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 oilfield 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 oilfield cements. Our approach involves predicting cement compositions, particlesize distributions, and thickening-time curves from the diffuse reflectance infrared Fourier transform spectrum of neat cement powders.