Analysis of real-time reservoir monitoring : reservoirs, strategies, & modeling.

Mani, Seethambal S. ; van Bloemen Waanders, Bart Gustaaf ; Cooper, Scott Patrick ; Jakaboski, Blake Elaine ; Normann, Randy Allen ; Jennings, Jim (University of Texas at Austin, Austin, TX) ; Gilbert, Bob (University of Texas at Austin, Austin, TX) ; Lake, Larry W. (University of Texas at Austin, Austin, TX) ; Weiss, Chester Joseph ; Lorenz, John Clay ; Elbring, Gregory Jay ; Wheeler, Mary Fanett (University of Texas at Austin, Austin, TX) ; Thomas, Sunil G. (University of Texas at Austin, Austin, TX) ; Rightley, Michael J. ; Rodriguez, Adolfo (University of Texas at Austin, Austin, TX) ; Klie, Hector (University of Texas at Austin, Austin, TX) ; Banchs, Rafael (University of Texas at Austin, Austin, TX) ; Nunez, Emilio J. (University of Texas at Austin, Austin, TX) ; Jablonowski, Chris (University of Texas at Austin, Austin, TX)

Sandia National Laboratories

关键词: Natural Gas Fields;    Hydrocarbons;    Management;    02 Petroleum;    Monitoring;   

DOI  :  10.2172/966614
RP-ID  :  SAND2006-7503
RP-ID  :  AC04-94AL85000
RP-ID  :  966614

美国|英语

来源: UNT Digital Library

【 摘 要 】

The project objective was to detail better ways to assess and exploit intelligent oil and gas field information through improved modeling, sensor technology, and process control to increase ultimate recovery of domestic hydrocarbons. To meet this objective we investigated the use of permanent downhole sensors systems (Smart Wells) whose data is fed real-time into computational reservoir models that are integrated with optimized production control systems. The project utilized a three-pronged approach (1) a value of information analysis to address the economic advantages, (2) reservoir simulation modeling and control optimization to prove the capability, and (3) evaluation of new generation sensor packaging to survive the borehole environment for long periods of time. The Value of Information (VOI) decision tree method was developed and used to assess the economic advantage of using the proposed technology; the VOI demonstrated the increased subsurface resolution through additional sensor data. Our findings show that the VOI studies are a practical means of ascertaining the value associated with a technology, in this case application of sensors to production. The procedure acknowledges the uncertainty in predictions but nevertheless assigns monetary value to the predictions. The best aspect of the procedure is that it builds consensus within interdisciplinary teams The reservoir simulation and modeling aspect of the project was developed to show the capability of exploiting sensor information both for reservoir characterization and to optimize control of the production system. Our findings indicate history matching is improved as more information is added to the objective function, clearly indicating that sensor information can help in reducing the uncertainty associated with reservoir characterization. Additional findings and approaches used are described in detail within the report. The next generation sensors aspect of the project evaluated sensors and packaging survivability issues. Our findings indicate that packaging represents the most significant technical challenge associated with application of sensors in the downhole environment for long periods (5+ years) of time. These issues are described in detail within the report. The impact of successful reservoir monitoring programs and coincident improved reservoir management is measured by the production of additional oil and gas volumes from existing reservoirs, revitalization of nearly depleted reservoirs, possible re-establishment of already abandoned reservoirs, and improved economics for all cases. Smart Well monitoring provides the means to understand how a reservoir process is developing and to provide active reservoir management. At the same time it also provides data for developing high-fidelity simulation models. This work has been a joint effort with Sandia National Laboratories and UT-Austin's Bureau of Economic Geology, Department of Petroleum and Geosystems Engineering, and the Institute of Computational and Engineering Mathematics.