Well - Placement Optimization Using the Quality Map Approach a Report Submitted to the Department of Petroleum Engineering of Stanford University in Partial Fulfillment of the Requirements for the Degree of Master of Science

Determination of optimal well locations is a challenging task because engineering and geologic variables affecting reservoir performance are often nonlinearly correlated and uncertain. This study presents an approach where an optimization technique based on a “quality map” in combination with genetic and polytope algorithms is used in determining optimal well locations. The objective of the study was to investigate the applicability of the quality map concept to optimal determination of well locations. The quality map attempts to simplify the complex and diverse parameters governing fluid flow through porous media into a simple two-dimensional representation of the reservoir. Two approaches are presented: the Basic Quality Map (BQM) approach and the Modified Quality Map (MQM) approach. The BQM approach, in contrast to other optimization methods, does not require simulation runs once the quality map is in place. The fitness function for any given well configuration is obtained through an inverse distance weighting method. Results obtained from the BQM approach showed that wells appeared to be placed sequentially although the optimization process was simultaneous. This counterintuitive feature of the basic quality map approach gave suboptimal results in some situations. An attempt was made to remove this “static” feature by calibrating “quality paths” using streamlines. This however did not lead to any improvement. The study however found the quality map concept useful as a screening tool in an optimization method that uses the numerical simulator as the true fitness function coupled with a decline proxy. The screening of possible well locations provided by the quality map led to significant reductions in the number of flow simulation runs and the use of the decline proxy resulted in remarkable CPU time savings. This approach was the basis of the MQM method.