Fractures are the prime conduits of flow for hydrocarbons in reservoir rocks. Identification and characterization of the fracture network yields valuable information for accurate reservoir evaluation. This study aims to portray the benefits and limitations for various existing fracture characterization methods and define strategic workflows for automated fracture characterization targeting both conventional and unconventional reservoirs separately. While traditional seismic provides qualitative information of fractures and faults on a macro scale, acoustics and other petrophysical logs provide a more comprehensive picture on a meso and micro level. High resolution image logs, with shallow depth of investigation are considered the industry standard for analysis of fractures. However, it is imperative to understand the framework of fracture in both near and far field. Various reservoir-specific collaborative workflows have been elucidated for a consistent evaluation of fracture network, results of which are further segregated using class-based machine learning techniques. This study embarks on understanding the critical requirements for fracture characterization in different lithological settings. Conventional reservoirs have good intrinsic porosity and permeability, yet presence of fractures further enhances the flow capacity. In clastic reservoirs, fractures provide an additional permeability assist to an already producible reservoir. In carbonate reservoirs, overall reservoir and production quality exclusively depends on presence of extensive fracture network as it quantitatively controls the fluid flow interactions among otherwise isolated vugs. Devoid of intrinsic porosity and permeability, the presence of open-extensive fractures is even more critical in unconventional reservoirs such as basement, shale-gas/oil and coal-bed methane, since it demarcates the reservoir zone and defines the economic viability for hydrocarbon exploration in reservoirs. Different forward modeling approaches using the best of conventional logs, borehole images, acoustic data (anisotropy analysis, borehole reflection survey and stoneley waveforms) and magnetic resonance logs have been presented to provide reservoir-specific fracture characterization. Linking the resolution and depth of investigation of different available techniques is vital for the determination of openness and extent of the fractures into the formation. The key innovative aspect of this project is the emphasis on an end-to-end suitable quantitative analysis of flow contributing fractures in different conventional and unconventional reservoirs. Successful establishment of this approach capturing critical information will be the stepping-stone for developing machine learning techniques for field level assessment.
The biggest clastic reservoir based in Kuwait has been facing evaluation challenges over the thick intervals of highly laminated thin hydrocarbon layers. Conventional wireline tools have a limitation on resolution when it comes to addressing these thin beds. Therefore, the reserves are usually underestimated, and thin pays are often overlooked. This paper presents the integration of a variety of advanced Wireline tools in order to correctly evaluate and compute reserves from these thin pay zones. Acquisition of the triaxial induction tool enabled the study of resistivity anisotropy and the identification of thin pay zones through the distinct reading of the resistivity of the thin sand reservoir. The thin layers have also been further validated using high resolution advanced thin bed analysis from image logs. Advanced spectroscopy and NMR data were used to quantitively define the sand and shale fractions within the thin beds. These measurements were critical to input to improve the resistivity interpretation followed by a reliable estimate of the saturation. High resolution dielectric measurements provided resistivity-independent saturation information enhancing the NMR interpretation using water-filled porosity which was a key input into the identification of the heavy oil presence in Burgan. The newly identified thin pay zones have been further validated using the fluid sampling confirming presence of hydrocarbons with greater understanding of its properties and uniquely quantifying the mobile fluid fractions. The additional available reserves can only be properly determined by combining data from multiple sources to achieve a comprehensive evaluation. Resistivity anisotropy was observed based on the separation of vertical and horizontal resistivities and was therefore investigated to understand its root-cause over different zones. By integrating the results from the dielectric dispersion measurements, the diffusion-based NMR data, spectroscopy data, borehole image interpretation and high-resolution sand count delineation of different lithologic units at a finer scale, we were able to identify thin bedded sand-shale intervals in addition to pin-pointing the heavy oil intervals. Hydrocarbon saturations of individual sand layers showed improvement in hydrocarbon volumes, improvement in permeabilities across the studied zones and increased net pay estimations by 12%. Results from the fluid sampling performed across the newly identified thin pays have validated the advanced logging interpretation results and the presence of hydrocarbons. These intervals were overlooked by the standard basic evaluation and the reservoir potential has been revisited following the latest integrated advanced results. By combining the results of all these advanced wireline answer products, we were able to properly identify and quantify the additional available reserves and therefore change the classification of these reservoirs from poor to excellent with new development plan in place. The paper demonstrates the value solution of the high vertical resolutions taking advantage of the latest advanced technologies to enhance the characterization of laminated thin beds. The integrated advanced solution has enabled improved reservoir potential by the identification of new pay zones initially overlooked by the standard basic measurements.
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