Managing Artificial Intelligence in Supply Chains for Oil & Gas Industry in India

AI is driving the evolution of interconnected digital supply networks. This change can help supply chain managers be more dynamic, flexible, and efficient in their planning and execution. Supply chain planning has always been a data-rich, analytical process. But as linear supply chains evolve into interconnected digital supply networks (DSNs), powered by advanced technologies and interconnected systems, the way we think about supply chain planning could fundamentally shift. Organizations can use intelligent technologies to make smarter planning decisions, enabling them to reduce costs, remove reliance on “tribal knowledge,” gain deeper and broader insights into their supply chains, dramatically improve decision-making processes, and increase the agility of their DSN.

How AI techniques support Oil & Gas Industry

AI creates a simulated experience that plays a vital role in the decision-making process.
The use of machine learning techniques and case-based reasoning to reference previous knowledge of similar situations could save time, money and manpower across the first stage of the E&P process. Machine learning and other big data applications could save the oil and gas industry as much as $50 billion in the coming decade, according to management consulting firm McKinsey & Company.

AI can be used in optimising the drilling process and enhancing operational efficiency, leading to a reduction in drilling costs. Drilling engineers employ AI-driven and reality-enabled platforms that have better control over rates of penetration (ROP) enhancement, drilling equipment condition, risk recognition, and procedural decision making.

AI systems have the ability to automate and optimise data-rich processes. They have the potential to mitigate risks, enhance productivity, remove redundancy and minimise operational costs. When AI intersects with seismic data, the outcome is a much more accurate depiction of the sweet spot sources.

Executing AI in the Oil and Gas Industry

Presently the future outlook of oil and gas is challenging owning too many factors. The most prominent of these are low oil prices, the emergence of new hydrocarbon sources, increasing penetration of renewable energy, electric vehicles, strict carbon regulations, and better energy storage technologies.
Despite shrinking margins, most upstream companies have sufficient budgets to invest in a robust AI strategy from exploration to production. This strategy should include solutions that improve project design and evaluation, enable unmanned drilling operations, increase reliability on the ecosystem, and predict maintenance needs. Such capabilities will not only increase efficiency but also support profitable growth.

Upstream stacks

Upstream exploration stack

Exploration companies can build capacities to analyze geological data by digitizing all the data and knowledge gathered or developed by them. Applying advanced analytics and machine learning techniques on such big data sets will provide exploration teams with precise and meaningful insights.

Upstream project stack

Industry 4.0 will change how organizations design, evaluate and choose the best project by allowing them to consider as many parameters as possible. When advanced analytics and digital modeling techniques are applied to various data inputs, they create a digital framework that can generate and evaluate an exhaustive number of projects. Based on these evaluations, companies can then choose projects that suit their chosen parameters.

Way forward on leveraging AI

  • Planning and forecasting

On a macro scale, deep machine learning can help increase awareness of macroeconomic trends to drive investment decisions in exploration and production. Economic conditions and even weather patterns can be considered to determine where investments should take place as well as intensity of production.

  • Eliminate costly risks in drilling

Drilling is an expensive and risky investment, and applying AI in the operational planning and execution stages can significantly improve well planning, real-time drilling optimization, frictional drag estimation, and well-cleaning predictions. Additionally, geoscientists can better assess variables such as the rate of penetration (ROP) improvement, well integrity, operational troubleshooting, drilling equipment condition recognition, real-time drilling risk recognition, and operational decision-making.

When drilling, machine-learning software takes into consideration a plethora of factors, such as seismic vibrations, thermal gradients, and strata permeability, along with more traditional data such as pressure differentials. AI can help optimize drilling operations by driving decisions such as direction and speed in real time, and it can predict failure of equipment such as semi-submersible pumps (ESPs) to reduce unplanned downtime and equipment costs.

  • Well reservoir facility management

Wells, reservoirs, and facility management includes integration of multiple disciplines- reservoir engineering, geology, production technology, petrophysics, operations, and seismic interpretation. AI can help to create tools that allow asset teams to build professional understanding and identify opportunities to improve operational performance.
AI techniques can also be applied in other activities such as reservoir characterization, modelling and field surveillance. Fuzzy logic, artificial neural networks and expert systems are used extensively across the industry to accurately characterize reservoirs in order to attain optimum production level. Today, AI systems form the backbone of digital oil field (DOF) concepts and implementations. However, there is still great potential for new ways to optimize field development and production costs, prolong field life, and increase the recovery factor.

  • Predictive maintenance

Today, artificial intelligence is taking the industry by storm. AI-powered software and sensor hardware enable us to use very large amounts of data to gain real-time responses on the best future course of action. With predictive analytics and cognitive security, for example, oil and gas companies can operate equipment safely and securely while receiving recommendations on how to avoid future equipment failure or mediate potential security breaches.

  • Oil and gas well surveying and inspections

Drones have been part of the oil and gas industry since 2013 when ConocoPhillips used the Boeing ScanEagle drone in trials in the Chukchi Sea. In January, Sky-Futures completed the first drone inspection in the Gulf of Mexico.

While drones are primarily used in the midstream sector, they can be applied to almost every aspect of the industry, including land surveying and mapping, well and pipeline inspections, and security. Technology is being developed to enable drones to detect early methane leaks. In addition, one day, drones could be used to find oil and gas reservoirs underlying remote uninhabited regions, from the comfort of a warm office.

  • Remote logistics

As logistics to offshore locations is always a challenge, AI-enhanced drones can be used to deliver materials to remote offshore locations.

Creating Road map

Designing an AI roadmap requires a holistic understanding of the entire organization including its capabilities, priorities, culture, and digital maturity level. As such, it demands the direct involvement of C-level executives, particularly CEOs who possess in-depth understanding of the organization.

Studies show that the integrated and connected assets of oil and gas companies can generate as much as terabytes of data per day. Despite this, many companies still lack the capabilities to leverage this information for relevant business insights. To overcome this digital bottleneck, companies must look at adopting Industry 4.0 using an enterprise-wide and holistic approach.

The first step is to analyze the organization’s current situation and goals. The key questions to be considered are:

• What are the strategic goals and objectives over the next few years?
• What technologies and systems are currently implemented?
• How do these technologies and systems operate within the company?

Conclusion

Today’s oil and gas industry has bееn transformеd by two industry downturns in onе dеcadе. Although adoption of nеw hard tеchnology such as dirеctional drilling and hydraulic fracturing (fracking) has hеlpеd, thе oil and gas industry nееds to continuе to innovatе in today’s low-pricе markеt to survivе. AI has thе potеntial to diffеrеntiatе companiеs that thrivе and thosе that arе lеft bеhind.

Thе promisе of AI is alrеady bеing rеalizеd in thе oil and gas industry. Еarly adoptеrs arе taking advantagе of thеir position to gеt a hеad start on thе compеtition and protеct thеir assеts. Thе industry has always lеvеragеd tеchnology to adapt to changе, and еarly adoptеrs havе always bеnеfitеd thе most. As compеtition in thе oil and gas industry continuеs to hеat up, companiеs cannot afford to bе lеft bеhind. For thosе that undеrstand and sеizе thе opportunitiеs inhеrеnt in adopting cognitivе tеchnologiеs, thе futurе looks bright.