Halliburton Unveils Cloud Software to Speed Up Oil and Gas Investments

2026-11-03 companies

Houston, Tuesday, 3 November 2026.
Co-developed with Aker BP to halve project delivery times, Halliburton’s new platform unifies technical and financial data, empowering energy producers to rapidly identify optimal asset investments.

Houston-Based Energy Giant Introduces AI-Enhanced Investment Platform

Oilfield services leader Halliburton Company (NYSE: HAL) has launched Agile Asset Management (AAM), a cloud-native artificial intelligence platform designed to streamline upstream energy investment decisions. Developed within the company’s Landmark software ecosystem, the tool integrates subsurface geological data, well construction metrics, facility operational parameters, and economic forecasting models into a unified decision-support environment. The release, announced on November 2, 2026, positions the Houston-headquartered firm at the forefront of AI-driven capital allocation in the global energy sector. According to company executives, the platform enables multidisciplinary teams to compare development scenarios, assess risk profiles, and trace every assumption throughout the asset lifecycle with unprecedented speed and transparency [1][2][4][5].

Collaborative Development with Aker BP Accelerates Time-to-First-Oil Targets

Halliburton’s new solution is the result of a strategic partnership with Norwegian operator Aker BP, which contributed domain expertise to co-develop capabilities aligned with its aggressive efficiency targets. Aker BP has publicly committed to reducing its time to first oil by 50%, a goal that demands fundamental changes in field development planning and execution. Tommy Sigmundstad, Aker BP’s Senior Vice President of Drilling and Wells, Supply Chain and Logistics, emphasized that this collaboration transforms how development alternatives are evaluated: “Together, we co-developed Agile Asset Management to change how we plan and evaluate field development. By orchestrating data and workflows in one shared environment, AAM enables our teams to evaluate more development alternatives, understand risks and cross-domain impacts earlier, and maintain a traceable and auditable basis for every decision.” [2][3][5]

Agentic AI and Open Architecture Redefine Decision-Making Workflows

At the core of AAM’s functionality is proprietary Agentic AI, which automates data retrieval, quality validation, and user guidance across disparate technical and commercial datasets. The platform’s open architecture ensures seamless integration with Halliburton’s DecisionSpace 365 suite, third-party software applications, and operator-specific tools, creating a single source of truth for asset investment decisions. Tony Antoun, Senior Vice President of Halliburton Landmark, highlighted the platform’s collaborative benefits: “Agile Asset Management brings technical and economic work into one collaborative environment so multidisciplinary teams can evaluate more development alternatives with speed and confidence. AAM gives management a clear, traceable record of the data and assumptions behind the preferred field development case.” [2][3][5][6]

Strategic Context and Industry Implications

The introduction of AAM follows Halliburton’s recent acquisition of Keystone AS, a Norway-based software provider specializing in AI-native well planning and execution tools. This acquisition strengthens the company’s integrated digital well construction capabilities and complements the existing Digital Well Program suite, creating a cohesive ecosystem from reservoir characterization to rig operations. Industry analysts view this convergence of AI, cloud computing, and open workflow standards as a bellwether for the broader energy technology landscape. While the quantitative impact on project delivery timelines remains to be measured, the platform’s design explicitly targets reductions in evaluation cycles and capital deployment lead times, positioning operators to better navigate volatile commodity cycles and tightening capital discipline requirements [3][4][6].

Sources

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