Geological simulation during the drilling process based on interpolation by inverse distance weighting to account for the lateral variability of the section being developed

Background. In difficult lithological and facial conditions, the tasks of geological steering require improved approaches to constructing a geological section during drilling. The selected method should be prompt, detailed, and capable of flexible adjustment of the section properties based on information received during drilling.

Aim. To apply a geological simulation algorithm to reconstruct the properties of a section with a high lateral variability, enabling flexible adjustment of test wells and the required update rate in real time.

Methods. The problem was solved using the mathematical method of inverse distance weighting (IDW), i.e., a type of deterministic multivariate interpolation based on calculating values at unknown points using a weighted average of known points. The IDW method is effectively used to interpolate diverse data sets, including temperature, substance concentration, and other spatially dependent variables. This method was used to reconstruct the geological section under study without conflicting with seismic data. The results were compared with those obtained by alternative specialized 3D geophysical simulation programs.

Results. The approach of reconstructing a synthetic section along the actual wellbore was used. This approach meets the requirements of geological steering in terms of promptness and detail, providing a geological picture consistent with such a priori data as wellbore logs of the surrounding area and seismic attributes of the drilling area.

Conclusion. To mitigate the shortcomings of grid models and the DSC method, we used an algorithm to develop a reservoir model along the horizontal well of interest. The possibility of adjusting the influence of the test well during section formation allowed seismic data to be taken into account in the simulation process. High convergence of models constructed using the proposed approach and the kriging method is demonstrated, and their application to real data is demonstrated.

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