Abstract
We present the recent advancements and integration of two related R&D initiatives: one focused on numerical modeling for dynamic data analysis (DDA) and the other on automating DDA workflows for reservoir surveillance.
In the realm of numerical modeling, we introduce novel techniques and models for Pressure Transient Analysis (PTA) and Rate Transient Analysis (RTA), to address complex geometries, multiphase flow and thermal effects. Key innovations include an automated discretization kernel for the efficient setup of transient models with increasing geometrical complexity, and an upscaling method tailored to preserve transient, multiphase flow regimes. We show how these techniques enable the integration of PTA results into full-field reservoir models, via dynamic sector modeling.
We then explore how numerical models can be integrated into a broader framework for automated reservoir monitoring workflows. A next-generation DDA platform is presented, built on a microservice architecture orchestrated via Kubernetes. This infrastructure supports robust data management and intelligent data handling (featuring mirroring, event detection, adaptive filtering, and depth conversion). A flexible API layer allows users to define tasks and trigger microservice-based analyses programmatically, as new production data becomes available. We demonstrate fully automated PTA and RTA workflows enabled by this system, showcasing its potential for continuous, real-time reservoir surveillance in both conventional and unconventional contexts.
Looking forward, ongoing efforts aim to embed transient numerical modeling into these automated workflows, especially for nonlinear multiphase scenarios.
The framework described provides a scalable, state-of-the-art solution integrating physics-based modeling and automation for efficient reservoir analysis. This is directly aligned with ongoing digital oilfield initiatives and the evolution of modern reservoir surveillance strategies.
Bio
Vincent Artus is the Regional Manager for North America at KAPPA Engineering. He holds a PhD in Reservoir Engineering from Paris University and the Institut Français du Pétrole (now IFPEN), followed by a postdoctoral fellowship at Stanford University. He began his career as a Research Engineer at IFPEN, focusing on upscaling and stochastic optimization. In 2005, Vincent joined KAPPA Engineering, where he is leading numerical developments. He has published about 30 communications in peer-reviewed journals and international conferences, in the fields of stochastic modeling and numerical simulation.