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Identifying promising areas for the placement of new wells in a mature oilfield presents a challenging task, requiring a complex integrated analysis of geological and production data. Full field reservoir simulation model reflects all relevant information about subsurface properties and historical production, making it a great tool to quickly evaluate relative performance of potential infill locations. However, evaluation of all possible scenarios and well patterns for infill drilling using dynamic simulator can be a time-consuming and computationally prohibitive process. Historically, this problem was commonly addressed by leveraging optimization algorithms such as "Genetic Algorithm", "Particle Swarm Optimization" or "Ensemble based optimizations". These algorithms help to minimize the required number of simulation runs for assessing optimal well placement to a few hundred cases. However, a significant drawback of these approaches is their lack of transparency, when the final recommended scenario with the best well performance might lack a coherent underlying rationale. For example, it could be unclear why all infill wells should be drilled in a certain region of the oilfield or positioned very close to other offset producers. This paper introduces an innovative approach to streamline the selection process for drilling infill wells. By conducting a focused set of simulation runs, our method employs a unique design that enables enhanced post-processing of the results. This leads to the generation of clear visualizations highlighting the most promising areas within the field. The effectiveness of this methodology has been demonstrated through its application in determining optimal infill producer locations within the Korolev oilfield in Western Kazakhstan.
Identifying promising areas for the placement of new wells in a mature oilfield presents a challenging task, requiring a complex integrated analysis of geological and production data. Full field reservoir simulation model reflects all relevant information about subsurface properties and historical production, making it a great tool to quickly evaluate relative performance of potential infill locations. However, evaluation of all possible scenarios and well patterns for infill drilling using dynamic simulator can be a time-consuming and computationally prohibitive process. Historically, this problem was commonly addressed by leveraging optimization algorithms such as "Genetic Algorithm", "Particle Swarm Optimization" or "Ensemble based optimizations". These algorithms help to minimize the required number of simulation runs for assessing optimal well placement to a few hundred cases. However, a significant drawback of these approaches is their lack of transparency, when the final recommended scenario with the best well performance might lack a coherent underlying rationale. For example, it could be unclear why all infill wells should be drilled in a certain region of the oilfield or positioned very close to other offset producers. This paper introduces an innovative approach to streamline the selection process for drilling infill wells. By conducting a focused set of simulation runs, our method employs a unique design that enables enhanced post-processing of the results. This leads to the generation of clear visualizations highlighting the most promising areas within the field. The effectiveness of this methodology has been demonstrated through its application in determining optimal infill producer locations within the Korolev oilfield in Western Kazakhstan.
Korolev Large Scale Pilot (KLSP) Project is a part of the Improved Oil Recovery (IOR) project intended to gather operational and subsurface learnings to improve understanding of Korolev reservoir response to water injection and incremental waterflood recovery potential. The main objective of this paper is to share facilities design, waterflooding monitoring techniques and operational lessons learned and best practices. The process for the waterflooding evaluation includes nested pilot projects which provide key information to make the decision whether to stop or proceed with the project using the data as input for well completions and facility designs. The large-scale pilot equipment set consists of 3 water source wells, a pumping station and 2 water injection wells. One of the main goals of KLSP is to allow surveillance data gathering to evaluate the pilot efficiency, to improve potential full field waterflood design, and to decrease the uncertainty of existing hydrodynamic models for the optimization of asset development strategies in the future. The existing surveillance plan includes fall-off tests at the injection wells, water source well sampling for water compositional analysis, injection logging tests for the estimation of zonal injection allocation, pulse test program between production and injection wells for the reservoir connectivity assessment, step-rate tests for injectivity evaluation and continuous reservoir pressure trend monitoring via permanent/temporary pressure gauges installed at Korolev production wells. A comprehensive water breakthrough monitoring strategy has been put in place, which includes tracer injection, water wash index analysis, robust wellhead sampling plan and daily offset production well water-cut measurements through the multiphase flowmeter. An early water breakthrough response plan has been developed for the cases of a sudden increase in water-cuts at the offset wells. The plan accounts for both KLSP's needs for data collection and the plant water production constraints. This paper gives an overview of the KLSP IOR project's operations challenges, reservoir uncertainties and surveillance operations, while also sharing facilities design, waterflooding monitoring techniques and overall design approach of the pilot waterflooding in Korolev field.
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