Quantifying Incremental Oil Production and Economics of Using Intelligent Completion as a Tool for Reservoir Management
CHAPTER ONE
OBJECTIVES OF THE STUDY
The objectives of this study are:
- To quantify the incremental oil production from the application of IWC as a reservoir management tool
- To ascertain the economic viability of IWC compared to non-intelligent completion
- To guide reservoir management team in the decision to use intelligent completion or not
CHAPTER TWO
LITERATURE REVIEW
Since 1997, when the first intelligent well (IW) was installed in Saga Snorre TLP North Sea- Norway, intelligent well technology has been used in many kinds of production wells all over the world, including off-shore wells, vertical conventional wells, horizontal wells and multilateral wells. Before 1997, all wells were completed with a common completion including hydraulically sliding sleeves and tubing. The evolution of downhole gauges, sliding sleeves and surface controlled subsurface safety valves resulted in the development of intelligent wells. (Dekui et al., 2012)
From 1997, several studies have been published to demonstrate the importance of the application and benefits of intelligent well completion (IWC), especially for multiple reservoirs where commingled production is the main production strategy (Jalali et al., 1998; Lucas et al., 2001). The application and potential benefits of IWC for production from a single reservoir have been demonstrated in several studies (Yu et al., 2000; Yeten and Jalali, 2001; Jansen et al., 2002; Valvatne et al., 2003).
Sharma (2002) presented a method to apply real options theory to quantify the value of intelligent well applications, including the value of reducing project volatility and risk. He described how mathematical model can be incorporated into a larger workflow process to assess entire asset portfolios which can be used as a tool for screening reservoir assets for potential IWC applications and also help optimize the design of the completion.
Yeten et al. (2004) determined the optimal performance of IWs using gradient based optimization technique in conjunction with a reservoir simulator. They considered the effect of uncertainty in reservoir description and equipment reliability and noted that downhole control can compensate to some extent for geological uncertainty, even when there is the possibility of equipment failure. They also noted that, the impact of equipment reliability was related to both the timing and type of failure; generally the earlier the valves failed the larger the negative impact.
Vachon and Furui (2005) illustrated how IWC can enhance the electrical submersible pump (ESP) performance and add flexibility by using downhole chokes to optimize ESP performance. Their study focused on single ESP wells producing from multiple pay zones. It was established that the intelligent completion systems with remotely controlled chokes allow for optimal production rates, maintain the optimum ESP operating range and reduce risk of pump failure. Thus, IWC eliminates the expense of intervention and the associated loss in production, due to extended ESP life, reduction of cost of replacing damaged pumps and pump down time.
Sakowski (2005) looked at the impact of intelligent well completions on total economics of field development. Reservoir performance analysis and economic evaluation tools were used to quantify the value of IWC. IWC projects performed better in relatively cost sensitive environments since they can maintain oil production while reducing the capital and operating costs. He noted that the ability to respond to expected changes in reservoir performance is also a valued benefit and the technology has advanced rapidly from more high-cost, offshore application environment to more revenue-sensitive operating environment due to the ability to clearly demonstrate economic value of IWC over alternate conventional completions.
Aggrey et al. (2006) employed a synthetic reservoir to explore and compare the value of extensive, accurate measurements with a higher chance of system failure with the deployment of lower resolution sensors of greater reliability. A methodology to calculate the value of information and expected opportunity loss parameters for IWC of different capabilities was developed. It was shown that value creation from IWC and real time optimization is strongly dependent on the ability of the system to function properly throughout the equipment’s specified lifetime.
Aggrey and Davies (2007) presented an enabler for IWC decision making process where stochastic coupling of the reliability profile and reservoir performance is employed. The proposed workflow allows the inclusion of conventional stochastic analysis for economic and geologic risks. The evaluated scenarios showed increased value potential for IWC implementation.
Addiego-Guevara et al. (2008) investigated whether simple reactive control strategies based on a feedback loop between inflow control valve (ICV) settings and surface or downhole measurements can enhance production and mitigate reservoir uncertainty if they are designed to work across a range of production scenarios. The implementation of an intelligent horizontal well in a thin oil rim reservoir in the presence of reservoir uncertainty was assessed. They evaluated the benefit of using two completions in conjunction with surface and downhole monitoring. It was found that reactive control strategy can insure against reservoir uncertainty. However, a simple reactive control strategy using variable ICVs adjusted in response to downhole measurements of phase flow rates yielded a neutral or positive return regardless of reservoir behavior. They suggested that downhole reservoir imaging techniques which can monitor fluid flow and saturation changes at a distance from the well may be used in a proactive feedback loop.
CHAPTER THREE
METHODOLOGY
INTRODUCTION
Oil and gas production operations involve risks as a result of uncertainties from reservoir properties, equipment failure as well as unforeseen future events; hence evaluation and justification of field development projects are extremely important, as projects incur huge sums of money and once a field is developed, the whole architecture cannot be changed entirely.
This section describes the methodology used to compare the application of intelligent well completions (IWC) vs. non-intelligent completions in reservoir management. In the presentation, we consider data acquisition, oil recovery and the economics involved in both completions.
Case studies of reservoirs where intelligent completions have been employed were analyzed. The tools used for this analysis include material balance software (MBAL) for production forecast using decline curve analysis and Monte Carlo-Simulation-software (a spread sheet add-in, Oracle Crystal Ball) for the uncertainty analysis of the economics of the two types of completions. Figure 3.1 illustrates the major steps in the workflow which is patterned after the model proposed by Sakowski et al, (2005); the detailed step-by-step procedure used in their work is illustrated by Figure 3.2.
CHAPTER FOUR
RESULTS AND DISCUSSION OF CASE STUDIES IN RESERVOIR MANAGEMENT
INTRODUCTION
The results obtained from the analysis are presented and discussed in this Chapter. Sample worked examples are presented in the Appendix.
Actual field cases where IWC has been proposed and implemented as solution to the challenges in reservoir management in the field were evaluated and compared with non-intelligent completions.
CHAPTER FIVE
CONCLUSIONS AND RECOMMENDATIONS
SUMMARY AND CONCLUSIONS
This study compares the application of intelligent well completions versus non-intelligent or conventional well completions for reservoir management. The objectives of this study is to present a methodology which guides the planning and decision making process of implementing IWC, as well as to present the benefits and limitations of IWC.
The case studies from four fields where IWC has been implemented were analyzed to examine the applications of IWC. Daily production history from both IWC and non-intelligent completion was history matched and prediction runs carried out. The criteria for judging the feasibility of implementing IWC includes ease of data acquisition and well monitoring, incremental oil recovery, NPV, Discounted payout period, Profitability Index and Growth Rate of Return.
Based on the results of the analysis of the case studies presented in this work the following conclusions are drawn:
- Incremental oil recovery from IWC vs. Non-IWC ranges from 21.6% to38%.
- IWC proves to be more economically viable compared to Non-IWC; NPV from IWC exceeds that from Non-IWC by 17.5% to 40.6% for the case studies evaluated in this work.
- NPV is dependent on oil
- Comparing field operating costs of IWC vs. Non-IWC shows that IWC OPEX can be reduced from 9% to45%.
- Payout time for IWC is a week to a month less than that of the Non-IWC but
- IWC implementation is justifiable for the cases considered, except for IWC installation in well B – 30B (Oseberg field) which did not yield the expected benefits due to the failure of the surface-controlled downhole flow control valves. Failure of downhole control devices would limit the profitability and justification for IWC
RECOMMENDATIONS
Based on methodology and results of this study, the following recommendations are suggested:
- Decline Curve Analysis was used for the oil production forecast in this work. It is recommended that reservoir simulation be used for production forecast in similar analysis.
- Production period of five to six years after IWC implementation was considered in this work. The entire life of the field, from IWC implementation to abandonment should be considered in quantifying the benefits of IWC.
- Statistics of all intelligent completions implemented, with their level of success, the risk of failure, types of failure and the vendors should be compiled to guide the planning process and actual field deployment of intelligent
- Pre-installation or pre-development test should be carried out to ascertain the reliability of the intelligent
- More research should be done on the intelligent system hardware to improve its reliability.
REFERENCES
- Addiego-Guevara E. A., Jackson M. D. and Giddins M. A.: “Insurance Value of Intelligent Well Technology against Reservoir Uncertainty”, SPE 113918 presented at the 2008 SPE Improved Oil Recovery Symposium, Tulsa, Oklahoma, April 19-23,
- Aggrey G. H. and Davies D. R.: “A Rigorous Stochastic Coupling of Reliability and Reservoir Performance When Defining the Value of Intelligent Wells”, SPE 107197 presented at the Offshore Europe, Aberdeen, Scotland, September 4-7,
- Aggrey G. H. et al.: “Data Richness and Reliability in Smart-Field Management – Is There Value?”, SPE 102867 presented at SPE Annual Technical Conference and Exhibition, San Antonio, Texas, September 24-27,
- Ajayi, A. and Konopczynski, M. R.: “Intelligent-Well Technology Reduced Water Production in a Multilateral Oil Producer”, SPE 102982 presented in the SPE Annual Technical Conference and Exhibition, San Antonio, September 24-27
- Barreto C. E. A. G. et al.: “Use of Water Cut to Optimize Conventional and Smart Wells”, SPE 150908 presented at the North Africa Technical Conference and Exhibition, Cairo, Egypt, February 20-22,
- Behrouz* T., et al.: “Economic Evaluation of Smart Well Technology – A case Study”, presented at the 14thInternational Oil, Gas & Petrochemical Congress, Iran,
- Brock W. R., et al.: “Application of Intelligent-Completion Technology in a Triple-Zone Gravel Packed Commingled Producer”, SPE 101021 presented in the SPE Annual Technical Conference and Exhibition, San Antonio, September 24-27,
