Hot spot hunting: Optimising the staged development of shale plays

Abstract

The development of unconventional plays tends to unfold in many stages, each of which involves incremental investment and a reduction in the geological uncertainty of the reservoir. These two characteristics yield a large decision space where future decisions are optimised based on the near-continuous arrival of new information. This managerial flexibility can be exploited by operators during the development of unconventional plays.We introduce a methodology that demonstrates how value can be created by a staged and partial development of a shale play that would have been unprofitable if fully developed. Compared to existing methods the novel methodology is more consistent with the characteristics of how plays are currently developed as existing methods assume that upon a successful appraisal stage a play is developed in its entirety in a single development phase.As more data become available after each development phase of the play, the potential of the remaining undrilled locations is updated using Bayes's rule. The method is couched in geostatistical principles, combined with an algorithm that allows for a continuous optimisation of drilling targets.An example of a shale gas project has been investigated that consists of 225 possible drilling targets each containing 10 well locations. A maximum of 200 wells can be produced by drilling 20 of the 225 targets. The mean performance of the well population is uncertain. The scenario with the highest mean well performance yields a value of −160MM USD and the expected project value across all scenarios of mean well performance equals −920MM USD, given that all 200 wells are drilled at randomly chosen drilling targets. In the model presented in this study the resource can be developed in up to 19 stages upon completion of an appraisal programme. After each development stage an assessment is made where, and if, the next batch of 10 wells should be drilled. This strategy of stage-wise development yields an expected value of 49.2MM USD. The spatial dependency of well performance enables the algorithm to restrict the development of the play to the most prolific areas.The appraisal programme provides a view on the variability of well performance across the play. A trade off exists between the size, and the consequential accuracy, of the appraisal programme and the cost of appraising. The example illustrates that the expected project value increases from 33.3MM USD for an appraisal programme in which two locations were appraised, to a maximum of 49.2MM USD after the appraisal of four locations, and subsequently decreases to 29.0MM USD after having appraised eight locations.The assumptions around the variability of Estimated Ultimate Recovery (EUR) used in our example are informed by data from 10,000 horizontal wells located in the Mississippian Barnett Shale in the Fort Worth basin in Texas.