Rudy Lai

AI @ ConocoPhillips

Focus on upstream oil & gas
Industry
Last updated
July 3, 2025 at 10:44 AM

Summary

  • ConocoPhillips has consistently expanded its application of AI and digital technologies from 2021 through 2025, integrating AI-enabled platforms like seismic HPC, cloud-based DELFI environment, and digital twins to optimize exploration, drilling, and production operations, leading to operational efficiency gains such as drilling optimization by over 60 feet per day.
  • Key leadership, including CEO Ryan Lance and Chief Digital and Information Officer Pragati Mathur, have driven a strategic push towards AI adoption including AI patent filings (five in Q4 2023 and three in Q1 2024) and using AI/ML workflows for decision-making especially in the Permian Basin, underscoring AI’s role in cost reduction and risk mitigation.
  • By 2025, ConocoPhillips is leveraging advanced AI workflows and agentic AI models for autonomous data extraction and decision support while navigating workforce adjustments influenced by AI-driven automation, highlighting a transformative phase balancing innovation with workforce impact.

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5 AI Use Cases at ConocoPhillips

Decision Automation
2025
Traditional
Generative
Agentic
Outcome
Risk
Using agentic AI models and large language models, ConocoPhillips automates data extraction and integrates multiple sources to support rapid, economically sound field development and operational decisions. [1][2]
Market Forecasting
2025
Traditional
Generative
Agentic
Outcome
Revenue
AI techniques are used to analyze energy market dynamics and forecast demand, particularly focusing on AI-driven gas demand and LNG strategies to guide investment and production planning. [1][2]
Exploration Optimization
2024
Traditional
Generative
Agentic
Outcome
Revenue
ConocoPhillips employs machine learning algorithms to analyze vast geological and seismic data, improving identification of potential drilling sites and optimizing exploration success rates. [1][2]
Digital Twins
2023
Traditional
Generative
Agentic
Outcome
Risk
Adoption of digital twin technology allows real-time simulation and monitoring across the asset portfolio to improve operational visibility, maintenance planning, and risk reduction. [1]
Operational Efficiency
2023
Traditional
Generative
Agentic
Outcome
Costs
AI and ML workflows enable ConocoPhillips to optimize drilling parameters and managing artificial lift systems to reduce costs and improve operational productivity, such as increasing drilling advancement by over 60 feet per day. [1][2]

Timeline

2025 Q4: no updates

2025 Q3

4 updates

Announced workforce reductions up to 3200 jobs linked to AI-driven operational efficiency; deployed agentic AI models autonomously extracting and processing vital data; senior leadership including Pragati Mathur highlighted AI's transformative role.

2025 Q2: no updates

2025 Q1

4 updates

Highlighted market strategy analyses linking AI demand to energy markets; used AI for plunger lift optimization and energy demand forecasting; emphasized the role of AI in industry transformation amid market volatility.

2024 Q4

2 updates

Featured in AI-related stock and investment analyses emphasizing ConocoPhillips' positioning in AI-driven energy market dynamics.

2024 Q3

3 updates

Advanced use of automated AI and ML workflows enhanced decision-making efficiency and economic outcomes for Permian Basin assets; engaged with Raptor Data for plug and abandonment operations.

2024 Q2

2 updates

Reported ongoing AI-related patent activities and strategic consideration of AI alongside broader electrification and sustainability efforts.

2024 Q1

3 updates

Filed multiple AI-related patents focused on optimizing shut-in pressure management; expanded use of machine learning algorithms for geological data analysis to identify drilling sites; held conferences to highlight AI's role in transforming reservoir challenges.

2023 Q4

2 updates

Initiated portfolio-wide adoption of digital twin technology across major asset classes to improve operational visibility and decision-making.

2023 Q3

2 updates

Use of machine learning models to optimize operational parameters in the Lower 48 operations, improving drilling performance by more than 60 feet per day; also signed an AI-related agreement with Wyld Networks.

2023 Q2: no updates

2023 Q1: no updates

2022 Q4: no updates

2022 Q3: no updates

2022 Q2: no updates

2022 Q1

1 updates

Enterprise-wide deployment of Schlumberger's cloud-based DELFI cognitive E&P environment to enhance exploration and production efficiency.

2021 Q4: no updates

2021 Q3: no updates

2021 Q2: no updates

2021 Q1

1 updates

Launched a comprehensive seismic processing, data analytics, and machine learning platform leveraging Spark for interprocess communication and enhanced data handling.