Optimization under Uncertainty for Reliable Unconventional Play Evaluation.A Case Study in Vaca Muerta Shale Gas Blocks,Argentina

摘要

The objective of this work is to present a comprehensive workflow to optimize the value of a hydrocarbon asset evaluation project under high degrees of uncertainty.This workflow is applicable to both conventional and unconventional assets.However,because of the considerable level of subsurface uncertainty and high initial costs(mainly due to drilling and hydraulic fracturing operations),unconventional resources are good examples for demonstrating the benefits of the workflow.For the case of an unconventional asset,well spacing and perforation cluster spacing are usually the decision parameters that need to be optimized to increase its value.The workflow begins with the construction of a representative base case single well gas simulation model for production history matching.Petrophysical,geological,geomechanical,stimulation,completions and production data are interpreted and analyzed together to better understand drivers that could be influencing the production.If this can be repeated with several wells in the block with sufficient production data,the process is enriched as so the level of confidence,as the range of history-matching parameters from these different wells across the block can be captured for sensitivity and uncertainty analysis.Several sets of sensitivities and uncertainty runs are then performed to get a probabilistic production profile in the presence of the most influential parameters.It is important to highlight that usually,the limited number of wells,short production histories,different dynamic behavior in neighboring blocks and the lack of necessary data to help understand well performance all contribute to the high uncertainty in predicting production.Given the high cost of drilling and hydraulic fracturing and on the other hand the high gas price in Argentina,optimizing well spacing and cluster spacing are critical parameters in the process of unconventional resource evaluation considering the high degree of uncertainty.