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.
Most field engineers and geoscientists find the estimation of borehole salinity using multiple mud reports to be a tedious task. The existing process involves using spreadsheets with multiple charts for conversion and requires the user to juggle from charts to reports to calculators at the same time. Depending on the mud vendor, the standard of estimation and equations change, making the process more user intensive. However, these equations can be strategically used in a programming language to automate this exhaustive and manual process of estimation. Any open-source code editor can be used to run the codes and generate borehole salinity at any depth desired. Borehole salinity is an important parameter as it influences the correction of neutron porosity and associated measurements to be used for petrophysical evaluation. In this study we first outline the current industrywide used methodology of estimating borehole salinity using mud reports supplied by vendors. The input parameters, calculation standards, and equations vary based on mud type and vendor. We also outline the increased complexity and decreased efficiency of the existing estimation process by focusing on two factors: first, equations are mostly embedded in a spreadsheet, which still requires manual interventions such as copying and editing values from large numbers of mud reports. Second, it can be time consuming, and the user needs hours-long training to comprehend the process. We then discuss the novel automated process where a suite of scripts written in open-source Python language runs via any open-source code editor. By using the popular Python library and DataFrame, tabular data from mud reports can be detected and pertinent values can be used as input for necessary calculation using the equations and charts already embedded in the scripts, which eventually generates salinity values in less than a minute. This project aims to deliver an automated solution to estimate borehole salinity. This methodology can be adopted by engineers on the rig and geoscientists in the office to calculate salinity values instantaneously without using any conversion chart or complicated equations whatsoever. In a case study using 20 samples from a typical mud vendor, we show that the new process is time saving and produces accurate borehole salinity values that are the same as values calculated using a manual technique. It is also a zero-cost process as open-source yet licensed software is used for estimation and needs little training for operation. The key innovative aspect of this project is to create a stepping-stone towards automation of day-to-day routine tasks that are being executed manually in the office and at the rig site. Existing salinity estimation has remained unchanged since early 2000 and calls for an update as the industry is taking aggressive steps towards automation. Borehole salinity automation is a first of its kind and its successful establishment will encourage more automation of similar calculation-based workflows.
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