The paper discusses on reservoir souring study in a deep water subsea green field as a result of seawater injection. The objectives are to determine likelihood, timing of reservoir souring to happen and amount of expected produced H2S. Offshore deep water development involves considerable CAPEX investment hence reservoir souring requires to be assessed in order to make techno-commercial judgement involving formulating the field development plan, upfront identification of prevention & mitigation strategy, operating strategy and project economics. The study started by performing data gathering involving among others field information, PVT, mineralogy, water analysis data, and production and injection profile. Subsequently, 2D reservoir modelling and 3D reservoir modelling was built. Sensitivities cases were run by varying the injection rate, nutrient loading, rock abstraction capacity, sulphate content, injection temperature and bacteria growth time. This is followed by sensitivity analyses for mitigation options using biocide injection, nitrate injection, H2S scavenging and sulphate removal in the field. Based on the results obtained, prevention and mitigation strategy has been evaluated and ranked followed by comparison with nearby analogue fields. The modelling results of all scenarios indicate that reservoir souring will happen in the field and beyond HSE safety limit. For some scenarios, the H2S partial pressure exceeds NACE limit before end of field life, hence requiring team to re-evaluate material selection options. Water injection rate and rock abstraction capacity have the largest impact to the H2S breakthrough time. Sensitivity analyses for mitigation options have been conducted based on consideration of having options of biocide injection, nitrate injection, H2S scavenging and sulphate removal in the field. Biocide injection does not have considerable effects on H2S level. Nitrate injection only partially reduces H2S generation mainly due to high nutrient content in the reservoir and high sulphate content in the injected seawater. On the other hand, sulphate removal analyses indicate its effectiveness in preventing reservoir from becoming sour. The outcome of the study is then incorporated in the field development plan and operating strategy. The paper highlighted comprehensive step by step approach to understand reservoir souring potential in a deep water development via 2D and 3D modelling approach. This can be included as an important procedure in field development especially involving high CAPEX development whereby critical decision making need to be made upfront. In addition, benchmarking, and learnings from nearby deep water fields help to identify best preventive and remedial option for reservoir souring.

This paper demonstrates that data-driven approach can address to resolve the big data complexity, enhance reservoir characterization and accelerate history matching process into an offshore mature field with many challenges such as complex reservoir geology with high properties variation, fair correlation between seismic amplitude and reservoir properties, multi-stacked completion of production/injection strings, commingled production system from multiple zones, mechanical leaking issue and sparse production allocation. By these complexities, classical technique of integrated full-field modelling cannot be done easily. In addition, it is difficult to acquire reservoir engineering insights for reservoir characterization. Data-driven reservoir modelling approach is applied to tackle the technical challenges. A workflow is proposed for rapid quality check of big data and integrated with systematic reservoir engineering analyses. In data-driven approach, current understanding of geology and physics is relieved with field measurements data as the foundation of constructing the model. The model is kept at high level and introduce further detail where needed within the bound of uncertainty to achieve history match. Data-driven approach has successfully improved reservoir characterization and provide cost-effective technique to accelerate history match process. The quality of big data (i.e. pressure, correct production allocation of each zone) plays key role in data-driven approach. Through early valuable insights from engineers, it significantly reduced the iterations number for history match purposes between engineers and geologist. In this study, it cuts from commonly in months to be weeks. Consequently, field-level and well-level history match can be satisfactorily achieved with deviation within 5%. The blind testing has been conducted to validate data-driven approach and improve confidence level with the model for further field development opportunities such as waterflood optimization, stimulation, new well placement, effective completion and enhanced oil recovery. The results can be reconciled with geological understanding, which will be very useful and suitable in current oil price situation as a cost-effective technique, especially for mature fields with large data and have complexity in geological and production system. The data-driven approach can be deployed to other neighbour mature fields and can improve the level of confidence to support fast business decisions.

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