Generalized Multi-Scale Stochastic Reservoir Opportunity Index for enhanced well placement optimization under uncertainty in green and brownfields

Well placement planning is one of the challenging issues in any field development plan. Reservoir engineers always confront problem that which point should be drilled to achieve highest recovery factor and/or maximum sweep efficiency. In this paper, we use Opportunity Index (ROI) as a spatial measure productivity potential for greenfields, hybridizes reservoir static properties, and brownfields, ROI replaced by Dynamic Measure (DM), takes into account current dynamic properties addition properties. The purpose using these criteria diminish search region optimization algorithms consequence, reduce computational time cost optimization, are main challenges well problems. However, considering significant subsurface uncertainty, probabilistic definition (SROI) or DM (SDM) needed, since there exists an infinite number possible distribution maps To build SROI SDM maps, k -means clustering technique used extract limited characteristic realizations can reasonably span uncertainties. addition, determine optimum clustered realizations, Higher-Order Singular Value Decomposition (HOSVD) method applied also compress data large models lower-dimensional space. Additionally, introduce multiscale density (D 2 D DM), distinguish between regions high (or SDM) arbitrary neighborhood windows from local maxima with low values vicinity. Generally, develop implement new systematic approach both green brownfields on synthetic model. This relies utilization multi-scale improve initial guess algorithm. Narrowing down algorithm substantially speed up convergence hence would reduced 4.