In this paper, the low-permeability reservoir was subdivided into several units based on three models; in the first model, porosity, permeability, pore sizes, and shale volume were used as an input in the heterogeneous rock analysis clustering workflow to define rock units; in the second model, rock types were defined using flow zone index. The third flow unit discriminator was proposed by the author; the model is based on relation between porosity, permeability, irreducible water saturation, and pore size distribution. Also, Wyllie–Rose equation for permeability in tight reservoir was core-calibrated, and coefficients e, d, and Kw were established. The reservoir is built of thin layers of sandstones with variable porosity, permeability, pore sizes, and irreducible water. The research was performed in two wells where as input well log data, the laboratory results of mercury injection porosimetry, permeability measurements, and nuclear magnetic resonance data were used. Furthermore, it was investigated whether the presence of fractures identified on XRMI images were strictly related to one flow unit.

The complexity of shale formation interpretation requires an accurate evaluation of a detailed petrophysical model in association with the analysis of the geomechanical properties. Mineralogy plays an important role in controlling shale’s mechanical properties, among which one of the most problematic parameters to establish is the Biot’s coefficient. Although, this parameter is necessary to determine the magnitude of the effective stresses acting in the reservoir, it is not included in the standard protocols used in Poland. This paper presents a comprehensive petrophysical and geomechanical evaluation of the unconventional reservoirs of lower Paleozoic age formation: lower Silurian and Ordovician deposits located in the onshore part of the Baltic Basin (Poland). In this study, the Biot’s coefficient from well-log data was calculated. Initially, a calibrated rock-physics model was derived to provide a set of relationships between the elastic and petrophysical properties. Based on an accurate, calibrated petrophysical model, the effective bulk modulus along with the Biot’s coefficient and horizontal stresses were calculated. Ultimately, the tectonic regime was determined. Using full-waveform sonic data analysis, the horizontal anisotropy was estimated. The directions of maximum and minimum horizontal stress were established based on several X-tended Range Micro Imager images of breakout structures and drilling-induced fractures.

The petrophysical properties of Miocene mudstones and gas bearing-heteroliths were the main scope of the work performed in one of the multihorizon gas fields in the Polish Carpathian Foredeep. Ten boreholes were the subject of petrophysical interpretation. The analyzed interval covered seven gas-bearing Miocene horizons belonging to Sarmatian and Badenian deposits. The water saturation in shaly sand and mudstone intervals was calculated using the Montaron connectivity theory approach and was compared with Simandoux water saturation. Additionally, the Kohonen neural network was used for qualitative interpretation of four PSUs (petrophysically similar units), which represent the deposits of comparable petrophysical parameters. This approach allowed us to identify the sediment group with the highest probability of hydrocarbon saturation. Then, the spatial distribution of PSUs and reservoir parameters was carried out in Petrel. The resolution of the model was selected to reflect the variability of log-derived parameters. The reconstruction of the spatial distribution of shale volume, porosity, and permeability was performed with standard parametric modeling procedures using the Gaussian random function simulation stochastic algorithm, while PSU distribution and hydrocarbon saturation (SH) required a separate approach. The distribution into PSU groups was carried out by facies classification. Predefined ranges of clay volume, effective porosity, and permeability were used as discriminators to achieve spatial distribution of the PSU groups. The spatial distribution of hydrocarbon saturation was performed by creating the meta-attribute of this parameter and then reducing the derived pseudo-saturation model to physical values. Results included the creation of maps of hydrocarbon saturation that show the preferable areas with the highest hydrocarbon saturation for each gas horizon.

This study presents a novel approach to the parametrization of 3D PETRO FACIES and SEISMO FACIES using supervised and unsupervised learning, supported by a coherent structural and stratigraphic framework, to enhance understanding of the presence of hydrocarbons in the Dzików–Uszkowce region. The prediction relies on selected seismic attributes and well logging data, which are essential in hydrocarbon exploration. Three-dimensional seismic data, a crucial source of information, reflect the propagation velocity of elastic waves influenced by lithological formations and reservoir fluids. However, seismic response similarities complicate accurate seismic image interpretation. Three-dimensional seismic data were also used to build a structural–stratigraphic model that partitions the study area into coeval strata, enabling spatial analysis of the machine learning results. In the 3D seismic model, PETRO FACIES classification achieved an overall accuracy of 80% (SD = 0.01), effectively distinguishing sandstone- and mudstone-dominated facies (RT1–RT4) with F1 scores between 0.65 and 0.85. RESERVOIR FACIES prediction, covering seven hydrocarbon system classes, reached an accuracy of 70% (SD = 0.01). However, class-level performance varied substantially. Non-productive zones such as HNF (No Flow) were identified with high precision (0.82) and recall (0.84, F1 = 0.83), while mixed-saturation facies (HWGS, BSWGS) showed moderate performance (F1 = 0.74–0.81). In contrast, gas-saturated classes (BSGS and HGS) suffered from extremely low F1 scores (0.08 and 0.12, respectively), with recalls as low as 5–7%, highlighting the model’s difficulty in discriminating these units from water-saturated or mixed facies due to overlapping seismic responses and limited training data for gas-rich intervals. To enhance reservoir characterization, SEISMO FACIES analysis identified 12 distinct seismic facies using key attributes. An additional facies (facies 13) was defined to characterize gas-saturated sandstones with high reservoir quality and accumulation potential. Refinements were performed using borehole data on hydrocarbon-bearing zones and clay volume (VCL), applying a 0.3 VCL cutoff and filtering specific facies to isolate zones with confirmed gas presence. The same approach was applied to PETRO FACIES and a new RT facie was extracted. This integrated approach improved mapping of lithological variability and hydrocarbon saturation in complex geological settings. The results were validated against two blind wells that were excluded from the machine learning process. Knowledge of the presence of gas in well N-1 and its absence in well D-24 guided verification of the models within the structural–stratigraphic framework.

Geomechanical characterization plays a key role in optimizing the stimulation treatment of tight reservoir formations. Petrophysical models help classify the reservoir rock as the conventional or unconventional type and determine hydrocarbon-saturated zones. Geomechanical and petrophysical models are fundamentally based on well-log data that provide reliable and high-resolution information, and are used to determine various relationships between measured borehole parameters and modeled physical rock properties in 3D space, with the support of seismic data. This paper presents the geomechanical characterization of the Middle Cambrian (Cm2) sediments from Eastern Pomerania, north Poland. To achieve the aim of this study, 1D well-log-based and 3D models based on seismic data of the rocks’ petrophysical, elastic, and strength properties, as well as numerical methods, were used. The analysis of the Middle Cambrian deposits revealed vertical and horizontal heterogeneity in brittleness, the direction of horizontal stresses, and the fracturing pressure required to initiate hydraulic fractures. The most prone to fracturing is the gas-saturated tight sandstones belonging to the Paradoxides Paradoxissimus formation of Cm2, exhibiting the highest brittleness and highest fracturing pressure necessary to stimulate this unconventional reservoir formation.