Combining geostatistics with Bayesian updating to continually optimize drilling strategy in shale-gas plays

Abstract

We present a new methodology to evaluate subsurface uncertainty during the development of shale-gas plays. Even after many wells are drilled, the average well production and the variation of well performance (economics) remain highly uncertain. The ability to delineate a shale play with the fewest wells and to focus drilling in the most-productive areas is a major factor in commercial success. The importance of probabilistic modeling in managing uncertainty in shale-gas plays is emphasized in several studies. The objective of this study is to develop a practical 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 number of wells to optimize production volumes and rates. Drilling policies must take account of many already-drilled locations, possible new drilling locations, spatial dependencies between performance at those different (possible) well locations, and the extent of uncertainty concerning whether a well will be economical. These factors cause typical valuation methodologies to be impractical because of the “curse of dimensionality.” In this study, an unconventional play is divided into numerous segments. These segments are referred to as cells. In each cell, a fixed number of wells can be drilled. The chance of success (CoS) [of a well with a net present value (NPV) greater than zero] in any given cell is itself considered to be an uncertain variable. An initial probability distribution for the CoS 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 (with Bayes theorem) to create an updated probability distribution for the CoS of the relevant grid cell. Thus, our initial estimate can be continuously updated as more actual outcomes are realized. Second, the influence of the new CoS on the surrounding cells, because of spatial correlation, is updated by use of indicator Kriging (IK), a geostatistical technique. This combination of Bayesian update (BU) and IK is referred to as the BU-IK method. The methodology proposed in this study informs the development of drilling policies for shale-gas opportunities with a probabilistic model that accounts for the uncertainty in the CoS 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 is applied to a well set from the Barnett shale in Texas.