As oil and gas exploration and development forays into unconventional plays, more specifically, basement exploration, its characterization and understanding have become very important. The present study aims at understanding the reservoir quality in terms of complex mineralogy and lithology variations, porosity, fracture properties and distribution near and away from the borehole using an integrated approach with the help of elemental spectroscopy, borehole acoustic imager, borehole micro-resistivity imager, nuclear magnetic resonance and borehole acoustic reflection survey. A comprehensive petrophysical characterization of different mineralo-facies of basement was carried out using elemental spectroscopy, formation micro-resistivity imager, borehole acoustic imager and combinable magnetic resonance along with basic open-hole data. Two distinct rock groups were identified – silica rich, iron poor zones having open fractures with good fracture density, porosity and aperture and silica poor, iron rich zones with no open fractures, poor fracture density, porosity and apertures. The zones with open fractures were the prime zones identified for further testing and completion. However, the near well bore analysis could not explain the oil flow from one zone having open fractures, whereas another similar zone showed no flow. Borehole Acoustic Reflection Survey processing was attempted to understand how extent of fractures beyond the borehole wall contributed to productivity from a well. The presence of laterally continuous fracture network at an interval that coincides with the depths from which the well is flowing, in turn validated from production log data, explained fluid flow from basement. Furthermore, the absence of such network can cause no flow even though near well-bore possible open fractures are present. Present study established the fact that, identification of potential open fractured zones in basement is a lead for reservoir zone delineation, however, a lateral extent of such basement reservoir facies is the key for successful basement hydrocarbon exploration.

The present study attempts to use 3D slowness time coherence (STC) technique to characterize the far-field fractures based on the reflector locations and attributes such as the dip and azimuth of fractures. These, in integration with the rest of the available data are used to accurately characterize the producing horizons in fractured basement reservoirs. The first step of the workflow involves the generation of 2D image to see if there are evidences of near and far wellbore reflectors. Since this is subjective in nature and does not directly provide quantitative results for discrete reflections, a new automated sonic imaging technique – 3D slowness time coherence (STC), has been incorporated to address this challenge. This method complements the image by providing the dip and azimuth for each event. The 2D and 3D maps of the reflectors can be readily available to integrate with the interpretations provided by other measurements, to better correlate and map the producing horizons. A field example is presented from the western offshore, India in which a fractured basement reservoir was examined using 3D STC technique to provide insight to the near and far field fracture network around the borehole. Few of the interpreted fractures from the resistivity image and conventional sonic fracture analysis coincide with the far field 3D STC reflectors, indicated by significant acoustic impedance. Further, the zones where the near and far field events coincide, represent a producing horizon. Comparing the near wellbore structures from the borehole images with the reflectors identified through the far field sonic imaging workflow provides necessary information to confirm the structural setting and characteristics of fractures away from the borehole. For the present case, it indicates the continuity of the fracture network away from the wellbore and explains the possibility of high production from the reservoir horizon. This study opens new perspective for identifying and evaluating fractured basement reservoirs using the sonic imaging technique. As more wells are drilled, it will be possible to better correlate and map the producing horizons in the field. This will allow better planning of location of future wells and help in optimizing field economics. A robust, automated and synergistic approach is used to locate and characterize individual arrival events which allows a more reliable understanding of the fracture extent and geologic structures. The 2D and 3D visualizations/maps can be readily integrated with the interpretations provided by other measurements.

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.