Abstract

We present a new methodology for improving the economic returns of shale gas plays. The development of an economically efficient drilling programme in such plays is a challenging task, requiring a large number of wells. Even after a relatively large number of wells have been drilled, the average well production and the variation of well performance (economics) remains highly uncertain. The ability to delineate a shale play with the fewest number of wells and to focus drilling in the most productive areas is an important driver of commercial success.

The importance of probabilistic modelling in managing uncertainty in shale gas plays has been explicitly emphasised in a number of studies. The objective of this study is to develop a practical valuation methodology that addresses these complexities and is dynamic, in the sense that the optimal drilling strategy can be continually updated as we learn the outcome of each well drilled.

Maximizing the returns from a shale gas play is essentially a problem of choosing well locations and numbers to optimize production volumes & rates. Drilling policies have to take account of a large number of already-drilled locations, possible new drilling locations, spatial dependencies between performance at those different (possible) well locations and the extent of uncertainty as to whether or not a well will be economic. These factors cause typical valuation methodologies to be impractical due to the "curse of dimensionality".

In this study an unconventional play is divided into cells. In each cell a fixed number of wells can be drilled. The chance of success (of a well having an NPV greater than zero) in any given cell is itself considered to be an uncertain variable. An initial probability distribution for the chance of success of each cell is derived from analogous plays plus any available information about the specific play.

The methodology proceeds as follows. First, as each new well (or group of new wells) is drilled, the outcome is used in combination with the prior probability distribution (using Bayes Theorem) to create an updated probability distribution for the chance of success of the relevant grid cell. Thus, our initial estimate can be continuously updated as we get more and more actual outcomes. Second, the influence of the new chance of success on the surrounding cells, due to spatial correlation, is updated using indicator kriging, a geostatistical technique.

The methodology proposed in this study informs the development of drilling policies for shale gas opportunities by using a probabilistic model that accounts for the uncertainty in the chance of success and its spatial dependency. The use of cells to represent a set of wells simplifies the analysis and greatly reduces the computing requirements. The methodology has been applied to a well set from the Barnett Shale, Texas, United States of America.

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