Case Study: Petrophysical Characterization in Unconventional Reservoir in the Burgan Field

Abstract

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.

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 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 Excess 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 the hydrocarbon independent spectral component from T2 spectra, and its saturation, that shows good agreement with the results obtained with 2D NMR.

This study proves that NMR data is key in the petrophysical evaluation of organic rich shales specifically for water saturation. 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.