Compositional analysis of reservoir rock is a vital aspect of oil exploration and production activities. In a broad sense, knowing the mineral composition of a reservoir can help with characterization and interpretation of depositional environments. On a smaller scale, identifying mineralogy helps calibrate well logs, identify formations, design drilling and completion programs, and screen for intervals with potential problem minerals, such as swelling clays. The petroleum industry utilizes two main methods to find compositional mineralogy, x-ray diffraction (XRD) and thin section analysis. Both methods are time consuming, expensive, and destructive. An alternative method for compositional analysis that includes quantitative mineralogy is a valuable prospect, especially if it had the potential to characterize the total organic content (TOC).
The remote sensing community has been using infrared spectroscopy to analyze mineralogy for years. Within the last ten years, the advancement of infrared spectrometers and processing programs have allowed infrared spectra to be taken and analyzed faster and easier than before. The objective of this study is to apply techniques used in remote sensing for quantitatively finding mineralogy to the petroleum industry. While developing a new methodology to compositionally analyze reservoir rock, a database of infrared spectra of relevant minerals has been compiled. This database was used to unmix spectra using a constrained linear least-squares algorithm that is used in the remote sensing community. A core has been scanned using a hand-held infrared spectrometer. Results of the best method show RMS error from mineral abundance to be under five percent.
|Commitee:||Abdelsalam, Mohamed, Pashin, Jack|
|School:||Oklahoma State University|
|School Location:||United States -- Oklahoma|
|Source:||MAI 55/05M(E), Masters Abstracts International|
|Subjects:||Geology, Mineralogy, Petroleum Geology|
|Keywords:||Fourier, Mineralogy, Reservoir, Spectroscopy, Transform, XRD|
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