DOI•
Geophysical Aspects of Reservoir Characterization of Tight Gas Play in the Dnieper-Donets Basin
23 Nov 2021-
About: The article was published on 2021-11-23 and is currently open access. It has received None citations till now. The article focuses on the topics: Tight gas & Reservoir modeling.
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TL;DR: In this article, the authors analyzed new velocity data in addition to literature data derived from sonic log, seismic, and laboratory measurements for clastic silicate rocks and demonstrated simple systematic relationships between compressional and shear wave velocities.
Abstract: New velocity data in addition to literature data derived from sonic log, seismic, and laboratory measurements are analyzed for clastic silicate rocks. These data demonstrate simple systematic relationships between compressional and shear wave velocities. For water-saturated clastic silicate rocks, shear wave veloci ty is approximately linearly related to compressional wave velocity and the compressional-to-shear velocity ratio decreases with increasing compressional velocity. Laboratory data for dry sandstones indicate a nearly constant compressional-to-shear velocity ratio with rigidity approximately equal to bulk modulus. Ideal models for regular packings of spheres and cracked solids exhibit behavior similar to the observed water saturated and dry trends. For dry rigidity equal to dry bulk modulus, Gassmann's equations predict velocities in close agreement with data from the water-saturated
1,379 citations
TL;DR: In this article, a new, practical and theoretically correct methodology is proposed for identi$cation and characterization of hydraulic units based on a modified Kozeny-Carmen equation and the concept of mean hydraulic raditis.
Abstract: Understanding complex variations in pare geomet~ within different Iithofacies is the key to improved reservoir description and exploitation. Core data provide in~ornration on various depositional and diagenetic controls on pore geometry. Variations in pore geometrical attributes in rum, de$ne the existenceof distinct zones(hydraulic units) with similar f?uid-jlow characteristics. Classic discrimination of mck types has been based on subjective geological observations and on empirical relationships between the log of permeability versus porosity. Howevec for any porosity within a given mck type,permeability can vary by several orders of nragnitnde, which indicates the existenceof severalflow units. In this papec a new, practical and theoretically correct methodology is proposedfor identi$cation and characterization of hydraulic units widtin mappable geological units (facies). The technique is based on a modified Kozeny-Carmen equation and the conceptof mean hydraulic raditis. The equation indicatesIhat for any hydraulic unit, a log-log p!ot of a “Reservoir Quality index,” (RQI), which is equal to 0.0314 ~. versus a “Normalized PorosityIndex” (+=) which is equal to WI-W should yield a straight line with a unit slope. 7he intercept of the unit slope line with +Z = 1, designated as the “FIow Zme Indicator” (M), is a unique parameter for each hydraulic unit. RQI, 4, and FZI are based on stressed potvsity and permeability data measuredon core samples.
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TL;DR: In this article, the authors make a synthetic review of the pros and cons associated with each Pc classification method and propose an innovative way to take the best of several known Pc classifiers in defining a straightforward technique to derive robust Pc clusters and equations.
Abstract: Lab capillary pressure Pc measurements are routinely performed on core plug samples. Applications of Pc curve measurements are mainly to derive some relationships between water saturation Sw and the height H above the Free-Water-Level FWL. The resulting correlations can then be cross-checked vs. the Elogs-derived Sw so as to confirm the overall consistency of the Sw model. Ultimately, the Sw - H relationship can be used as input to the 3D model. In addition, screening of Pc – Sw relationships often allows defining robust rock-types, based on observation of Pc clusters vs. the sample porosity, permeability, mineralogy and geological facies. In the case of MICP, the rock-type classification may be further enhanced by looking at the Pore-Throat-Radius PTR distributions, related to the derivative of the Pc curve. Several methods to derive Sw – Pc relationships and classify Pc clusters are possible (e.g. Leverett, Thomeer, Johnson, Cuddy, Skelt, ..) and have been documented and published in the past. In this paper, we make a synthetic review of the pros and cons associated to each Pc classification method. Eventually, we propose an innovative way to take the best of several known Pc classification methods in defining a straightforward technique to derive robust Pc clusters and equations.
6 citations