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Peciko geological modeling: possible and relevant scales for modeling a complex giant gas field in a mudstone dominated deltaic environment

Proceedings Title : Proc. Indon. Petrol. Assoc., 30th Ann. Conv., 2005

Geological modeling is not an objective in itself. It may have different purposes such as estimating IGIP, validating well locations, optimizing well trajectory, reservoir studies and constraining geological models for production forecast. Geological model methodology depends on (i) the main objectives of the model, (ii) the available data, and (iii) the geological understanding of the field. The geological model evolves with the life of the field. Peciko is a challenging gas reservoir regarding geological modeling due to several factors. These include (i) complex geology, associated with a mud dominated deltaic environment of deposition for the reservoir section, (ii) a large gross gas column of 2000 m contained within several tens of reservoirs. (iii) poor seismic resolution, and (iv) an active drilling campaign with approximately 20 wells drilled per year.During early field appraisal, geological modeling is aimed at estimating the initial gas in place (IGIP). For this purpose, delineation wells allow the definition of a geological layering scheme and the construction of multi-2D models comprising 39 layers. Subsequently, during field development and production, the main objectives are to validate future well locations and to geologically constrain flow models for production optimization. Such a dynamic objective requires the main heterogeneity that controls fluid flow to be modeled. To achieve this objective, models have a fine elementary scale, corresponding with deltaic cycles. A total of 96 deltaic cycles corresponding to mouth bars deposit sequences are recognized within the field and are modeled with multi-2D techniques.3D object modeling is a further step if the proposed models do not sufficiently constrain flow simulations. Such an approach raises additional challenges including the stochastic distribution of modeled objects and requires a full transverse uncertainty workflow.

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