Over the past decade, significant supplies of natural gas have been discovered in shale. While the development of new technologies has driven down the cost of gas extraction, pursuing natural gas in shale continues to be risky and capital-intensive.

Producers seek the most productive zones in their unconventional basins, as well as continued improvement in hydraulic fracturing processes. Decreasing costs and reducing risk while maximizing gas production necessitates innovative, advanced analytical capabilities that can give you a comprehensive understanding of the reservoir heterogeneity in order to extract hidden predictive information, identify drivers and leading indicators of efficient well production, determine the best intervals for stimulation, and recommend optimum stimulation processes and frequencies. Modeling, simulating and predicting well productivity requires integrated exploratory, predictive and forecasting capabilities underpinned by advanced analytical models to unlock the true potential of each wellbore. Without the critical insight enabled by integrated analysis to pair productivity analysis with economic feasibility, companies face significant risk and uncertainty when developing new wells or optimizing production of extant wellbores.

This paper walks through a case study implemented in the Barnett asset in the United States, exemplifying data mining workflows that successfully improved hydrocarbon production. We shall detail analytical methodologies to explicate the optimization of these assets as additional to those workflows expanded upon in SPE paper 149784 presented in Abu Dhabi at the Middle East Unconventional Gas Conference and Exhibition.1 

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