Using Artificial Neural Networks to Predict the Quality and Performance of Oilfield Cements

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Inherent batch to batch variability, ageing 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 which allow the identification, characterisation and prediction of the variability of oilfield 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 he predicted with uncertainties of less than +/-10%. 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 emphasise the use of these techniques which provide the basis for a valuable quality control tool now finding commercial use in the oilfield.