Modelling of In-Situ Scale Deposition: The Impact of Reservoir and Well Geometries and Kinetic Reaction Rates

Abstract

Previous work has demonstrated how and where the mixingof incompatible brines occurs in waterflooded reservoirs, and what the impact would be on scale prevention strategies in terms of timing and placement of squeeze treatments. This paper extends this work, by modelling the resulting in-situ depositionprocess. The location of maximum scale deposition and the resulting brine compositions at the production well are calculated for a range of sensitivities, including reservoir geometry (1D, 2D areal, 2D vertical, 3D), well geometry (location and orientation within field and with respect to other wells and the aquifer), and reaction rate (ranging from no precipitation to equilibrium). In conventional systems with no aquifer, it is demonstrated that maximum scale deposition occurs in the immediate vicinity of the production wellbore, and therefore low produced cation concentrations indicate inadequate squeeze treatments. In systems where water injection is into the aquifer, low cation concentrations may also result from deposition deeper within the reservoir. Maximum scale dropout still occurs as the fluids approach the production well, but sufficiently far from the wellbore to be unaffected by squeeze treatments, or to have any major impact on productivity. The reaction rate is critical in determining the amount of scale deposition, but even under equilibrium conditions, sufficient concentrations of scaling ions are delivered to the production well to necessitate squeezing the well, although using lower volumes of inhibitor. Once cation concentrations have been reduced, it is predicted that they will never pick up again. This paper also discusses some of the limitations of modelling such systems, which include the determination of the kinetic reaction rates, size of the mixing zone, and the impact on permeability. Although the thermodynamics are fairly well understood, the kinetics are much more difficult. The size of the mixing zone is affected by numerical dispersion, and computationally intensive techniques are required to overcome this problem. Previous experience shows that formation damage factors are very difficult to extrapolate from coreflood data because there is a great difference between the dimensions of the mixing zone in the reservoir and the core plug. Introduction Previously presented work has shown the effect of brine mixing under various flow conditions, both idealised1,2(1D, 2D vertical, 2D areal, 3D) and actual reservoir conditions3,4(Alba Reservoir, North Sea). For the majority of these calculations a conventional reservoir simulator has been used. The advantages of using a conventional simulator are that a large proportion of waterflooded reservoirs have a field model dataset already available, the addition of tracer tracking to model the propagation of the mixing zone is relatively straightforward to implement, and the results are easy to visualise. This technique is quite powerful for demonstrating the movement of the water front and also the mixing zone relative to the production wells. Even within a given field the behaviour may vary quite markedly, depending on the configuration of neighbouring wells and the reservoir geometry, as was shown in the Alba case3.

Using ultrasonic as a new approach for elimination of inorganic scales (NaCl): an experimental study

“…In the production process, where decreases in pressure and temperature mostly take place in the nearwellbore region, NaCl in porous media and perforations largely precipitates and causes skin in the reservoir (Kleinitz et al 2001;Paulo et al 2001). To induce crystallization in the reservoir, various factors such as supersaturation, nucleation and precipitation are required (Crabtree et al 1999;Mackay 2003). The induced skin greatly reduces permeability, causing a slope rise in the inflow performance relationships used for evaluating reservoir deliverability (Economides et al 2012).…”

Re-development of the Frøy Field: Selection of the Injection Water

“…The accuracy of these calculations in modelling reservoir systems where in situ BaSO 4 precipitation is known to occur has been established (Mackay, 2003), and may be attributed to the simplicity of the precipitation reaction and the relatively low solubility of BaSO 4 , which means that extensive precipitation in the reservoir where convection rates are low is a reasonable assumption. CaCO 3 precipitation cannot, however, be calculated accurately using STARS, due to the greater complexity of the reaction and the higher solubility.…”

The Optimisation of a Scale Management and Monitoring Program for During the Production-Decline Phase of the Life Cycle

“…This in situ stripping mechanism has been proposed, studied and validated by comparing model results with field data for a large number of fields, and results have been published extensively 4,5,[11][12][13][14][15][16][17][18] . In most of the systems described in these publications there has been an excess of sulphate ions in the injection brine relative to the concentration of barium ions in the formation brines.…”

“…To stop scale squeezing a well is more difficult to predict and normally requires an assessment of the amount of scale possible to form at seawater/injection water proportions of >70% in the produced water. But scale predictions at this end of the mixing spectrum are not always accurate due to the presence of trace residuals of scale squeeze chemicals, impact of scale ion stripping 13,16 and the impact of water rates in high production wells can mean that relatively small predicted mass of scale in mg per litre of water could be a significant issue in a well producing e.g. 10,000 BWPD.…”