Conventional approaches for reserves identification and quantification use formation evaluation (FE) logs and cores; the former being usually corrected for environmental artefacts before they are used in petrophysical models. The corrections are based on logging environmental assumptions which are generally valid for vertical wells and have been developed and continuously used since wireline logging exists. FE logs acquired in high-angle wells are exposed to different environmental conditions for which the traditional corrections do not provide sufficient corrections. Asymmetric invasion and tool eccentricity result in an inaccurate estimation of reservoir porosity. Separation of apparent resistivity curves and polarization horns cause uncertainty in saturation estimates. Due to different depths of investigation, deep-reading FE logs obtain responses from multiple formations, which create large shoulder bed effects. True formation resistivity may be underestimated if conductive layers exist in the sensitive volume of the resistivity measurements.

The accumulation of these uncertainties lead to inappropriate decision making from petrophysical background information. Hydrocarbon distribution along well trajectories can be erroneous causing false completion decisions and potential production limitations. Saturation logs affected by geometric effects provide inaccurate saturation-height models which result in a wrong assumption about residual hydrocarbons and water production. Increasing saturation uncertainties also falsify the distinction between mobile and irreducible hydrocarbon, so that the actual production differs with what was expected. Early water breakthrough with tremendous treatment efforts may be the result of such erroneous decisions. Eliminating the uncertainty on FE logs in high-angle wells allows better and more accurate decision making leading to a reduction in time and costs.

We present different case studies for pay zone identification, and hydrocarbon volume correction based on petrophysics applied on a formation response model. Resistivity modeling is based on a 1D fast forward modeling algorithm implemented in an inversion scheme. Additional conventional logs (neutron, density, gamma ray) are modelled geometrically using weighted average functions to correct for shoulder bed effects. Of significant importance is the use of common formation boundaries, which are interpreted on borehole images to provide a consistent formation response model.

Modeling results from different case histories are presented, and the differences to conventional petrophysical evaluation are highlighted demonstrating the added value for operating companies. Higher hydrocarbon saturations calculated from modelled logs and more precise boundary placement may have changed the decisions with respect to completion design and production strategy in earlier stages. This study shows that the error in hydrocarbon estimation based on traditional FE techniques can be significant (11%).

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