AI-driven reservoir fluid inflow performance relationship (IPR) estimation offers engineers a better understanding of decline in reservoir pressure, enabling them to identify optimal lift methods to boost production. In the case of unconventional wells—where reservoir pressure declines rapidly—traditional inflow performance models fall short. This challenge necessitated the development of an alternative IPR estimation approach powered by artificial intelligence.
Using Azure-based AI/ML models, LTM enabled intelligent reservoir pressure estimation to support inflow performance relationship (IPR). Key aspects of the solution included:
Developed an AI/ML model to forecast inflow performance for unconventional wells, enabling proactive production planning and optimization.
Leveraged inputs derived from physics-based models to enhance the accuracy and reliability of predictions, ensuring alignment with subsurface dynamics.
Incorporated reservoir pressure estimation capabilities to support real-time decision-making and optimize well performance under varying operational conditions
A robust workflow combining simulation and machine learning estimated IPR for unconventional wells.
Enhanced transient IPR modelling enabled better planning and output. Predicting key subsurface metrics like bottom-hole pressure, drawdown, and productivity index led to more efficient production
Enhances physics-based models with ML insights, improving accuracy and efficiency.
Better forecasting capabilities by anticipating pressure and drawdown trends to enable proactive well management. This helped in establishing a fit-for-purpose artificial lift method to maintain productivity.
LTM’s Azure-based AI/ML solution transformed IPR estimation for the client by replacing manual, error-prone tasks with intelligent, predictive models. By combining physics-based inputs with machine learning, the solution delivered accurate reservoir pressure forecasts, real-time visibility into well performance, and more proactive well management. The result? Better production efficiency, lower operational overhead, and quicker, data-driven decisions across subsurface operations. By enabling scalable, data-led optimization, this solution is setting a new standard for how the energy industry approaches digital transformation in the field.
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