The Toarcian Oceanic Anoxic Event (T-OAE) interval was cored at Colle di Sogno and Gajum in the Lombardy Basin (Southern Alps, northern Italy). The Sogno and Gajum cores recovered 26.83 and 31.18 stratigraphic metres, respectively, of pelagic sediments consisting of marly limestones, marlstone, marly claystone, and black shale. Drilling at both sites resulted in 100 % recovery of unweathered material. The pelagic succession comprises a relatively expanded black shale interval of 4.98 m in the Sogno core and 15.35 m in the Gajum core, with lower and upper boundaries without evidence of hiatuses. The Sogno and Gajum cores can be considered reference sections for the pelagic lower Toarcian interval of the western Tethys and will provide high-resolution micropaleontological, inorganic and organic geochemical, isotopic multiproxy data. Integrated stratigraphy and cyclostratigraphy are predicted to result in estimates of durations and rates to model the ecosystem resilience to the extreme perturbations of the T-OAE and gain a better understanding of current global changes and help provide better projections of future scenarios.Published by Copernicus Publications on behalf of the IODP and the ICDP.

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In carbonate reservoirs, the estimation of a reliable permeability log is a long-standing problem mainly because of the inherent multi-scale heterogeneities. The conventional approach relies on core-calibrated algorithms applied to open-hole (OH) logs. In general, this static log-based prediction uses to underestimate the actual dynamic performance of the wells and an ad-hoc integration with production logging tool (PLT) and well test (WT) analyses represents a required step to correct the initial estimation. However, it is critical, and at once challenging, to define the relation between dynamic-based corrections and OH characterization outcomes. An elegant solution is here proposed that makes use of predictive analytics applied on special core analyses (SCAL), nuclear magnetic resonance (NMR) log modeling, and multi-rate PLT/WT interpretations. The methodology is presented for a complex oil-bearing carbonate reservoir and it starts with an advanced NMR characterization performed downhole for more than 100 wells, and after a rigorous calibration with SCAL. The main outputs are a robust porosity partition (in terms of micropore, mesopore and macropore contributions), and a physics-based permeability formula. Although the match with core data demonstrates the reliability of the applied NMR rock characterization, log permeability underestimates the actual dynamic performances obtained from WT, as expected. At the same time, multi-rate PLT data from more than 150 wells are used to compute an apparent permeability value for each perforated interval, automatically consistent with the associated WT interpretation. Finally, both static and dynamic characterization outputs are used as inputs for a dual random forest (RF) template. In detail, the first RF algorithm learns through experience how NMR porosity partition and core-calibrated permeability are related to PLT/WT apparent permeability values, after considering the proper change of scale. Next, the second RF is utilized to estimate the uncertainty associated to the previous step, still in a completely data-driven way. Hence, the so-defined dual model provides a continuous automatic flow-calibrated permeability log, together with its confidence interval, directly from static NMR responses. The presented methodology allows dynamic data to be incorporate efficiently into a static workflow by means of a pure data-driven analytics approach. The latter is able to shed light on the statistical relationships hidden in the available datasets, thus leading to a more accurate permeability estimation. It is also shown how this provides fundamental information for perforation strategy optimization and reservoir modeling purposes in such carbonate rocks.

This paper discusses how an integrated data-driven analytics (DDA), mechanistic petrophysical and mineralogical modeling can enhance the characterization of reservoirs selected for Carbon Capture, Utilization and Storage (CCUS) projects. The approach makes use of exhaustive core datasets to generate synthetic mineralogical curves at wells, hence expanding the available log information. This allows a robust and complete quantitative analysis of storage and sealing intervals through a DDA-informed physics-based methodology. The growth of interest around CCUS pushes towards in-depth analyses of reservoir layers, as well as of the sealing ones. In brown fields the available open-hole (OH) logs might not be enough for a detailed lithological and petrophysical characterization, which is mandatory to establish the storage capacity of the assets. Hence, the proposed methodology starts from X-Ray Powder Diffraction (XRD) core data representative of the field under investigation for both reservoir and non-reservoir sections. Next, DDA is used to generate synthetic volumetric fractions of given minerals after an ensemble learning relating core mineralogy and selected logs. The DDA-based log mineralogy and the other available OH logs are then input for conventional mechanistic models to obtain final petrophysical and mineralogical properties. The added value is demonstrated through a real case study, where a CCUS project is ongoing for a mature field. From the mineralogical standpoint, experimental studies performed for several cored wells show a wide composition variety with different coexisting phases including carbonates, silicates, feldspars, micas and clays. The main criticality is represented by the OH log datasets that are often incomplete and ineffective to provide a straightforward formation evaluation consistent with the complexity highlighted by core analyses. Therefore, after the calibration of the ensemble learner with hundreds of XRD data, high-frequency dielectric and reprocessed nuclear logs, the DDA steps have been successfully applied to about a hundred wells for obtaining synthetic mineralogical curves. These augment the information of the available measured logs and allow the definition of a physics-based interpretation model able to properly characterize both the reservoir and caprock layers. In addition, the most reactive facies to carbon dioxide are recognized and represent another significant step forward to evaluate the sealing efficiency and integrity over time at field scale. The presented workflow is deemed able to provide a strong mineralogical and petrophysical characterization template in case of incomplete/not exhaustive wellbore dataset. The outcomes are fundamental for several aspects in CCUS projects, including reservoir modeling, geomechanics, geochemistry, monitoring phases and risk management.