Thanks to the evolution of the technology and techniques to characterize organic rich shales, various options are available to perform a petrophysical evaluation, ranging from the most basic to the most complex, advanced ones. A robust petrophysical model is critical for the accuracy of the reservoir characterization; therefore, obtaining a reliable petrophysical analysis based on well logging data that can also support reservoir modeling and early wellsite decisions, was a key objective for this well drilled in an unconventional reservoir in the Burgan Field. For these reasons, technologically advanced logging services like Nuclear Magnetic Resonance and Nuclear Spectroscopy, along with conventional ones, including Spectral Gamma Ray, were logged in this well. The problem, then, is connecting all the valuable information obtained from different sources to define a petrophysical model. Are all sources reaching the same conclusion? This study aims to find how these different technologies and techniques can be interconnected to build a strong petrophysical model. Along with advanced technology, conventional logs were analyzed with the Organic Shale Petrophysics (OSP) technique which is based on the Gas Research Institute. Water saturation was obtained from NMR data using the T1T2 maps and DT2 maps and Blind Source Separation based on Independent Component Analysis (BSS-ICA). The most relevant results of the petrophysical analysis are as follows: Improvement in Fluid Saturations and Analysis:. The DT2 and T1T2 maps proved to resolve the fluid components in the organic matter rich section of the reservoir. Kerogen indicators identified with the Spectral Gamma Ray, OSP analysis and the NMR undercall porosity are in good agreement. The fluid saturation model applied to the 2D-NMR results and T1T2 maps, was iteratively improved based on the cross-correlation between Organic Carbon provided from Nuclear Spectroscopy (NS) and undercall porosity from NMR. NS-NMR data consistency: In this regard, the Organic Carbon from Nuclear Spectroscopy analysis matches with the NMR undercall porosity, proving the consistency of the log data and the applied analyses. In addition, a machine learning tool BSS-ICA was used to determine from the T2 spectra the independent spectral component of the hydrocarbon and its saturation, that shows good agreement with the results obtained with the traditional 2D NMR. This study proves that NMR data is key in the petrophysical evaluation of organic rich shales specifically for water saturation. The values of cementation exponent, m, and saturation exponent, n, used in the OSP Archie-based saturation model were confirmed with NMR data. These results, along with Nuclear Spectroscopy data, can be used to future optimization of Organic Shale Petrophysics ( OSP), to obtain a more precise petrophysical evaluation. This is will be extremely beneficial since these optimized parameters can be used for calibration when advance technology is not available.
Formation evaluation in a gas condensate carbonates reservoir with high temperature and pressure is very challenging: low porosity and gas have an effect on reserve estimation and fluid typing identification. A complex of or state-of-the-art petrophysical studies were implemented for the first time in Europe in the Machukhske field in Ukraine, which helped to estimate the reservoir properties, rock quality, permeability and fluid typing of the main challenging productive carbonate reservoir of the Tournasian formation at a qualitatively new level. The 8.5" section was drilled through the Tournasian formation with oil-based mud and a composite logging suite with high pressure and temperature (P, T) ratings was deployed. Gamma Ray, Neutron, Resistivity, Density and Formation Testing tools were run along with latest generation of multifrequency, focused Nuclear Magnetic Resonance (NMR) wireline tool. Longitudinal (T1 ) and transversal relaxation time (T2) distributions were calculated from multifrequency echo trains of raw NMR data to evaluate hydrocarbon porosity and saturations. The evaluation of T2 spectra used blind source separation driven by statistical independent component analysis (BSS-ICA), a machine learning algorithm. These results were then compared against those obtained from traditional two-dimensional NMR (2D-NMR) maps, specifically the T1T2 maps, that rely on the simultaneous inversion for T1 and T2. An adequate data acquisition sequences or logging activations ensured a suitable magnitude of the borehole signal, which enabled tool to apply long polarization times needed to detect volatile fluids. Conventional logs and core data were integrated with NMR results to minimize uncertainties, mathematical artifacts, and different effects. Rock quality indicators based on NMR porosity fractions and acoustic velocities were calculated and revealed some rock heterogeneities or porosity-lithology facies. In challenging borehole condition with high P & T, high quality composite logging suite data was successfully obtained. An advanced reservoir characterization study was performed by integrating the NMR data with conventional logs which also helped to reduce the uncertainty in formation evaluation by clearly identifying pay and shale zones, deeper understanding of the storage and flow capacity of reservoir and the furthermore, providing necessary parameters for optimizing completion design. An innovative study was carried out which helped not only meet objective of the well, but also results became reference for detailing the geological and hydrodynamic models of Machukske gas condensate field. The geological and technological model of the field was updated, and further field development strategies were optimized.
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