DEVELOPMENT OF AN ARTIFICIAL NEURAL NETWORK BASED EXPERT SYSTEM TO DETERMINE THE LOCATION OF HORIZONTAL WELL IN A THREE-PHASE RESERVOIR WITH A SIMULTANEOUS GAS CAP AND BOTTOM WATER DRIVE

Open Access
Author:
Alquisom, Mohammed Mattoq
Graduate Program:
Earth Sciences
Degree:
Master of Science
Document Type:
Master Thesis
Date of Defense:
June 17, 2016
Committee Members:
  • Turgay Ertekin, Thesis Advisor
  • Li Li, Committee Member
  • Eugene C Morgan, Committee Member
Keywords:
  • Neural Network
  • ANN
  • Simulator
  • Reservoir
  • Cresting
  • Horizontal well
  • Gas Cap
  • Water Drive
  • Ertekin
  • Three-Phase
Abstract:
The oil and gas industry is continuously trying to increase hydrocarbon’s recovery in order to meet the high demand for energy in the world. Increasing the production rate of hydrocarbon compromises the lifespan of the reservoir. Throughout last decays, a number of processes have been developed in the oil and gas industry to increase the hydrocarbon recovery while minimizing their effect on the life of the reservoir. One of these techniques is the horizontal well drilling. This drilling method allows higher recovery of hydrocarbons by increasing the contract area between the casing and the oil zone. However, high production rate from the horizontal well will result in phenomenon called cresting. The time at which it occurs is called breakthrough time. The goal for any production engineer is to delay breakthrough time as much as possible. The delay of this time will result in increasing the lifetime of the reservoir by maintaining the natural driving forces represented by water drive and gas cap in the reservoir. In this study artificial neural network is utilized to construct a reliable tool to predict the production profiles namely: oil rate, gas rate, water rate, cumulative oil, cumulative gas, cumulative water, gas oil ratio, water oil ration and water cut, that lies within the reservoir and design properties for this study. A synthetic three-phase reservoir with a gas cap and bottom water drive is constructed using a commercial reservoir simulator to simulate and validate. After that, 600 different scenarios were generated using a range of reservoir properties along with different depth at which horizontal well will be placed. These different scenarios were used to train the ANN in order to make it predict the production profiles mentioned above within an error range of 5-15%. A graphical user interface (GUI) was developed to make this model user-friendly. A user will be asked to input the required reservoir properties and the design property in the form of numbers and then the user will be able to obtain production profiles along with gas oil ratio, water oil ratio and water cut profiles.