Numerical Model Representation Of Multi-stage Hydrualically Fractured Horizontal Wells Located In Shale Gas Reservoirs Using Neural Networks

Open Access
Bodipat, Kanin
Graduate Program:
Energy and Mineral Engineering
Master of Science
Document Type:
Master Thesis
Date of Defense:
August 08, 2011
Committee Members:
  • Turgay Ertekin, Thesis Advisor
  • numerical model
  • simulation
  • hydraulic fracturing
  • horizontal well
  • shale gas
  • neural networks
  • ANN
Numerical representation of hydrocarbon reservoirs has seen increase usage and sophistication due to recent surge in demands for unconventional hydrocarbon resources. Unlike the conventional fields most oil companies are familiar with; these unconventional fields often require special recovery techniques that significantly elevate the project size and funds required. One of the popular recovery method used that has proven highly successful is hydraulic fracturing combined with horizontal wells. Sometimes referred to as “fracjob”, this process has seen extensive use in US shale gas reservoirs that were in the past regarded as non-recoverable sources. Due to the effectiveness of fracturing shale combined with horizontal wellbore, several reservoir models have been proposed to represent how these techniques would affect the characteristics of the reservoir. The particular model used in this thesis is the “crushed zone” model that represents the effect of multiple stages of hydraulic fracturing as anelliptical shape around the horizontal well that sees an increased in fracture permeability and smaller fracture spacing. To study the validity of this particular reservoir representation an artificial neural-network (ANN) is created using production data generated from a commercial simulator. This first network is then used to predict production performances over a period of 50 years. A second network is then required to establish a quantitative relationship between the crushed zone model and another hydraulic fracturing model called the “transverse fracture” model. In addition, an error filter neural network has been created to improve upon the predictions of the second network. The results of the network were all within the expected ranges and the ANN can then be used to predict new gas rates and equivalent transverse fracture representations for any reservoir that has its characteristics within the range of the training data