Thin-interbedded low-permeability sandstone reservoirs present unique challenges due to frequent alternations between sand and mudstone layers along with pronounced vertical differences in rock properties. These characteristics complicate efforts to identify productive zones known as sweet spots and to forecast how wells will perform over time.
A recent study introduces a method that combines physical principles with an adaptive weighting system to improve evaluation accuracy. The technique adjusts the influence of various data inputs dynamically while respecting established physical laws governing fluid flow and rock behavior. This balance helps reduce uncertainties that arise when traditional statistical models are applied to highly heterogeneous formations.
Researchers tested the approach on field data from several wells. Results showed clearer identification of intervals with better production potential compared with conventional weighting schemes. The method also provided more reliable estimates of long-term output by incorporating constraints that prevent unrealistic predictions.
Industry experts note that such reservoirs are common in many basins worldwide. Improved sweet-spot mapping can support more efficient drilling and completion decisions, potentially lowering costs and environmental impact. The physics-constrained framework appears adaptable to other complex geological settings where data quality varies.
Further validation across additional sites is planned to refine the algorithm and expand its use. The work contributes to ongoing efforts in reservoir characterization that integrate numerical modeling with real-world observations.
Overall the study demonstrates how hybrid optimization strategies can address longstanding difficulties in evaluating thinly layered low-permeability systems. Continued development may lead to broader application in resource assessment and field development planning.
