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Abstract

Summary

Modelling fracture intensity at the reservoir scale is challenging due to the scarcity of spatially exhaustive data and the sampling bias caused by the small support size of wellbore image logs. Multiple attempts have aimed at accounting for this sampling bias, but most have treated the fracture intensity measured from well logs as hard data that needs to be honoured during geostatistical modelling. In this paper, we demonstrate that there may be large uncertainties associated with upscaling well log derived fracture intensities to the reservoir scale, and then provide a mechanism for quantifying this uncertainty and provide a workflow for propagating it through the reservoir modelling process. Specifically, we use Bayesian inference from data collected empirically from a set of fit-for-purpose prior fracture networks. We then develop a workflow to model the reservoir fracture intensity uncertainty away from the wells, while integrating non-linear multivariate secondary data. 3D models of probability density function of reservoir fracture intensity are thus obtained for the entire reservoir, which can then be used to generate different scenarios of discrete fracture networks. Models created with this approach are compared between different simulation methods, demonstrating the value of accounting for non-linearity in secondary data.

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/content/papers/10.3997/2214-4609.201902234
2019-09-02
2024-04-26

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Quantifying Uncertainty of Fracture Intensity in Reservoirs
Conference Proceedings 2019, 1 (2019); https://doi.org/10.3997/2214-4609.201902234
/content/papers/10.3997/2214-4609.201902234
/content/papers/10.3997/2214-4609.201902234
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