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Combined Geostatistics and Bayesian Updating Optimize Drilling in Shale-Gas Plays

Fig. 1—Map of the Barnett shale-gas play and location of the Training and Test areas.

The development of an economically efficient drilling program in shale-gas plays is a challenging task, requiring a large number of wells; even with many wells, the average well production and the variation of well performance (economics) remain 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. Here, we present a new methodology for improving the economic returns of shale-gas plays.

Introduction

The methodology proposed in this study informs the development of drilling policies for shale-gas opportunities by use of a probabilistic model that accounts for the uncertainty in the chance of success (COS) and its spatial dependency. The model developed in this study shows how a prior view of the COS across a shale-gas play is updated as more wells are drilled. Inferred chances of success of undrilled well locations allow decision makers to react to newly arriving well data and optimize the development of a shale-gas resource.

The main contributions of this paper are (1) to establish and quantify any spatial dependencies between the performances of shale-gas wells, an assessment based upon data from the Barnett shale; (2) present a comprehensive method for updating COS probabilities consistently with the arrival of new information in a play with spatial dependencies; and (3) demonstrate the approach through an analysis of data from the Barnett shale.

For a discussion of previous work performed in uncertainty resolution in inconventional gas plays, including the role played by spatial dependency, please see the complete paper.

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 164816, “Combining Geostatistics With Bayesian Updating To Continually Optimize Drilling Strategy in Shale-Gas Plays,” by B.J.A. Willigers, BG Group; S. Begg, University of Adelaide; and R.B. Bratvold, University of Stavanger, prepared for the 2013 EAGE Annual Conference and Exhibition incorporating SPE Europec, London, 10–13 June. The paper has not been peer reviewed.
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Combined Geostatistics and Bayesian Updating Optimize Drilling in Shale-Gas Plays

01 October 2014

Volume: 66 | Issue: 10

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