已发表: 06/05/2026
已发表: 06/05/2026
Petoro manages substantial assets and resources on the Norwegian continental shelf (NCS), producing 1.1 million bbls (oil equivalent) per day, accounting for approximately 17% of oil production, one third of gas supplied, and one third of remaining reserves. With around 80 employees overseeing 48 of 97 fields in production and 187 licenses, Petoro is the largest partner on the NCS. In 2025 alone, drilling and well investments reached NOK 12 billion, with a cash flow of NOK 243 billion and NOK 33 billion in investments.
The Petoro and SLB collaboration exemplifies how transformative solutions from Innovation Factori move from experimental prototypes to full enterprise integration. The journey begins with ideation and experimentation using the design thinking process, proving that new approaches are feasible on a small dataset. Deployable prototypes are validated in pilot projects on targeted assets, and successful solutions are scaled for broader application on more assets in the portfolio.
Innovation Factori brought together a diverse team from multiple domains and product teams: Data science, field development planning, well design, drilling, cloud architecture, and more to ideate, test, and implement solutions.
This innovation framework emphasizes:
The innovative workflow aims at enhancing the selection of sidetrack wells opportunities in mature fields by overcoming various challenges, including manually managing a vast volume of over 1,000 sidetrack opportunities per field, grappling with low-quality data, and the absence of a unified collaboration platform between drilling and reservoir engineering domains. The initiative determined the most effective places and methods to drill new wells or re-enter sidetracks to maximize resource recovery and operational efficiency. By using a portfolio-based approach, the solution is taking into account dependency effects between the drilling targets and how and when the sidetracked are scheduled.
There are numerous possible combinations of sidetrack paths, target zones, and drilling parameters (such as depth, direction, and trajectory), making the decision process complex. The factors influencing drilling decisions are constantly changing, including geological data, well performance, operational constraints, and economic considerations.
By systematically analyzing these scenarios using advanced data science and AI tools, field development teams can make data-driven decisions on where and how to drill, optimizing resource extraction, minimizing costs, and reducing risks. This approach leverages large datasets and sophisticated models to evaluate countless possible options, ensuring that the selected drilling opportunities align with both technical feasibility and business objectives.
Because the field development solution described above relies on analyzing data from multiple domains—reservoir, well design, production, and more—data quality is paramount.
Critical information for field development is often embedded in reports and time series of varying formats in different databases and file shares. Manual data curation is labor-intensive, error-prone, and time-consuming, making it difficult to ensure data quality. The lack of standardized, repeatable workflows further complicates the process.
Two of the solutions the team developed to address these challenges was the Well Schematic Extractor app and the Well History app, both agentic workflows enabled by the Lumi™ data and AI platform that uses large language models (LLMs) with context and instructions to extract data from well reports and log files. This approach surpasses traditional natural language processing (NLP) methods, providing domain-contextualized insights for the workflow.
Key features include:
The Lumi platform module—Lumi AI workspace—was used to generate new AI-enabled workflows to extract data from multiple data sources, delivering a threefold productivity improvement over traditional manual approaches. For example, preliminary user testing showed that QC throughput increased from two schematics per day to six or seven per day. The interactive tool enables visual QC with an AI conversational assistant, making it easier to find relevant information and ensure data accuracy.
Petoro was enabled to maximize the value of its assets, while mitigating risks associated with field development.
Other benefits include:
The project has fostered a close working relationship between Petoro and SLB, paving the way for future collaborations and continued innovation. While the solution is tailored to the specific needs of the project, its principles are globally applicable. The ability to revisit each well, with minimum effort over its lifetime—demonstrates the value of generative AI to uncover new opportunities in even the most mature fields.
Testing ideas digitally, rather than through costly operational trials, has proven to be a game-changer for cost optimization and returns.
With a strong foundation of end-to-end experience, Petoro and SLB are poised to amplify their impact—helping to drive industry-wide change. The collaboration was anchored by the expertise of the Innovation Factori Oslo team, and is a testament to the transformative power of AI and digital innovation in the energy sector. By combining deep domain knowledge with cutting-edge technology and leveraging the creative approach of Innovation Factori, they are optimizing field development, while setting a new benchmark for what’s possible on the Norwegian continental shelf—and beyond.
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