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Integration of Subsurface and Surface Intelligence for Asset Performance Management
Halliburton and Shape Digital collaborate to integrate physics-based reservoir modeling with applied AI to optimize production workflows and industrial automation.
www.halliburton.com
The strategic cooperation between Halliburton and Shape Digital addresses the technical requirement for unified asset performance management (APM) by connecting subsurface domain science with surface operational data. This integrated approach targets the upstream and midstream oil and gas sectors, focusing on reservoir management, equipment reliability, and energy efficiency.
Technical Context and Collaborative Framework
In complex production environments, the separation of subsurface models and surface operational data often leads to fragmented decision-making. Halliburton Landmark provides the Digital Field Solver® (DFS), a system that integrates reservoir, well, and production network models. Shape Digital contributes its applied AI portfolio, including the Lighthouse, Aura, and Reef platforms, which specialize in equipment reliability and safety analytics.
The cooperation is necessitated by the need to scale digital infrastructure across the entire production lifecycle. By combining Halliburton’s physics-based modeling with Shape Digital’s data-driven AI, the partners create a feedback loop where surface constraints — such as equipment health — inform subsurface flow simulations.
Unified Production Planning and Operational Logic
The technical solution functions by synchronizing Landmark’s reservoir and network models with continuous updates from Shape Digital’s AI modules.
- Constraint Mapping: Shape Digital evaluates live and historical sensor data to determine equipment reliability and facility constraints.
- System Analysis: These parameters are fed into Landmark’s DFS to analyze their impact on flow dynamics and production targets.
- Energy Optimization: The system connects facilities data (process performance and energy consumption) with real-time production schedules. This allows operators to calculate the trade-offs between energy efficiency and throughput using a unified system context rather than isolated process variables.
Safety and Asset Integrity Integration
The implementation focuses on reducing the latency between anomaly detection and operational response. By evaluating facilities’ process safety indicators alongside well-integrity models, the solution identifies how subsurface pressure or temperature changes propagate risk to surface assets.
This architecture utilizes standardized interfaces to aggregate data from disparate sources, creating a shared system view for maintenance, safety, and engineering teams. The elimination of disconnected workflows aims to enhance process stability and maintainability.
Expected Operational Impact
The integration of these technologies supports predictive, asset-level decision-making. By applying AI to historical and real-time operational data within the context of high-fidelity engineering models, the collaboration provides:
The implementation focuses on reducing the latency between anomaly detection and operational response. By evaluating facilities’ process safety indicators alongside well-integrity models, the solution identifies how subsurface pressure or temperature changes propagate risk to surface assets.
This architecture utilizes standardized interfaces to aggregate data from disparate sources, creating a shared system view for maintenance, safety, and engineering teams. The elimination of disconnected workflows aims to enhance process stability and maintainability.
Expected Operational Impact
The integration of these technologies supports predictive, asset-level decision-making. By applying AI to historical and real-time operational data within the context of high-fidelity engineering models, the collaboration provides:
- Earlier Constraint Identification: Reducing unplanned downtime through predictive equipment health monitoring integrated into flow models.
- Increased Execution Consistency: Aligning subsurface potential with surface processing capacity to minimize production variances.
- Enhanced Safety Visibility: Correlating well-integrity data with surface risk indicators to improve hazard identification.
This technical framework reflects a transition toward automated, science-based decision systems within the global energy infrastructure.
Edited by Evgeny Churilov, Induportals Media - Adapted by AI.
www.halliburton.com
Edited by Evgeny Churilov, Induportals Media - Adapted by AI.
www.halliburton.com
