Renata Mutalova, Anton Morozov, Andrei Osiptsov , Albert Vainshtein, Evgeny Burnaev, Egor Shel, Grigory Paderin

Growing amount of fracturing stimulation jobs in the recent two decades resulted in a significant amount of measured data available for construction of predictive models via machine learning (ML). Simulataneous evolution of machine learning has made it possible to apply algorithms on the hydraulic fracture database. A typical multistage fracturing job on a near-horizontal well today involves a significant number of stages. The post-fracturing production analysis (e.g., from production logging tools) reveals evidence that different stages produce very non-uniformly, and up to 30% may not be producing at all due to a combination of geomechanics and fracturing design factors. Hence, there is a significant room for fracturing design optimization, and the wealthy of field data combined with ML techniques opens a new road for solving this optimization problem. However, ML algorithms are only applicable when there is a comprehensive, well structured digital database. This paper summarizes the efforts into 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 ML.