The development of unconventional formations in the oil & gas industry has been largely achieved through a trial and error approach since its beginning. Some of the reasons for this might be the initial lack of technical knowledge or the significant time required to properly conduct a technical study before an optimized field test could be recommended. To the latter point, the technical study approach described in this paper, which utilizes advanced data analytics, provides a streamlined methodology that once developed can be repeated at a desired frequency with updated field data, to quickly arrive at statistically significant findings which can be immediately incorporated into a field test or pilot recommendation.
Keywords:production control, reservoir geomechanics, reservoir characterization, asset and portfolio management, artificial intelligence, upstream oil & gas, data analytic, well productivity, contribution, machine learning
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2020. Unconventional Resources Technology Conference