DETERMINATION OF THE REQUIREMENT FOR TRANSPORTATION AND TECHNOLOGICAL MACHINES BY CLUSTERIZATION OF OIL AND GAS PRODUCTION DEPARTMENTS
The article considers the analysis of production indicators of oil and gas production departments with the aim of clustering them for the subsequent determination of the need for automobiles and technological machines. The departments have different sizes, power, are in different conditions, are characterized by different performance indicators, but at the same time they are equipped with vehicles according to the same standards. This leads to problems in ensuring the uninterrupted transport and technological service of the main production. In a number of departments, situations arise when the planned number of transport and technological machines is not enough to perform technological operations for the repair or maintenance of wells. In this case, vehicles are sent from another sub-division, thereby limiting their own transport service capabilities. Fleet planning often takes place taking into account the historical conditions of the department, which is generally applicable for old departments with an established well stock, but practically does not work for newly formed departments with large volumes of newly commissioned wells and complicated production conditions. These subdivisions are equipped with vehicles in relation to existing workshops with similar indicators, which most often leads to an insufficient number of machines and downtime of the main production due to lack of machines. In this regard, it is necessary to search for and justify those production indicators of departments that determine their differentiation. The aim of the paper is to increase the efficiency of transport and technological service of oil and gas production facilities based on determining the patterns of influence of production indicators of production and gas shops on the need for transport and technological machines and developing, on this basis, differentiated standards for equipping units with vehicles. Using machine learning methods, the clustering of production units was carried out, and the factors that determine the distribution of departments into four groups were identified. The main factors include the stock of wells in the department and the degree of complexity of this stock. Groups are determined by the degree of change in these factors. The presented approach and the resulting distribution can be used as a basis for more efficient standardization of the needs of departments in automobiles and technological machines and also as part of decision support systems for vehicle fleet management.