Identification of reservoir rocks in the geological section of the Visean deposits of the Lypovodolynske field

Abstract

This paper focuses on identifying reservoir rocks within the Viséan deposits of the Lypovodolynske oil and gas condensate field, using an integrated analysis of well logging data (geophysical well surveys, GWS). The Viséan stage consists of lower and upper sublevels of terrigenous-carbonate sequences forming several prospective productive horizons. The study focuses on two types of reservoir rock: terrigenous rocks (such as sandstones and siltstones) and carbonate rocks (such as the limestones of the lower Viséan 'plate'), which differ in terms of their lithological composition, pore space structure and physical properties. Terrigenous reservoirs exhibit relatively uniform porosity and a clear response in geophysical fields, enabling them to be identified efficiently using traditional methods. In contrast, carbonate reservoirs are more difficult to recognise due to heterogeneous porosity, including secondary fracture and cavernous porosity, which affects the representation of geological parameters in GWS data. This work proposes a methodology that comprehensively correlates baseline and normalised (scaled) curves from various geophysical methods (gamma-ray logging, acoustic logging, lateral logging, neutron-gamma logging and pulsed neutron-neutron logging). This improves the accuracy with which reservoir layers can be identified. This approach identifies potential reservoir intervals when the normalised curve values exceed the baseline, enabling precise delineation of terrigenous and carbonate layers. Well No. 26-Lypovodolynska was used as a case study to construct a logging curve panel and determine reservoir intervals, with the results confirmed by actual oil and gas inflows. The study reveals that different combinations of logging curves yield slightly different reservoir intervals, indicating the presence of multiple porosity types within the geological section. Analysis of current international and Ukrainian research indicates that combining different GWS methods with mathematical models and machine learning techniques is the most effective way to enhance reservoir identification in complex carbonate formations. The results confirm the practical effectiveness and applicability of the proposed methodology in geological modelling, resource assessment and field development planning in oil and gas condensate fields with similar geological conditions. The study also emphasises the need to further develop integrated GWS data interpretation techniques adapted to the local features of reservoir rocks, in order to improve the accuracy with which they can be identified within the geological sections of wells.