Continuous Estimation of Ultimate Recovery
- Stephanie Marie Currie (Texas A&M U.) | Dilhan Ilk | Tom Blasingame
- Document ID
- Society of Petroleum Engineers
- SPE Unconventional Gas Conference, 23-25 February, Pittsburgh, Pennsylvania, USA
- Publication Date
- Document Type
- Conference Paper
- 2010. Society of Petroleum Engineers
- 5.5 Reservoir Simulation, 5.6.4 Drillstem/Well Testing, 5.8.9 HP/HT reservoirs, 4.3.4 Scale, 3.2.3 Hydraulic Fracturing Design, Implementation and Optimisation, 5.7 Reserves Evaluation, 5.8.1 Tight Gas, 2 Well Completion
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Gas in place/reserves estimation in unconventional (low/ultra-low permeability) reservoirs has become a topic of increased interest as more of these resources are being developed domestically and internationally. Production data from these unconventional gas reservoirs exhibit extended periods of transient/transition flow behavior that often lead to the over-estimation of gas in place/reserves with the use of simple rate-time extrapolation techniques.
In this work we show that the analysis of production data (particularly rate-time data) before the onset of boundary dominated flow for these unconventional gas reservoirs leads to significant overestimation of gas in place/reserves. Consequently, we introduce the concept of "continuous estimation of ultimate recovery" (or Continuous EUR) where estimation of reserves from a single gas well is performed in a dynamic fashion — in other words we continuously estimate the reserves for selected time intervals throughout the producing life of the well.
We have applied the Continuous EUR method by using simple rate-time relations such as the Arps' "hyperbolic" rate decline relation and the power-law exponential rate decline relation. We present the application of the Continuous EUR method with a tight gas data set and four (tight gas) field data sets. Our analyses show that the distinction in the flow behavior is more evident and the uncertainty in reserves estimation decreases significantly when reserves are evaluated continuously.
The estimation of reserves in unconventional reservoir systems is problematic due to the extremely complex geology, and because of the very low permeability of the reservoirs, longer transient flow periods are being exhibited throughout the producing life of the well. Under these circumstances the uncertainty in reserves estimation is high and usually the reserves are over-estimated.
From a practical standpoint, simple rate-time relations are used in order to estimate reserves in unconventional reservoir systems. These relations (i.e., Arps' rate-time relations) are very simple to use, but they usually over-predict the reserves as they are only applicable for the boundary-dominated flow regime whereas unconventional reservoirs exhibit very long transient flow periods (years or even decades).
In this work we propose the use of the simple rate-time relations in a dynamic (continuous) manner to reduce the uncertainty in reserve estimates. We specify a time interval amongst the production sequence, and for each interval we perform extrapolation to obtain the maximum production (i.e., the estimation of ultimate recovery). For example, for 500 days of production data (i.e., rate-time data) we may select a 100-day time interval and the maximum production would be estimated using 100 days, 200 days, 300 days, 400 days, and 500 days of production data (in principle, the interval is arbitrary — it could even be daily, but such a small interval would require an automated procedure, which is not addressed in this paper). We choose to call this procedure as the continuous estimation of reserves or Continuous EUR approach. For our purposes we use the Arps' hyperbolic rate-time and the power-law exponential rate-time relations for the continuous estimation of reserves.
Hyperbolic Rate Decline Function:
The most common method of well performance (i.e. rate-time data) analysis involves the empirical rate-time relations presented by Arps . These equations were developed to analyze data exhibiting boundary-dominated flow behavior. Arps' equations are widely used to forecast production behavior and estimate reserves.
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