Part 1: DINOSAUR IN OIL AND GAS? WHAT’S YOUR FUTURE?

TOP 3 TRENDS IN AI FOR 2025

Artificial Intelligence (AI) is rapidly evolving, reshaping industries and driving innovation. By 2025, three key trends are set to transform the oil and gas sector:

1. Generative AI and Large Language Models (LLMs)

What It Is: Generative AI, and more specifically large language models (LLM’s) such as OpenAI’s ChatGPT, Google’s Gemeni, and Anthropic’s Claude, are revolutionizing our interactions with technology. These models can produce human-like text, create images, compose music, and even write code.

Why It Matters: In the oil and gas industry, generative AI can uncover new revenue streams and cost-saving opportunities while personalizing workflows. By automating everything from reports to operational procedures, businesses can operate more efficiently, making this trend impossible to ignore.

2. AI-Augmented Decision-Making

What It Is: AI is being integrated into decision-making processes, offering real-time insights that help engineers and leaders make informed choices. This encompasses financial forecasting, risk management, performance diagnostics, and supply chain optimization.

Why It Matters: AI’s ability to process vast amounts of data enhances strategic decision-making, allowing organizations to anticipate trends and identify risks with greater accuracy. In production optimization, intelligent surveillance operations powered by advanced digital twins and predictive capabilities are game-changers, leading to revenue consolidation beyond mere cost savings.

3. Ethical AI and Explainability

What It Is: As AI systems proliferate, there is a heightened focus on ensuring these technologies are ethical, transparent, and understandable. This trend emphasizes the development of AI models that are accessible to non-experts and free from bias.

Why It Matters: Trust in AI is crucial for widespread adoption. By prioritizing ethics and explainability, companies can ensure their AI systems are effective, fair, and accountable, which is vital in sectors like finance and environmental management.

Evolving or Extinct? Why You May Feel Like a Dinosaur in Oil and Gas

Despite rapid advancements in AI, the oil and gas industry, can feel antiquated. Here’s why:

1. Slow Adoption of New Technology The oil and gas sector has historically lagged in adopting new technologies. While AI and IoT have gained traction in many fields, their integration in oil and gas is still nascent due to the large installed base of legacy systems and the asymmetric balance between risk and reward characteristic of many heavy industries.
2. Cultural Resistance A strong cultural emphasis on established practices can hinder innovation. Much of the experience and knowledge of the current oil & gas workforce is in traditional tried and true methods and workflows, making the transition to digital tools and learning opportunities challenging.
3. Focus on Physical Infrastructure Unlike tech industries dominated by software and data, the oil and gas industry relies heavily on physical assets and infrastructure. While AI can optimize processes, the industry's core focus on rigs, pipelines, and refineries can create a perception that digital transformation is a luxury or a fad. ?

However, change is on the horizon. The only way the industry will be able to maintain its safety and operational excellence record in the face of shrinking budgets and increasing flight of capital to renewables will be through digital transformation. As energy diversification and decarbonization gain momentum, the oil and gas industry will increasingly embrace AI and digital solutions. Adapting to these trends now can help professionals avoid becoming the dinosaurs of tomorrow.

REAL TANGIBLE AI OUTCOMES

Case Study 1: Drilling Efficiency in West Texas

Value: Reduced the planned Bottom Hole Assemblies (BHA’s) from 12 to 8.

Overview: In a competitive landscape, efficient drilling is crucial. By utilizing advanced data analytics and automation, field engineers can optimize drilling operations to save costs and enhance performance.

Key Strategies:

1. Data-Driven Decision Making: Machine learning algorithms analyze historical drilling data to identify patterns, offering real-time recommendations for optimal BHA configurations.
2. Statistical Modeling: Accurate models predict drilling challenges and optimize BHA components, minimizing downtime.
3. Collaborative Workflows: Platforms that facilitate data sharing streamline planning and enable rapid adjustments.

Outcomes:

• Efficiency Gains: Reducing the BHA leads to lower costs and operational time, enhancing overall operational efficiency.
• Increased Agility: Automated systems facilitate quick adaptations to drilling plans, reducing risks.

AI Approach:

• Machine Learning Algorithms?analyze historical drilling data to predict optimal drilling parameters (weight on bit, rotation speed) in real-time. A reinforcement learning model can adapt parameters based on immediate drilling conditions, maximizing penetration rates while minimizing costs.

Human Process:

• Engineers manually analyze historical data and rely on experience to adjust drilling parameters. This can be time-consuming and may not adapt quickly to changing conditions, potentially leading to inefficiencies or increased cost.

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Case Study 2: Production Optimization of Offshore GoM Operations

Value: Achieved a 10% increase in incremental production through intelligent surveillance.

Overview: Maintaining optimal flow rates while managing reservoir pressures is critical. An intelligent surveillance system continuously monitors and adjusts production parameters for maximum efficiency.

Key Strategies:

1. Real-Time Intelligent Surface and Subsurface Monitoring: Sensors and IoT devices gather data on pressure, temperature, and flow rates for immediate assessment.
2. Adaptive Control Systems: Machine learning algorithms adapt to changing conditions, dynamically optimizing drawdown levels.
3. Reliable Models : Robust models ensure that system adjustments are based on consistent, validated data.

Outcomes:

• Production Increase: The intelligent surveillance program leads to a measurable 10% increase in production.
• Sustainability: Continuous optimization minimizes waste and enhances resource use.

AI Approach:

· Machine Learning Models: Implement anomaly detection algorithms (e.g., isolation forests, auto encoders) to identify unusual patterns in production data that may indicate equipment failures, subsurface changes, or operational inefficiencies.

·? Alerts and Notifications: Set up automated alerts for operators when anomalies are detected, enabling swift intervention to mitigate potential issues.

Human Process:

Manual Data Collection

• Periodic Monitoring: Operators manually collect production data at set intervals, often relying on paper logs or basic spreadsheets.
• Human Error: Manual entry can lead to errors or inconsistencies in data, impacting decision-making.

Static Reporting

• Basic Analytics: Reports are often generated using basic statistical methods or simple spreadsheets, lacking the depth and speed of AI-driven analytics.
• Delayed Insights: Insights from traditional reporting can be slow, resulting in missed opportunities for optimization or early intervention.

While the prospect of AI replacing humans in various roles poses significant challenges, it also opens up exciting opportunities for transformation and growth. The critical task for the industry will be to navigate this transition thoughtfully, ensuring that individuals can effectively adapt to an AI-driven landscape. Prioritizing continuous learning, reskilling, and ethical considerations will be essential as we move toward a future where AI is deeply integrated into our lives. By fostering an environment that supports these initiatives, we can harness the benefits of AI while minimizing its disruptive effects. AI will not replace humans in oil and gas wholesale; rather traditional humans workers will be replaced by humans who are able to leverage AI to advantage.        

COMMON ASSUMPTIONS AND BLOCKERS IN AI ADOPTION

When discussing AI in oil and gas, several common assumptions or blockers can impede effective integration:

1. "AI Has Been Around for Many Years—What’s the Big Deal?"

Assumption: Some believe AI's current capabilities are merely extensions of past technologies.

Reality: AI is neither entirely new nor is it the same old. The underlying sensing, mathematical, computational, and information technologies have all been advancing for decades, but the pace of advancement has now become exponential propelled by the exploding computational backbone and cloud infrastructure and accelerated by an avalanche of never-ending data. As such, Modern AI, especially deep learning and generative AI, far surpasses earlier systems in processing power, data analysis, and creative output.

2. "AI Is Just Hype—It Can’t Replace Human Expertise."

Assumption: AI cannot match the intuition and experience of seasoned professionals.

Reality: AI will primarily augment human expertise, handling repetitive tasks and analyzing vast datasets, allowing professionals to focus on higher value tasks such as strategic decision-making. There are certain tasks that can indeed be replaced entirely by machines, but most will invariably require humans in the loop for a variety of technical, operational, political, ethical or other reasons.

3. "Our Existing Systems Work Fine—Why Do We Need AI?"

Assumption: Many organizations believe their current technologies are sufficient.

Reality: While existing systems may work, AI can significantly improve efficiency, accuracy, and uncover new opportunities.

4. "AI Is Too Complex and Expensive to Implement."

Assumption: There’s a belief that AI adoption requires massive investments, accessible only to large organizations.

Reality: Lower barriers to entry, such as cloud-based services and pre-built models, make AI feasible for all organizations. The long-term ROI always outweighs initial costs.

5. "AI Will Solve All Our Problems Instantly."

Assumption: Some view AI as a magic solution to all operational challenges.

Reality: AI requires careful planning, clear objectives, and ongoing management. It is most effective when integrated into a well-defined strategy.

6. "AI Will Not Cover My Operations"

Assumption:?AI encompasses a broad range of applications, including various aspects of Machine Learning. Advanced Machine Learning models can learn from both data and experience, making them valuable tools for understanding physical concepts.

Reality: By leveraging hybrid models, we can combine the speed of statistical approaches with the insights of complex engineering principles. This integration results in an optimized computational model that accurately reflects the behavior of physical assets, leading to improved operational efficiency and accurate decision-making.

Authors:

Camilo Mejia Farzad Sunavala Hani Elshahawi

Contributors:

Pat Bernard Rebecca Nye

Part 2 : Decarbonization Agents...... How real is Decarbonization?