In light of the huge investments needed to acquire sediment cores and the growing need of energy-providing companies to predict reservoir quality, it is remarkable that the standard workflow in core analysis has not been optimized to extract as much information from cores as possible. The Integrated Core Analysis (ICA) protocol presented in this study provides a statistical framework for multivariate calibration and prediction of a wide range of properties measured in core, based on integration of high-resolution X-ray fluorescence core-scanning (XRF-CS) proxy records with records of sparsely sampled petrophysical data and sedimentological core descriptions. The downscaling of numerical data involves the application of invertible transformations to remove range constraints on data, followed by calibration with Partial Least Squares regression through cross validation. Categorical data (i.e. lithofacies) are downscaled by associating each class with a statistical model based on the XRF-CS data. All data points are reassigned to their most likely class by applying Quadratic Discriminant Analysis. The result of the ICA is a multivariate data set in which all properties are specified at the same high resolution along the core with their prediction uncertainties. Downscaling of all variables to 1-cm vertical resolution permits investigation of the variability among petrophysical properties, geochemical proxies, and lithofacies memberships. Petrographic analysis is fundamental for interpretation of the XRF-CS records (element-mineral affinity) and for understanding the sedimentological controls on predicted petrophysical properties. Application of the ICA protocol to a 32 m thick, heterogeneous, Upper Carboniferous fluvial sandstone interval resulted in a near 30-fold increase of the petrophysical data base, which allowed identification of the main depositional and diagenetic controls on the spatial distribution of reservoir quality. Successful implementation of the novel ICA protocol will greatly increase the economic value of legacy core data in studies that aim to re-use depleted hydrocarbon reservoirs.@en

In Reservoir Quality (RQ) studies, only cores provide an intact sample of the sedimentary rock, which permits various parameters, such as mineralogical composition, texture, diagenetic alterations and porosity‐permeability distribution to be measured. However, state‐of‐the‐art routine core analysis (RCA) workflow for RQ prediction is still based on a mere collection of such measurements acquired with different analytical techniques and at different resolutions/scales. This protocol does not include operator‐bias evaluation and usually fails at integrating the continuous sedimentological core description with spot measurements on plugs and thin sections. Moreover, RCA data rarely have verified uncertainty specifications, thus hampering statistically‐rigorous extrapolation of spot measurements such as petrographic description, to the entire reservoir volume. Because many sediment properties are interrelated, e.g. porosity is governed by grain size distribution, which in turn is related to mineralogical composition with a specific chemical expression, an integrated analysis of all these data is the best way to understand and predict their behaviour. Non‐destructive, in‐situ hyperspectral X‐ray fluorescence core‐scanning (XRF‐CS) technology provides a spatially continuous, cm‐scale “big‐data” environment in which the relationship between the different variables can be statistically explored. Based on this approach, we have integrated the sedimentological description of a fluvial Carboniferous core with the quantitative petrographic and chemical (i.e. inductively coupled plasma optical emission spectrometry) analysis and plug‐derived poro‐perm measurements, using the XRF-CS output. The calibration (Weltje & Tjallingii, 2008; Weltje et al., 2015; Bloemsma, 2015) and multivariate statistical analysis of these integrated data allowed us to identify the different diagenetic zones affecting reservoir quality, and to predict their occurrence across three different reservoir intervals. This is the first step of the so‐called Integrated Core Analysis (ICA) workflow that we are developing, which intends to overcome some of the main above mentioned issues.@en