Shale well completion design has historically been based on geomechanical modeling, empirical field trials, reservoir simulation, and/or engineering judgment. These approaches are often less than optimal due to:
non-unique characterizations of key reservoir and completion properties,
the statistical nature of these properties, and
time and expense required to perform studies. This paper presents a method for overcoming these difficultiesusing a simple completion performance model that uses production and wellhead pressure data from nearby analog wells. The method is based on performance considerations that are most significantly controlled by only three primary "lumped parameters".
The workflow uses the historical performance of analog wells to constrain ranges of key completion and petrophysical parameters, from which estimated distribution functions can be established. A Monte Carlo method is used to stochastically evaluate expected undeveloped well economics for different well and completion designs. The methodology can be rapidly applied to evaluate a large number of alternatives that address:
lateral length, proppant amount,
number of completion stages, and
number of perforation clusters per stage.
Though the approach focuses on the reservoir engineering consequences of different designs, there are obviously other considerations that should be taken into account, particularly those based on geology, drilling, and completions engineering. This approach allows for those considerations to be incorporated.
The example cases described in this paper are for dry and retrograde condensate gases. The methodology can, however, be extended to volatile and black oil shale wells.