NEURON NETWORK TECHNOLOGIES APPLICATION IN LITHOLOGICAL-FACIAL DECOMPOSITION OF THE DEVONIAN AND CARBONIFEROUS SEDIMENTS ACCORDING TO GEOLOGICAL-GEOPHYSICAL DATA (NORTHWESTERN PART OF THE DNIEPER-DONETS DEPRESSION) - KARPENKO, Ivan; LEVONIUK, Serhii; KARPENKO, Oleksii; LOKTIEV, Andrii (Visnyk of Taras Shevchenko National University of Kyiv. Geology, 2024)
Background. The growth of hydrocarbon reserves in the old oil and gas producing regions can be realized to a large extent due to the reinterpretation of geological and geophysical data on the old fund of wells (both within existing deposits and on areas that, under certain circumstances, were at one time assessed as unpromising). At this stage of geological exploration work, considerable work is being done to reinterprete the data of geophysical studies in the missed intervals of wells of the old fund, including exploratory wells. Similar works are carried out on deep horizons (Devonian), as well as on Mesozoic deposits, which in the last century were not of primary importance in terms of prospects in many areas. The available geological and geophysical material indicates significant difficulties in the correct identification of layers within the specified strata. Methods. The results of geophysical, petrographic and petrophysical studies on the deposits are analyzed. The methods of statistical analysis were applied in order to establish the possibility of using pattern recognition methods for lithological-facies dissection of well sections based on well-logging data. Results. Thanks to the use of neural network technology, the reliability of the traditional geological interpretation of the welllogging data was verified. The reliability of establishing the lithological-facies affiliation of individual layers in Upper Devonian, Lower, and Middle Carboniferous well sections has been increased by using an ensemble of calculated neural networks. An original approach to the implementation of neural network technology in the interpretation of geological and geophysical data is proposed. Сonclusions. The article examines the geophysical characteristics of potentially promising horizons that were previously overlooked. The problems to be solved are indicated, as well as ways to increase the reliability of the geological interpretation of welllogging data in layers of different ages. In particular, the possibility and expediency of using machine learning based on neural network technologies is considered.
Keywords: horizon; reservoir; lithology; Devonian; neural networks; well logging