This article takes an approach to creating a machine learning model for the oil and gas industry. This task is dedicated to the most up-to-date issues of machine learning and artificial intelligence. One of the goals of this research was to build a model to predict the possible risks arising in the process of drilling wells. Drilling of wells for oil and gas production is a highly complex and expensive part of reservoir development. Thus, together with injury prevention, there is a goal to save cost expenditures on downtime and repair of drilling equipment. Nowadays, companies have begun to look for ways to improve the efficiency of drilling and minimize non-production time with the help of new technologies. To support decisions in a narrow time frame, it is valuable to have an early warning system. Such a decision support system will help an engineer to intervene in the drilling process and prevent high expenses of unproductive time and equipment repair due to a problem. This work describes a comparison of machine learning algorithms for anomaly detection during well drilling. In particular, machine learning algorithms will make it possible to make decisions when determining the geometry of the grid of wells—the nature of the relative position of production and injection wells at the production facility. Development systems are most often subdivided into the following: placement of wells along a symmetric grid, and placement of wells along a non-symmetric grid (mainly in rows). The tested models classify drilling problems based on historical data from previously drilled wells. To validate anomaly detection algorithms, we used historical logs of drilling problems for 67 wells at a large brownfield in Siberia, Russia. Wells with problems were selected and analyzed. It should be noted that out of the 67 wells, 20 wells were drilled without expenses for unproductive time. The experiential results illustrate that a model based on gradient boosting can classify the complications in the drilling process better than other models.
Aluminum master alloys with rare earth metals are widely studied by many scientists around the world, but research on the production of Al-Er master alloys is still limited. The purpose of this work is to study the microstructure parameters of aluminum-erbium master alloys obtained by metallothermic reduction of salt mixtures containing erbium oxide or fluoride. The structural features were investigated by optical and scanning electron microscopy, and the dependence of the microhardness of the eutectic and solid solution fields of obtained master alloys on the content of erbium in the master alloy was determined. Studies have shown that master alloys obtained by metallothermic reduction of erbium compounds from chloride–fluoride melts are characterized by a uniform distribution of Al3Er intermetallic compounds in the volume of double eutectic [(Al) + Al3Er] and have a strong grain refinement effect. The analysis of the microstructure showed that the structure of the master alloys varies depending on the content of erbium. When the content of erbium in the master alloy is up to 6 wt.%, the eutectic structure is preserved. When the content of erbium in the master alloy is 8 wt.% or more, the structure becomes a solid solution with individual inclusions of various shapes and intermetallic compounds.
The research investigates the process of synthesis of magnesium master alloy with zinc and yttrium. Based on the analysis of state diagrams and requirements for fluxes for smelting of magnesium alloys, the composition of the saline mixture was chosen. X-ray phase analysis of the molten salt mixture showed that during the melting process, yttrium fluoride partially interacted with sodium and potassium chlorides, forming complex salts: Na 1.5 Y 2.5 F 9 , NaYF 4 , Na 5 Y 9 F 32 , and KY 7 F 22 , which are the source for yttrium recovery. Differential thermal analysis (DTA) determined the temperature ranges and values of thermal effects of melting and crystallization of a mixture of the KCl-NaCl-CaCl 2 -YF 3 salt in the recovery of yttrium compounds by a magnesium-zinc alloy. It was determined that interaction within the system begins at a temperature equal to the initial melting point of zinc, and occurs in the range from 415°C to 672°C. As a result of series of experimental meltings, the basic laws of the synthesis of magnesium-zinc-yttrium master alloys from the selected technological salt mixture, as well as the main factors of the metallothermic process, affecting the degree of yttrium reduction were revealed. The metallographic study of the alloys obtained showed that the samples consisted of solid solutions of Mg x Zn y and intermetallic compounds of Mg x Y y Zn z , which were located along the boundaries of dendritic cells. The proposed method of recovery of yttrium fluoride from the chloride melt allows extracting up to 97.2 % of yttrium.
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