Coupled CFD-DEM Modeling for Volumetric Sand Production Prediction and Proactive Wellbore Stability
Sustaining petroleum production targets requires developing marginal, weakly consolidated offshore reservoirs. However, continuous volumetric sand production causes severe borehole collapse and surface facility erosion. Legacy analytical Critical Drawdown Pressure (CDP) baselines assume an ideal post-yield elastoplastic state, ignoring transient rock-fluid degradation and yielding forecasting errors exceeding 20%. To resolve this multi-scale gap, this study presents a coupled simulation framework split into two computational nodes. In Node 1, an offline two-way coupled Computational Fluid Dynamics-Discrete Element Method (CFD-DEM) Digital Core tracks discrete particle kinematics and pore-scale hydrodynamics. A 1,200-run synthetic dataset generated via Latin Hypercube Sampling (LHS) mapped three sanding regimes, capturing wormhole propagation where localized porosity spikes from 0.24 to 0.85. In Node 2, a lightweight Physics-Informed Machine Learning (PIML) neural network is trained on this dataset. Unlike unconstrained black-box models, the PIML surrogate embeds fluid-solid mass conservation and Navier-Stokes partial differential equations (PDEs) directly into its loss function via automatic differentiation. When validated against a 24-month blind historical dataset from the Gulf of Guinea, the PIML surrogate achieved near-perfect fitment with an R2 of 0.97, an RMSE of 1.2 lb/1000 bbl, and an AAPRE of 7.4%, marking a 78.3% error reduction over legacy baselines without non-physical artifacts. Retroactively deployed as a dynamic virtual choke advisor, the surrogate minimized unexpected downhole cleanouts to zero, yielding a net 70.3% ($2.6 million) lifecycle operating expenditure (OPEX) reduction. The framework proves that physics-bounded intelligence successfully replaces reactive workflows to safely optimize drawdowns in unconsolidated assets.