AI-239 · Source-linked project profile. Read the scope, reported result and what remains unknown.
Open the interactive projectHow to read this evidenceA high-speed machine-learning surrogate model provides rapid predictions of pressure, gas saturation and other reservoir parameters from geological and operating conditions, supporting reservoir-efficiency and pressure-rise evaluation plus optimization and sensitivity analysis for CCS decision-making.
The stated benefit is reduced simulation time and the ability to evaluate parameters quickly; no measured speedup, accuracy figure, validation result or project result is quantified.
This is an engineering decision-support model, not evidence of AI control of an operating CCS asset. No specific field, injection project, go-live date, user role, validation method, accuracy, quantified time reduction or economic result is reported.
Adds a dated, named machine-learning application for CCS reservoir evaluation at an operator absent from the current profile set, with practical simulation and decision-support tasks rather than a general AI strategy statement.
Printed p. 41 (PDF physical page 42), “Thorough Utilization of Digital Technology,” Case example 2: “Accelerating CCS simulation using machine learning”; official integrated-report index dates publication 4 July 2025.
Printed pp. 39–42 (PDF pp. 40–43), Technical Strategies and Digital Strategy sections; CCS surrogate was not discussed in the section reviewed.
Latest Report section states Integrated Report 2025 published July 7, 2026; Back Numbers section dates Integrated Report 2024 July 4, 2025.
A high-speed machine-learning surrogate model provides rapid predictions of pressure, gas saturation and other reservoir parameters from geological and operating conditions, supporting reservoir-efficiency and pressure-rise evaluation plus optimization and sensitivity analysis for CCS decision-making.
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