A Study on the Analysis of the Formation of High Water Saturation zones around Well perforations

Open Access
Author:
Alrumah, Muhammad
Graduate Program:
Petroleum and Natural Gas Engineering
Degree:
Doctor of Philosophy
Document Type:
Dissertation
Date of Defense:
August 05, 2011
Committee Members:
  • Turgay Ertekin, Dissertation Advisor
  • Turgay Ertekin, Committee Chair
  • Robert W Watson, Committee Member
  • Yilin Wang, Committee Member
  • Li Li, Committee Member
  • Savash Yavuzkurt, Committee Member
Keywords:
  • composite reservoir
  • water coning
  • water saturation
Abstract:
The water produced with oil as a result of water coning is a serious problem as it decreases well productivity and increases the cost of operation. The main cause of water coning is the pressure drawdown near the wellbore. Producing a well in an oil reservoir with a bottom water drive will cause the original oil-water contact to rise towards the well in the shape of a cone. As production continues, the height of the cone increases. Once the water reaches the wellbore, the water starts to be produced and water production increases with time while the oil production decreases. The first part of this thesis is concerned with the analysis of pressure transient data in an oil reservoir with edge water. A 3-D numerical model is used to generate pressure transient data for a vertical well. The reservoir is considered as a composite reservoir where the inner zone contains the oil phase, and the outer zone contains the water phase. Procedures are proposed to help estimate the distance to the discontinuity under certain conditions. These procedures can be applied using the derivative of the pressure buildup test data if the mobility ratio is less than unity. Some of the parameters that control the accuracy of the proposed analysis procedure are the producing time and the external reservoir radius. The second part of this study is concerned with the water coning phenomena. Artificial neural networks are developed to predict water saturation around vertical and horizontal wells with a good accuracy in oil reservoirs experiencing a bottom water drive. The data used to develop the neural networks are from a numerical simulation model for reservoirs created using synthetic data. The neural networks have been found to effectively predict the change of water saturation over the time and show the development of the water cone.