Data-driven model for fracturing design optimization: focus on building digital database and production forecast.

Growing amount of hydraulic fracturing (HF) jobs in the recent two decades resulted in a significant amount of measured data available for construction of predictive models via machine learning (ML). There is a significant room for fracturing design optimization. We propose a data-driven model for fracturing design optimization, where 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 vertical, inclined and near-horizontal wells from about twenty 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 6000 of data points, compared to typical databases available in the literature, comprising tens or hundreds of points at best. Each point in the data base is based on the vector of 92 input variables (the reservoir, well and the frac design parameters). Production data is characterized by 16 parameters, including the target, cumulative oil production. The focus is made on data gathering from various sources, data preprocessing and development of the architecture of a database as well as solving the production forecast problem via ML. 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.