A novel approach to a quantitative estimate of permeability from resistivity log measurements

Description of the material. In this paper a novel methodology for the estimation of the formation permeability, based on the integration of resistivity modeling and near wellbore modeling, is presented. Results obtained from the application to a real case is shown and discussed. The well log interpretation process provides a reliable estimation of the main petrophysical parameters such as porosity, fluid saturations and shale content, but the formation permeability is traditionally obtained through laboratory tests on plugs, at the scale of centimeters, and through well test interpretation, at the scale of tens or hundreds of meters. However, log measurements, and in particular resistivity logs, are strongly affected by the presence of the near wellbore zone invaded by mud filtrate. In turn, the extension of the invaded zone depends on formation properties and, in particular, on permeability. As a consequence, the resistivity measured by the tools (the apparent resistivity) has to be properly corrected through a resistivity modeling process to obtain the true formation resistivity and the geometry and resistivity of the invaded zone. Resistivity profiles within the invaded zone are function of fluid properties, petrophysical properties and rock-fluid interaction properties. The novelty of the approach is to numerically simulate the mud invasion phenomenon and match the resistivity profile provided by resistivity modeling to estimate the formation permeability. In the proposed methodology the match of the resistivity profile is obtained by integrating the near wellbore simulator with an optimization algorithm. Application. This novel approach was applied to a heterogeneous shaly-sand oil-bearing reservoir in the Norwegian offshore area. The analyzed sequence was characterized by a high degree of variations in the layers’ thickness, from meters down to below tools’ vertical resolution. A complete set of wireline logs were acquired in the considered well; several cores were cut and routine and special core analyses performed.

Results, Observations, and Conclusions. First, a conventional petrophysical characterization was achieved and the appropriate resistivity corrections were calculated. Then, the modeled resistivity was used as the input for the optimization algorithm so as to obtain a continuous quantitative estimation of permeability in the entire logged interval. The results were satisfactorily compared to core measurements: in both thick conventional layers and thinner beds the match was very accurate.

Significance of subject matter. The new approach provided a robust permeability estimate also in un-cored intervals and, more generally, can be used to predict permeability in un-cored and un-tested wells.

Description of the material. In this paper a novel methodology for the estimation of the formation permeability, based on the integration of resistivity modeling and near wellbore modeling, is presented. Results obtained from the application to a real case is shown and discussed. The well log interpretation process provides a reliable estimation of the main petrophysical parameters such as porosity, fluid saturations and shale content, but the formation permeability is traditionally obtained through laboratory tests on plugs, at the scale of centimeters, and through well test interpretation, at the scale of tens or hundreds of meters. However, log measurements, and in particular resistivity logs, are strongly affected by the presence of the near wellbore zone invaded by mud filtrate. In turn, the extension of the invaded zone depends on formation properties and, in particular, on permeability. As a consequence, the resistivity measured by the tools (the apparent resistivity) has to be properly corrected through a resistivity modeling process to obtain the true formation resistivity and the geometry and resistivity of the invaded zone. Resistivity profiles within the invaded zone are function of fluid properties, petrophysical properties and rock-fluid interaction properties. The novelty of the approach is to numerically simulate the mud invasion phenomenon and match the resistivity profile provided by resistivity modeling to estimate the formation permeability. In the proposed methodology the match of the resistivity profile is obtained by integrating the near wellbore simulator with an optimization algorithm. Application. This novel approach was applied to a heterogeneous shaly-sand oil-bearing reservoir in the Norwegian offshore area. The analyzed sequence was characterized by a high degree of variations in the layers’ thickness, from meters down to below tools’ vertical resolution. A complete set of wireline logs were acquired in the considered well; several cores were cut and routine and special core analyses performed.

Results, Observations, and Conclusions. First, a conventional petrophysical characterization was achieved and the appropriate resistivity corrections were calculated. Then, the modeled resistivity was used as the input for the optimization algorithm so as to obtain a continuous quantitative estimation of permeability in the entire logged interval. The results were satisfactorily compared to core measurements: in both thick conventional layers and thinner beds the match was very accurate.

Significance of subject matter. The new approach provided a robust permeability estimate also in un-cored intervals and, more generally, can be used to predict permeability in un-cored and un-tested wells.


ISSN 1121-9041

CiteScore:
2020: 3.8
CiteScore measures the average citations received per peer-reviewed document published in this title.
CiteScore values are based on citation counts in a range of four years (e.g. 2016-2019) to peer-reviewed documents (articles, reviews, conference papers, data papers and book chapters) published in the same four calendar years, divided by the number of these documents in these same four years (e.g. 2016 —19).
Source Normalized Impact per Paper (SNIP):
2019: 1.307
SNIP measures contextual citation impact by weighting citations based on the total number of citations in a subject field.
SCImago Journal Rank (SJR)
2019: o.657
SJR is a prestige metric based on the idea that not all citations are the same. SJR uses a similar algorithm as the Google page rank; it provides a quantitative and a qualitative measure of the journal's impact.
Journal Metrics: CiteScore: 1.0 , Source Normalized Impact per Paper (SNIP): 0.381 SCImago Journal Rank (SJR): 0.163

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