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The organization of the management of an oil and gas producing enterprise has not experienced major changes over the past few decades. At the same time, extractive enterprises face numerous problems and challenges: - Deteriorating stock quality, oil price volatility and volatility of market in general, These problems are accompanied by a decrease in the efficiency of production: an increase in the water content of the stock, an increase in the number of downtime and complications on the stock, the appearance of unproductive lifting-costs All this leads to a drop in the economic efficiency of asset development. Current challenges, such as flexible production management, its economic efficiency, deterioration of the structure and quality of the resource base, the need for system-based linking of a large amount of additional information about the field on the one hand and a huge variety of calculation and digital tools on the other, dictate the need for an integrated transformation of the principles of production management of an oil and gas producing enterprise. We offer you to familiarize yourself with the approach to the organization of oil and gas production management based on an effective process model and integrated digital solutions, the purpose of which is to optimize the operation of the field and ensure the growth of free cash flow from production. (Figure 1)

The paper describes the principal possibility of using machine learning methods for verifying and restoring the quality of oilfield measurements. Basic methods for screening incorrect values have been given and approaches for solving three problems have been recommended: Correctness analysis of well logging data Quality control of physical and chemical fluid properties (PVT-studies) Separation between the base production and effect from well interventions (WI) to predict the performance of hydraulic fracturing (frac). The main deliverable is a set of algorithms based on machine learning methods, which allows to automatically process large volumes of field data. A number of approaches is proposed, including using modern methods of machine learning, to restore the missing values and the quality of algorithms operation.