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Dielectric dispersion measurements are increasingly used by petrophysicists to reduce uncertainty in their hydrocarbon saturation analysis, and subsequent reserves estimation, especially when encountered with challenging environments. Some of these challenges are related to variable or unknown formation water salinity and/or a changing rock texture which is a common attribute of carbonate reservoirs found in the Middle East. A new multi-frequency, multi-spacing dielectric logging service, utilizes a sensor array scheme which provides wave attenuation and phase difference measurements at multiple depths of investigation up to 8 inches inside the formation. The improvement in depth of investigation provides a better measurement of true formation properties, however, also provides a higher likelihood of measuring radial heterogeneity due to spatially variable shallow mud-filtrate invasion. Meaningful petrophysical interpretation requires an accurate electromagnetic (EM) inversion, which accommodates this heterogeneity, while converting raw tool measurements to true formation dielectric properties. Forward modeling solvers are typically beset with a slow processing speed precluding use of complex, albeit representative, formation petrophysical models. An artificial neural network (ANN) has been trained to significantly speed up the forward solver, thus leading to implementation and real-time execution of a complex multi-layer radial inversion algorithm. The paper describes, in detail, the development, training and validation of both the ANN network and the inversion algorithm. The presented algorithm and ANN inversion has shown ability to accurately resolve mud filtrate invasion profile as well as the true formation properties of individual layers. Examples are presented which demonstrate that comprehensive, multi-frequency, multi-array, EM data sets are inverted efficiently for dis-similar dielectric properties of both invaded and non-invaded formation layers around the wellbore. The results are further utilized for accurate hydrocarbon quantification otherwise not achieved by conventional resistivity based saturation techniques. This paper presents the development of a new EM inversion algorithm and an artificial neural network (ANN) trained to significantly speed up the solution of this algorithm. This approach leads to a fast turnaround for an accurate petrophysical analysis, reserves estimate and completion decisions.

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A new wire-line high-definition formation resistivity imaging instrument employing a 'two-electrode' measurement configuration was developed for application in low-resistivity formations drilled with non-conductive (oil-based) mud. The new instrument and measurement principles are described with modeled synthetic responses. Several field log examples with image interpretation are shown.The high-spatial resolution of a 'two-electrode' arrangement has been well documented for nearly 25 years in conductive boreholes [1] and has been used in oil-based mud (OBM) for more than a decade [2]. However, the lower formation resistivity limit in OBM often precluded realizing the benefits in many important offshore deep-water plays, such as offshore East Malaysia and the Gulf of Mexico. The new instrument employs multi-frequency impedance measurements to overcome this low-resistivity low-contrast (LRLC) formation limitation.The imaging device measures electrical impedance across electrodes and produces two image logs: an image of the real part of impedance that is calibrated to yield formation resistivity and an image of the imaginary part of impedance that conveys information about image quality and borehole rugosity. The instrument employs six individually articulated pads, each containing ten sensor electrodes that provide a 79% surface coverage in an 8-in borehole. Typical spatial-resolution is better than 0.8-in vertically and 0.3-in azimuthally, providing a high-definition image of the near-wellbore formation. Pad-to-pad vertical offset is less than 6 inches. This short offset means there is never any pad overlap and guarantees high coverage of borehole surface while the instrument rotates during logging.Presented are field examples from typical logging environments found offshore Malaysia. Detailed geologic features such as turbidite thin-beds, slumping, debrites, faulting and fracturing in low resistivity formations are clearly resolved in highdefinition image logs. The high-definition images deliver information necessary for detailed sedimentological studies, reducing uncertainty when calculating hydrocarbon saturations in LRLC sandstone successions, and provide more precise and accurate net-to-gross calculations.