World Summit on Management Sciences
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Accepted Abstracts

Development of Hydrocarbon Resources on Soft Computing and Data Mining Technologies Base

Hasanov Ramiz* 
Azerbaijan State University of Oil and Industry, Azerbaijan

Citation: Ramiz H (2020) Development of Hydrocarbon Resources on Soft Computing and Data Mining Technologies Base. SciTech Management Sciences 2020. 

Received: August 18, 2020         Accepted: August 27, 2020         Published: August 27, 2020


Though there are big resources of mathematical conceptions, methods and theories on decision making in the economy, geologico-technological processes control spheres and other corresponding spheres last decades, complex mathematical theories don’t make it possible to solve all existing problems. The reason of it is the intolerableness of mathematical theories to inaccuracy and incompleteness of reality. A number of works have been devoted to the existing fuzzy logics and probability theories in scientific literature and they are used to consider indefiniteness of medium and information on decision-making in complex technological spheres. There are a number of works in scientific literature concerning development of complex technological process as drilling of wells on the basis of statistic methods. But solution of such task requires considering of factors and medium characterized by indefiniteness. For this reason with the purpose of decision making the development of the control system on the well drilling process in indefiniteness condition has been considered. There was a need in intellectual analysis of data to eliminate shortage in indefiniteness condition. With this purpose classic methods complex–Fuzzy technology and Data mining technology have been used. In geologico-technological researches geological condition, receiving and description of information, prognosing of drilling indices, evaluation of drilling efficiency, information structure used in setting up of dependences of drilling regime parameters are characterized by various types of indefiniteness. The fact that information considered in these spheres consists of mixture of fuzzy and probable indefiniteness and various scientific approaches are being worked out in this sphere.
Geological, technico-technological and ecological information in the process of oil spheres development characterizes the condition expressed by indefiniteness. During the collection and processing the information all attempts made for obtaining complete information are directed to precise information on a whole process, and it requires precise processing of indefinite, fuzzy information. Strong interpretation ability of fuzzy logics, tolerance to indefiniteness make it possible to get complete information. Analysis of oil-spheres using statistic methods depending on the information processing brings to various results, and it sometimes conforms partly with the experts’ studies in this sphere, sometimes it brings to the opposite results. For eliminating this shortage there is need for intellectual analysis of data. There is a complex of classic methods directed to the increase of oil production coefficient from the layers in the development of oil spheres and currently it is being applied. Dependence of the analyzed data on each other and imprecise analysis of a number of factors influencing the results and limited information due to the shortage of human’s calculation ability, form difficulties in making right decision using classic decision making methods. Comparative analysis of the results obtained by classic methods and intellectual analysis of data have been considered in the article. The suggested approach is based on fuzzy logics and Data mining technology. Application of fuzzy clusterization gives several possible variants and it widens choice of decision making alternatives.
Keywords: Oil well drilling, Statistic methods decision making, Well drilling control, Fuzzy theories, Probability theories, Data mining technology, Intellectual analysis, Fuzzy-C-means algorythm, Clusterization task, Choice of alternatives, Interpretation, Cluster centers, Neuron nets.