Integrated optimization of scale inhibitor squeeze treatment

Abstract

Scale inhibitor (SI) squeeze treatment is one of the most widely adopted techniques to control scale deposition. In this technique, chemical scale inhibitor is injected into the near-wellbore area where it retains in the formation and then slowly releases in the produced water when the well is back in production, preventing scale formation at a concentration of few ppm. The injection process normally starts with a preflush to condition the rock, and then the main slug (containing the SI) is injected followed by an overflush to push the chemical further deep into the reservoir. Before production, a shut-in period is also considered for more SI retention in the formation. The aim of chemical inhibition is to delay the deposition kinetics so that scaling issues are deferred from subsurface to surface, where a much easier access allows easier handling of the deposition risk. The main goal of this thesis is to present an integrated study of how to optimize the squeeze treatment design based on the well conditions and by considering the operational constraints. SI concentration, main treatment volume and the overflush volume are considered for squeeze design optimization. Using the sensitivity study, the optimum inhibitor concentration in the main slug is identified. The sensitivity results show that the most efficient squeeze treatment is achieved when the SI is deployed with the highest possible concentration, given the formation damage issues are avoided. In most cases, the well is normally planned to be protected for a target lifetime, this will result in protecting the well until the next treatment becomes available. The squeeze lifetime function was shown to be differentiable against the squeeze parameters, hence a gradient-based optimization algorithm, specifically Gradient Descent (GD) algorithm was applied to optimize the main treatment and the overflush volume for a given target squeeze lifetime. This will result in identifying the squeeze “Iso-Lifetime” curve, which presents all the possible squeeze designs that provide the target lifetime, using the optimum SI concentration. Based on the iso-lifetime designs, a cost analysis was carried out to find the optimum treatment, where the CPB (total cost of squeeze per barrel of water protected during the production period) was minimized, and the design with the lowest CPB was selected as the optimum one. For the cases with some flexibility in treatment lifetime, the same approach as described above was employed for a range of target lifetimes to identify the optimum target. The target lifetime that demonstrates the minimum CPB was identified as the optimum target lifetime which can be considered to optimize the treatment in a single well for long-term. Using this procedure, the optimum long-term strategy for squeeze treatment in a case study was provided. Multi-well squeeze design optimization was also studied in this thesis. Multi-well cases may include scenarios such as treating two or more wells connected to a subsea manifold or treating several single wells in the field where several wells of the same field are treated simultaneously in a squeeze campaign. A supply vessel is normally used to deliver the SI to the wells in a single trip. Due to the limitation of storage capacity on the vessel, the amount of inhibitor which can be used is limited, hence the available amount of inhibitor onboard should be optimally distributed among the wells. The squeeze campaign design was optimized for two field cases, minimizing the total inhibitor volume and the total downtime/pumping time, using the Multi-Objective Particle Swarm Optimization (MOPSO) method. Once the wells are squeezed, they should all reach the target lifetime of the campaign. This is essential such that all wells are protected until the next campaign. Finally, the Pareto Front was identified for the field, including the optimum squeeze campaign designs with the minimum cost, leading to the optimum inhibitor allocation strategy. The associated uncertainties with squeeze optimization were also considered in optimization. These uncertainties are mainly related to the retention isotherm which is normally derived by history matching. There might be several isotherms resulting in a reasonable history match. This causes uncertainty in squeeze lifetime prediction. Uncertainty quantification is considered in this research by evaluating the P10/P50/P90 percentiles using the likelihood function. Finally, scale treatment optimization in geothermal reservoirs was investigated and the most efficient scale treatment strategy in a geothermal doublet system was identified by considering three different techniques of SI deployment: continuous injection downhole, squeeze treatment and batch injection in the injector well. Optimum design for each of the methods was studied considering different reservoir conditions, and the optimization results were compared, providing the best scale treatment strategy in the reservoir.