Application of Conformance Control Techniques in Tengiz Field (Russian)
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Cited by 8 publications
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Conventional preformed particle gels suffer from insufficient salt tolerance and weak mechanical properties after water absorption, which reduce the water shutoff effect in mature oilfields. In this paper, a nanocomposite particle gel (NCPG) is synthesized by copolymerization of acrylamide (AM) and 2-acrylamido-2-methylpropane sulfonic acid (AMPS) using laponite RD (LPT) as a physical cross-linker and N,N-methylene-bisacrylamide (MBA) as a chemical cross-linker via in situ free radical polymerization. Compared with the NCPG without LPT, both the swelling rate and mechanical properties of NCPG added with LPT are found to be improved. In addition, the pore sizes of the network of the swollen NCPG are smaller than those of the sample without LPT, and the thermal stability is also slightly enhanced. The swelling rate of NCPG increases with increasing AMPS concentration. The water absorbency of NCPG first increases and then decreases with increasing MBA and APS concentrations. The NCPG is sensitive to alkaline medium due to the presence of sulfonic acid groups on the molecular chains of the NCPG. The synthesized NCPG exhibits good salt tolerance at 80 °C in formation water. The plugging rate of the NCPG to a sand-pack is above 90%, and the residual resistance factor reaches 19.2 under reservoir conditions. These results indicate that the NCPG may have potential application for water shutoff treatment in mature oilfields.
Conventional preformed particle gels suffer from insufficient salt tolerance and weak mechanical properties after water absorption, which reduce the water shutoff effect in mature oilfields. In this paper, a nanocomposite particle gel (NCPG) is synthesized by copolymerization of acrylamide (AM) and 2-acrylamido-2-methylpropane sulfonic acid (AMPS) using laponite RD (LPT) as a physical cross-linker and N,N-methylene-bisacrylamide (MBA) as a chemical cross-linker via in situ free radical polymerization. Compared with the NCPG without LPT, both the swelling rate and mechanical properties of NCPG added with LPT are found to be improved. In addition, the pore sizes of the network of the swollen NCPG are smaller than those of the sample without LPT, and the thermal stability is also slightly enhanced. The swelling rate of NCPG increases with increasing AMPS concentration. The water absorbency of NCPG first increases and then decreases with increasing MBA and APS concentrations. The NCPG is sensitive to alkaline medium due to the presence of sulfonic acid groups on the molecular chains of the NCPG. The synthesized NCPG exhibits good salt tolerance at 80 °C in formation water. The plugging rate of the NCPG to a sand-pack is above 90%, and the residual resistance factor reaches 19.2 under reservoir conditions. These results indicate that the NCPG may have potential application for water shutoff treatment in mature oilfields.
Sour gas injection operation has been implemented in Tengiz since 2008 and will be expanded as part of a future growth project. Due to limited gas handling capacity, producing wells at high GOR has been a challenge, resulting in potential well shutdowns. The objective of this study was to establish an efficient optimization workflow to improve vertical/areal sweep, thereby maximizing recovery under operation constraints. This will be enabled through conformance control completions that have been installed in many production/injection wells. A Dual-Porosity and Dual-Permeability (DPDK) compositional simulation model with advanced Field Management (FM) logic was used to perform the study. Vertical conformance control was implemented in the model enabling completion control of 4 compartments per well. A model-based optimization workflow was defined to maximize recovery. Objective functions considered were incremental recovery 1) after 5 years, and 2) at the end of concession. Control parameters considered for optimization are 1) injection allocation rate, 2) production allocation rate, 3) vertical completion compartments for injectors and producers. A combination of different optimization techniques e.g., Genetic Algorithm and Machine-Learning sampling method were utilized in an iterative manner. It was quickly realized that due to the number of mixed categorical and continuous control parameters and non-linearity in simulation response, the optimization problem became almost infeasible. In addition, the problem also became more complex with multiple time-varying operational constraints. Parameterization of the control variables, such as schedule and/or FM rules optimization were revisited. One observation from this study was that a hybrid approach of considering schedule-based optimization was the best way to maximize short term objectives while rule-based FM optimization was the best alternative for long term objective function improvement. This hybrid approach helped to improve practicality of applying optimization results into field operational guidelines. Several optimization techniques were tested for the study using both conceptual and full-field Tengiz models, realizing the utility of some techniques that could help in many field control parameters. However, all these optimization techniques required more than 2000 simulation runs to achieve optimal results, which was not practical for the study due to constraints in computational timing. It was observed that limiting control parameters to around 50 helped to achieve optimal results for the objective functions by conducting 500 simulation runs. These limited number of parameters were selected from flow diagnostics and heavy-hitter analyses from the pool of original 800+ control parameters. The novelty of this study includes three folds: 1) The model-based optimization outcome obtained in this study has been implemented in the field operations with observation of increased recovery 2) the hybrid optimization of both schedule and operation rule provided practicality in terms of optimization performance as well as application to the field operation 3) provides lessons learned from the application of optimization techniques ranging from conventional Genetic Algorithm to Machine-Learning supported technique.
Wells in Tengiz and Korolev oil fields are equipped with data transmitting devices, which provide real-time process data used by Production engineers for continuous production monitoring and identification of unusual process conditions. Monitoring and analysis of each well performance becomes a tedious process with growing well inventory. Up until recently, real-time data from wellsite transmitters was not used to its full potential to simplify and automate well performance analysis. To improve the quality of daily well performance monitoring and detection of abnormal process conditions, sets of data rules have been developed to create alerts and screens with real-time process data managed by exception. These alerts and screens help to identify malfunctioning equipment and changes in operating conditions. Timely evaluation of critical conditions helps to proactively prepare a mitigation plan and prevent unscheduled well shutdowns. Data management by exception allows automatic filtering of big data sets and draws attention only to wells with deviations from the stable operating regime. Detailed review of highlighted well conditions helps to differentiate between malfunctioning equipment and actual changes in operating conditions. Fast identification of the issues allows taking preventative actions to maintain process stability of each producing well. Implementation of these tools significantly reduced number of unscheduled well shutdowns due to leaks in Surface Controlled Subsurface Safety Valve (SCSSV) hydraulic system and pneumatic valves control system. The screens also help to identify malfunctioning equipment including pressure and temperature gauges, pressure downhole gauges (PDHGs) and multiphase flow meters (MPFMs), as well as flow assurance issues such as hydrate formation. Developed data rules can be useful for any field equipped with data transmitting devices. This paper aims to share the best practices of using real-time operational data analytics to identify malfunctioning equipment, changing operating conditions and other process related issues to maintain stable production process.
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