An Integrated Technique for Production Data Analysis With Application to Mature Fields
- Razi Gaskari (Merrick Systems, Inc.) | Shahab D. Mohaghegh (West Virginia U.) | Jalal Jalali (West Virginia U.)
- Document ID
- Society of Petroleum Engineers
- SPE Production & Operations
- Publication Date
- November 2007
- Document Type
- Journal Paper
- 403 - 416
- 2007. Society of Petroleum Engineers
- 4.3.4 Scale, 4.6 Natural Gas, 7.6.6 Artificial Intelligence, 7.6.4 Data Mining, 2.5.1 Fracture design and containment, 3 Production and Well Operations, 5.5.8 History Matching, 5.5 Reservoir Simulation, 5.1 Reservoir Characterisation, 1.6 Drilling Operations, 5.8.3 Coal Seam Gas
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The most common data that engineers can count on, especially in mature fields, is production rate data. Practical methods for production data analysis (PDA) have come a long way since their introduction several decades ago and fall into two categories: decline curve analysis (DCA) and type curve matching (TCM). DCA is independent of any reservoir characteristics, and TCM is a subjective procedure.
State of the art in PDA can provide reasonable reservoir characteristics, but it has two shortcomings: First, for reservoir characterization, the process requires bottomhole or wellhead pressure data in addition to rate data. Bottomhole or wellhead pressure data are not usually available in most of the mature fields. Second, a technique that would allow the integration of results from hundreds of individual wells into a cohesive fieldwide or reservoirwide analysis for business decision making is not part of today's PDA tool kit.
To overcome these shortcomings, a new methodology is introduced in this paper that has three unique specifications:
• It does not "require?? pressure data, bottomhole or wellhead (but it can make use of it, if available, to enhance accuracy of results).
• It integrates DCA, TCM, and numerical reservoir simulation or history matching (HM) to iteratively converge to a near unique set of reservoir characteristics for each well.
• It uses fuzzy pattern recognition technology to achieve fieldwide decisions from the findings of the analysis.
Techniques for PDA have improved significantly over the past several years. These techniques are used to provide information on reservoir permeability, fracture length, fracture conductivity, well drainage area, original gas in place (OGIP), estimated ultimate recovery (EUR), and skin. Although several methods are available to characterize the reservoir, there is not a unified method that always yields the most reliable answer.
DCA is a method to fit observed production rates of individual wells, group of wells, or reservoirs by a mathematical function to predict the performance of the future production by extrapolating the fitted decline function.
Arps (1945) introduced the DCA method in the 1940s. The method is a mathematical equation with no physical basis other than the equation that shows a declining trend. Arps' method is still being used because of its simplicity. In the early 1980s, Fetkvoich (1985) introduced DCA by type curves. Fetkovich used Arps' decline curves along with type curves for transient radial symmetric flow of low-compressibility liquids at constant bottomhole pressures. Fetkovich related Arps' decline parameters to some reservoir engineering parameters for production against constant bottomhole pressures. Several other type curves have been developed by Carter (1985), Fraim & Wattenbarger (1987), Palacio & Blasingame (1993) and Agarwal et al. (1999) and others for different well and reservoir conditions.
Several commercial PDA tools have been developed for the oil and gas industry. These commercial applications use DCA, TCM, and/or HM (using reservoir simulation) independent from each other without integrating these techniques. Furthermore, no other technique that is currently in use provides facilities to integrate the results from individual well analysis into a fieldwide (reservoirwide) analysis.
|File Size||3 MB||Number of Pages||14|
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