Abstract

The reservoir quality prediction is carried out at the exploration scale through software that try to model the diagenetic evolution of the reservoir. The input data are quantitative petrographic data, core analysis results and the burial and thermal history of the wells either 1D or 3D (PSM). The procedure starts from the calibration of the model on a well with cores or sidewall cores for petrographic-diagenetic data and RCA and with a calibrated burial and thermal history. Once calibrated, the model extends to the whole area of interest following the 3D burial and thermal history model of the reservoir.

The extension of the approach to the reservoir scale requires a dedicated work-flow that comprises the following points:

  • -

    Identification of the main diagenetic issues from petrographic studies;

  • -

    Use of the information coming from logs of the non-cored wells in a diagenetic perspective; this step comprehends the realization of detailed CPI of wells and, if necessary, additional mineralogical analyses in order to fix a valuable mineralogical model;

  • -

    3D burial and thermal history reconstruction at the reservoir model scale using the reservoir model surfaces; the step implies the reconciliation of the regional explorative model with the layers and cell dimension of the reservoir one;

  • -

    Modelling of specific diagenetic phenomena through transport-reaction models, in order to assess the areal distribution of diagenetic drivers in the reservoir to be used as trends; as an example, carbonate cementation though faults is one of the issues; in this step, also the structural evolution of the reservoir is a key point;

  • -

    Reservoir quality prediction maps of the reservoir layers;

  • -

    Use of the maps as soft drivers in the reservoir models and results comparison with other model scenarios (e.g. inversion-based model) ;

  • -

    Evaluate the match with model prognosis and new well results

The work-flow was applied to a Cretaceous reservoir in West Africa. The reservoir quality variations are usually subtle at the reservoir scale and the effect of local variables may be strong. The resulting reservoir quality maps provided trends for properties distribution in the reservoir model obtaining remarkably different results compared to the inversion-based distribution and providing additional data for uncertainty analysis.

The reservoir quality prediction at the reservoir scale implies a new approach to the diagenetic problems and to the integration of log and petrographic data. The work-flow has to encompass all data in a collaboration space in order to produce a valuable tool for reservoir models.

You can access this article if you purchase or spend a download.