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AN INNOVATIVE DIELECTRIC DISPERSION MEASUREMENT FOR BETTER EVALUATION OF THIN LAYERED RESERVOIRS APPLIED

IN A SOUTH ITALY WELL

M. Pirrone, N. Bona, M.T. Galli, F. Pampuri, Eni e&p, O. Faivre , M. Han, M. Hizem, L. Mosse, Schlumberger

This paper was presented at the 10th Offshore Mediterranean Conference and Exhibition in Ravenna, Italy, March 23-25, 2011. It was selected for presentation by OMC 2011 Programme Committee following review of information contained in the abstract submitted by the author(s). The Paper as presented at OMC 2011 has not been reviewed by the Programme Committee. ABSTRACT Modelling and exploiting very thin layered reservoirs is challenging and requires the capability to describe complex petrophysical models and to simulate accurate distributions of the petrophysical properties; this process often benefits from the availability of technologically advanced, fit-for-purpose measurements. A new dielectric dispersion tool was successfully field tested in thin layered low resistivity pays, whose layer thickness was always below the resolution capabilities of the standard logging tools; the high resolution measurements provided by new dielectric dispersion tool comes with the continuous measurement of dielectric dispersion at multiple frequencies in the MHz to GHz range. This paper describes the innovative petrophysical interpretation of a thin layered turbiditic sequence in the Pleistocenic levels of the Iblean Offshore (Sicily); the sequence was characterized by a high degree of variation of the layers’ thickness (from meters down to laminations), therefore the conventional interpretation was unable to explain the actual gas production rates in thin layers. In order to achieve the best petrophysical characterisation, a full set of WL logs was acquired, together with the new tool to be tested (dielectric dispersion tool) and wireline formation tester. A core was also cut in thin layers. During the interpretation, the gas shows from the masterlog were used to improve the definition of the zones with potential gas presence. The previous interpretation was improved by the integration of the broad frequency dielectric spectra made available by the dielectric dispersion tool, together with a suitable petrophysical dispersion model used to fully exploit the total tool measurements. The availability of a high resolution quantitative evaluation of the shale content and water filled porosity improved the definition of the reservoir geometry and petrophysical properties providing a better definition of SW profiles and a more robust evaluation of GOIP. INTRODUCTION The distribution and the quantification of the petrophysical properties in thin layered formations is still challenging in hydrocarbon exploration and production while the world demand for hydrocarbons forces these low resistivity reservoirs in a central position. New tools and interpretation models have been developed to improve their characterization [1], [2], [3], [4]; in this scenario the introduction of a new dielectric dispersion tool can play a fundamental role in providing powerful additional information for their petrophysical interpretation [5]. The strength of the aforementioned dielectric dispersion measurements is here illustrated by means of a suitable and representative case study. A brief description of the tool theory and radial processing is presented as well as the dielectric interpretation model, followed by the most significant results provided by this new approach when compared to the previous interpretation.

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The example comes from a field located in the southern Mediterranean offshore; the well (Well A) is vertical and was drilled in a gas bearing shaly sand turbiditic sequence. The reservoir is extremely complex since thick sand bodies (up to a few meters) are inter-bedded with thinly laminated sections where bed thickness can be lower than 1 cm. Formation water salinity is high (about 35 ppk), while the drilling fluid is a relatively fresh water based mud (FW-UD-LU, density 1.38 g/cc, salinity rarely exceeds 10 ppk). The well experienced encouraging gas shows during drilling. The presence of gas is also confirmed by pressure measurements and by a well test performed in a selected interval. A core was cut in a thin layers section confirming the presence of shale/silty-shale with thin/very thin inter-layered sand beds. A comprehensive set of wireline logs (standard and high resolution) has been acquired to provide a reliable formation evaluation: spectral gamma ray, resistivity, sonic, density, neutron, nuclear magnetic resonance and image logs. In addition, the new dielectric dispersion tool (Dielectric Scanner*) has been run, for the first time in this type of reservoir, in order to get a more accurate petrophysical model. Globally, the quality of the acquired data is very good, with exception of the image log which is biased by large borehole effects. CONVENTIONAL INTERPRETATION In the petrophysical characterization of thin layers, a conventional analysis has often proved to be meaningless; therefore, a fit-for-purpose workflow (TLA-C, Thin Layers Analysis & Characterization) has been followed in the interpretation of Well A [2], [3]. TLA-C is based upon three modelling steps:

1. Thin Layers Analysis: the objective is the definition of the geometrical model of the formation, represented as a sequence of sand and shale layers. The input to the process must be a high resolution log such as a micro-resistivity curve (from an image log) or the attenuation/conductivity log from a dielectric tool. The output is a binary lithology log expressing the thickness of the recognized sand layers (figure 1, track 7).

2. Resistivity modelling: the objective is to correct the resistivity profile of the thin layers for invasion and shoulder effects. The output is a modelled resistivity curve with high vertical resolution (figure 1, track 3).

3. Porosity modelling: the objective is to provide a total porosity curve (with appropriate vertical resolution), starting form predefined end-points (figure 1, track 8).

Once resistivity and porosity have been properly defined a standard water saturation analysis (Archie, Indonesia, Dual Water, etc.) can be performed (figure 1, track 9). A compared analysis of resistivity, sonic and water saturation curves provides a classification of the reservoir fluids in terms of GAS (red flag), GAS_POSSIBLE (orange), GAS_TRACES (yellow), WATER_POSSIBLE (light blue) and WATER (blue) (figure 1, track 10). The main drawback of this approach is that the binary output of sand and shale cannot describe a mixed lithology and, moreover, cannot quantify the volume of clay. The availability of Dielectric Scanner* allows the execution of a complete petrophysical formation evaluation in thin layers, overcoming the limitations of the TLA-C methodology just described. Combining the tool potentiality with a suitable petrophysical model a significant enhancement in the characterization of the reservoir quality can be achieved. (*) Mark of Schlumberger

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Fig 1: Thin Layers Analysis & Characterization

DIELECTRIC SCANNER* TOOL THEORY The Dielectric Scanner* tool measures the relative permittivity and conductivity ⁄ of the formation at different frequencies. These two physical parameters are the real and imaginary part of a single complex number, namely the complex permittivity:

⁄ where is the circular frequency and is the vacuum permittivity. This quantity drives the electromagnetic waves propagations in the formation (in non ferromagnetic materials which is dominant in geological formations). The conductivity term is sometimes written as

′′ , where is the conductivity at DC, and ′′ are the dielectric losses. To be sensitive to the relative permittivity, the operating frequency should be larger than few MHz. In the MHz to GHz range, the relative permittivity and conductivity dispersion (variation with frequency) of silicates is mainly driven by the pore water volume fraction , by the water DC conductivity (or water salinity) and by the clay content of the formation. The dielectric dispersion model that formalizes this sensitivity will be presented in a section below. The Dielectric Scanner* tool is a propagation tool: transmitters emit microwaves which propagate into the formation and reach several receivers. The amplitude and phase of the propagated waves with respect to the emitted ones depend on the complex permittivity of the formation, the wave frequency and the transmitter-receiver spacing. As wave’s frequency and tool’s geometry are known, the formation relative permittivity and conductivity are computed from amplitude and phase inversion.

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Dielectric Scanner* performs a continuous measurement of formation complex permittivity at four different frequencies, from about 20MHz up to about 1GHz. The tool is pad-mounted with two transmitters’ antennas and ten symmetrically located receivers’ antennas (see figure 2). Transmitters and eight receivers possess two orthogonal polarization modes, allowing anisotropy assessment. At a given operating frequency, the symmetry of the tool enables the creation of nine gain-free, borehole-compensated, attenuation and phase-shift measurements, and hence nine relative permittivity and conductivity measurements, each with a different radial sensitivity response. The tool finally measures 36 relative permittivities and 36 conductivities, nine couples at every frequency. All these measurements are used to obtain a dielectric radial profile near the wellbore. The measurements’ depths of investigation are mainly driven by the transmitter-receiver spacing: about four inches for the furthest arrays translating into a shallower radial sensitivity when compared to usual low frequency induction or laterolog tools. The theoretical vertical resolution of these measurements is about 1 inch.

Fig 2: Dielectric Scanner* sketch and pad layout

In the present paper, a classical mudcake correction model is used to interpret the radial response of the measurements (see figure 3). A mudcake is present on the borehole wall followed by the formation zone labelled XO. The radial analysis provides the thickness of the mudcake and the relative permittivity and conductivity dispersion of the formation [5], [6].

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Fig 3: Mudcake correction model

DIELECTRIC INTERPRETATION MODEL An extensive experimental and theoretical study was performed in order to develop a suitable interpretation model for the dielectric dispersion measurements in turbiditic, high salinity (about 35 ppk) shaly-sands. The following tasks were accomplished: (1) Creation of a statistically representative database containing conductivity and permittivity spectra, mineralogical and petrophysical data from more than hundred samples coming from different cored wells; (2) Understanding of the physical mechanisms influencing rock’s electro-magnetic properties at high frequency; (3) Development of a physically-based model that relates the measured conductivity and permittivity spectra to water content, water salinity and clay-related parameters; (4) Verification of the theoretical model against the measurements and definition of its main limitations and validity range for use in log interpretation. In the end, the dielectric dispersion measurements in the formation is reconciled with the dielectric dispersion model to extract the water volume fraction , the water salinity and a clay related parameter. In particular, the final model used in the present paper reads

, , , , with

, , , where is the petrophysical model (adapted from the one described in [7]), is the effective non-conductive phase real permittivity (involving rock grains and hydrocarbon), the parameter that will be further related to clay, represents the frequency, is the water complex permittivity, the water model that relates this permittivity to the water salinity , the pressure and the temperature . A suitable mathematical inversion in turn transforms the permittivity and conductivity dispersion for each layer into petrophysical parameters, given , and (figure 4).

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Fig 4: Petrophysical inversion example and scheme

The blue markers are the measured permittivities and the red ones are the measured conductivities with their corresponding uncertainties. The four permittivities and four conductivities are fitted together with the petrophysical model: the black curves represent the model’s best fit to the measurements given their individual uncertainties (figure 4a). The final output is the set of water-filled porosity, water salinity and parameter (figure 4b). 0, 1,

2 and 3 are the four measuring frequencies. A sensitivity analysis on the different parameters shows that the low-frequency dielectric dispersion is mainly driven by for both the permittivity and conductivity. For a shaly sand matrix, when clay content increases, the dielectric dispersion is enhanced, thus the parameter increases. Two well-known facts contributes to this behaviour (see [8] for more details):

• the geometric or textural heterogeneities of clay particles inducing important interfacial polarization effects;

• the low frequency bound layer and membrane polarization adding a new contribution to dielectric dispersion.

Both phenomena can be related to a particular characteristic of a shaly sand: the Cation Exchange Capacity (CEC). CEC represents the ability of a clay mineral to form an electrical double layer: it corresponds to the excess of cations over anions on the surface of a solid. It is expected that the CEC should be the bridge linking the response of the theoretical dispersion model, and in particular the , with the “shaliness” of the formation. In order to test the conjectured correlation between and CEC, several laboratory measurements were done on selected plugs coming from different cored wells. In particular, dielectric dispersions were measured and the petrophysical model applied. The output of the model inversion was then validated against the known porosities and brine salinities of the fully water saturated plugs. The overall good match between predicted and experimental parameters (water volume and water salinity) confirms the quality of the chosen model. Finally, the “ vs CEC” correlation is indeed confirmed (figure 5) and in particular, the red points correspond to plugs from Well A.

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Fig 5: Experimental correlation between CEC and model parameter

It is important to mention that no important changes in the correlation have been observed regarding partially saturated samples. Moreover, the same correlation is also confirmed on the field and can be safely transferred to the logs so that no corrections need to be taken into account for well site conditions. Once the CEC has been obtained, physically-based assumptions (see, for example, [9]) together with a good knowledge of rock mineralogy also yield a direct estimation of the bound water content and/or the volume of clay (dry and wet), schematically:

,…

, This workflow will be crucial in the definition of the quality of the reservoir as we are going to show in the following sections. DISCUSSION OF THE RESULTS In order to illustrate the additional information provided by Dielectric Scanner* and to compare these results with the previous interpretation (figure 1) two intervals with different characteristics have been chosen and will be properly described. Cored interval The interval consists of 2 distinct zones: a thinly laminated section (down to cm scale) from top to about XX10.35 m and a thick silty-shale section inter-bedded with thin silty laminations and split into 2 sub-zones by a thick sand layer at XX12 m. The interval was cored but, unfortunately, due to the unconsolidated nature of the formation, the core was only partially recovered and in very bad conditions. Sand layers, in particular, were mostly washed out; for this reason, porosity and permeability couldn’t be measured with sufficient reliability. On the contrary, a few samples could be cut in the shale sections and analyzed, so that direct measurements were available for validation and further calibration: grain density; mineralogy, granulometry; CEC.

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Fig 6: Core photography and sedimentological description

The results of the application of the new dielectric interpretation model are shown (with their uncertainties) in figure 7:

• Track 3 and track 4: continuous dispersion measurements (EPSI and COND) for the four frequencies; at each depth level, figure 4a could be reproduced;

• Track 5: water filled porosity (PWXO) from dielectric petrophysical model inversion and total porosity (PHIX) from density-neutron logs;

• Track 6: fluid salinity (FSXO) from dielectric petrophysical model inversion ; • Track 7: parameter (MN) from dielectric petrophysical model inversion; • Track 8: water saturation from previous interpretation (SWT) and from dielectric

petrophysical model (SWXO); SW is scaled between 1.0 and 0.5; the coloured zones, corresponding to SAND beds, highlight the hydrocarbon content;

• Track 9: anisotropy flag computed from Dielectric Scanner’s measurements; • Track 10: mineralogy from core; • Track 11: clay mineralogy from core.

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Fig 7: Dielectric dispersion output

As it can be seen in figure 7, the lithological classification provided by the anisotropy flag (track 9) is in very good agreement with the previous interpretation (track 8) and the other data (image log, mineralogy from core); the thin laminations, described also by sedimentologists, are highlighted by the variations in EPSI and COND (tracks 3 and 4) even where the image log, due to bad hole, is not so clear. The salinity indicator (FSXO, track 6) takes advantage of the shallow depth of investigation of the tool, showing the variations due to the mixing of mud filtrate (about 10 ppk) and formation water (about 35 ppk). A high FSXO value implies that invasion is negligible and, therefore, permeability rather low. Under this light, FSXO can be also used as a reservoir quality indicator. The parameter (MN, track 7) and the anisotropy flag may be used as qualitative lithological indicators; for a more quantitative estimation of clay a new set of results is computed. CEC is obtained through the correlation illustrated in figure 5; in the analyzed example bound water volume (and hence clay volume) is calculated using the relationship among CEC, grain density and total porosity as described in [9]. These outputs provide the added value of a high resolution petrophysical characterization in thin layered environments. This is shown in figure 8 where the Dielectric Scanner* CEC measurement, and post-computed CBW and VCL are compared to the other well data and interpretation results. CEC (track 9, red curve) computed from the correlation shown in figure 5 is compared to lab measurements (green dots) and the results are satisfactory. The same agreement can be seen in track 12, where the estimated dry clay fraction (orange curve) is compared to the lab measurements (the blue dots represent grain size < 2 micron). The quantitative estimate of clay (bound water and wet clay volume) is shown in tracks 10 and 11, compared to the correspondent volumes from NMR interpretation (through a T2 cutoff of 3ms used to define the clay bound water volume). In the top thinly laminated section CBW and the volume of wet clay are lower than the NMR results, this discrepancy is due to a lower vertical resolution of the NMR tool which averages the response and minimizes the contribution of the very thin sand and silt layers. These very thin layers are clearly visible in the image (track 2) and have also been described by the sedimentologists (figure 6).

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The upper and lower shale sub-zones may look very similar on the image, probably due to its low quality, but the dielectric response highlights two important features:

• The real clay content (CBW and VCL, tracks 10 and 11) is lower than predicted by NMR, thus implying the presence of a significant amount of very fine grains which are not clay minerals;

• The 2 sub-zones are more different than shown by the image since a decreasing trend in clay content is visible in the upper sub-zone.

The interval has been recognized as generally gas bearing by the previous interpretation (figure 8, track 6); the gas potential of the thicker beds (red flag) is confirmed by SWXO (track 7, green fill), while the thin beds, where the presence of gas is more uncertain, are in fact disproved by the new interpretation. The analysis of the salinity profile, FSXO (track 4), highlights the presence of invasion in the sand beds, where the value is close to mud filtrate salinity, while in shalier or clay beds the value is in the range of the water formation salinity.

Fig 8: Dielectric model output versus previous interpretation and core data

Tested interval The interval (figure 9) is mostly shaly with sparse thin (about 15 cm) and only three thicker (from 30 to 60 cm) sand beds. The modelled porosity of the sand beds is rather high (32 %). The lithological classification (tracks 8, 10 and 11) highlights the presence of three main facies: the three sand bodies seen also by TLA-C (@ YY92.36 m, @ YY95.81 m and @ YY99.64 m), and a further differentiation in the shaly background, where the thick massive shale in the bottom half (YY98.38 m – YY99.64 m) is characterized by a higher, more homogeneous clay content with respect to the inter-bedded sections at the top and bottom. This interval has been tested (Qgas 166 KSm3/g) producing dry gas. According to the previous interpretation the main producers are the thick bodies (track 6, red flag), while the presence of gas in the thinner beds is uncertain (track 6, orange and yellow flag). This prediction is confirmed by the new dielectric interpretation in the thick beds and often improved in the thinner which are “upgraded” to certain GAS by SWXO (track 7, green fill). The salinity profile FSXO (track 4) is in agreement with the lithological and fluid classification, and shows a deeper invasion (expected to correspond to better permeability) in the thick sands than in the thinner beds.

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Thanks to the integration of conventional and dielectric interpretations, it is now possible to characterize:

• the clean sand reservoir (thick sand bodies); • an additional reservoir facies (of lower quality) consisting of thin alternated silty-sands

(with low invasion and low clay content), from top down to YY95.32 m; • the massive non productive shales with high clay content; • a non-reservoir facies of alternated shaly-sands and silty-shales with no invasion and

medium to high clay content (from YY96.20 m down to YY98.39 m), possibly inter-bedded with sparse gas bearing thin beds.

Fig 9: Dielectric model output versus previous interpretation

CONCLUSIONS A new interpretation model has been developed to maximize the added value of a new dielectric dispersion logging tool in thin layered sand-shale reservoirs. The field test application proved that a correct interpretation of the multi frequency data provides:

• a direct gas indicator which doesn’t require an often time consuming interpretation; • a high resolution lithological classification together with the quantitative estimate of

clay content, through dielectric dispersion analysis and CEC evaluation, thus overcoming the limitation of conventional logging tools and interpretation models;

• a qualitative indicator of the reservoir quality, based on a combination of apparent formation water salinity and CEC.

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ACKNOWLEDGEMENTS The authors wish to thank Eni e&p, Edison SpA and Schlumberger for permission to publish this work. They also wish to thank Massimiliano Borghi, Werner Klopf, Nikita Seleznev and Giovanni Tumbiolo for their help and support through this work. REFERENCES [1] Rosthal, R. et al.,”Field tests of an experimental fully triaxial induction tool”, SPWLA, 2003 [2] Chelini, V. et al., ”Petrophysical characterization of thin layered reservoirs: a case history from the Adriatic Basin”, OMC, 2009 [3] Galli, M.T. et al., “An integrated petrophysical interpretation methodology in thin layers and low resistivity pays”, OMC, 2005 [4] Thomas, E.C. et al., “The distribution of shale in sandstones and its effect upon porosity”, SPWLA, 1975 [5] Hizem, M. et al., “Dielectric dispersion: a new wireline dielectric measurement”, SPE, 2008 [6] Mosse, L et al., “Dielectric Dispersion Logging in Heavy Oil: A case study from the Orinoco belt”, SPWLA, 2009 [7] Stroud, D. et al., “Analytical model for the dielectric response of brine-saturated rocks”, Phys. Rev. B 34, 5145, 1986 [8] Chelidze, T. et al., “Electrical spectroscopy of porous rocks: a review – I. Theoretical models”, Geophysical Journal International, 137: 1-15, 1999 [9] Clavier C., et al., "Theoretical and experimental bases for the Dual Water model for interpretation of shaly sands", SPE, 1991