Hype Aside: Real-World Use Cases of Artificial Intelligence in the Oil and Gas Industry

The oil and gas sector (O&G) has long been putting a premium on tradition and caution rather than innovation. Is there a place for AI in oil and gas?

Half of the surveyed industry executives say they have already started using AI to help solve organizational challenges — and it’s easy to see why. From the earliest stage of exploration to the final steps of distribution, artificial intelligence promises to streamline processes while slashing operational expenses and addressing safety concerns.

So how exactly does AI drive O&G companies’ growth agenda?

AI in all its forms to serve the needs of the oil and gas industry

Capturing value from data is made possible with these forms of AI used in the oil and gas industry:

• Machine learning: Analyzes data for pattern recognition to predict outcomes and, overall, optimize tasks across various operational levels. Often used in reservoir exploration, drilling, or fault detection.
• Deep learning: As a more advanced form of ML, deep learning utilizes complex neural networks for tasks like precise seismic analysis to process data and identify more complex details within it.
• Generative AI: Learning from existing datasets, gen AI creates, for example, new data samples, emergency instructions, or smart summaries.
• Natural Language Processing (NLP) and computer vision: Interpret human language and visual data for tasks like report generation and quality control. 
• Edge AI: Processes data locally on IoT devices without relying on cloud storage or internet connectivity.

The ways AI in oil and gas transforms the industry across all stages

Permeating into each stage of the supply chain, use of AI in the oil and gas industry enables companies to achieve operational efficiency and come closer to sustainable development and net zero.

Upstream AI applications in oil and gas: exploration and production

Explore four examples of how Artificial Intelligence aids firms during exploration and production (E&P).

Predicting reservoir’s exact location, quality, and size

Reservoir engineers analyze tons of electromagnetic and seismic data to discover new hydrocarbon deposits. 

The traditional exploration approach is expensive, risky, and prone to mistakes, as it heavily relies on human fieldwork. Drilling a well in the wrong spot can cost $5 to $20 million per site. 

AI in oil and gas exploration reduces the likelihood of such costly surprises. How?

• Seismic images interpretation. Geoscientists can remove noise, improve resolution, detect subtle features, or even generate additional data samples with AI if imagery quality is poor or incomplete.
• Understanding reservoirs better. Generative AI is used to analyze geological maps, production data, and well logs to create geo-models of hydrocarbon reservoirs. Such models help engineers control fluid movement and predict the long-term performance of a well.
• Informed oil well placement. A powerful duo — IoT devices and edge computing — enables the local processing of real-time sensor data, including pressure, temperature, and flow rates, to be used for geological models without relying on external computing systems. As stated by Deloitte, the time required to create geo-models for oil well placement can be reduced from months to hours, thanks to Edge AI.

Increasing extraction rates with automated drilling

With hefty costs involved, no wonder oil and gas companies seek to hammer drilling operations home on the first try. A helping hand here is drilling optimization — another application of AI in the oil and gas industry.

Predictive intelligence allows engineers to convert cross-sourced historical and real-time data into actionable insights for drilling preparation. Armed with advanced ML algorithms, geosteering teams analyze terabytes of historical data to configure optimal parameters, such as weight on bit, drilling speed, angle, etc. Meanwhile, neural networks leverage real-time drilling data to predict the likelihood of stuck pipe events, enabling proactive measures. 

The result? Machine learning for oil and gas reduces the risk of drill-bit failures and optimizes extraction rates.

Proactively identifying and preventing equipment failures

Oil and gas producers put significant effort into overseeing E&P equipment and scheduling maintenance activities.

While manual monitoring is error-prone, time-consuming, and lacks the ability to leverage historical data, automated fault detection helps identify patterns, predict potential failures, and optimize operations.

Drones and robots with tiny sensors and cameras scan each equipment component with laser precision. Cracks, corrosion, and other potential signs of wear are identified by pre-trained ML models. Thus, AI performs 24/7 meticulous real-time inspection without human intervention, minimizing operational risks and expenses.

An example of AI-enabled asset maintenance in action can be found at Shell. They utilize an ML-based predictive analytics solution that helps avoid critical equipment outages and identify cases when maintenance is needed. Now, its staff have more time for engineering instead of analyzing mountains of data, while the company reduces production losses and maintenance costs.

Equipping field workers with AI assistants 

High pressures, heat, flammable substances, and other factors have led to many tragic safety incidents during exploration. With virtual field assistants, drilling rig crews, well operators, and technicians have quicker and easier access to critical information. Conversational AI assistants are easily integrated into field-friendly devices, guaranteeing round-the-clock availability and proving to be more effective for emergencies than human-staffed call centers.

Midstream use cases of AI in the oil and gas industry: storage and transportation

Here are practical applications of artificial Intelligence in midstream oil and gas leaders should know.

Inspecting storage facilities

Optical gas imaging (OGI) cameras installed on robots or unmanned drones capture large amounts of data. Generative AI sums it all up in natural language, saving hours that used to be spent on the manual review of the footage. 

AI-generated assistive summaries enhance the efficiency of oil & gas operations, especially in large industrial facilities or outdoor environments. Moreover, operators can take remedial actions without entering potentially dangerous areas.

Planning the safest and fastest routes for logistics vessels

Logistics specialists analyze vast amounts of data related to weather, route hazards, port congestions, vessel conditions, and other operational factors. Advanced analytics help them reveal insights to plan the most cost-effective tank routes.

Not only do AI optimization algorithms ensure on-time delivery, but they also identify risks and adjust the route on the go without increasing the planned transit time.

A great example of using algorithmic shipping & maritime route optimization can also be observed at Shell. Their LNG Shipping Accelerator gathers all critical infrastructure data in a single place for freight operators to reduce waiting time at ports and fuel usage, resulting in timely and nature-positive energy delivery.

Downstream artificial intelligence applications in the oil and gas industry: refinery and distribution

By embracing AI, downstream firms achieve refinery & distribution cost reduction and reach regulatory compliance faster in several ways.

Honing the refinery process

The application of AI in downstream oil and gas companies involves real-time monitoring systems that optimize refineries. Those systems monitor operations and collect data during distillation, catalytic cracking, and hydrogenation. They also screen data from energy meters and equipment sensors.

All this contributes to boosting petrochemical throughput, minimizing energy consumption, and identifying potential safety hazards.

Meeting quality standards faster

AI aids oil and gas refinery companies to meet key quality standards, including ISO, API, ASTM, and others.

Straight from the production lines, ML algorithms and predictive AI models analyze the produced diesel, lubricants, jet fuel, natural gas, liquefied petroleum gas, oil petrochemicals, etc., against the standards. 

By predicting deviations in product quality before they occur, production specialists can make corrections to minimize waste and ensure production reliability and environmental sustainability.

Reinforcing product research and development 

Generative AI in the oil and gas industry allows petrochemical engineers to accelerate the materials development process and bring down R&D costs by:

• designing new chemical compounds with diverse compositions, simulating how these virtual assets behave under different conditions before actual physical production;
• establishing the most efficient experimental procedures for probing or optimizing materials;
• developing high-entropy alloys (HEAs) with excellent physical, chemical, and mechanical properties.

Cross-stream AI applications in the oil and gas industry

Some AI use cases in oil and gas are so versatile that they cover more than just one segment, functioning across multiple layers of the industry.

Planning asset maintenance proactively

AI-powered predictive maintenance goes beyond upstream. 

It forecasts failure in pipelines, pump stations, or processing plant equipment to prevent costly repairs. Casting their nets wide, predictive maintenance models optimize performance and extend the infrastructure life across the whole oil & gas supply chain. 

Besides, gen AI creates efficient maintenance schedules by analyzing data like equipment usage, production needs, and required costs. 

Automating mission-critical supply chain processes

Apart from optimizing costly transits of crude oil or LNG via barges and tankers, advanced analytics algorithms enhance the following:

• other transportation methods, such as pipelines, trucks, and railroads,
• distribution network configuration, including the number and locations of storage facilities, transportation routes, and inventory levels at each facility.

Using gen AI, oil and gas companies and logistics providers automate mission-critical supply chain processes:

• procurement: materials demand forecasting, identifying the most suitable suppliers;
• on-shore and off-shore inventory management: improving asset tracking;
• route planning: identifying current traffic conditions, tuning optimal delivery timing, vehicle tracking, fuel-efficient routing;
• contingency planning: running what-if scenarios in a digital twin environment to develop custom multi-purpose mitigation strategies.

Generative AI in oil and gas adds resilience to planning so all stakeholders make the supply chain run like clockwork.

AI for oil and gas: impact at a glance

Here’s a pack with all the examined use cases to highlight the impact AI solutions make on different tasks of O&G professionals.

A pAI in the sky? What’s holding O&G companies back in adopting artificial intelligence

The challenges of capturing AI perks are now so often reiterated that they are bordering on clichés, commonplace enough to cover everyone with one sweeping generalization, yet too vague to shed light on the unique struggles faced by a specific industry: 

• Keep your eye on the business case, not just the tech,
• Focus on satisfying and listening to users,
• Don’t let perfection be the enemy of progress; fail fast, learn fast,
• Agility rocks; bureaucracy sucks,
• and all that jazz.

While these insights are true, they are far from the whole story. The real reasons oil and gas firms hinder squeezing value from digital are more sector-focused:

• Physical orientation. Plants, terminals, offshore platforms, or thousands of miles of pipelines are not subject to swift adjustments. Oil and gas executives may fairly ask: Should we burden our complex capital ecosystem with additional obscure stuff? Technology investments need strong evidence to prove they’ll get bang for every buck without compromising asset performance.
• Safety monitoring and compliance. O&G companies have always adhered to a web of local rules, environmental regulations, and international treaties. Institutional pressures and enormous attention to safety make decision-makers slow to innovate.
• Engineering vs. digital mindset. The domination of engineers at the top management level is the energy industry’s hallmark. To that end, the oil & gas sector is pervaded by an engineer-driven approach with its expectation of a guaranteed result, whereas adopting whatever cutting-edge technology requires a digital, or flexible mindset.
• Reliance on external partners. Fine-tuned collaboration between all supply chain stakeholders is the heart of the energy sector. Given the interdependent nature of the operations, aligning all these stakeholders and their systems becomes a significant hurdle.
• Lengthy careers, limited diversification. O&G C-level managers often spend decades within the same company, which fosters a cautious culture of following traditions and focusing on surviving, often at the expense of driving innovative change.

These factors, flavored by shaky political stability, globally scattered operations, and fluctuating profit margins, account for decision-making inertia regarding AI use in the oil and gas industry.

3 steps to start your AI project in the oil and gas sector

All the above challenges should not necessarily hold you back. A recent EY survey showed more than 92% of oil and gas companies are either currently investing in AI or plan to in the next two years. AI in the oil and gas market, estimated at $2.98 billion in 2024, is expected to reach $5.17 billion by 2029, growing at a CAGR of 11.68% during 2024-2029.

So what should oil and gas executives start with to achieve AI payoffs?

Address data issues

The most critical factor in laying the solid groundwork for AI adoption is high-quality data.

What can you do? 

• Identify all your raw data sources (equipment, sensors, satellite imagery, etc.). 
• Review the ways you collect and store geological data, historical maintenance records, etc.
• Enrich your data science and big data competencies, if needed, to facilitate security and quality.

Identify use cases  

While AI can be a powerful tool, it won’t be a silver bullet that transforms every process overnight.

Forget the idea of a one-size-fits-all enterprise AI solution that magically fixes everything in your company.

Instead, focus on business areas that rely on high volumes of raw data and directly impact revenue, costs, risk management, or other crucial aspects.

Quick wins from improving bit-sized processes will create room for larger AI initiatives.

Create a deployment strategy 

Deployment pathway varies by the industry segment, but these five criteria are universal to consider:

• draw on a cost-benefit analysis 
• consider the impact on people and operations 
• create a responsible implementation framework 
• ensure data integrity 
• establish effective governance

These considerations help build a win-win deployment strategy for business growth and a sustainable future.

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