  Growing amount of hydraulic fracturing (HF) jobs in the recent two decades
resulted in a significant amount of measured data available for development of
predictive models via machine learning (ML). In multistage fractured
completions, post-fracturing production analysis reveals that different stages
produce very non-uniformly due to a combination of geomechanics and fracturing
design factors. Hence, there is a significant room for improvement of current
design practices. The workflow is essentially split into two stages. As a
result of the first stage, the present paper summarizes the efforts into the
creation of a digital database of field data from several thousands of
multistage HF jobs on wells from circa 20 different oilfields in Western
Siberia, Russia. In terms of the number of points (fracturing jobs), the
present database is a rare case of a representative dataset of about 5000 data
points. Each point in the data base contains the vector of 92 input variables
(the reservoir, well and the frac design parameters) and the vector of
production data, which is characterized by 16 parameters, including the target,
cumulative oil production. Data preparation has been done using various ML
techniques: the problem of missing values in the database is solved with
collaborative filtering for data imputation; outliers are removed using
visualisation of cluster data structure by t-SNE algorithm. The production
forecast problem is solved via CatBoost algorithm. Prediction capability of the
model is measured with the coefficient of determination (R^2) and reached
0.815. The inverse problem (selecting an optimum set of fracturing design
parameters to maximize production) will be considered in the second part of the
study to be published in another paper, along with a recommendation system for
advising DESC and production stimulation engineers on an optimized fracturing
design.
