We describe a stacked model for predicting the cumulative fluid production for an oil well with a multistage-fracture completion based on a combination of Ridge Regression and CatBoost algorithms. The model is developed based on an extended digital field data base of reservoir, well and fracturing design parameters. The database now includes more than 5000 wells from 23 oilfields of Western Siberia (Russia), with 6687 fracturing operations in total. Starting with 387 parameters characterizing each well, including construction, reservoir properties, fracturing design features and production, we end up with 38 key parameters used as input features for each well in the model training process. The model demonstrates physically explainable dependencies plots of the target on the design parameters (number of stages, proppant mass, average and final proppant concentrations and fluid rate). We developed a set of methods including those based on the use of Euclidean distance and clustering techniques to perform similar (offset) wells search, which is useful for a field engineer to analyze earlier fracturing treatments on similar wells. These approaches are also adapted for obtaining the optimization parameters boundaries for the particular pilot well, as part of the field testing campaign of the methodology. An inverse problem (selecting an optimum set of fracturing design parameters to maximize production) is formulated as optimizing a high dimensional black box approximation function constrained by boundaries and solved with four different optimization methods: surrogate-based optimization, sequential least squares programming, particle swarm optimization and differential evolution. A recommendation system containing all the above methods is designed to advise a production stimulation engineer on an optimized fracturing design.

The study provides insights into the development of a data-driven model for hydraulic fracturing design optimization. We make a specific focus on practical aspects of testing the model in the field. Database for hydraulic fracturing treatments is built on the data from 22 oilfields in Western Siberia, Russia. The database contains about 5500 points with formation, well and fracturing process parameters, the target feature for model is a cumulative fluid production for 3 months. System and method for searching offset (similar) wells is also developed, tested and validated. Authors developed the model for predicting cumulative production that is used for futher hydraulic fracturing design optimization.

The results are presented from quantum-chemical modeling of the chemisorption of atomic hydrogen and fluorine on the surface of a hexagonal boron nitride monosheet containing a number of stable intrinsic, impurity, and complex defects in different charge states. The objects of study are antisite defects, interstitial atoms, vacancy defects, carbon and oxygen impurities, and complex "impurity + vacancy" defects. The configurations of molecular orbitals (MOs) for the studied defects are analyzed. It is found that hydrogen is more actively with nitrogen atoms in most of the considered cases, while fluorine exhibits higher activity toward boron atoms.

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 (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 improvement of current design practices. 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 in the creation of a digital database of field data from several thousands of multistage HF jobs on vertical, inclined and near-horizontal 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, compared to typical databases available in the literature, comprising tens or hundreds of points at best. 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. The focus is made on data gathering from various sources, data preprocessing and development of the architecture of the 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 visualization 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 (R2) 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.

The results are presented from quantum-chemical simulations of the interaction between F– and FHF– ions and monovacancy and divacancy defects in graphene. The energy characteristics of fluorine chemisorption from ion associates with water molecules are determined. It is shown that the vacancies affect the parameters of chemisorption: the activation energy falls and the heat of adsorption rises, compared to those of an ordered graphene sheet. The relationship between the heat of chemisorption and the degree of fluorine coverage is studied. The characteristics of the reaction between vacancy defects and F–, FHF–, and hydroxonium ions are compared.

A numerical simulation for the spherical indentation of elastoplastic media was performed. A range of hardening exponents, friction coefficients and yield strength was used. The obtained solutions were used to evaluate the stresses and strains at the contact edge. Influence of friction and material’s parameters on the sigma-epsilon contact pair and a comparison with an analytical equation is presented