PREDICTION OF WATER CONE FORMATION IN A NATURALLY FRACTURED RESERVOIR WITH AQUIFER DRIVE -AN ARTIFICIAL EXPERT APPLICATION

Open Access
Author:
Bae, Chang Eon
Graduate Program:
Petroleum and Natural Gas Engineering
Degree:
Master of Science
Document Type:
Master Thesis
Date of Defense:
May 05, 2015
Committee Members:
  • Turgay Ertekin, Thesis Advisor
Keywords:
  • watercone
  • coning
  • naturally fractured reservoirs
  • artificial neural network
Abstract:
In the recent past, highly productive reservoirs found are naturally fractured and have a complex geologic setting. Typically in a geological situation where a nearby strong water drive exists, production performance and ultimate recoveries of the reservoir are comparatively higher. A strong water drive often results in high production, but may pose difficulties in production due to significant water influx. Hence, an accurate water influx predictive model helps in lowering the uncertainty and reducing the risk. This study attempts to use Artificial Expert Systems to predict water coning behavior around horizontal wells in naturally fractured reservoirs with a strong water drive. By using a numerical simulator, various reservoir conditions and scenarios are simulated. With the help of these simulated datasets, an artificial expert system based tool is developed to enable engineers to predict water coning behavior in a given horizontal well instantaneously.