Philip Fletcher

Determining cement composition by Fourier transform infrared spectroscopy

Abstract A diffuse reflectance mid-infrared Fourier transform spectroscopy (DRIFTS) method is described for obtaining high quality Fourier transform infrared (FTIR) spectra of cements. DRIFT spectra of synthetic C3S, C2S, C3A, and C4AF and of pure gypsum, bassanite, anhydrite, syngenite, and calcite are shown. Typical spectra of American Petroleum Institute class G and class A cements display characteristic features which can be related qualitatively to variations in the constituent minerals. For quantitative analysis, the FTIR spectra of 156 cements of varied origin and known elemental composition have been used to construct multivariate calibration models. These relate the spectrum to composition (expressed in terms of nine mineral components and five minor oxides) and allow the composition of unknown cements to be determined rapidly from the FTIR spectrum alone. Error estimates are given.

Using Artificial Neural Networks to Predict the Quality and Performance of Oil-Field Cements

Inherent batch-to-batch variability, aging, and contamination are major factors contributing to variability in oil-field cement-slurry performance. Of particular concern are problems encountered when a slurry is formulated with one cement sample and used with a batch having different properties. Such variability imposes a heavy burden on performance testing and is often a major factor in operational failure. We describe methods that allow the identification, characterization, and prediction of the variability of oil-field cements. Our approach involves predicting cement compositions, particle-size distributions, and thickening-time curves from the diffuse reflectance infrared Fourier transform spectrum of neat cement powders. Predictions make use of artificial neural networks. Slurry formulation thickening times can be predicted with uncertainties of less than 10 percent. Composition and particle-size distributions can be predicted with uncertainties a little greater than measurement error, but general trends and differences between cements can be determined reliably. Our research shows that many key cement properties are captured within the Fourier transform infrared spectra of cement powders and can be predicted from these spectra using suitable neural network techniques. Several case studies are given to emphasize the use of these techniques, which provide the basis for a valuable quality control tool now finding commercial use in the oil field.