Search Results (294)

Preservation design is characterized by high interactions among different skills, including both architectural and engineering field. When the architectural heritage is composed of the ruins of a medieval fortress, the contribution of archeological studies is fundamental to recognize the different construction phases of the building. This work presents the most recent stratigraphic analyses conducted on the fortress of Vogogna, a military masonry castle in Ossola Valley, Piedmont, whose origin is lost in time and provides further support to define the correct interpretation of the architectural artifact. Previous studies showed several shortcomings concerning the historical evolution of the structure and a precise geometrical survey. The authors developed a geometrical model of the archeological site, through advanced survey techniques, and analyzed the historical maps of the cadasters to investigate additions and transformations of the abandoned fortress and its connection with the rural and natural surrounding fields. The updated information provided new indications for the past uses of the building, and the digital model allowed further considerations on the geometrical characteristics of the structures, addressing some choices for the final reuse proposal for the site, today at the center of a wider project to enhance the cultural heritage in the Vogogna area. Full article

Fluid flow prediction in clastic heterogeneous reservoirs is a universal issue, especially when diagenetic development supplants structural and depositional controls. We consider this issue in the Middle Miocene Belayim Formation of the Gulf of Suez, a principal syn-rift reservoir where extreme, diagenetically induced pore system heterogeneity thwarts production. Although fault compartmentalization is understood as creating first-order traps, sub-seismic diagenetic controls on permeability anisotropy and reservoir within these traps are not restricted. This study uses a comprehensive set of petrophysical logs (ray gamma, resistivity, density, neutrons, sonic) of four key wells in the western field of Tawila (Tw-1, Tw-3, TW-4, TN-1). We apply an integrated workflow that explicitly derives permeability from petrophysical logs and populates it within a seismically defined structural framework. This study assesses diagenetic controls over reservoir permeability and fluid flow. It has the following primary objectives: (1) to characterize complicated diagenetic assemblage utilizing sophisticated petrophysical crossplots; (2) to quantify the role of shale distribution morphologies in affecting porosity effectiveness utilizing the Thomas–Stieber model; (3) to define hydraulic flow units (HFUs) based on pore throat geometry; and (4) to synthesize these observations within a predictive 3D reservoir model. This multiparadigm methodology, involving M-N crossplotting, Thomas–Stieber modeling, and saturation analysis, deconstructs Tawila West field reservoir complexity. Diagenesis that has the potential to destroy or create reservoir quality, namely the general occlusion of pore throats by dispersed, authigenic clays (e.g., illite) and anhydrite cement filling pores, is discovered to be the dominant control of fluid flow, defining seven unique hydraulic flow units (HFUs) bisecting the individual stratigraphic units. We show that reservoir units with comparable depositional porosity display order-of-magnitude permeability variation (e.g., >100 mD versus <1 mD) because of this diagenetic alteration, primarily via pore throat clogging resulting from widespread authigenic illite and pore occupation anhydrite cement, as quantitatively exemplified by our HFU characterization. A 3D model depicts a definitive NW-SE trend towards greater shale volume and degrading reservoir quality, explaining mysterious dry holes on structurally valid highs. Critically, these diagenetic superimpressions can replace the influence of structural geometry on reservoir performance. Therefore, we determine that a paradigm shift from a highly structured control model to an integrated petrophysical and mineralogical approach is needed. Sweet spot prediction relies upon predicting diagenetic facies distribution as a control over permeability anisotropy. Full article

Background/Objectives—Traditional forensic investigations often analyze digital, physical, and criminological evidence separately, leading to fragmented timelines and reduced accuracy in reconstructing complex events. To address these gaps, this study proposes the Digital Stratigraphy Framework (DSF), inspired by archaeological stratigraphy, to integrate heterogeneous evidence into structured, temporally ordered layers. DSF aims to reduce asynchronous inconsistencies, minimize false associations, and enhance interpretability across digital, behavioral, geospatial, and excavation evidence. Methods—DSF employs Hierarchical Pattern Mining (HPM) to detect recurring behavioral patterns and Forensic Sequence Alignment (FSA) to synchronize evidence layers temporally and contextually. The framework was tested on the CSI-DS2025 dataset containing 25,000 multimodal, stratified records, including digital logs, geospatial data, criminological reports, and excavation notes. Evaluation used 10-fold cross-validation, Bayesian hyperparameter tuning, and structured train-validation-test splits. Metrics included accuracy, precision, recall, F1-score, and Stratigraphic Reconstruction Consistency (SRC), alongside ablation and runtime assessments. Results—DSF achieved 92.6% accuracy, 93.1% precision, 90.5% recall, 91.3% F1-score, and an SRC of 0.89, outperforming baseline models. False associations were reduced by 18%, confirming effective cross-layer alignment and computational efficiency. Conclusions—By applying stratigraphic principles to forensic analytics, DSF enables accurate, interpretable, and legally robust evidence reconstruction. The framework establishes a scalable foundation for real-time investigative applications and multi-modal evidence integration, offering significant improvements over traditional fragmented approaches. Full article

The indigenous community of Quisquinchir, in Saraguro (Loja, Ecuador), is facing a process of transformation of the rural Andean landscape associated with internal and external migration, as well as the influence of foreign architectural models. The new buildings symbolize, in the collective imagination, modernity and progress; however, they are alien to the natural environment characterized by the practice of agricultural and livestock activities. Although previous studies have described the loss of Andean vernacular architecture, its recent evolution in clear typologies has not been systematized. The objective of this study is to assess the current state of traditional dwellings and understand how migration reconfigures the landscape, collective memory, building traditions, and cultural identity of their inhabitants. Based on direct observation, photographic and stratigraphic analysis, and secondary sources, five typologies were identified: traditional one-story, traditional two-story, hybrid one-story, hybrid two-story, and eclectic. This classification indicates the replacement of earthen walls with cement blocks in 37% of the dwellings and of tile roofs with zinc roofs in 29%. However, 35% of the houses retain their traditional morphology and materials. These results and their classification are fundamental contributions to the design of local public policies that generate adequate interventions respectful of the environment. Full article

The West Qinling Orogenic Belt (WQOB) contains a sedimentary succession that is approximately 15 km thick, spanning from the Carboniferous to the Jurassic period. This succession offers critical insights into the tectonic evolution of the Paleo-Tethys Ocean. While previous models have suggested various depositional environments, the late Paleozoic to Mesozoic tectonic evolution of the WQOB is still not fully understood. In this study, we incorporate new detrital zircon U-Pb age data and whole-rock geochemical analyses from six stratigraphic units, dating back to the Carboniferous to Triassic periods in the Xiahe–Hezuo region, alongside existing datasets. The detrital zircon age spectra from the WQOB reveal three distinct groups: Devonian–Carboniferous strata exhibit dominant Neoproterozoic (~800–900 Ma) zircon populations, whereas Permian–Triassic rock samples show prominent Paleoproterozoic (1840–1880 Ma) and Archean (2450–2500 Ma) peaks. A minor Neoproterozoic component in Permian spectra disappears by the Triassic, while Jurassic–Cretaceous assemblages lack Precambrian grains. These trends reflect evolving source terranes linked to Paleo-Tethyan subduction dynamics. Furthermore, the geochemical signatures of the Devonian–Triassic clastic rocks align with the composition of upper continental crust, indicating a tectonic relationship with continental island arcs and active continental margins. By synthesizing these findings with established detrital zircon ages, magmatic records, and geophysical data, we propose that the WQOB underwent pre-Triassic tectonic evolution that was marked by pre-Triassic subduction and localized extension during the process of continental underthrusting. Full article

Hydraulic fracturing technology significantly enhances reservoir conductivity by creating artificial fractures, serving as a crucial means for the economically viable development of low-permeability reservoirs. Accurate prediction of post-fracturing productivity is essential for optimizing fracturing parameter design and establishing scientific production strategies. However, current limitations in understanding post-fracturing production dynamics and the lack of efficient prediction methods severely constrain the evaluation of fracturing effectiveness and the adjustment of development plans. This study proposes a machine learning-based method for predicting post-fracturing productivity in multi-layer commingled production wells and validates its effectiveness using a key block from the PetroChina North China Huabei Oilfield Company. During the data preprocessing stage, the three-sigma rule, median absolute deviation, and density-based spatial clustering of applications with noise were employed to detect outliers, while missing values were imputed using the K-nearest neighbors method. Feature selection was performed using Pearson correlation coefficient and variance inflation factor, resulting in the identification of twelve key parameters as input features. The coefficient of determination served as the evaluation metric, and model hyperparameters were optimized using grid search combined with cross-validation. To address the multi-layer commingled production challenge, seven distinct datasets incorporating production parameters were constructed based on four geological parameter partitioning methods: thickness ratio, porosity–thickness product ratio, permeability–thickness product ratio, and porosity–permeability–thickness product ratio. Twelve machine learning models were then applied for training. Through comparative analysis, the most suitable productivity prediction model for the block was selected, and the block’s productivity patterns were revealed. The results show that after training with block-partitioned data, the accuracy of all models has improved; further stratigraphic subdivision based on block partitioning has led the models to reach peak performance. However, data volume is a critical limiting factor—for blocks with insufficient data, stratigraphic subdivision instead results in a decline in prediction performance. Full article

The distribution and quality of the Lower Cretaceous reservoir sandstone units of the Mesozoic–Cenozoic Scotian Basin, offshore eastern Canada, is well known in producing fields but difficult to extrapolate to less-explored areas of the deep-basin floor. Prediction of reservoir risk is complicated by salt tectonism and the strong influence of diagenesis on reservoir quality. This study investigates the burial diagenetic dissolution of detrital K-feldspar in the subarkosic sandstones and the preservation of the resulting secondary porosity. K-feldspar abundance declines with increasing depth, creating secondary porosity, which in open systems is preserved but in closed systems is clogged by carbonates and clays. The distribution of detrital K-feldspar has been simulated using forward stratigraphic modeling and is compared to thermal modeling, fault mapping, and sand distribution to determine the risk due to the reservoir quality, illustrated as common risk segment maps. Sand deposits have the lowest risk of poor reservoir quality along the shelf edge and upper slope of the central and western basin, where growth faulting created an open diagenetic system. This novel combination of petrographic study and forward modeling has applications to other regions where diagenesis has a strong influence on the reservoir quality, such as the Gulf of Mexico. Full article

On 8 June 2025, the Mw 6.4 Paratebueno earthquake struck the eastern foothills of the Eastern Andes, Colombia. The event occurred near the Guaicáramo fault, along the eastern margin of the Eastern Cordillera fold-and-thrust belt. To investigate its rupture characteristics and tectonic implications, we utilized ALOS-2 and Sentinel-1 SAR data to derive coseismic deformation fields. Source geometry and slip distribution were inverted with the Okada dislocation model, and static Coulomb failure stress change were calculated to assess the triggering relationship with the 2023 Mw 6.2 Meta-Cundinamarca earthquake. The results reveal maximum line-of-sight displacements of 43 cm, 23 cm and 32 cm, respectively, caused by a northwest-dipping blind reverse fault (strike ~213°, dip 58°) with ~5 m maximum slip concentrated at depths of 8–12 km, without surface rupture. Combining geological and stratigraphic evidence, including regional structures and sedimentary cover thickness, this event implies a transition from a normal fault to reverse fault due to ongoing shortening of fold-and-thrust belt, consistent with a thick-skinned tectonic origin. Coulomb stress modeling suggests the 2023 event promoted the 2025 rupture, and the combined effect of the two events further increased stress on the southeastern Guaicáramo fault, implying elevated seismic hazard. Full article

Thin-bedded carbonate reservoirs face significant challenges in characterization and development due to their thin formation thickness, strong interlayer heterogeneity, and rapid sedimentary transformation. In recent years, horizontal wells have played an increasingly important role in improving the productivity of thin-bedded carbonate reservoirs. However, building accurate geological models from horizontal well data is a major challenge for geoscientists. Using Middle East Oilfield A as a case study, this paper analyzes the specific challenges of horizontal well geomodeling and proposes a dedicated strategy for integrating horizontal well-derived constraints into the geological modeling workflow. To address the challenges of structural modeling constrained by horizontal well data, this study proposes three methodologies: stratigraphic layer iteration, virtual control point generation, and localized grid refinement. These techniques collectively enable the construction of a higher-fidelity structural framework that rigorously honors hard well data constraints while incorporating geological plausibility. To address the challenges posed by the spatial configuration of vertical and horizontal wells and the dominant trajectory patterns of horizontal wells, this study introduces two complementary approaches: the exclusion of horizontal well section data (relying solely on vertical wells) and the selective extraction of representative horizontal well section data for variogram derivation. These methods collectively enable the construction of a geologically realistic reservoir model that accurately captures the spatial distribution of reservoir properties. These methodologies not only effectively leverage the rich geological information from horizontal wells but also mitigate spatial clustering effects inherent to such data. Validation through development well production data confirms robust performance, providing transferable insights for reservoir characterization in analogous fields worldwide. Full article

This research presents the calibration of a constitutive model to replicate the cyclic performance of soils using a Bayesian framework. This study uses data from laboratory-conducted consolidated undrained isotropic cyclic triaxial tests and numerical tools to estimate optimal parameters by the application of Slice Sampling in a Bayesian analysis and to determinate the uncertainty of the model. For each calibrated parameter in the model, a probability distribution was obtained from the Markov chain. The means and the standard deviations from the distributions are compared with the laboratory results by the simulation of a series of consolidated undrained isotropic cyclic triaxial tests and a numerical model for a deposit that replicates the Wildlife’s stratigraphic characteristics. The calibrated model response offers a good approximation of the recorded data and the uncertainty due to the model is evaluated. The results of this study demonstrate that Bayesian calibration can reliably quantify parameter uncertainty, reveal parameter correlations that deterministic methods overlook, and improve confidence in liquefaction assessments. This probabilistic framework provides a robust basis for extending calibration to other soil types and site conditions. Full article

Long and continuous lacustrine sediments in Southwest China provide exceptional records of the Indian summer monsoon (ISM) evolution. Rock magnetic and environmental magnetic methods have significant roles in these lacustrine studies. However, lacustrine sedimentary environments are complex and magnetic mineral signatures can be altered by post-depositional processes. This study applies isothermal remanent magnetization (IRM) component unmixing methods to lacustrine sediments from the Heqing core, to identify and quantify magnetic mineral components. We analyzed 104 samples based on lithological variations and magnetic susceptibility (χ) to examine the composition of magnetic minerals and their relative contributions. Three distinct magnetic components were identified in IRM component unmixing results: a low-coercivity detrital component, a medium-coercivity authigenic component, and a hard magnetic component. Based on rock magnetic results, the medium-coercivity component was attributed to greigite. These components exhibit stratigraphic trends that reflect changes in paleoenvironmental conditions. The medium-coercivity component shows an upwards decrease, indicating a significant change in ISM science at about 1.8 Ma. The study highlights the importance of considering post-depositional processes when interpreting magnetic mineral signatures in lacustrine sediments. The CLG model, combined with conventional rock magnetic analyses, provides a rapid approach for characterizing magnetic assemblages in weakly magnetic sediments. Full article

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. Full article

This study investigates the Holocene evolution of the Laraquete-Carampangue strandplain on the tectonically active coast of south-central Chile using ground penetrating radar and light detection and ranging data. The Laraquete-Carampangue strandplain, on the tectonically active coast of south-central Chile, is a rare accretionary feature in a region dominated by rocky shorelines and limited sediment supply. The light detection and ranging data-derived digital elevation model reveals a complex geomorphology comprising 52 beach ridges, aeolian dunes, and fluvial paleochannels, while ground penetrating radar radargrams uncover marine and aeolian facies influenced by past seismic and climatic events. We interpret these units in the frame of past seismic and climatic events. Our geomorphological and stratigraphic findings suggest that the strandplain progradation was driven by relative sea-level changes associated with Holocene seismic cycles and climate change. We propose that the transition from drier to humid conditions in the late Holocene triggered the onset of dune formation at the end of the Little Ice Age. This integrated approach highlights the interplay of tectonic and climatic forcings in shaping coastal landforms, offering insights into their long-term response to environmental change. Full article

This study presents the first integrated, basin-scale analysis of helium distribution and its geological controls within the Ordos Basin, one of China’s most prospective cratonic gas provinces. Through comprehensive sampling and experimental analysis of the helium content in natural gas, combined with high-resolution gravity and magnetic data processed using the normalized vertical derivative of the total horizontal derivative (NVDR-THDR) method, we reveal significant spatial heterogeneity in helium enrichment. The results show that helium concentrations are generally higher along the basin margins and structurally complex zones, while central areas are relatively depleted. Helium primarily originates from the radioactive decay of uranium (U) and thorium (Th) within metamorphic and magmatic basement rocks. Fault systems act as efficient vertical migration pathways, enabling deep-sourced helium to accumulate in structurally and stratigraphically favorable traps. This study proposes a new enrichment mode, “basement-sourced helium generation, fault-mediated migration, and caprock-controlled preservation”, which highlights the synergistic roles of basement lithology, deep-seated faults, and sealing capacity in controlling helium distribution. This model is supported by the observed alignment of high helium concentrations with zones of strong basement magnetism and major fault intersections. These findings advance our understanding of helium accumulation mechanisms in stable cratonic settings and provide a predictive framework for helium exploration in similar geological contexts worldwide. Full article

Cone Penetration Tests with pore pressure measurements (CPTu) are widely used in geotechnical site investigations due to their high-resolution profiling capabilities. However, traditional interpretation methods—such as the Soil Behavior Type Index ($I_c$)—often fail to capture the internal heterogeneity typical of mining tailings deposits. This study presents a machine learning-based approach to enhance stratigraphic interpretation from CPTu data. Four unsupervised clustering algorithms—k-means, DBSCAN, MeanShift, and Affinity Propagation—were evaluated using a dataset of 12 CPTu soundings collected over a 19-year period from an iron tailings dam in Brazil. Clustering performance was assessed through visual inspection, stratigraphic consistency, and comparison with $I_c$-based profiles. k-means and MeanShift produced the most consistent stratigraphic segmentation, clearly delineating depositional layers, consolidated zones, and transitions linked to dam raising. In contrast, DBSCAN and Affinity Propagation either over-fragmented or failed to identify meaningful structures. The results demonstrate that clustering methods can reveal behavioral trends not detected by $I_c$ alone, offering a complementary perspective for understanding depositional and mechanical evolution in tailings. Integrating clustering outputs with conventional geotechnical indices improves the interpretability of CPTu profiles, supporting more informed geomechanical modeling, dam monitoring, and design. The approach provides a replicable methodology for data-rich environments with high spatial and temporal variability. Full article