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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.
A prolific Southeast Asia onshore oilfield has enjoyed scale free production for many years before recently experiencing a series of unexpected and harsh calcite scaling events. Well watercuts were barely measurable, yet mineral scale deposits accumulated quickly across topside wellhead chokes and within downstream flowlines. This paper describes the scale management experience, and the specific challenges presented by this extraordinarily low well water cut, low pH, calcium carbonate scaling environment. To the knowledge of the authors, no previous literature works have been published regarding such an unusual and aggressive mineral scale control scenario. A detailed analysis of the scaling experience is provided, including plant layout, scaling locations, scale surveillance and monitoring programs, laboratory testing, product selection and implementation, and scale inhibitor efficacy surveillance and monitoring programs. The surveillance and application techniques themselves are notable, and feature important lessons learned for addressing similar very low water cut and moderate pH calcium carbonate scaling scenarios. For example, under ultra-low watercut high temperature well production conditions, it was found that a heavily diluted scale inhibitor was necessary to achieve optimum scale control, and a detailed laboratory and field implementation process is described that led to this key learning lesson. The sudden and immediate nature of the occurrence demanded a fast-track laboratory testing approach to rapidly identify a suitable scale inhibitor for the high temperature topside calcium carbonate scaling scenario. The streamlined selection program is detailed, however what could not be readily tested for via conventional laboratory testing was the effect of <1% water cut, and how the product would perform in that environment. A risk-managed field surveillance program was initiated to determine field efficiency of the identified polymeric scale inhibitor and involved field-trialing on a single well using a temporary restriction orifice plate (ROP) to modify the residence time of the injected chemical. The technique proved very successful and identifed that product dispersibility was important, and that dilution of the active scale inhibitor had a positive effect on dispersibility for optimum inhibitor action. The lessons learned were rolled out to all at-risk field producers with positive results. The ongoing success of this program continues and will be detailed in the manuscript and presentation. This paper demonstrates a unique situation of calcium carbonate scale formation and control that utilized a previously unreported and analytical surveillance approach. The cumulative performance derived by improving not only chemical selection, but the way the wells were managed via surveillance and chemical management decision making processes is compelling and of value to other production chemists working in the scaling arena.
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