Abstract

The system where sulphate scaling damage occurs is determined by two governing parameters: the kinetics coefficient characterising the velocity of chemical reaction and the formation damage coefficient reflecting permeability decrease due to salt precipitation. We derived an analytical model-based method for determination of kinetics and formation damage coefficients from production well data consisting of barium concentrations in the produced water and of well productivity decline.

We analyse production data for five scaled-up producers from giant offshore field A, submitted to seawater flooding (Campos Basin, Brazil) in order to predict productivity index and to plan the well stimulation program. The wells are completed by gravel packs. Complete mixing of sea- and formation waters in production well neighbourhoods in the reservoir under consideration was assumed in previous works. Using this assumption, quasi steady state model for reactive flow around production well is formulated. We obtained values of the two sulphate scaling damage parameters. The two coefficient values were used for prediction of productivity decline for these wells.

Both coefficient values as determined for five wells are inside the variation intervals for scale damage coefficients obtained from coreflood data. It allows concluding that the productivity damage in wells under investigation was caused by sulphate scaling and validates the mathematical model. It also permits to perform a reliable prediction of well productivity. The values for kinetics and formation damage coefficients as obtained from well and laboratory data are recommended for use in reservoir modelling of sulphate scaling.

Sulphate scaling can have a disastrous impact on oil production in waterflood projects with incompatible injected and formation waters. This is due to precipitation of barium / strontium sulphate from the mixture of both waters and the consequent permeability reduction The Ba/SrSO4 scaling is a chronicle disaster in waterflood projects with incompatible injected and formation waters. This phenomenon is attributed to precipitation of barium/strontium sulphate from the mixture of both waters and the consequent permeability reduction resulting in loss of well productivity 1,2.

The sulphate scaling productivity decline phenomenon has been long recognized in North Sea reservoirs 3 and in Campos Basin fields of Brazil 4,5,6.

Decision-making on scale prevention, removal and on stimulation of scaled-up wells is based on scale damage prediction provided by reliable mathematical modelling with coefficients determined from laboratory or field data.

Several numerical 7,8,9 and analytical 10,11 models describing sulphate scaling under laboratory and field conditions are available in the literature. Chemical reaction options in commercial simulators allow for sulphate modelling on field scale.

Mathematical models for sulphate scaling contain the reaction rate coefficient characterising the intensity of chemical reaction (so called reaction velocity). The reaction rate coefficient is proportional to flow velocity for small velocities, and the proportionality coefficient is called the kinetics coefficient 12,13,14. The kinetics coefficient is determined by properties of rocks and fluids, by shape of deposit and by thermodynamics conditions.

Another governing parameter is the formation damage coefficient reflecting permeability decrease due to salt precipitation 15. The formation damage coefficient also depends on rock and fluid properties. Like permeability or capillary pressure, the kinetics and formation damage coefficients cannot be predicted theoretically for real rocks and fluids.

Scale deposition profile during coreflood is non-uniform because the reagent concentrations decrease along the core due to chemical reaction. So, the sulphate scaling coefficients cannot be directly measured in reactive coreflood tests.

The same applies to scale deposition around production wells.

Therefore, the coefficients must be determined from either laboratory or field data by solution of inverse problems.

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