Because decline curve analysis is not grounded in fundamental theory but is based on empirical observation, there are some major assumptions that need to be made in order to come up with an appropriate forecast. These assumptions can derail a forecast and lead the evaluator in the wrong direction.

Decline analysis is by far the simplest way to analyze declining production rates and forecast the future performance of oil and gas wells. However, because decline curve analysis is not grounded in fundamental theory but is based on empirical observation, there are some major assumptions that need to be made in order to come up with an appropriate forecast. These assumptions can derail a forecast and lead the evaluator in the wrong direction. This article will illustrate some of the assumptions and will also discuss how combining multiple analysis methods can help negate these assumptions and increase confidence in production forecasts.

There is a breadth of assumptions that need to be considered when performing a decline analysis. One of the major assumptions that empirical decline analysis makes is that the operating and reservoir variables that cause the historical decline will continue unchanged during the forecast period. This means that any reservoir conditions, such as the number of producing wells, pressure depletion, and relative permeability, are assumed to remain the same from the historical period through the forecast period. The same is true for operating conditions: tubing size, choke settings, compression, and operating hours are to remain constant. The sandface flowing pressure must also be considered; it must remain relatively constant before a reliable set of decline parameters can be extracted. And finally, the Arps equations used to generate these forecasts are theoretically only valid during boundary-dominated flow.

If there are so many assumptions associated with traditional decline methods, how should the data be analyzed? The recommended approach involves combining multiple analysis methods to increase confidence in the production forecasts. Modern methods such as combining traditional decline analysis with another method that considers the flowing pressure are one option. Another option, if the flowing pressures are not available, is to combine the traditional decline analysis with a method that can calculate the fluids in place, such as a material balance or a volumetric analysis. By combining these methods, the range of answers that a decline analysis can generate will be constrained.

To illustrate how combining methods can work, a decline analysis has been done on a high-permeability gas well. The results of this analysis suggest an estimated ultimate recovery (EUR) of 10 billion standard cubic feet (Bscf). But when an additional method is considered, in this case a flowing material balance, a conflicting result emerges. The flowing material balance suggests an original gas in place of only 6 Bscf, and when an abandonment pressure of 250 pounds per square inch absolute (psia) is considered, the EUR decreases to 4.6 Bscf. Obviously something is wrong: the decline analysis is predicting an EUR that is roughly twice what the flowing material balance is suggesting.

The issue here is that the empirical decline analysis assumes a constant flowing pressure, but in this case it is not constant-the flowing pressure is decreasing. One of the major assumptions associated with decline analysis has been ignored, thereby generating a misleading forecast. But by combining methods that use both the flowing pressures and the rates, an overestimation of recoverables can be avoided.

In the energy industry the accurate booking of reserves is imperative to the way business is run. We can mitigate the risk of overbooking reserves by using additional analysis methods in combination with traditional decline analysis. To ensure due diligence in reserve estimation is met, we must remind ourselves of the assumptions that traditional decline analysis makes. With the use of additional methods, results generated from a decline analysis can be constrained to increase confidence in production forecasts.

Kristina Epp, Reservoir Engineer, IHS
Posted July 16, 2015

This article was published by S&P Global Commodity Insights and not by S&P Global Ratings, which is a separately managed division of S&P Global.