Produced water was sampled and measured repeatedly during production from an offshore field, and an extensive brine-chemistry data set was developed. Systematic analysis of this data set enables an in-depth study of brine/brine and brine/rock interactions occurring in the reservoir, with the objective of improving the prediction and management of scale formation, along with improving its prevention and remediation.A study of the individual-ion trends in the produced brine by use of the plot types developed for the reacting-ions toolkit (Ishkov et al. 2009) provides insights into the components that are involved in in-situ geochemical reactions as the brines are displaced through the reservoir, and how the precipitation and dissolution of minerals and the ion-exchange reactions occurring within the reservoir can be identified. This information is then used to better evaluate the scale risk at the production wells.A thermodynamic prediction model is used to calculate the risk of scale precipitation in a series of individual produced-water samples, thus providing an evaluation of the actual scaling risk in these samples, rather than the usual theoretical estimate, on the basis of the endpoint formation-and injection-brine compositions and the erroneous assumption that no reactions in the reservoir impact the produced-water composition. Nonetheless, the usual effects of temperature, pressure, and brine composition are accounted for in these calculations by use of classical thermodynamics. The comparison of theoretical and actual results indicates that geochemical reactions taking place in this given reservoir lead to ion depletion, which greatly reduces the severity and potential for scale formation. However, ion-exchange reactions are also observed, and these too affect the scale risk and the effectiveness of scale inhibitors in preventing deposition.Additionally, comprehensive analysis by use of a geochemical model is conducted to predict the evolution of the produced-brine compositions at the production wells and to test the assumptions about which in-situ reactions are occurring. A good match between the predictions from this geochemical model and the observed produced-brine compositions is obtained, suggesting that the key reactions included in the geochemical model are representative of actual field behavior. This helps to establish confidence that the model can be used as a predictive tool in this field.

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Summary Oilfield-scale deposition is one of the important flow-assurance challenges facing the oil industry. There are a number of methods to mitigate oilfield scale, such as reducing sulfates in the injected brine, reducing water flow, removing damage by using dissolvers or physically by milling or reperforating, and inhibition, which is particularly recommended if a severe risk of sulfate-scale deposition is present. Inhibition consists of injecting a chemical that prevents the deposition of scale, either by stopping nucleation or by retarding crystal growth. The inhibiting chemicals are either injected in a dedicated continuous line or bullheaded as a batch treatment into the formation, commonly known as a scale-squeeze treatment. In general, scale-squeeze treatments consist of the following stages: preflush to condition the formation or act as a buffer to displace tubing fluids; the main treatment, where the main pill of chemical is injected; overflush to displace the chemical deep into the reservoir; a shut-in stage to allow further chemical retention; and placing the well back in production. The well will be protected as long as the concentration of the chemical in the produced brine is greater than a certain threshold, commonly known as minimum inhibitor concentration (MIC). This value is usually between 1 and 20 ppm. The most important factor in a squeeze-treatment design is the squeeze lifetime, which is determined by the volume of water or days of production where the chemical-return concentration is greater than the MIC. The main purpose of this paper is to describe the automatic optimization of squeeze-treatment designs using an optimization algorithm, in particular particle-swarm optimization (PSO). The algorithm provides a number of optimal designs, which result in squeeze lifetimes close to the target. To determine the most efficient design of the optimal designs identified by the algorithm, the following objectives were considered: operational-deployment costs, chemical cost, total-injected-water volume, and squeeze-treatment lifetime. Operational-deployment costs include the support vessel, pump, and tank hire. There might not be a single design optimizing all objectives, and thus the problem becomes a multiobjective optimization. Therefore, a number of Pareto optimal solutions exist. These designs are not dominated by any other design and cannot be bettered. Calculating the Pareto is essential to identify the most efficient design (i.e., the most cost-effective design.)