Identification of Reservoir Rocks in the Geological Section of the Visean Deposits of the Lypovodolynske Field

Abstract

The article addresses the identification of reservoir rocks within the Viséan deposits of the Lypovodolynske oil and gas condensate field based on an integrated analysis of well logging data (geophysical well surveys, GWS). The Viséan stage is represented by lower and upper sublevels, consisting of terrigenous-carbonate sequences that form several prospective productive horizons. The study focuses on two types of reservoir rocks: terrigenous (sandstones and siltstones) and carbonate (limestones of the lower Viséan “plate”), which differ in lithological composition, pore space structure, and physical properties. Terrigenous reservoirs exhibit relatively uniform porosity and clear response in geophysical fields, allowing efficient identification using traditional methods. In contrast, the recognition of carbonate reservoirs is complicated by heterogeneous porosity, including secondary fracture and cavernous porosity, affecting the representation of geological parameters in GWS data.

This work proposes a comprehensive methodology for correlating baseline and normalized (scaled) curves of various geophysical methods (gamma-ray logging, acoustic logging, lateral logging, neutron-gamma logging, and pulsed neutron-neutron logging) to improve the accuracy of reservoir layer identification. The approach identifies potential reservoir intervals when the normalized curve values exceed the baseline, enabling precise delineation of terrigenous and carbonate layers. Using well No. 26-Lypovodolynska as a case study, a logging curve panel was constructed, and reservoir intervals were determined, results which were 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 shows that integrating various GWS methods with mathematical models and machine learning techniques is the most promising approach to improving reservoir identification in complex carbonate formations.

The results confirm the practical effectiveness of the proposed methodology and its applicability in geological modeling, resource assessment, and field development planning in oil and gas condensate fields with similar geological conditions. The study also emphasizes the need for further development of integrated GWS data interpretation techniques, adapted to local reservoir rock features in order to improve the accuracy of their identification within the geological section of wells.