Carbonate and sulphide scales can form in CO2 and/or H2S-rich environments in a process which we refer to as "auto-scaling", i.e. these scales form in the produced brine due to a change in conditions such as pressure and temperature, not due to brine mixing. Particularly in production systems, carbonate and sulphide scales can form due to the evolution of CO2 and H2S from the aqueous phase to the gas phase caused by a pressure decrease. Carbonate scale formation in this manner is broadly understood; however, there are details of precisely how this occurs in auto-scaling processes which are not widely appreciated.

Measuring the water composition at surface locations (e.g. at the separator) does not give a full indication per se of the amount of scale that has precipitated upstream of the sampling point. However, the composition of the water before precipitation occurs is required for predicting the scaling potential of the system, and this information is seldom available. In this paper, we propose a model that accounts for this issue, and that accurately calculates the carbonate and sulphide scaling profiles in CO2 and/or H2S-rich production systems by knowing only commonly available surface data – i.e. pressure, temperature, and fluid compositions (water, gas, and oil). A rigorous workflow which can do this calculation using any aqueous scale prediction model along with a PVT Model has already been published by the authors (Verri et al, 2017a). The current paper describes a new model to do these calculations which also includes an approach for estimating both the "correct" scaling case within a range of cases up to the "worst case" carbonate scaling scenario.

A scale prediction model has been developed to include a three-phase flash algorithm (using the Peng-Robinson Equation of State) coupled with an aqueous electrolyte model (using the Pitzer equations as the activity model). This model is used to run a demonstration example showing the procedure to calculate accurate auto-scaling profiles in CO2 and/or H2S-rich production systems, which is based on building a sensitivity analysis on the ions directly involved in precipitation reactions. We also note that auto-scaling profiles in production systems are commonly obtained by sectioning the production system – either by parameterising depth with pressure and temperature, or by selecting specific locations (e.g. DHSV, wellhead, etc.). Then, established guidelines to treat scale (or not) based on the calculated saturation ratios and precipitated masses of scale can be applied. We show that such an approach is not optimal and that it can lead to under or over-estimation of scale treatments. Furthermore, building on our previous method (Verri et al 2017a) we propose an approach to model the cumulative amount of scale formed under full equilibrium conditions, which is not dependent on how the production system is sectioned. By doing so, the correct amount of scale formed in the production system is always calculated, thus avoiding non-optimum scale treatments.

Our approach focuses on calculating the correct auto-scaling profiles in CO2 and/or H2S-rich production systems, and on correctly interpreting the results obtained by thermodynamic modelling and it can be easily integrated with commonly available scale prediction software.

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