 Machine Learning Approaches for Predicting Lithological and Petrophysical Parameters in Hydrocarbon Exploration: A Case Study from the Carpathian ForedeepDrozd Arkadiusz (),
Topór Tomasz,
Lis-Śledziona Anita and
Sowiżdżał Krzysztof
Energies, 2025, vol. 18, issue 17, 1-28 Abstract: 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. Keywords: seismic facies; heterolithes; gas saturated zones; machine learning; hydrocarbon prediction; reservoir geology (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:17:p:4521-:d:1732881 Access Statistics for this article Energies is currently edited by Ms. Cassie Shen More articles in Energies from MDPI |
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