Achieving Net-Zero: How SLB Leverages AI and Machine Learning in the Energy Transition

SLB, a global leader in energy services and technology, has set an ambitious goal of achieving net-zero emissions by 2050. Central to this mission is the deployment of cutting-edge Artificial Intelligence and Machine Learning technologies across its product portfolio and operations. By embedding these technologies into products like DELFI, Neuro Autonomous Solutions, and End-to-End Emissions Solutions (E2E), SLB is driving the energy transition while enabling its clients to reduce their carbon footprints.

Here’s how SLB integrates AI and ML into its product ecosystem to lead the way towards sustainable energy future.

Optimizing Operations with AI-Driven Solutions

DELFI Cognitive E&P Environment

DELFI, SLB's AI-powered cloud platform, revolutionizes how exploration and production (E&P) data are analyzed. By integrating vast datasets with advanced ML algorithms, DELFI enables operators to:

• Optimize Well Construction: DELFI provides real-time recommendations for drilling operations, reducing energy consumption and emissions. The platform's ability to predict operational challenges minimizes non-productive time, leading to lower environmental impact.
• Streamline Reservoir Management: By analyzing petrophysical and seismic data, DELFI enhances reservoir understanding, improving recovery rates and reducing the need for additional drilling.

Neuro Solutions for Autonomous Drilling

The Neuro Autonomous Solutions product line integrates AI into drilling operations, automating tasks traditionally reliant on human decision-making. Neuro achieves:

• Energy Efficiency: AI algorithms in Neuro optimize bit placement and trajectory in real-time, minimizing the energy required for operations.
• Reduced Emissions: By increasing precision, Neuro lowers material waste and emissions, advancing SLB's decarbonization goals.

Driving Carbon Capture and Storage (CCS) Innovation

End-to-End Emissions Solutions (E2E)

SLB End-to-end Emissions Solutions platform integrates AI to address the full lifecycle of emissions management. Key capabilities include:

• Seismic Data Analytics: AI models process complex geological data to identify and evaluate CO₂ storage reservoirs.
• Real-Time Monitoring: Advanced ML algorithms track injected CO₂ to ensure secure and permanent storage, reducing risks of leakage.
• Compliance Assurance: AI-driven tools ensure projects meet regulatory requirements, supporting scalable CCS deployment.

Ora Intelligent Wireline Platform

The Ora Intelligent Wireline Platform is a next-generation wireline logging solution from SLB that integrates advanced Artificial Intelligence (AI) and Machine Learning (ML) technologies. Designed for precision and real-time decision-making, Ora is transforming how subsurface data is acquired, analyzed, and applied, especially in Carbon Capture and Storage (CCS) projects. Its innovative capabilities enable operators to optimize CO₂ storage site selection, monitor injection performance, and ensure long-term storage security.

• Dynamic Reservoir Characterization: AI models analyze petrophysical data to create detailed 3D models of the subsurface, allowing operators to assess storage capacity and seal integrity with unprecedented accuracy.
• Enhanced Decision-Making: Operators can make real-time adjustments to CCS operations based on live data, minimizing risks and optimizing storage performance.
• Fracture Detection and Mapping: ML algorithms process imaging data to detect natural fractures and faults that could compromise CO₂ containment.
• Caprock Integrity Assessment: Ora evaluates the strength and continuity of caprock layers, ensuring the long-term security of stored CO₂.

Transforming Renewable Energy with AI

SLB applies its AI expertise to accelerate renewable energy adoption and integration.

GAIA Environmental Intelligence

The GAIA Environmental Intelligence platform combines satellite data, geospatial analytics, and AI models to identify the most suitable locations for renewable energy projects, such as solar farms and wind turbines. GAIA’s capabilities ensure projects are designed to maximize energy production while minimizing environmental and social impacts.

• Site Feasibility Analysis: AI algorithms process data on solar irradiation, wind patterns, topography, and environmental constraints to identify high-potential renewable energy sites.
• Environmental Impact Assessment: GAIA uses geospatial data to evaluate factors like biodiversity, land usage, and water resources, ensuring project designs adhere to sustainability standards.
• Real-Time Insights: The platform provides up-to-date weather forecasts and climate trends, enabling operators to plan for seasonal and long-term changes in renewable energy generation.

Energy Storage Optimization

Efficient energy storage is essential for renewable energy integration, particularly given the intermittent nature of solar and wind power. SLB’s advanced battery management systems utilize AI and ML to enhance energy storage solutions.

• Dynamic Charging Optimization: AI models analyze usage patterns, grid demands, and battery conditions to optimize charging and discharging cycles, prolonging battery life and improving efficiency.
• Degradation Prediction: ML algorithms monitor and predict battery health, identifying potential failures before they occur. This reduces downtime and ensures consistent energy availability.
• Grid Balancing: AI tools manage energy storage systems to balance supply and demand, stabilizing renewable energy grids and reducing reliance on fossil fuel backup systems.

Reducing Emissions in Oil and Gas Operations

Emission Detection and Quantification with Sensia

The Sensia product suite integrates advanced AI with IoT-enabled sensors, drones, and edge computing to monitor and manage methane emissions across oil and gas facilities. Methane, a potent greenhouse gas, is a key focus for SLB in its mission to decarbonize operations.

• Real-Time Leak Detection: Sensia employs AI algorithms to process data from IoT sensors and drones, identifying methane leaks in real time with high precision. Example: Drone-mounted sensors equipped with hyperspectral imaging can detect methane plumes, while AI pinpoints the exact source, allowing for immediate intervention.
• Quantification and Reporting: AI-powered analytics measure the volume and rate of methane emissions, providing operators with actionable data to comply with environmental regulations.
• Predictive Maintenance: By analyzing historical data and identifying patterns, Sensia’s ML models predict equipment malfunctions or pipeline weaknesses that could lead to future leaks.

Targeted Mitigation Strategies

Sensia doesn’t stop at detection; it enables swift and effective mitigation:

• Automated Alerts: When a leak is detected, operators receive real-time alerts via a centralized dashboard. The system provides actionable insights, such as the size of the leak and recommended actions.
• Emission Reduction Plans: AI tools generate targeted mitigation strategies, such as valve adjustments or pipeline repairs, to minimize downtime and emissions simultaneously.

Cameron’s Low-Emission Valves and Systems

SLB’s Cameron product line integrates AI into the design and operation of low-emission valves and systems to address fugitive emissions—small leaks that can occur in pipelines, valves, and other equipment.

• AI-Enhanced Flow Control: Advanced algorithms optimize valve performance in real time, ensuring precise control over fluid flow and reducing the risk of leaks.
• Fugitive Emission Reduction: Cameron’s valves are engineered with sealing technologies and AI-powered diagnostics to minimize gas emissions in both midstream and downstream operations.
• Performance Monitoring: Embedded AI systems continuously monitor valve performance, identifying potential seal failures or wear-and-tear issues before they escalate.

• Energy Efficiency: Optimized flow control reduces energy use in pumping and compression systems, further lowering the carbon footprint of operations..

Enhancing Digital Workflows for Sustainability

Avocet Integrated Operations Platform

The Avocet platform is a powerful tool that uses AI and advanced analytics to streamline operations, minimize energy consumption, and maximize production efficiency across the value chain.

• Real-Time Monitoring and Analysis: Avocet processes historical and real-time data from field operations, identifying inefficiencies such as excessive energy consumption or equipment underperformance.
• Predictive Maintenance: AI models in Avocet anticipate equipment failures and maintenance needs, reducing unplanned downtime and associated energy waste.
• Dynamic Optimization: The platform provides actionable recommendations for optimizing production strategies, such as adjusting pump schedules, compressor settings, or injection rates, to achieve energy efficiency and minimize emissions.

Digital Twins

SLB’s digital twin technology takes operational optimization to the next level by creating virtual replicas of physical systems. These AI-driven models enable operators to simulate, analyze, and refine workflows without the risks or costs of physical testing.

• Energy Use Prediction: Digital twins analyze energy consumption patterns, identifying areas for improvement to reduce operational carbon footprints.
• Emissions Modeling: AI-powered twins simulate scenarios to predict emissions across operations, allowing operators to implement proactive measures to stay within environmental compliance thresholds.
• Equipment Lifecycle Optimization: By monitoring wear and tear, digital twins predict equipment degradation and recommend timely replacements or adjustments, extending asset life and reducing waste.

Proactive Decision-Making: Digital twins provide operators with insights into potential operational bottlenecks, allowing for preemptive interventions. For instance, if a simulation shows that a specific pump is likely to fail under increased load, operators can proactively address the issue, preventing costly downtime and associated emissions.

AI for Sustainability Metrics and Reporting

SLB recognizes the importance of accurate, transparent, and actionable sustainability metrics to drive meaningful progress toward net-zero goals. AI-powered platforms like Agora Edge and SLB’s advanced emissions reporting tools provide operators with the insights and automation needed to measure, manage, and report environmental performance effectively. These solutions enable businesses to align their operations with global sustainability targets while ensuring regulatory compliance.

Agora Edge AI

The Agora Edge AI platform is a powerful tool that collects, analyzes, and visualizes critical sustainability data from field operations, providing operators with a comprehensive understanding of their environmental impact.

• Data Integration: Agora Edge gathers data from IoT sensors, edge devices, and operational systems, creating a unified platform for monitoring key sustainability metrics.
• Energy Consumption: Tracks energy use across equipment and operations, identifying inefficiencies and recommending optimizations.
• Carbon Emissions: Monitors real-time greenhouse gas (GHG) emissions, providing granular data to pinpoint emission sources.
• Water Usage: Analyzes water consumption patterns, highlighting opportunities for conservation and improved resource management.
• AI-Driven Insights: Advanced machine learning algorithms process field data to identify trends, correlations, and anomalies, empowering operators to take proactive measures.
• Dynamic Benchmarks: The platform compares operational metrics against industry standards and sustainability benchmarks, enabling operators to set and achieve ambitious environmental targets.

Emissions Reporting

SLB’s AI-powered emissions reporting tools streamline the complex process of tracking and documenting carbon footprints, ensuring accuracy, transparency, and compliance with international standards. These tools help operators align with frameworks such as the Greenhouse Gas (GHG) Protocol, Sustainability Accounting Standards Board (SASB), and Task Force on Climate-Related Financial Disclosures (TCFD).

• Automated Data Aggregation and Validation: AI consolidates data from multiple sources, validates its accuracy, and formats it for reporting, drastically reducing manual effort.
• Compliance with Global Frameworks: SLB’s tools ensure that emissions reporting meets regulatory and voluntary standards, such as Scope 1, 2, and 3 emissions tracking under the GHG Protocol.
• Scenario Analysis and Modeling: Advanced ML algorithms simulate the impact of potential operational changes on emissions, enabling operators to design effective mitigation strategies.
• Customizable Reports: Operators can generate tailored emissions reports for internal stakeholders, regulators, and investors, highlighting key achievements and areas for improvement.

A Comprehensive Approach to Net-Zero

SLB’s integration of AI and ML across its product lines underscores its commitment to sustainability and innovation.

As SLB continues to develop and deploy AI-driven solutions, it solidifies its role as a leader in the energy transition, setting a benchmark for the industry in achieving net-zero goals while maintaining operational excellence.

By harnessing technology, expertise, and a vision for a sustainable future, SLB is proving that the path to net-zero is not only feasible but also transformative for the energy sector.