Machine learning on field data for hydraulic fracturing design optimization: Digital database and production forecast model

A. Morozov, D. Popkov, V. Duplyakov, R. Mutalova, A. Osiptsov, A. Vainshtein, E. Burnaev, E. Shel, G. Paderin

Research output: Contribution to conferencePaperpeer-review

3 Citations (Scopus)

Abstract

Increasing amount of hydraulic fracturing (HF) jobs in the recent two decades brought in a significant amount of measured data available for development of predictive models via machine learning (ML). In multistage fractured completions, post-fracturing production 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. Therefore, there is a significant room for fracture design optimization. We propose a data-driven model for fracturing design optimization, where the workflow is essentially split into two stages: prediction of 12-month cumulative oil production and maximizing the target by optimizing HF design parameters. In this work, the first stage is considered, and the result of the ML model’s prediction of the target is 81.5% on test set.

Original languageEnglish
DOIs
Publication statusPublished - 2020
Event1st EAGE Digitalization Conference and Exhibition - Vienna, Austria
Duration: 30 Nov 20203 Dec 2020

Conference

Conference1st EAGE Digitalization Conference and Exhibition
Country/TerritoryAustria
CityVienna
Period30/11/203/12/20

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