Machine learning on field data for hydraulic fracturing design optimization

This paper summarizes the efforts of the creation of a digital database of field data from several thousands of multistage hydraulic fracturing jobs on near-horizontal wells from several different oilfields in Western Siberia, Russia. In terms of the number of points (fracturing jobs), the present database is a rare case of an outstandingly representative dataset of thousands of cases, compared to typical databases available in the literature, comprising tens or hundreds of pints at best. The focus is made on data gathering from various sources, data preprocessing and development of the architecture of a database as well as solving fracture design optimization via machine learning. We work with the database composed from reservoir properties, fracturing designs and production data. Both a forward problem (prediction of production rate from fracturing design parameters) and an inverse problem (selecting an optimum set of frac design parameters to maximize production) are considered. A recommendation system is designed for advising a DESC engineer on an optimized fracturing design.

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