Optimizing O&G production with cloud, IoT, and AI technologies
The increasing global demand for oil and gas (O&G) will only continue to grow over time. Along with this, the volatility in prices and increasingly stringent environmental regulations will lead to challenges ranging from reducing costs to remain competitive to improving carbon and environmental footprints.
Processes involved in oil and gas industry
Producing and distributing oil and gas resources are extraordinarily complex, capital-intensive, and require state-of-the-art technology. Production is a multi-stage process of exploring, transporting, and converting to a finished product ready for market and each of the segments (upstream, midstream, and downstream) and face significant challenges ahead.
Upstream
The process of exploring and producing crude oil and natural gas is set to falter as the number nears all-time lows and the sector works to shake off the effects of the Covid-19 pandemic and ensuing oil market crash. Many of the industry challenges lie in this segment including harmful emissions, equipment failures, reduced yields, contamination due to leakages, and disrupted habitats due to extraction sites.
This segment is characterized by high investment capital, high risks, changing environmental conditions, and being technologically intensive.
Midstream
In addition to pipelines, railroads and trucks using tankers at sea to transport crude oil from wellheads to refineries or processed goods from refineries to their final destination is not only tricky but also hazardous. Pipeline explosions and oil spills are only a few examples of the obstacles faced with transportation across long distances.
This segment is marked by low capital risk and high regulation, and it is highly dependent on the success of the upstream processes.
Downstream
The downstream market is facing an unprecedented one-two punch: a reduction in demand, and a concurrent increase in supply. Typically, downstream players enjoy initial larger margins as crude prices drop faster than product prices. However, these tend to be short-lived, and as economic activities slow, the demand effect drives overall refining margins—and retail margins—downward. Deteriorating demand and profit margins for fuels produced at refineries are reducing operations ranging from cutting refinery runs, to closing units, to completely shutting down refineries. These conflating factors are increasing pressure on refiners’ balance sheets, currently strained from a low-margin business environment.
This segment depends on market demand, price, and government regulations as companies need to ensure they have enough inventory to fulfill the changing customer demands.
Optimize oil and gas production processes using modern solutions
Neal Analytics has developed several solutions to address many of the challenges identified above using technologies like machine learning, deep reinforcement learning, and other analytics. These solutions range from helping O&G organizations predict demand, consumption, asset maintenance and scheduling to identifying workforce needs and optimizing production.
A brief synopsis of each solution is included below:
Upstream
Identify well condition through sensors signals: A modernized edge architecture and IoT analytics to assess sensor signals in real-time and detect faults to prevent downtime.
Avoiding severe pump jack failure: Identify patterns of failure occurrences and remotely diagnose pre-failure conditions through machine learning.
Detect potential shutdowns to allow preventive maintenance action: View past machine failures and alerts and help the operations team with what-if-scenarios analysis to adjust control parameters when needed through an AI framework.
Refining yield optimization: Determine the best refinery output (based on market conditions) and schedule refinery equipment accordingly to optimize yield by training an AI model.
Equipment failure root cause identification: Detect anomalies and build forecasting models using sensor signals and operational historian data to detect potential causes of equipment failures.
Midstream
Scheduled tank pickup optimization through tank levels forecasting: Building a predictive model to forecast tank level using on-site sensor reading and historical data.
Optimize oil, natural gas, and LNG (Liquefied Natural Gas) transportation: Analyze and optimize transportation routes, gaining visibility into all locations, suppliers, and customers in real-time using cloud, analytics, and AI-powered solutions.
Optimize maintenance schedules based on risk probabilities: Analyze the survival probability of assets and predict future failures and performance degradation using a multi-variate prediction algorithm.
Pipeline predictive maintenance: Implement condition-based alerts using sensor signals and operational historian data to reduce or prevent downtime and increase throughput.
Hydrocarbon loss detection: Create a digital twin to reduce data-to-decision time for prognostic corrective actions by finding the root causes for the hydrocarbon losses.
Downstream
Pricing optimization: Develop non-linear regression models and machine learning techniques to help evaluate the oil and gas market and forecast prices based on historical data and other economic factors.
Fuel station analytics: Leverage forecasting and analytics to optimize planning and automate and streamline processes for a better customer experience by providing superior quality fuel with short waiting times.
Demand forecasting: Leverage advanced demand forecasting solution to understand demand drivers, optimize business operations, and strategize sales plans.
Inventory management: Maintain the right amount of inventory to meet changing customer demands using technologies like machine learning, advanced analytics, and AI.
Conclusion
The need for oil and gas will continue for several decades. In response, companies are striving to find innovative solutions that enable them to produce cleaner, safer, and more affordable products to best competition and comply with government regulations.
Technology will continue to mature, and innovation will help optimize the production flow from upstream to downstream. Neal’s solutions for O&G are developed to improve operations within each of these three segments through cloud, IoT, and AI technologies alleviating many of these challenges.
We have worked with leading O&G companies to deliver best-in-class solutions to improve operational efficiencies and drive business performance. If you want to learn more, please contact us!
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