XGBoost is a scalable ensemble technique based on gradient boosting that has demonstrated to be a reliable and efficient machine learning challenge solver. This work proposes a practical analysis of how this novel technique works in terms of training speed, generalization performance and parameter setup. In addition, a comprehensive comparison between XGBoost, random forests and gradient boosting has been performed using carefully tuned models as well as using the default settings. The results of this comparison may indicate that XGBoost is not necessarily the best choice under all circumstances. Finally an extensive analysis of XGBoost parametrization tuning process is carried out.

Journal of Petroleum Exploration and Production Technology manuscript No. (will be inserted by the editor) Abstract In this paper we present a data-driven model for forecasting the production increase after hydraulic fracturing (HF). We use data from fracturing jobs performed at one of the Siberian oilfields. The data includes features, characterizing the jobs, and a geological information. To predict an oil rate after the fracturing machine learning (ML) technique was applied. The MLbased prediction is compared to a prediction based on the experience of reservoir and production engineers responsible for the HFjob planning.

In order to bridge the gap of more than 15m between the drilling bit and high-fidelity rock type sensors during the directional drilling, we present a novel approach for identifying rock type at the drilling bit. The approach is based on application of machine learning techniques for Measurements While Drilling (MWD) data. We demonstrate capabilities of the developed approach for distinguishing between the rock types corresponding to (1) a target oil bearing interval of a reservoir and (2) a non-productive shale layer and compare it to more traditional physics-driven approaches. The dataset includes MWD data and lithology mapping along multiple wellbores obtained by processing of Logging While Drilling (LWD) measurements from a massive drilling effort on one of the major newly developed oilfield in the North of Western Siberia. We compare various machine-learning algorithms, examine extra features coming from physical modeling of drilling mechanics, and show that the classification error can be reduced from 13.5% to 9%.