance costs by up to 40% in the oil and gas industry. Oil and gas companies on the cloud can reduce their IT costs by eliminating the need for expensive hardware, software licenses, and maintenance. By using cloud services, companies can pay only for the resources they use, and scale up or down as needed.
Security and Compliance
In a highly complex cloud environment, a unified view of the security posture becomes challenging. This can make security systems and applications prone to some serious cyber threats. For instance, a hacker group known by the name Darkside facilitated a highly malicious ransomware that shut down the largest fuel pipeline in the US. This resulted in big supply shortages, plummeting the gasoline prices to $3/gallon.
For enhancing cloud security, an integrated approach that blends identity and information-centric solutions for security access is the need of the hour. This ranges from setting up identity and access management, implementing cloud security policies, to establishing a DR and backup solution, encrypting data in transit and motion, ensuring compliance, and enforcing cloud access security brokers for securing the entire infrastructure. A combination of these two solutions offers a contextual and unified view of identifying, prioritizing, and remediating risks with accuracy and speed.
Empower Oil and Gas Industries with Cloud4C’s Cloud Analytics
Currently, the oil and gas industry is witnessing an increasing adoption of predictive analytics. According to Facts and Factors, the global oil and gas analytics market size will grow beyond $60 billion at a 20% CAGR by 2026. However, the sheer volume of data can make it difficult for enterprises to extract actionable insights. This is where an expert partner in managed cloud services with predictive analytics comes in.
Cloud4C, a global cloud managed services provider, offers cloud analytics and services that help enterprises become intelligent. With Cloud4C's data analytics solutions, enterprises can make informed decisions and optimize operations, reducing risks and driving growth.
Cloud4C's cloud managed services and predictive analytics solutions and services are designed to meet the unique needs of each enterprise. They offer a range of managed cloud services and expertise in data analytics and can provide customized solutions that meet the specific needs of each client. With Cloud4C's managed cloud services, enterprises can unlock the full potential of their data and build a successful future.
Interested in knowing more about our cloud services? Contact us today!