Abstract
The best petrophysical models are based on direct measurements from the core. Unfortunately, core is not available in many cases, either for economic, logistical, or historical reasons. In this study, we needed to construct a detailed Field Development Plan (FDP) for the small, marginal B-9 field in the Western Offshore Basin, India, which did not merit core acquisition. The objective is to propose a workflow for building a petrophysical model with limited data sets instead of a typical FDP workflow. After analyzing the assumptions, limitations, and uncertainties involved in the petrophysical model, we used advanced petrophysical logs to reduce uncertainty and create a robust petrophysical model. We carried out a log-based petrophysical study to determine the volume of shale, porosity, saturation, and permeability. The advanced petrophysical logs including spectroscopy, nuclear magnetic resonance (NMR), formation pressures, and well testing data are used to calibrate the petrophysical model. Spectroscopy data are used to calibrate the mineralogical volumes and grain density, whereas the porosity is calibrated from NMR data. We calibrated log-derived permeability results with NMR permeability and mobility from well test data. We used heterogeneous rock analysis on petrophysical outputs to carry out petrophysical rock typing (PRT). This has helped in establishing the porosity-permeability relationship and saturation-height model for each PRT. In the absence of irreducible water saturation ([Formula: see text]) information from the core, NMR-derived [Formula: see text] is calculated and then used to calibrate the saturation model. Log-derived permeability and saturation are estimated, which agrees well with the available testing data. This provided a robust petrophysical model that served as a basis for geologic static and reservoir dynamic models. The gas-down-to and water-up-to methods are used to establish the contacts. The resulting saturation height model agreed well with the saturations derived from the log, which gave us confidence in our dynamic model.
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