During Middle Triassic times, the Peri-Tethys Basin bordered the north-western Tethys shelf and was connected to the open Tethys Ocean via three seaways. Today, Lower Muschelkalk carbonates of this epeiric sea cover large parts of Central Europe, documenting the evolution of a low-relief, homoclinal, mud-dominated ramp system during the Anisian. In view of their geotectonic/climatic setting, depositional processes, facies architecture, and distribution, the rocks are considered as an outcrop analogue for layer-cake reservoirs of world-wide importance, e.g. the Permo-Triassic Khuff or Jurassic Arab carbonates in the Middle East. In general, two different reservoir types and their interplay might be considered: The proximal stacks of muddy dolostones (NW part of the basin) and the more distally developed grainy limestones (central and SE part of the basin). The rather uncommon depositional setting with minor relief and minimal accommodation contributed to both, the stratal and lateral facies development, and to unusual and possibly even "inverted" facies patterns with thick, grainy facies found in the more distal environments. Based on litho- and microfacies analyses, six main facies types are distinguished, building characteristic cyclic facies successions of different hierarchies. The stratal architecture of small-scale depositional sequ ences systematically changes in relation to their relative proximal-distal position on the Muschelkalk ramp system. Here, we present porosity and permeability data of the different facies types and within the basin-wide sequence stratigraphic framework. Dolo-wacke-/packstones and peloid grainstones attain the highest porosities of up to 24%, whereas bioclastic grainstones show porosities of up to 8%. The platy and nodular mud-/wackestone and most of the bioclastic wacke-/packstones typically show porosities below 2%. Even in the most porous strata, permeabilities do not exceed 10 mD, and only a few carbonates show higher permeabilities up to 90 mD. Within large-scale, third-order depositional sequences late highstand deposits represent the most permeable sediments.
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Fluid substitution plays the key role in reservoir characterization, leading to enhance understanding of the influence of fluids on seismic parameters. In general, fluid substitution tool assumes that the Earth is as an isotropic medium, which may not represent the practical field situation. Nevertheless, anisotropic fluid substitution provides important insights into the processes that control the anisotropic seismic response of a fractured rock when subjected to CO2 injection for enhanced oil recovery and its geological sequestration. Here, we examine the influence of fluid substitution in a porous yet fractured reservoir for quantitative interpretation of seismic data. This investigation involves anisotropic Gassmann’s equation and linear slip theory for fluid substitution in a transversely isotropic media with a horizontal axis of symmetry (HTI). We present a synthetic case by conceptualizing a double-layered half-space model with upper layer as shale and bottom layer as HTI sandstone, representing an Indian mature reservoir. The effects of variation in background porosity and fracture weaknesses on anisotropic (Thomsen’s) parameters, acoustic parameters including amplitude variation with angle have also been discussed. We observe that brine and oil sands to be associated with the highest elastic moduli, while CO2 sands exhibit contrasting trend. It is noteworthy that CO2 is more sensitive to fracture weakness when compared to the other reservoir fluids such as hydrocarbons and brines, as P-wave moduli (as much as 37.1%) and velocity (as high as 12.2%) reduces significantly with the increase in fracture weakness. Further, Gassmann’s assumption is validated as we noticed unchanged values in shear-wave moduli and shear-wave splitting parameter (γ) for various fluid types.
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The present research applies six empirical, three statistical, and two soft computing methods to predict water saturation of an oil reservoir. The employed empirical models are ‘Archie (Trans AIME 146(1):54–62, 1942),’ ‘DeWitte (Oil Gas J 49(16):120–134, 1950),’ ‘Poupon et al. (J Petrol Technol 6(6):27–34, 1954),’ ‘Simandoux (Revue deI’Institut Francais du.Petrol, 1963),’ ‘Poupon and Leveaux (1971),’ and ‘Schlumberger (Log interpretation principles/applications, p. 235, 7th printing. Houston, 1998)’; statistical methods are ‘multiple variable regression,’ ‘fine tree, medium tree, coarse tree-based regression tree,’ and ‘bagged tree, boosted tree-based tree ensembles’; and soft computing techniques are ‘support vector machine (SVM)’ and ‘Levenberg–Marquardt (LM), Bayesian regularization (BR), and scaled conjugate gradient (SCG)- based artificial neural network (ANN).’ In addition, log variables are ranked based on their significance in water saturation modeling. To achieve the goals, 521 data points are selected from three wells. Each data point has laboratory-derived core water saturation information and six well log features, such as gamma ray (GR), bulk density (RHOB), sonic travel time (DT), true resistivity (LLD), neutron porosity (φN), and Depth. Statistical indexes, namely regression coefficient, mean squared error, root mean squared error, average absolute percentage error, minimum absolute error percentage, and maximum absolute error percentage, are used to compare the prediction efficiency of study methods. Results show that the empirical models provide exceedingly poor prediction efficiency. Within the study models, fine tree, medium tree-based regression tree; bagged tree, boosted tree-based tree ensembles; fine Gaussian SVM; ANN with LM; and ANN with BR are very efficient predictive strategies. The log ranking reveals that GR and DT are the most important, whereas RHOB and φN are the least vital predictor variables in water saturation prediction.
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