Tensor-Based Modal Decomposition and Sparse Sensor Placement for the Brugge Field Simulation Model

Abstract

Sparse monitoring of reservoir pressure and saturation requires numerical methods that retain gridded structure while reconstructing full fields from few observations. We present a four-dimensional tensor-based modal decomposition (TBMD) and sparse reconstruction framework for coupled pressure-saturation fields. The approach uses an explicit property mode, mode-4 pivoted orthogonal-triangular (QR) ranking of grid-wide spatial-property fibers, and tensor-based compressive sensing for both grid-wide and existing-well measurement operators. The Brugge benchmark is evaluated using 10 well-control realizations with fixed geology. Each realization is processed independently as a tensor of size 139 x 48 x 2 x 134 under an 80/20 temporal split with training-fitted property-wise min-max normalization. In the joint pressure-saturation well-only study, increasing the number of instrumented wells from 1 to 10 reduces the relative Frobenius error from about 0.57 to 0.20, increases the Structural Similarity Index Measure from about 0.47 to 0.88, and raises the peak signal-to-noise ratio from about 33.8 to 37 dB. By 20 wells, the relative error drops to about 0.11. The results support the feasibility of tensor-structured sparse reconstruction for coupled reservoir fields and provide a basis for controlled comparisons with alternative reduced-order and sparse-sensing methods.