Progressive-Recursive Self-Organizing Maps PR-SOM for Identifying Potential Drilling Target Areas

摘要

Identifying potential productive reservoir units for infill drilling is a major challenge in developing giant fields in order to meet production targets and extend plateau. A common practice in identifying potential drilling locations is using oil saturation from production logs or near-by wells performance. Literature already recommended combining static and dynamic parameters from reservoir models (geological or numerical) to calculate cell performance indices such as Reservoir Opportunity Index (ROI) or Simulation Opportunity Index (SOI). It is difficult for those methodologies to provide volumetric representation of the hydrocarbons in potential areas since they do not specify the details of the clustering mechanism of similarly performing cells. The proposed algorithm allows the reservoir engineer to, progressively and recursively, define hydrocarbon sweet spots areas. In this work, progressive-recursive self-organizing maps (PR-SOM) is developed and tested on a carbonate reservoir model. PR-SOM is an unsupervised artificial intelligence neural network algorithm that classifies the simulation grid cells into potential drilling targets by using a progressive list of dynamic or static reservoir parameters to identify similarly 'good' contiguous regions. This is achieved by applying SOM on geomodels based on relative permeability, fluid saturation, pressure, and displacing fluid influx in the first iteration. The second iteration, PR-SOM explores the already selected regions and applies SOM on mobile and immobile hydrocarbons. The last iteration recursively applies PR-SOM to identify areas away from existing wells on the already defined regions from last iteration. The algorithm allows further definition of sweet spots based on more parameters and will further increase the potential value of the classified regions. PR-SOM was applied to a carbonate reservoir with the objective of identifying un-swept areas as potential candidates for infill drilling. To compare the resulting potential target regions, an implementation of sweet spot identification algorithm and conventional approaches were applied on the same field. The results show that PR-SOM generated more accurate and conservative regions reducing the risks and increasing the confidence in the designated regions. In addition to obtaining more accurate clustering results, using PRSOM allows extending the search to increase the value of required targets whereas previous work has to adhere to the original selected parameters (static or dynamic) for region identification and selection.