Reservoir souring refers to the generation of hydrogen sulphide (H~) in originally sweet reservoirs that have been subjected to production operations such as (sea)water flooding. The most plausible cause of reservoir souring is the growth of sulphate-reducing bacteria (SRB) in the zone where seawater mixes with formation water. In the mixing zone the components that support the SRB's lifeoxidant and nutrients -are present.Inorganic reactions are not considered important in the generation of H 2 S. They are important, however, in the scavenging of H 2 S, since many ironcontaining minerals are capable of reacting with H 2 S, forming pyrite or pyrrhotite. For this reason, the types and quantities of iron-containing minerals within the reservoir have been studied using petrographic and isotopic techniques.As a first step towards quantifying the effects of H 2 S production by SRB, a 10 analytical transport model has been developed. It describes the production of H 2 S in the mixing zone and the compound's transport through a reservoir. The partitioning of H 2 S between the fluid phases and the possibility of scavenging by iron-containing minerals have also been included in the model.References and illustrations at end of paper. 369The model has served to calibrate a numerical simulator, which can be used for modelling souring under more realistic settings. It is demonstrated how the distribution of nutrients, sulphates and H 2 S actually observed in a producing field can be used to assess the existence of souring chemical reactions as well as the mechanisms that give rise to them.
In the petroleum industry, history-matched reservoir models are used to aid the field development decision-making process. Traditionally, models have been history-matched by reservoir engineers in the dynamic domain only. Ideally, if any changes are required to static parameters as result of history matching the dynamic model, then these should be reflected directly in the static reservoir model, ensuring consistency between the static and dynamic domain. In addition, static model uncertainties are often not evaluated in the dynamic domain, which can result in the detailed modeling of geological features that have little impact on the dynamic behavior of the reservoir or the resulting development decision. This paper demonstrates a workflow where the reservoir simulator and static modeling package are closely linked to promote a more integrated approach to reservoir model construction, facilitating the interaction between subsurface disciplines. Using either the reservoir simulator or the static modeling package as the platform, the output of the workflow is a sensitivity analysis of the uncertainties related to structure, rock properties, fluids and rock-fluid interactions. Computer-assisted history matching methods (i.e. adjoint-based and Design of Experiments) are used to find the parameter values that result in a history match model. The workflow is described for both a synthetic model and also a reservoir model from a real field case. This methodology results in improved history-matched models and a better understanding of the static and dynamic subsurface uncertainties and their importance, leading to more informed decision-making. Furthermore, it is anticipated that it will result in faster accomplishment of the history matching studies. The method presented here can significantly enhance the understanding of the impact of both static and dynamic subsurface uncertainties on development decisions. In addition, it offers a platform where all subsurface professionals involved in reservoir model construction and simulation can more optimally focus their efforts on improving the integrated understanding of their reservoirs.
This paper discusses the modeling of a fluvial reservoir system. Construction of a detailed reservoir model was followed by a coarser numerical simulation model. During the history matching of the latter, we found that fluid transmissibilities had to be reduced significantly to reproduce the observed pressure differentials. Fault-related features are thought to be the reason that the sand continuity is so much lower than that predicted with sedimentological considerations only.
The field is located onshore Abu Dhabi and has been in production for more than 50 years. It covers an area of approximately 1500 square kilometers and has 20 reservoirs with producible hydrocarbons comprising a series of stacked oil and gas reservoirs with differing drive mechanisms and development maturity, including in-field exploration targets. This paper describes the work undertaken to build a full field Shared Earth Model (SEM) to support future drilling to maximize the field recovery and extend the field lifetime. The key business deliverable for the SEM was to allow more effective assurance of planned well trajectories in a highly congested surface/subsurface environment, thus increasing the safety of such operations. As a consequence, the SEM had to extend from ground level to the deepest penetrated reservoirs. The reservoir model is constrained by more than 70 seismic horizons and by more than 40,000 well markers of various vintages; this alone represented a significant modeling challenge. Automated QC & QA workflows were used heavily to analyse the input data and the model during each of the model construction iterations. The reservoir units are faulted but these faults do not extend to the surface. Due to limitations in the representation of such faults in pillar grids and a lack of continuity of fault interpretations from one reservoir to the next, it was decided to represent faults as properties and not as fault planes. This not only allowed adjustment per reservoir, without requiring a rebuild of the whole structural model, but also identified clearly a zone of uncertainty around the predicted faults, assisting well planning efforts. A major focus of the project was to provide an evergreen model which could be kept up to date with ADCO's substantial drilling program. Therefore considerable effort was made to hand over not just a model, but also training in the key workflow's and work practices which would ensure that ADCO staff have the skills in house to update and maintain the model. This was achieved through extensive use of Petrel workflows, which enabled quick & structured model updates and through several training sessions in Abu Dhabi, run over 3–4 days. This proved to be an effective mechanism to hand over the model and workflows to ADCO staff.
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