ABSTRACT
Inherent batch to batch variability, ageing and contamination are thought to be major factors contributing to variability in 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. This situation occurs regularly in operations. Such variability imposes a heavy burden on performance testing and is often a major factor in operational failure.
Methods are described herein which allow the identification, characterisation and prediction of cement variability for Class G, Class A and Class H cements. Specifically, the technique involves predicting cement compositions, particle size distributions and thickening time curves from the Diffuse Reflectance Infrared Fourier Transform (DRIFT) spectrum of neat cement powders. Predictions are based on either linear statistical procedures or non-linear Artificial Neural Networks (ANNs).
For some simple slurry formulations thickening times can be 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 adequately.
The results establish the infrared spectrum of cements as a ‘signature’ of cement performance and composition since it yields subtle information on the nature and condition of the cement which is not given by API measurements.
A summary of the main features of this technique is as follows:
Predicts the performance of some cement slurries
Detects the presence of major contaminants
Predicts cement composition and particle size distributions
Identifies the nature and extent of cement ageing
Predicts the effect of ageing on slurry performance
Identifies and quantifies differences between cement samples
Differentiates cement/silica blends from neat cements
Detects non-API cements
The potential benefits of the technique can be summarised as follows:
Improves the assessment of cement quality or expected cement performance
Helps avoid operational failures due to batch to batch variation, ageing or contamination
Establishes a basis on which to reject or accept cements
Helps to identify suitable local cements in new locations
Improves the efficiency of formulation design by identifying performance characteristics of cement
The objectives of the paper are (a) to review the factors controlling cement hydration and variability, and show that many of these properties are captured within the FTIR spectrum of cement and (b) to show that performance and composition can be predicted from spectra using suitable statistical techniques. Several cases studies are given to emphasise the use of the techniques.
