Although generative AI is still evolving, it’s proven to be more than a hyped-up term. Over the past few months, industries – from banking to medical services – eagerly explored LLMs on various levels. Analysts are also enthusiastic about generative AI; thus, McKinsey believes it could create additional value potential above what could be unlocked by other AI”.
So, it’s only natural for an industry as competitive as oil and gas to leverage trailblazing technology. A recent Accenture study states that “62% of oil and gas companies are already using or planning to implement generative AI, ” proving the industry’s growing embrace of this transformative technology. Indeed, generative AI can potentially disrupt the oil and gas sector, from exploration and production to refining and distribution to general operations and sustainability practices. In this piece, we’ll explore the disruptive influence of generative AI and its ability to unlock new opportunities and efficiencies for oil and gas.
Geospatial analysis and exploration
Streamlining exploration processes is among the top priorities in the energy industry. And only some technology can handle it better than generative AI. It can significantly improve decision-making when it comes to drilling locations and budgeting. With the ability to process masses of data, discover patterns and create simulations, GenAI can help find new oil and gas deposits and offer the most rewarding exploration activities.
As a result, exploration efforts can be more targeted and efficient, enabling companies to reduce the time and resources required to locate new reserves and ensure they remain ahead of their competitors.
Transportation and distribution
Oil and gas products must arrive at intended markets on time. So, streamlined transportation and distribution are vital for the industry. Yet, these parts of the industry lifecycle are some of the most challenging, involving many parties.
By analysing logistical data, generative AI can optimise distribution and transportation networks. This helps businesses to efficiently distribute resources, manage suppliers, and maximise shipping schedules and routes. Using Gen AI for transportation and distribution results in significant cost savings and higher efficiency and enables companies to meet customer expectations, create value, and remain competitive.
Demand prediction and supply-chain management
Accurate and timely demand prediction is crucial in any industry. Processing masses of data, generative AI can transform demand forecasting and recourse allocation for energy companies. AI algorithms, analysing past projects, price fluctuations, and demand spikes, predict drilling needs. LLMs allow companies to adapt to changing market conditions in real time. They can promptly adjust production plans and logistics in response to extreme weather conditions or geopolitical events.
With Ggen AI, drilling companies can forecast customer demands and maximise profitability. While refineries can optimise production and inventory management. At the same time, businesses can predict the demand for downstream products and adapt production accordingly. Supply chain management is yet another area where AI can foster significant changes. AI-powered supplier performance evaluation simplifies searching for providers. Also, trained on historical data and market trends, LLMs enable precise cost estimation.
Maintenance and safety
Industry maintenance usually involves reactive or scheduled procedures at fixed intervals. This can result in unnecessary maintenance or unexpected breakdowns. Generative AI enables oil and gas companies to leverage predictive maintenance. By processing historical maintenance records and real-time operational data, it can predict equipment failures before they occur.
For example, offshore oil rigs usually have numerous pumps, compressors, and drilling equipment. AI-powered sensors scan these critical components 24/7. Then, LLMs analyse the data, define patterns and catch potential malfunctions. This way, operators can detect early warning signs and schedule maintenance activities, which prevents costly shutdowns before components fail.
Besides, AI-powered predictive maintenance can transform operations for refineries and pipelines – from increasing equipment lifespan to enhancing safety standards. Following established policies and procedure documentation, LLMs can provide users step-by-step instructions in specific troubleshooting scenarios involving equipment or processes, navigating standard problem-solving scenarios and simplifying standard procedures. Also, general AI-based risk analysis tools can scan industry news, regulatory updates, and internal reports, identify potential outcomes, and suggest mitigation strategies.
General operations, legal and finance
Apart from all the mentioned above, there are internal operational processes that oil and gas companies need to cater to. LLMs can also streamline operations, from training and onboarding to procedure automation to compliance management. Gen AI can assist in drafting legal documents and automate the generation of routine legal documents to save time and reduce errors. Oil and gas companies can use generative AI to review and analyse contracts for crucial terms, obligations, and potential risks. This could ensure compliance with legal standards, regulations, and internal policies. LLM-based solutions can monitor relevant changes in laws and regulations to alert legal teams about updates that may impact the organisation.
Besides, businesses can rely on LLMs to create interactive and adaptive training modules to revamp employee onboarding processes and provide personalised learning experiences. Also, companies can automate repetitive tasks with gen AI-powered scripts, allowing employees to focus on more strategic aspects of their roles.
Marketing and customer support
Conversational AI technologies have already proven their value for sales and marketing activities. However, LLMs can take commercial activities to the next level. For example, LLMs analyse competitor data. They could help departments process large amounts of information, identify trends, provide valuable insights, generate high-quality, human-like content materials, product descriptions, and more. This could save time and resources while maintaining a consistent and engaging brand voice.
Businesses can use generative AI to analyse customer reviews, social media mentions, and support tickets to gauge sentiment. This could help businesses to understand customer satisfaction, identify potential issues, and improve products or services.
Being under increasing environmental pressure, energy companies turn to more sustainable practices. These include increasing energy efficiency, utilising resources and reducing waste. And generative AI is one powerful tool that can help the industry achieve these goals. From improving emissions and waste management to better employee safety – LLMs can assist companies in their green initiatives.
Thus, AI is used to detect oil spills and hydrocarbon leaks so that companies can respond to such issues timely and efficient. We’ve already mentioned that LLMs enable real-time safety monitoring. AI-powered sensors in oil platforms track environmental conditions, equipment performance, and personnel activities. By processing these data, operators can detect safety deviations and prevent potential incidents.
Besides, gen AI helps reduce the risk of accidents when transporting hazardous materials. By analysing traffic data, weather conditions, and road infrastructure, AI-powered solutions for oil and gas help companies identify safer and more efficient transportation routes.
LLMs don’t just improve the bottom line for oil and gas companies but also help them stay committed to sustainability and corporate social responsibility, which will attract environmentally conscious investors and customers.
Of course, the energy industry is only beginning to realise the full potential of LLMs. There are new frontiers to explore and concerns to address. Data security and privacy remain issues as AI uses large datasets – often with confidential and private data. To reap the benefits of GenAI and eliminate risks, businesses must adhere to privacy laws and anonymisation practices. Companies need to deploy LLMs within ethical frameworks and governance standards. This will help avoid issues like biased model outputs or information leaks.
Besides, oil and gas companies try to improve their ESG performance and commit to net-zero emissions. They are adopting new technologies to operate their business with minimal environmental impact. So, another Gen AI-related concern companies must address is carbon footprint.
We know that the dose makes the poison, and the adoption of generative AI is still early. The oil and gas industry must balance the revolutionary potential of LLMs and potential uncertainties.
Now is the ideal time to start your Generative AI journey and reap its benefits for your company