# Development of a Digital Well Management System
###### Abstract  
The modern oil industry is characterized by a strong trend towards the digitalization of all technological processes. At the same time, during the transition of oil fields to the later stages of development, the issues of optimizing the consumed electricity become relevant. The purpose of this work is to develop a digital automated system for distributed control of production wells using elements of machine learning. The structure of information exchange within the framework of the automated system being created, consisting of three levels of automation, is proposed. Management of the extractive fund is supposed to be based on the work of four modules. The \"Complications\" module analyzes the operation of oil wells and peripheral equipment and, according to the embedded algorithms, evaluates the cause of the deviation, ways to eliminate it and the effectiveness of each method based on historical data. The \"Power Consumption Optimization\" module allows integrating algorithms into the well control system to reduce energy consumption by maintaining the most energy-efficient operation of pumping equipment or optimizing its operation time. The module \"Ensuring the well flow rate\" allows you to analyze and determine the reasons for the decrease in production rate, taking into account the parameters of the operation of adjacent wells. The Equipment Anomaly Prediction module is based on machine learning and helps reduce equipment downtime by predicting and automatically responding to potential deviations. As a result of using the proposed system, many goals of the oil company are achieved: specific energy consumption, oil shortages, and accident rate are reduced, while reducing the labor costs of engineering and technological personnel for processing the operation parameters of all process equipment. a 2023 6 31 202  
The modern oil industry is characterized by a strong trend towards digitalization, automation of all technological processes and the introduction of artificial intelligence in all components of oil production, transport and refining [11; 12; 13]. Thus, the development and use of intelligent control stations (ICS) is currently underway [14; 15]. These control stations are significantly superior to the currently used classical control stations. Their main advantage is the intelligent optimization of downhole pumping equipment operation, reduction of specific energy consumption, recording and analysis of well and downhole pumping equipment operation parameters, minimization of submersible electric motor (SEM) emergency shutdowns due to high-quality forecasting and automatic response to deviations [16; 17; 18]. There is a need to develop group control stations that allow you to simultaneously control the operation of several wells on one pad, as well as other oilfield equipment [19; 20]. Such equipment may include an automatic group metering unit, wellhead inhibitor dosing units, semi-automatic dewaxing units and automatic devices for cleaning pipe [21]. Most of the control stations being developed provide functions for analyzing data received from downhole pressure gauges, a telemetry system and entered into the system by engineering and technical personnel. The operation of these control stations does not involve taking into account the characteristics of the reservoir, well, or pump. The history of operation is not taken into account and predictive data analysis is not performed. As part of this work, a modern digital automated system for managing and optimizing the operation of the oil well stock (ACS) is presented. This system offers a new direction in the field of oil well cluster management, combines developments in the field of digital model of oil-producing facilities and artificial intelligence for process control. The rest of this paper is organized as follows: Section 2 describes the general concept of well cluster management and applied mathematical models. Section 3 describes the developed ACS modules, as well as experience with one of the modules.