Data processing: artificial intelligence – Neural network – Learning task
Patent
1997-07-21
1999-12-28
Hafiz, Tariq R.
Data processing: artificial intelligence
Neural network
Learning task
706 15, 706 21, 73 6441, 73 6443, G06F 1518
Patent
active
060094192
DESCRIPTION:
BRIEF SUMMARY
The present invention relates to a method for predicting properties of cement. In particular, the method provides a method of predicting the thickening time of a cement slurry although it is by no means restricted to this use.
In oilwell drilling, cement is used to secure a lining or casing inside a drilled hole. The casing is usually a steel tube and cement slurry is pumped between the casing and the borehole wall and allowed to set, so securing the casing in place. Casing is cemented into a borehole for various reasons. The casing prevents any enlargening of the hole by contact with the drill string or erosion by drilling mud and cuttings. This is particularly important where the borehole encounters soft underground formations. The cement also provides a seal between underground formations so that, for example, water at one level is not contaminated by oil flowing from another level via the borehole. Cement also seals the drilled wall of the borehole which can prevent gas entering the well while it is being drilled which can cause blow-out.
In a typical cementing operation, casing is run into the borehole and cement is pumped from the surface through the casing such that it fills the annulus between the outer surface of the casing and the borehole wall from the bottom up. During this operation it is necessary for the cement to flow relatively easily so that it can be pumped and can fill the entire annulus. Once the cement is in place, it is desirable that it should set in a relatively short time in order that drilling can recommence. The time after mixing a cement slurry at which pumping is no longer possible because the cement has started to set is known as the thickening time and the composition of the slurry is selected according to the required thickening time. A knowledge-based slurry design system has been proposed by Dowell Schlumberger of Saint Etienne, France (see paper from Fourth Annual Conference on Artificial Intelligence in Petroleum Exploration and Production, Texas A&M University 1991) which relies on a large cement slurry database which can be consulted for proposals of slurries according to the conditions chosen. Before a slurry is pumped into a well, it is necessary to test the thickening time of a sample of the slurry. Thickening time measurements are made in a pressurised consistometer which conforms to the specification defined in API Spec. 10. The measurement is performed by first mixing a cement slurry, in accordance with a defined procedure and specified solid to water ratio, prior to placing a required quantity of the slurry in a suitably calibrated consistometer. The subsequent consistometry measurement involves raising the temperature and pressure of the cement to specified values at specified rates while a consistency measurement is being made. After reaching the specified conditions, the temperature and pressure are stabilised and the consistency of the cement is measured continuously. The time at which the cement reaches a specified consistency is the thickening time. Thickening times can be measured in accordance with one of 20 different schedules. Each schedule has a specified heating rate leading to a specified final temperature and pressure.
Since the standard API measurement attempts to simulate, to some degree, the conditions encountered in a well, the time taken to test each slurry is considered to be close to the actual thickening time of the cement which can often be in the order of 3-6 hours. This, together with the nature of the equipment used, makes thickening time measurement expensive in terms of both cost and time.
It has been previously proposed to predict the thickening time of a cement by measuring one or more properties of the neat cement powder. This technique is based on regression analysis of the measured properties and observed thickening time (see Cement and Concrete Research. Vol 16. pp 190-198, 1986 Pergamon Press Ltd, Prediction of thickening time of well cements from Blaine air permeability, L P Hunt). In this article, it is proposed to predict t
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Coveney Peter Vivian
Fletcher Philip
Hafiz Tariq R.
Rhodes Jason W.
Schlumberger Technology Corporatin
Y'Barbo Douglas
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