Abstract
Abstract It has been known for a long time that oilfield chemicals used for different purposes (corrosion and scale inhibitors, scavengers, biocides, etc.) can modify the efficiency of each other. These cross-effects can exhibit adverse or beneficial impacts and may modify the overall corrosiveness of the medium to a great extent. However, there is no standard procedure in order to evaluate the cross-effects, i.e. the extent to which the effect of one of the chemicals is modified by the addition of another. The 2N Design of Experiment (DoE) method provides a robust and simple statistical way to evaluate the change in efficiency of oilfield chemicals owing to the addition of other additives. The 2N DoE method can also be applied to other systems. In the present work the effects and cross-effects in systems consisting of a corrosion inhibitor, as well as an oxygen and a hydrogen sulphide scavenger are investigated and successfully demonstrated in a typical oilfield corrosion system with electrochemical corrosion monitoring methods.
Highlights
The chemical treatment of wet oils that are produced is a widely used method for mitigating unfavorable phenomena in the production, transportation and processing of crude oils: corrosion, scaling, emulsion forming, etc
On the way from the oil well to the refinery a variety of oilfield treatment chemicals are added to the oil: corrosion and scale inhibitors, biocides, hydrogen sulfide and oxygen scavengers, demulsifiers, anti-foam agents, etc. [1–4]
The purpose of this work was to demonstrate the applicability of the 2N Design of Experiment (DoE) method for studying the factors and cross-effects of oilfield chemicals in a suitably chosen model system
Summary
The chemical treatment of wet oils that are produced is a widely used method for mitigating unfavorable phenomena in the production, transportation and processing of crude oils: corrosion, scaling, emulsion forming, etc. The effects of these chemicals are typically well defined in themselves, but the cross-effects, i.e. the influence on each other, are rarely discussed and even more rarely investigated, especially in situ. The reason for this is rather complex. The evaluation of such tests, if any, is rather problematic because if the effects of factors are strongly correlated (i.e. one or more “cross-effects” are significant in the system) the evaluation of the effects by usual means (i.e. least square model fitting [5–7]) is subject to a significant error, if not impossible
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