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Abstract

Improving the capital efficiency in oil and gas exploration and production, particularly in the unconventional (UNC) plays, becomes vitally important for the industry. Since it is evident that the existing geological and petro-physical methodologies and technologies that enjoy good success in conventional plays become not as effective when applied to UNC plays, more effective approaches are in high demand. However, in oil and gas exploration, the most critical phase is the early land appraisal and initial development of the so-called green fields wherein the available data is usually scarce. This poses a great challenge to both domain experts and Machine Learning practitioners. How can Machine Learning and its related techniques be applied to help in early land appraisal and sweetspotting to greatly improve the capital efficiency? This paper describes our recent advances in developing a Machine Learning sweetspotting workflow and presents our results and findings in identifying the higher production potential areas in an example. The workflow uses a imputation scheme and a more generic and powerful ensemble learning technique which combines the strengths of a set of different Machine Learning algorithms. Consequently, our new workflow has achieved very good results in terms of R^2 (0.83) from leave-one-out cross validation.

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/content/papers/10.3997/2214-4609.201701666
2017-06-12
2024-03-19
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201701666
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