Evaluation of Liquid Loading in the Pinedale Field: Integration of Smart Plunger Data and Mechanistic Modeling
- Oladele Olalekan Bello (Texas A&M University) | Gioia Falcone (Texas A&M University) | Jun Xu (Shell Intl E&P Co) | Stuart L. Scott (Shell EP-Americas) | John Douglas Harrod (Shell)
- Document ID
- Society of Petroleum Engineers
- SPE Production and Operations Symposium, 27-29 March, Oklahoma City, Oklahoma, USA
- Publication Date
- Document Type
- Conference Paper
- 2011. Society of Petroleum Engineers
- 5.3.2 Multiphase Flow, 2.2.2 Perforating, 3.1.1 Beam and related pumping techniques, 5.8.1 Tight Gas, 5.2 Reservoir Fluid Dynamics, 5.6.8 Well Performance Monitoring, Inflow Performance, 3.1 Artificial Lift Systems, 2 Well Completion, 3.1.5 Plunger lift, 3.1.8 Gas Well Deliquification
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Smart plungers1 can be used to enhance diagnostics and performance evaluation of liquid-loaded gas wells. The objective of this work is to develop a framework in which surface measurements and smart plunger data from the Pinedale field are analyzed using a mechanistic plunger lift model, so as to provide a methodology for maximizing well performance and reducing operational costs.
In this paper, the application of smart plunger technology in two Pinedale wells is presented. A description of the mechanistic model used for the analysis of the gathered transient flow data is also provided, including a discussion on changes to the model for different well completions (namely with or without packer). Inputs to the model are dynamic pressure and temperature, well completions, fluid properties and produced flow rates. Results from the two Pinedale wells are presented and critically reviewed. The results suggest that the proposed integrated approach is a feasible and practical methodology to optimize the performance of plunger lift cycles. Recommendations are then made for future technological advances in the development of an interpretation model for real-time analysis of smart plunger data, towards fully automated management of liquid loading in gas wells.
Plunger lifts are being used increasingly for gas well deliquification. The relationship between plunger-lift cycle operations and maximum hydrocarbon recovery is one important issue that has been the subject of intensive studies (Yang, 2010; Sask et al., 2010; Rowlan et al., 2009; Tang, 2009; Lea et al., 2007; Rowlan et al., 2006; Lea et al., 2003; Maggard, 2000; Chambliss, 2001; Wiggins et al., 1999; Gasbari and Wiggins, 1997; Avery and Evans, 1988; Lea, 1982; Foss and Gaul, 1965). Plunger-lift cycle operation is a complex problem that involves many decision variables and multiple objectives. Traditionally, plunger-lift cycle operation is based on heuristic procedures, embracing rule curves and subjective judgements by the operator. This approach provides general operation strategies for plunger-lift wells according to reservoir pressure, inflow performance relationship, gaswater ratios, static liquid levels in production tubing and annulus, casing and tubing pressures, plunger fall and rise velocities, wellhead gas flow rates, fluid properties, well completion data, shut-in time and production flow period. However, rule curves do not allow fine-tuning (and hence optimization) of the operation in response to changes in the prevailing conditions. Therefore, it would be valuable to use a more systematic approach to plunger-lift operation based on the information of hydrodynamic events from smart plunger technology and advanced mechanistic model in order to increase the cycle's efficiency. Smart plungers consist of traveling and stationary plungers that are equipped with temperature and pressure sensors and data loggers. The aim of this paper is to develop a framework for combining smart plunger data, wellhead production data and mechanistic modeling to identify and adjust key decision variables for the optimization of the plunger-lift cycle for well completions with and without packer. The framework is then applied to selected plunger-lift wells in the Pinedale field.
|File Size||1 MB||Number of Pages||13|