36 Oilfield Review

Zapping Rocks

By zapping a formation with microwave energy, dielectric logging tools can analyze

freshwater environments and identify movable hydrocarbons. The measurements

made by these tools are especially useful in characterizing heavy-oil reservoirs. After

a long period of niche application, a new tool is breathing life into this technology.

This resurgence is aided by a recently developed dispersion technique that evaluates

carbonate rock texture and shale effects in sandstones.

Romulo Carmona Petróleos de Venezuela, S.A.Caracas, Venezuela

Eric Decoster Rio de Janeiro, Brazil

Jim HemingwayHouston, Texas, USA

Mehdi HizemLaurent MosséTarek RizkClamart, France

Dale JulanderChevron U.S.A. Inc.Bakersfield, California, USA

Jeffrey LittleBakersfield, California

Tom McDonaldPerth, Western Australia, Australia

Jonathan MudePetroleum Development OmanMuscat, Sultanate of Oman

Nikita SeleznevCambridge, Massachusetts, USA

Oilfield Review Spring 2011: 23, no. 1. Copyright © 2011 Schlumberger.Dielectric Pro, Dielectric Scanner, EPT, FMI, HRLA, LithoDensity, MR Scanner, Platform Express, and Rt Scanner are marks of Schlumberger.

Petroleum technologists enjoy finding new meth-ods to poke, prod and probe the Earth. One such technique, dielectric logging, involves zapping a formation with microwaves to determine rock and fluid properties. Although not widely used within the petrophysics community, dielectric informa-tion answers a number of difficult interpretation questions. The success of a recently introduced dielectric tool is generating considerable interest because it provides information that isn’t readily available from standard logging suites.

Introduced to the oil and gas industry in the late 1970s, dielectric logging did not find univer-sal acceptance. Lack of acceptance of new tech-nologies is not unusual. Technologies often need time to evolve, gain a level of appreciation by users and, finally, be assimilated. The first com-mercial microwave oven, for example—a radi-cally new technology at the time—was introduced in 1947. It was taller than the average man and weighed more than three times as much. Not sur-prising, domestic sales were nonexistent. But

1. Serra O: Well Logging Handbook. Paris: Editions Technip, 2008.

2. Dispersion is the variation in dielectric permittivity and conductivity when measured at different frequencies.

3. Serra, reference 1.4. Named for James Clerk Maxwell, this set of partial

differential equations unifies the fundamentals of electricity and magnetism. There are four basic equations, but multiple iterations can be developed from them. For a full derivation of the equations related to electromagnetics and dielectric response: Serra, reference 1.

5. Depending on the reference source, microwaves are generally considered electromagnetic waves with wavelengths from 1 m to 1 mm, which corresponds to a frequency range of 300 MHz to 300 GHz.

Spring 2011 37

today, compact units that little resemble those early industrial-grade models are standard equip-ment in kitchens around the world.

Radically new technologies fall into different categories of acceptance. Some fully supplant older technologies. Others supplement existing methods without replacing them. In the example of the microwave oven, although it may be possi-ble to prepare a complete multicourse meal with one, rarely is it the primary method of meal prepa-ration. However, as a means for reheating food, a microwave oven is usually a better option than previous methods, such as a conventional oven. Clearly, it is a supplemental technology.

Similarly, a dielectric tool is a supplemental technology for the oil and gas industry. These tools were originally developed to analyze forma-tions with freshwater, low-salinity water or where water salinity was unknown. They respond pri-marily to the water in the pore network and mea-sure water-filled porosity. From water-filled porosity, resistivity-independent fluid saturations can be derived. Log analysts also combined dielectric measurements with data from deeper-reading tools to identify zones with hydrocarbon mobility, which is crucial information for evaluat-ing heavy-oil reservoirs.

Unfortunately, data quality for early- generation tools was frequently compromised by hole rugosity—a common condition in the environments in which these tools offered the greatest benefit—and measurement accuracy was difficult to quantify. After sparking initial interest within the petrophysics community, dielectric tools never reached a level of universal acceptance for formation evaluation. The intro-duction of nuclear magnetic resonance (NMR) tools in the 1990s virtually ended the use of microwave-based dielectric tools, except in some specialized applications.1

The recently introduced Dielectric Scanner multifrequency dielectric dispersion service is designed to overcome limitations of earlier tools. It has the ability to measure water-filled porosity, and, in conjunction with other porosity measure-ments, fluid saturations. Its collocated trans-mitter-receiver arrays probe the formation at multiple depths of investigation and offer stand-alone oil mobility assessment in heavy-oil reservoirs. In addition, the tool offers a new measurement—dielectric dispersion—with which petrophysicists can determine rock textural prop-erties and shale effects.2

This article presents the basic theory of dielectric measurements applied to petrophysics, including a description of the new dielectric dis-persion technique. Case studies describe textural

analysis of carbonates, evaluation of formations with variable- or low-salinity formation water and heavy-oil applications.

Microwave Frequency LoggingThree parameters define a rock electrically: magnetic permeability, electrical conductivity and dielectric permittivity.3 Reservoir rocks com-prise mostly nonmagnetic minerals, thus their magnetic permeability is negligible. Because the rock matrix has little conductivity, the electrical conductivity of the formation, the inverse of resistivity, is primarily a function of the fluids that fill the pore network and the connectivity of the pores. Formation conductivity is generally measured with induction and laterolog devices and is a crucial input, along with porosity, in Archie’s water saturation equation.

Dielectric permittivity is not a measurement that is generally considered when evaluating reser-voir rocks. It is defined as the frequency-dependent capacity of a medium to store energy from an applied field and is a function of the degree to which a material becomes polarized in the presence of an electric or electromagnetic field. A material’s dielectric permittivity, ε, can be expressed as its dielectric constant, which is the permittivity nor-malized to the lossless environment of a vacuum. The dimensionless dielectric constant is not really a constant because it is a function of the frequency of the electromagnetic field. It is computed from dielectric data using Maxwell’s equations.4

For most minerals and fluids found in reser-voir rocks, with the important exception of water, dielectric permittivity is quite low (above). For water, the absolute dielectric permittivity, ε*, comprises three terms: a real term related to polarizability, a complex term related to conduc-tivity at a given frequency and a second complex term related to dipolar relaxation (below).

Because of the large difference between matrix and water permittivities, a reservoir rock’s dielec-tric permittivity measured in the microwave range is primarily a function of the water filling the pores.5 The permittivity values of oil and the matrix are similar, and as a result, the presence of hydrocarbons makes it impossible to invert for

> Dielectric constants for common minerals, rocks and fluids.

Petroleum 2.0 to 2.4Gas 1.0

Water 56 to 80

Gypsum 4.16Anhydrite 6.35

Sandstone 4.65Dolostone 6.8Limestone 7.5 to 9.2

Shale 5 to 25Dry colloids 5.76Fresh water 78.3

Minerals, rocks, fluids Relative dielectric constant(relative to vacuum)

> Dielectric permittivity plot for water. The absolute dielectric permittivity, ε*, for bulk water comprises a combination of real and complex terms and is a function of the frequency of the electromagnetic field. The real component, εr (blue), is linear to about 1 GHz and then decreases as the frequency of the electromagnetic field increases. The complex conductivity term (black) depends on the frequency of the electromagnetic field, ω, and is normalized for the permittivity of vacuum, ε0. The conductivity component decreases as the frequency increases, especially across the frequency range used in downhole dielectric tools. The second complex term, iεx (purple), is related to dipolar relaxation and peaks around 20 GHz. It has minimal effect on the total permittivity measured by downhole tools because they operate in a frequency range below about 1.1 GHz.

Dipolar

Atomic Electronic

Bulk water 25°C

1.1 GHz 20 GHz Infrared Ultraviolet

Frequency

rε

xε

σωε

= + +i*r i x

0

0

ε ε εσωε

38 Oilfield Review

both water-filled porosity and total porosity using dielectric data alone. However, in conjunction with an independent porosity measurement, dielectric data can quantify fluid saturations.

A second factor affecting a rock’s dielectric permittivity and conductivity is the manner in

which its different constituents are mixed together. This factor is generally small when mea-sured at a frequency of about 1 GHz but domi-nates the measurement at lower frequencies. For this reason, rock texture and shale content can cause frequency-sensitive dispersion in both permittivity and conductivity measurements.

Schlumberger introduced the first commer-cial downhole device capable of measuring dielectric properties using microwaves, the EPT electromagnetic propagation tool, in the late 1970s.6 It operated at a single frequency of 1.1 GHz and measured attenuation and phase shift of waves traveling through the formation. Mathematical inversions were then applied to the attenuation and phase shift to derive petro-physical properties—including dielectric per-mittivity, conductivity and water-filled porosity (left). Petrophysicists determined fluid satura-tions by comparing this water-filled porosity to the total porosity.

After the introduction of the EPT tool, other service companies developed dielectric tools, each designed to operate at a company-chosen frequency. Because of the frequency dependence of dielectric information, data recorded at differ-ent frequencies often yielded different results and comparing the results between wells could be problematic. The differences can be attribut-able to the measurement’s sensitivity to rock tex-ture, clay content and fluid salinity. These sensitivities, however, were not well understood.

Water-filled porosity from the earliest tools was computed following the tpo method, which is based on the propagation time of the electromag-netic waves as they passed through the rock (below left). This calculation involved a simple transform that resembles the Wyllie equation used to compute sonic porosity. It requires knowl-edge of the water salinity and temperature to esti-mate the propagation time in formation water.

Formations, however, consist of more than just water. There are pore fluids—water, oil and gas—and minerals in the rock matrix. Relationships between each of these constituents, as they exist in the formation, can alter the electromagnetic waves. The tpo method was not adequate for com-puting water-filled porosity and, therefore, various mixing laws have been proposed to account for the interaction of the electromagnetic field with the various elements in the formation.7

The complex time average (CTA) method, com-bining both phase-shift and attenuation measure-ments, was an early technique for calculating petrophysical properties of a mixture. Two inde-pendent equations can be written, one for phase shift and one for attenuation of the signal, to deter-mine the volume of water in the pore network.

An alternate approach, the complex refrac-tive index (CRI) method, is based on Maxwell’s equations. Because of the time-dependent sinu-soidal nature of an electromagnetic field, the time derivative of Maxwell’s equations can be greatly simplified.8 It is reduced to two terms that

>Microwaves to petrophysics. The dielectric tool transmits an electromagnetic wave (red sine wave) with a frequency ω into a formation where, as a result of interactions with the fluids and minerals, its amplitude is attenuated and the velocity of the wave changes. The velocity change corresponds to a measureable phase shift. The change in amplitude, Α, and the phase shift of the wave (black sine wave) after it has passed through the media are measured at the receiver; they are functions of the initial frequency, ω, dielectric permittivity of the media, ε, the conductivity of the media, σ, and the transmitter-to-receiver spacing, r. The change in amplitude and phase shift are then inverted to output permittivity, conductivity and water-filled porosity, φ.

Transmitter ReceiverChange in amplitudePhase shift

Frequency,

} {φ

ƒ –1

ConductivityWater-filled porosity

PermittivityAmplitude change ΑPhase shift

Transmitter-to-receiver spacing, r

VacuumMedium

= ƒ r, , , )(Receiver voltageω ω

σ

σ

ε

ε

> Evolution of dielectric petrophysics. An early porosity transform for dielectric tools, the tpo method (top), looks similar to the Wyllie equation used to compute porosity from acoustic data. The transfrom is valid only for lossless traveltime, which is not representative of the downhole environment. The complex time average (CTA) method (middle) provides water-filled porosity from attenuation, traveltime and water saturation in the flushed zone. It includes corrections for losses, but is not as accurate as the complex refractive index (CRI) method (bottom). The CRI method uses the dielectric permittivity, ε*, measured at downhole conditions. Matrix, hydrocarbon and water permittivities, used in the equation, are also adjusted for downhole conditions. Water saturation is solved for using a total porosity, φT, provided by another source, such as the crossplot porosity from density and neutron tools.

φtpo tpma

tpwo tpma=

––

= lossless traveltimetpo = traveltime through the matrixtpma

tpwo = lossless traveltime through water

tpo methodEPT

= water saturation in the flushed zoneSxo

= attenuation through water = attenuation (tool measurement)

tpma = traveltime through the matrixtpw = lossy traveltime through watertph = lossy traveltime through hydrocarbon

tpl = lossy traveltime (tool measurement) CTA method

= porosityφ Sxotpw tph tpmaSxotpl = + +1– 1–

Sxo=

( ) ( )

w

Α

Α

wΑ

Α

= water saturationSφ = total porosity

CRI method = dielectric permittivity*

S S( ) ( ) )(* **

= + +1– 1– oil

= permittivity of the matrix = permittivity of water = permittivity of hydrocarbonoilw

ww

w

T m

m

wT

T

ε ε ε ε εεεε

φ φφ

φ φ

φ

Spring 2011 39

define the absolute dielectric permittivity, a real-number permittivity term and a complex frequency-dependent conductivity term.9 The complex number term consists of the angular fre-quency of the applied electromagnetic field and a conductivity that can be expressed as a real number. A single equation transforms the propa-gation time and attenuation into physical quanti-ties—permittivity and conductivity. Because matrix minerals and hydrocarbons are poor con-ductors and generally act as insulators, the con-ductivity signal is dominated by the water in the region sensed by the tool—the flushed zone. Solving for the dielectric conductivity provides the conductivity of the fluids that fill the pores in the near-wellbore region.

Mud filtrate from the invasion process enters the flushed zone and alters the properties of the fluids that were originally in place. This invasion is not uniform or easily quantified. Early methods for computing dielectric properties, such as the tpo method, assumed fixed values of fluid conduc-tivity. Directly solving for the conductivity of the fluid in this region, which is possible with the CRI method, provides more-accurate results for the water-filled porosity measurement. For this and other reasons, the CRI method has become the generally accepted technique for computing pet-rophysical properties from dielectric data.10

In addition, textural parameters of rocks, which are difficult to quantify from the tools used in conventional logging suites, can be derived from the dispersion of dielectric data made at mul-tiple frequencies. At frequencies around 1 GHz, tex-tural parameters have limited effects on outputs derived from the CRI method. An exception, how-ever, is high-salinity environments, which can enhance textural dispersion even with frequen-cies in the 1-GHz range. At lower frequencies, tex-tural effects significantly impact dielectric permittivity measurements—this is especially true in carbonate reservoirs.11 Several dispersion models have been developed to account for the frequency-dependent phenomenon.

A dispersion analysis, discussed below, has been developed that uses multifrequency dielec-tric outputs to quantify the cementation expo-nent, m, which is one of two crucial texture-related inputs in Archie’s water saturation equation. For carbonates, values for these parameters are gen-erally derived from core data, which are then applied to offset wells. The method used for mea-suring these parameters from core is a lengthy and expensive process. With continuous outputs of m for Archie’s equation from dielectric dispersion

information, petrophysicists can better evaluate carbonates using downhole data. Accurately char-acterizing texture in this rock type is important because an estimated 60% of the world’s remain-ing oil is found in carbonate reservoirs.

Dielectrics and Dipoles Materials that become polarized when exposed to a static electromagnetic field are referred to as dielectrics.12 A material’s susceptibility to polar-ization is directly related to its dielectric permit-tivity. There are three primary polarization

mechanisms that can be related to petrophysical properties: electronic polarization, molecular ori-entation and interfacial polarization (above). To understand how electromagnetic waves interact with various media, consider a porcelain mug, filled with coffee and placed in a microwave oven. The mug is essentially unaffected by the micro-waves as they pass through it, but the coffee in the mug heats rapidly. Accidently leaving a metal spoon in the mug can be disastrous because of the interaction of microwaves with good conductors such as metal.

6. A Russian dielectric tool predated the EPT tool by 10 years but had limited availability.

7. For more on the various mixing laws: Seleznev N, Boyd A and Habashy T: “Dielectric Mixing Laws for Fully and Partially Saturated Carbonate Rocks,” Transactions of the SPWLA 45th Annual Logging Symposium, Noordwijk, The Netherlands (June 6–9, 2004), paper CCC.

8. For assumptions made and the full derivation from Maxwell’s equations: Böttcher CJF and Bordewijk P: Theory of Electric Polarization: Dielectrics in Time-Dependent Fields, vol 2, 2nd ed. New York City: Elsevier Scientific Publishing Company (1978): 10–19.

9. A third complex number can be ignored for downhole applications.

10. The CRI method was proposed in Wharton RP, Hazen GA, Rau RN and Best DL: “Electromagnetic Propagation

> Polarization mechanisms. Several mechanisms related to a material’s polarizability affect dielectric measurements. For electronic polarization (top), balanced atomic structures may shift in the presence of an electromagnetic field, E, but the effects are minimal. In contrast, water molecules exhibit orientational polarization (middle) because they are dipolar. In the initial state, these easily polarizable water molecules are found as randomly oriented dipoles. When exposed to an electromagnetic field, they attempt to align with the direction of the field. Interfacial polarization for reservoir rocks (bottom) is influenced by the presence of charged clays, brine and oil in the pore network and the matrix minerals. Minerals and elements in the rock that might not be polarizable in isolation often behave differently in a mixture, exhibiting a larger permittivity value than any of the constituent components. This phenomenon is an example of the Maxwell-Wagner effect.

E = 0PolarizationType

Electronic

Orientational

Interfacial

MatrixSalt ionsWater

Oil

8+ 8+

Center of + and –

Center of –Center of +

8++

+

E

Logging: Advances in Technique and Interpretation,” paper SPE 9267, presented at the 55th SPE Annual Fall Technical Conference and Exhibition, Dallas, September 21–24, 1980.

For a comparison of the CTA and CRI methods: Cheruvier E and Suau J: “Applications of Micro-Wave Dielectric Measurements in Various Logging Environments,” Transactions of the SPWLA 27th Annual Logging Symposium, Dallas (June 9–13, 1986), paper MMM.

11. Kenyon WE: “Texture Effects on Megahertz Dielectric Properties of Calcite Rock Samples,” Journal of Applied Physics 55, no. 8 (April 15, 1984): 3153–3159.

12. Melrose DB and McPhedran RC: Electromagnetic Processes in Dispersive Media. Cambridge, England: Cambridge University Press, 1991.

40 Oilfield Review

These materials respond to electromagnetic energy differently because of their atomic and molecular properties and their intrinsic conductivi-ties. Rather than becoming polarized when struck by microwaves, metal objects, such as the spoon, may experience an induced current. This is because there are free electrons in the metal that move when it is exposed to the electromagnetic field. Resistance to current flow can generate extreme heat and the induced current may arc if a conduc-tive path is unavailable. Because they are electrical conductors, most metals have a dielectric permittiv-ity that can be a negative value. For this reason, metals are not generally classed as dielectrics.

The porcelain mug, on the other hand, is nom-inally affected by the electromagnetic field, and it becomes only slightly polarized. The origin of its polarization lies in the electronic clouds surrounding the nuclei of the atoms. When the electric field is applied, the electrons’ trajecto-ries shift. This phenomenon is called the elec-tronic polarization. The resulting dielectric constant, in the range from 5 to 7, is similar to that of reservoir rocks.13

The coffee, or more specifically, the water portion of the coffee, exhibits an entirely different behavior in the presence of the electromagnetic field. Water molecules—composed of two hydro-gen atoms and one oxygen atom—are asymmetri-cal: the centers of their positive and negative charges do not coincide. This asymmetry results in a permanent dipole moment for water mole-cules. Because of its much greater susceptibility to polarization, water’s dielectric constant is around 80—an order of magnitude higher than that of porcelain.

In the absence of an electric field, individual water dipoles point in random directions, so the net moment per unit volume is zero. However, when an electric field is applied, in addition to electronic polarization of the oxygen and hydro-gen atoms, the field tends to orient the individual dipoles, resulting in a net positive moment per unit volume. This effect is called orientational polarization. The collisions of the molecules in their thermal motion disorient the molecules and limit the net dipole moment per unit volume. Thus the magnitude of the orientational polariza-tion is a result of the type of polar molecule and its temperature.

Orientation of polar molecules under the influence of an applied field is not instantaneous. It requires a finite time due to the molecular moment of inertia and, as a result, there is resis-tance to realignment as the field reverses direc-tion. If the frequency of the applied field is sufficiently high, for instance in the microwave range, the polar molecules do not have enough time to orient along the field direction and the contribution of orientational polarization is diminished. The water molecules’ resistance to the rapidly changing polarity can be expressed as heat. This phenomenon is referred to as dipolar relaxation loss.

A dielectric phenomenon of saltwater, or brine, is that with increasing salinity, the conduc-tivity of a solution increases but the permittivity of the solution decreases. Adding salt to a solu-tion increases the number of water molecules nonrotationally bound to the NaCl molecules, thereby decreasing the orientational polariza-tion. At the same time, the concentration of ions

contributing to current conduction increases. A temperature increase has a similar effect on the solution properties: the solution conductivity will increase, and the solution permittivity will decrease due to the stronger effect of the thermal dipole disorientation.

As the electromagnetic wave passes through various media, it is altered by interaction with the media. The amplitude and the velocity of the wave decrease as a function of the amount of energy imparted, and the phase of the wave shifts. For materials with low dielectric constant values, such as the coffee mug or rock matrix, there are minimal effects on the returning elec-tromagnetic wave. In contrast, water’s high dielectric constant causes a large effect.

As early as the 1950s, petrophysicists experi-menting with microwaves recognized that the dielectric permittivity measurement from satu-rated core samples was controlled primarily by the amount of water in the pores and could be directly related to water-filled porosity. However, to compute the water fraction of a rock sample from dielectric measurements, the relationships between the dielectric properties of the constitu-ents that comprise the core sample must be known. Mixing laws were established under controlled laboratory conditions to model the effects of these relationships.

In the laboratory, dielectric properties can be measured by different methods employing vari-ous sample sizes and shapes. The measurement technique depends on the frequency of interest. For instance, the capacitive technique is typically employed for frequencies up to several MHz. The material is placed between the plates of a capacitor, and from the measurements of the capacitance the dielectric constant can be calcu-lated. This model works well if the wavelength is much longer than the space between the conduc-tor plates.

At high frequencies, it is difficult to measure the total voltage and current at the device ports. Because of the impedance of the probes and the dif-ficulty of placing the probe at the desired position, one cannot simply connect a voltmeter or a current probe and get accurate measurements. For frequen-cies in the GHz region, scientists developed tech-niques such as a transmission line or a microwave resonator. Transmission line methods are widely utilized because they allow for broadband measure-ments. The spanned bandwidth is limited, on the low end, by decreasing sensitivity to the sample’s dielectric constant with increasing wavelength. The maximum measurement frequency depends on the type of the transmission line, the forward model and the limitations of the acquisition system.

> Saturation from dielectric measurements. Petrophysicists generally use Archie’s water saturation equation, which requires inputs for porosity and resistivity. The dielectric method requires no resistivity. The simplified relationship shown here demonstrates how this is carried out. The dielectric porosity is a measurement of the water-filled portion of the porosity. When all the pore space is filled with water (left), the porosity from the dielectric tool, φDielectric, matches the total porosity measurement, φTotal, which must come from another source such as density-neutron crossplot porosity. Because their dielectric properties are similar, hydrocarbons are indistinguishable from the matrix for dielectric measurements. Thus, decreases in the porosity as measured by the dielectric tool that are not mirrored by the total porosity relate directly to increases in the volume of hydrocarbons (right).

30% Water, 70% Matrix 10% Water, 20% Oil, 70% Matrix

φφ φ

φTotal = 30%

Dielectric = 30%

Sw = 100%

Total = 30%

Dielectric = 10%

Sw = 33%

Spring 2011 41

Quantifying water-filled porosity from dielec-tric measurements is important because the ratio of the water-filled porosity to the total porosity represents the water saturation (previous page). The dielectric permittivity measurement can determine water saturation independent of a resistivity measurement—a critical and necessary input for Archie’s water saturation equation.14

Both freshwater and hydrocarbons have high resistivity values. Typical oilfield brines found in reservoir rocks have low resistivity. Archie’s equation is based on the assumption that a contrast exists between the resistivity of hydrocarbon-bearing formations and those filled with brine. It does not provide accurate satura-tion results in reservoirs with freshwater, low-salinity water or where the salinity of the formation water is unknown. In these environ-ments, the large contrast between the dielectric permittivity of hydrocarbons and water, regard-less of brine salinity, makes for an ideal satura-tion measurement.

Nuclear magnetic resonance (NMR) tools are also able to detect hydrocarbons in freshwa-ter environments by measuring the diffusion of the fluids.15 Because they do not rely on the resistivity of the fluids in the pore spaces to determine saturations, dielectric and NMR tools are often the primary means for quantifying hydrocarbon volumes in freshwater environ-ments or where the formation-water salinity is unknown. The dielectric tool measurement, however, must be combined with porosity from another source to provide hydrocarbon satura-

tions. The results do not depend on the hydro-carbon type or the pore network.

Dielectric and NMR tools have a shallow depth of investigation, which prevents them from fully supplanting traditional triple-combo logging suites. Whereas resistivity tools measure up to a few meters into the formation, the nature of NMR and dielectric measurements limits them to the first few centimeters from the wellbore wall: the flushed zone, where the virgin fluid has been invaded by mud-filtrate.

However, the shallow nature of the dielectric measurement provides important information about oil mobility. Comparing the saturation derived from dielectric measurements corre-sponding to the flushed zone with that of the virgin zone can help quantify the volume of oil flushed by water-base mud filtrate. This oil is movable and can be produced using primary production means; however, zones with oil that is not flushed generally require other methods, such as steam injection, water or CO2 floods or any of a multitude of enhanced oil recovery tech-niques to flush the oil from the rock. Ultimately, these data are best described as information that, when combined with other logging results, aides the petrophysicist in accurately character-izing the reservoir.

Dielectric tools, however, offer petrophysi-cists more than the ability to quantify water-filled

porosity and compute hydrocarbon volume. Using a newly developed measurement technique that relies on dielectric dispersion, the tools are also able to determine rock properties. This has been shown to be especially useful in carbonates but also provides insight for evaluating shaly sands.

Dispersion Because biological and sedimentological factors can produce a complicated pore network, car-bonates have a much more complex structure than siliciclastic rocks.16 The pore network may also be chemically altered through postdeposi-tional diagenesis.17 This makes evaluation of pet-rophysical properties of carbonates challenging— especially permeability and fluid saturations, which are not directly measured but derived from combinations of measurements using an appro-priate model.

Schlumberger researchers found that dielec-tric properties computed with a frequency of 1 GHz using the CRI technique were accurate for carbonate rock samples saturated with oil-brine mixtures (above). However, factors other than mineralogy and water content affect permittivity at lower frequencies.18 Permittivity dispersion measurements on two carbonate rocks with simi-lar porosity, mineralogy and water saturation high-lighted this frequency-dependent textural differ-ence. The observation of frequency dependence

13. Virtual Institute of Applied Science Encyclopedia: “Dielectric Constant,” http://www.vias.org/encyclopedia/phys_dielectric_const.htm (accessed February 11, 2011).

14. Poley JPh, Nooteboom JJ and de Waal PJ: “Use of V.H.F. Dielectric Measurements for Borehole Formation Analysis,” The Log Analyst 19, no. 3 (May–June, 1978): 8–30.

15. Akkurt R, Bachman HN, Minh CC, Flaum C, LaVigne J, Leveridge R, Carmona R, Crary S, Decoster E, Heaton N, Hurlimann MD, Looyestijn WJ, Mardon D and White J: “Nuclear Magnetic Resonance Comes Out of Its Shell,” Oilfield Review 20, no. 4 (Winter 2008/2009): 4–23.

16. For more on carbonate reservoir analysis: Al-Marzouqi MI, Budebes S, Sultan E, Bush I, Griffiths R, Gzara KBM, Ramamoorthy R, Husser A, Jeha Z, Roth J, Montaron B, Narhari SR, Singh SK and Poirer-Coutansais X: “Resolving Carbonate Complexity,” Oilfield Review 22, no. 2 (Summer 2010): 40–55.

17. Ali SA, Clark WJ, Moore WR and Dribus JR: “Diagenesis and Reservoir Quality,” Oilfield Review 22, no. 2 (Summer 2010): 14–27.

18. For more on the derivation of models used for textural inversion: Stroud D, Milton GW and De BR: “Analytical Model for the Dielectric Response of Brine-Saturated Rocks,” Petrophysical Review B 34, no. 8 (October 15, 1986): 5145–5153.

Baker PL, Kenyon WE and Kester JM: “EPT Interpretation Using a Textural Model,” Transactions of the SPWLA 26th Annual Logging Symposium, Dallas (June 17–20, 1985), paper DD.

Kenyon, reference 11.

> Dispersion in carbonates. Scientists found that, because of differences in rock texture, otherwise similar carbonates can have very different dielectric responses, especially at lower frequencies. Laboratory-measured values of permittivity of two different carbonate samples with similar porosity, permeability and saturating fluids are shown along with permittivity computed using the CRI method (black). The permittivity of Carbonate 2 (red) is similar to the results from the CRI method, but the permittivity of Carbonate 1 (green) is different. Neither sample provided an exact match—except around 1 GHz, which corresponds to the EPT tool’s operating frequency (red dashed line). Because other factors were equal, this frequency-related dispersion is associated with the different textures of the carbonate samples.

Frequency, MHz

EPT tooloperatingfrequencyPe

rmitt

ivity

102 103

50

40

30

20

10

15

45

35

25

Carbonate 1Carbonate 2CRI method

42 Oilfield Review

for dielectric properties led the scientists to develop a dielectric dispersion model to character-ize rock texture.

Researchers also experimented with permit-tivity and dielectric conductivity of siliciclastic core samples saturated with brines of different salinity.19 Although the permittivity of a dry sam-ple is constant over a wide range of frequencies, the permittivity values of the brine-soaked samples change with salinity, converging at fre-quencies around 1 GHz (above). The dielectric conductivities, however, are not linear, and the effect of the brine on the value of the conductiv-ity increases with the frequency of the applied electromagnetic field. Therefore, any variation in the dielectric permittivity with applied frequency must be related to either textural properties or fluid salinity.

Over the years, various models have been developed to quantify dispersion. The textural model utilizes geometric elements—platy grains—to account for differences in textural parameters. To validate the models, scientists acquired experimental dielectric permittivity and conductivity data using a wide range of fre-quencies for rocks with several distinct textures. They then used the dispersion model to fit their measurements. This inversion technique gener-ated results for dielectric permittivity and con-ductivity that more closely matched core measurements than with the traditional CRI technique (left).

> Effects of fluid salinity on dielectric measurements. Cores were saturated with four different brines ranging in resistivity from 4.890 to 0.051 ohm.m. Permittivity (left) and conductivity (right) were computed for a frequency range of 10 MHz to 10 GHz. The permittivity measurements converged around 1 GHz. For comparison, a baseline permittivity measurement was made on a dried core sample (blue). The core saturated with the highest salinity brine (green) displayed the highest dispersion and was the only one that did not converge at 1 GHz. Dielectric conductivity on the other hand, did not converge but increased with frequency for all four samples, demonstrating the dispersive effects of fluid salinity.

Frequency, MHz

Cond

uctiv

ity, S

/m

101 10210-2

10-1

100

103

Frequency, MHz

Per

mitt

ivity

101 102

10

20

30

40

50

60

1103

0.051 ohm.m0.211 ohm.m1.010 ohm.m4.890 ohm.mDried

= 15.6% φ φ = 15.6%

>Model comparison. Permittivity and conductivity (blue) from laboratory core measurements for a carbonate sample were compared to values computed using the CRI method (bottom, black) and the new dispersion textural model (top, red). The CRI method matches core-derived properties at 1 GHz; however, there is little agreement between the carbonate samples and the CRI method at lower frequencies, especially for conductivity. The textural model almost perfectly matches the core data. The example shown is one of several carbonate cores tested; all tested cores showed similar results. (Adapted from Seleznev et al, reference 19.)

Perm

ittiv

ity

Frequency, Hz

10106 107 108 109

20

30

Cond

uctiv

ity, S

/m

40

50

Frequency, Hz

0106 107 108 109

0.10

0.45

0.20

0.30

0.40

0.50

Perm

ittiv

ity

Frequency, Hz

10106 107 108 109

20

30

Cond

uctiv

ity, S

/m

40

50

Frequency, Hz

0106 107 108 109

0.10

0.45

0.20

0.30

0.40

0.50

0 40.455

0.40

Laboratory measurementTextural model

0 40.455

0.40

Laboratory measurementCRI method

Spring 2011 43

The textural method can be used to derive the cementation exponent, m, used in Archie’s water saturation equation. Cementation data computed using the textural model compared favorably with cementation exponents independently mea-sured from carbonate cores. Laboratory data were successfully modeled across a wide range of m-values from 1.7 to 2.9 (right). This technique has been used to explain carbonate texture–related resistivity variations that result in mis-leading saturation estimates (below right).

Dispersion effects are not limited to carbon-ate analysis; they can also be applied to shaly-sand evaluation. However, the dispersion models for shales are different from the one used for car-bonate analysis because the clays, which make up the shale, induce specific dispersion behaviors.

Shaly SandsQuantifying shaliness has been limited to corre-lations with gamma ray, sonic, neutron capture spectroscopy or differences in neutron and den-sity porosity logs. The results are not a direct measurement but are based on empirical infer-ences. The dielectric dispersion model directly quantifies shale effects such as those seen in laminated sand-shale sequences.20 This is espe-cially useful in freshwater shaly sands where the measured resistivity is determined in large part by the clay content. But applications of dielectric data for shaliness are not limited to just fresh-water. Because the dispersive response of a clay’s

> Cementation exponent for Archie’s water saturation equation. The cementation exponent, m, can be measured from core data, but it is a time-consuming process. The textural model, developed from dielectric dispersion analysis, was used to solve for m in a number of carbonate core samples. The crossplot of the values from both methods demonstrates close agreement over a wide range. The default value of 2 for Archie’s equation would not be appropriate for most of these samples for which the value ranges from 1.7 to 2.9. (Adapted from Seleznev et al, reference 19.)

Com

pute

d m

from

text

ural

mod

el

Laboratory measured m from cores

4.0

4.0

3.0

3.0

2.0

2.01.0

1.0

3.5

3.5

2.5

2.5

1.5

1.5

> Validating the dispersion model. Because of textural effects, computing Archie’s water saturation in carbonates using traditional techniques can yield incorrect results. In this example, the deep induction resistivity data (Track 5, red) are higher from X,764 to X,778 m (blue-shaded zone) than above or below. Water saturation computed using Archie’s equation (Track 3, red) with a fixed cementation exponent, m = 2, indicates the possible presence of oil (green shading) in this interval. The porosity from the dielectric tool (Track 6, blue) overlays the total porosity (black), which implies that there are no hydrocarbons. The dispersion-derived value for m (Track 2, blue) varies from 1.9 to 2.6 across this interval. Water saturation computed using this corrected m-value in Archie’s equation results in 100% water saturation (Track 3, black), which is more in line with expectations.

Salinity

Lith

olog

y

Depth,m

CaliperSaturation,

m = 2

Saturation,Corrected m

Gamma Ray

Dielectric Deep

Resistivity

Dielectric Shallow

Deep Induction Total Porosity

Dielectric Porosity

0 6

0

1.0 3.5

gAPI 100

0 100%

0 100%

16 1 ohm.m 1,000 50 % 0

50 % 01 ohm.m 1,000

1 ohm.m 1,000

in.50ppk

mOil

X,750

X,760

X,770

X,780

X,790

X,800

X,810

X,820

XX,770

19. Seleznev N, Habashy T, Boyd A and Hizem M: “Formation Properties Derived from a Multi-Frequency Dielectric Measurement,” Transactions of the SPWLA 47th Annual Logging Symposium, Veracruz, Mexico (June 4–7, 2006), paper VVV.

20. Laminated sands are characterized by intervals of stacked, thin sand and shale layers. The presence of the shale laminae results in lower bulk resistivity measurements and can mask the presence of hydrocarbons. Laminae thickness is generally below the resolution threshold of conventional logging tools.

44 Oilfield Review

dielectric properties directly relates to the phys-ics controlling its conductivity, the dispersion technique yields accurate clay estimation (left).21

As demonstrated with carbonates, the rela-tive permittivity computed from the CRI model may not match core-derived data at frequencies lower than 1 GHz. This dispersive behavior is also seen in shaly sands and sand-shale sequences but for different reasons. For these rocks, it corre-lates with the cation-exchange capacity (CEC) of the minerals in the formation, which relates to both the electrochemical polarization, also referred to as a double-layer effect, and to Maxwell-Wagner interfacial polarization. Both effects are present, and electrochemical effects dominate at lower salinity while interfacial polar-ization dominates at high salinities.

The CEC is the quantity of cations (positively charged ions) that a clay mineral can accommo-date on its negatively charged surface. Clays are aluminosilicates that have had some of their aluminum and silicon ions replaced by elements with a different valence, or charge. The presence of ions from clays enhances electrochemical interfacial polarization.22

Nonconductive elements found in the forma-tion, when mixed together, may exhibit dielectric conductivity that would not be present when these elements are in isolation. This is due to the geometric Maxwell-Wagner phenomenon, which is related to charge accumulation at the interface between brine and rock or brine and oil. Between these charged surfaces, the brine forms macro-scopic dipoles, which give rise to frequency-dependent macroscopic polarizations. When exposed to a low-frequency electromagnetic field, the dipoles reach equilibrium before the field changes direction. When exposed to a high-frequency field, the dipoles cannot follow the rapidly changing field, resulting in energy dissi-pation, increased electrical conductivity and reduced dielectric permittivity.23

In the Dielectric Scanner tool’s frequency range (20 MHz to 1 GHz), both electrochemical and geometric (Maxwell-Wagner) polarization mechanisms contribute to the overall dielectric dispersion measured in clay-containing forma-tions. The electrochemical response decreases

21. Myers MT: “A Saturation Interpretation Model for the Dielectric Constant of Shaly Sands,” paper 9118, presented at the Fifth Annual Society of Core Analysts Conference, San Antonio, Texas, USA, August 20–21, 1991.

22. Seleznev et al, reference 19.23. Toumelin E and Torres-Verdín C: “Pore-Scale Simulation

of KHz-GHz Electromagnetic Dispersion of Rocks: Effects of Rock Morphology, Pore Connectivity, and Electrical Double Layers,” Transactions of the SPWLA 50th Annual Logging Symposium, The Woodlands, Texas, USA (June 21–24, 2009), paper RRR.

> Interfacial polarization. Mixtures of sand and clay exhibit dispersive dielectric permittivity behavior depending on the clay type. The real permittivity measured in a smectite-water mix has a large frequency dependence—compare the real permittivity at 10 MHz with that at 1 GHz. For a kaolinite-water mixture, the effects are present, though less pronounced. There is little dispersion in the sand-water mixture. Because of the larger volume of bound water associated with smectite than with kaolinite, there is an associated decrease in permittivity with increased frequency. This correlation between dispersion and shale content and type can be used to compute the cation-exchange capacity (CEC) and quantify shale effects from dielectric data.

Frequency, MHz

Real

per

mitt

ivity

, εr

0

100

102 103101

200

300

400Smectite-water mixtureKaolinite-water mixture

Ottawa sand–water mixture

100

rΔε

> High-resolution hydrocarbon saturation. Differences in horizontal and vertical resistivities (Track 4) from a triaxial induction device, such as the Rt Scanner tool, can help interpreters identify anisotropy. However the laminations in the FMI fullbore formation microimager data (Track 1) are finer than the resolution of the induction tool or the density-neutron tools, as shown in the crossplot porosity (Track 5, black). This can result in an excessively high net-pay calculation. The vertical resolution of the saturation measurement from the Dielectric Scanner tool (Track 2, black) can be as small as 2.5 cm. The resolution difference is highlighted by comparing the Archie water saturation (Track 2, red) with the dielectric saturation (black). Incorporating dielectric data into the analysis results in a more accurate sand count and reserves estimate.

Lith

olog

y

Depth,ft

FMI ImageSw

Dielectric Vertical Resistivity

Horizontal Resistivity Dielectric Porosity

Crossplot Porosity

SwArchie

0 1 1,000ohm.m 50 0%

1 1,000ohm.m 50 0%

% 100

0 % 100

Oil

1,350

1,360

Hydrocarbon

Spring 2011 45

with increasing brine salinity. Maxwell-Wagner effects increase with increasing brine salinity. For a given brine salinity, an increase in the rock’s clay content causes an increase in its CEC value and an increase in its dielectric dispersion due to both the electrochemical and Maxwell-Wagner mechanisms simultaneously.

The relative importance of each mechanism is influenced by the brine salinity. For example, measurements of a vacuum-dried sample show no frequency dependence, but in sedimentary rocks, dielectric permittivity will increase with increased surface area and CEC. By relating dispersion from shale effects to the CEC, petrophysicists can quantify the shale content of reservoir rocks.

Attempts to determine clay volume as well as clay type are motivated by the need for a CEC input to water saturation equations. CEC deter-mines the effect of the clay on resistivity mea-surement as well as the bound water volume that needs to be excluded from the total porosity mea-surement so that water saturation and oil volume can be properly determined. Measuring CEC

directly rather than estimating it from clay type and volume is a simpler and more robust means of determining water saturation in shaly sands.

An added benefit of the dielectric measure-ment is the ability to directly measure shale con-tent and saturation at high resolution. Although techniques have been developed for measuring anisotropy with resistivity devices such as the Rt Scanner triaxial induction tool, this measure-ment does not have the vertical resolution of the dielectric tool. Nuclear porosity devices can pro-vide inputs for high-resolution saturation mea-surements, but the vertical resolution of these data is limited by physics and detector spacing. The dielectric measurement provides water-filled porosity at resolutions in the 2.5-cm [1-in.] range. The dielectric information allows petrophysicists to more accurately calculate reserves and esti-mate production than they currently can with resistivity and porosity from other sources, including new technology such as triaxial induc-tion tools (previous page, bottom).

The ability to measure shaliness and shale effects is crucial in characterizing anisotropic freshwater shaly-sand reservoirs. Interpreters identify the presence of hydrocarbons in anisotro-pic reservoirs by observing the difference between horizontal and vertical resistivities, such as those from the Rt Scanner tool. However, use of this technique is not effective in freshwater environ-ments because of the lack of contrast between the resistivity of freshwater, shale laminations and oil. Log analysts can, however, determine high-resolu-tion anisotropy using the transverse and longitudi-nal measurements from the Dielectric Scanner tool. From these data, shale effects and oil satura-tion can be quantified.

The Dielectric Scanner ToolMeasurements from electromagnetic devices that operate at frequencies in the kHz range, such as an induction tool, are better known than dielectric measurements acquired at very high frequencies. Lower-frequency measurements are dominated by the conductivity of the formation, but as the frequency increases, dielectric effects begin to appear and then predominate. Very high-frequency measurements offer the ability to eval-uate conductivity and permittivity simultaneously. In addition, obtaining information about texture and shaliness using dielectric dispersion requires a high-quality measurement acquired at multiple frequencies. The Dielectric Scanner tool was developed to provide a full dataset necessary for these applications (left).

> The Dielectric Scanner tool. This recently introduced tool incorporates several features to improve data acquisition and provide greater measurement accuracy. Unlike previous generation tools that used fixed pads, the Dielectric Scanner tool uses the caliper arm to push the articulated pad against the formation. The pad’s curvature also helps improve contact with the borehole wall. The transmitters (TA and TB) and antenna sets (RXA1 to RXA4 and RXB1 to RXB4) operate at discrete frequencies from 20 MHz to 1 GHz. Transmitters and antennas are collocated cross-dipoles and can operate simultaneously in transverse (red arrow) and in longitudinal (blue arrow) polarization modes. Two open electric dipoles (open-ended coaxial-cable probes) measure mudcake properties and provide quality control. For more-accurate fluid property input, the tool measures both temperature and pressure at the point of measurement. Borehole compensation is used to eliminate unbalanced transmitter-receiver pairs. For each measurement cycle, 72 attenuation and 72 phase measurements are made for each of the four frequencies. Depth of investigation is 2.5 cm to 10.2 cm [1 in. to 4 in.] depending on transmitter-to-receiver spacing and formation fluid properties.

Caliperarm

Articulated pad

RXA4 R

XA3 RXA2 R

XA1

RXB1 R

XB2 RXB3 R

XB4

TA

TB

Mudcake probe

46 Oilfield Review

The tool has a fully articulated pad to position the transmitters and receivers against the bore-hole wall. The pad shape is cylindrical and the antennas are designed to be perfect magnetic dipoles. Each of the two transmitters and eight receivers can operate with longitudinal or trans-verse polarization.24 The measurement is per-formed at four discrete frequencies from 20 MHz to approximately 1 GHz. Each measurement cycle includes 72 transmitter-receiver amplitudes and 72 phase measurements. Multiple transmitter-receiver pairs allow for borehole compensation, and a quality-control algorithm can extract unbalanced pairs and eliminate them from the computation. Depth of investigation (DOI)— a function of the transmitter-receiver spacing, operating frequency and formation properties—varies from 2.5 cm to 10.2 cm [1 in. to 4 in.]. A 2.5-cm vertical resolution is achieved.

Electric dipoles on the pad face provide two modes of operation. In propagation mode, they make the shallowest transverse measurement and are used to estimate mud properties. In reflection mode, they measure the dielectric properties of the material directly in front of the pad: mud or mudcake.

Because the tool acquires data in both longitudinal and transverse polarizations, high-resolution anisotropy effects can be quantified. Longitudinal polarization probes the permittivity and conductivity in a plane that is orthogonal to the tool axis (above left). Transverse polarization probes both horizontal and vertical permittivity and conductivity.

Temperature and pressure measurements are also needed for compensation in the dielectric models. Under downhole conditions, pressure has an appreciable effect on the dielectric prop-erties of water.25 The temperature, salinity and pressure dependencies should all be included in a dielectric model to produce accurate interpre-tation of the logs at downhole conditions. Temperature is measured with the integrated mud sensor and a dedicated sensor is used to measure hydrostatic pressure.

The tool investigates three main areas: radial information, geologic structure information and matrix texture (left). The data from the various transmitter-receiver pairs at all frequencies are inverted to output permittivities and conductivities for several layers: the mudcake, the near flushed zone and the far flushed zone. Petrophysical proper-ties can be computed using the CRI model for each of the four frequencies. Dispersion processing with

> Tool operational modes. Dielectric tools generate electromagnetic waves and create a field whose electric components (E) and magnetic components (H) are perpendicular to one another. The polarization of the wave determines the direction of the created fields. Longitudinal (left) and transverse (right) polarization modes correspond to measurements in horizontal and vertical planes with respect to the tool. Each mode generates a specific field orientation and shaped sensed region (insets). The colored bands represent multiple depths of investigation, which are functions of the transmitter-receiver spacing and formation properties. The sensed regions of the two modes overlap (bottom middle); differences in the measurements from the two orientations help identify anisotropy.

Longitudinal Transverse

H

HE

E

Longitudinalsensed region

Transversesensed region

Combinedsensed region

> Dimensions of dielectric measurements. With its four operating frequencies (F0 to F3) and four pairs of transmitter-receiver spacings (R1 to R4), the Dielectric Scanner tool has three investigation ranges: textural, radial and structural. The operating frequencies were chosen to exploit interfacial, molecular and electronic polarization mechanisms, which are related to textural and shale effects. The radial investigation is facilitated by four pairs of transmitter-receiver spacings that model the near-wellbore region, which includes mudcake and invaded zones, and, depending on the depth of invasion, may extend into the transition and virgin zones. Structural investigation is made possible by polarization orientation. Measuring in the horizontal and vertical planes allows identification of formation anisotropy at high resolution.

Interfacial polarization

F0

R1

R2

R3

R4

105 106 107 108 109 1010

F1 F2 F3

Virg

in zo

ne Tran

sitio

n zo

ne

Inva

ded

zone

Mud

cake Molecular orientation

Electronic polarization

Radia

l inve

stiga

tion:

multipl

e spa

cings

Stru

ctur

al in

vest

igat

ion:

mul

tiple

pol

ariza

tions Textural investigation: multiple frequencies

Frequency, Hz

Formation homogeneity

Anisotropy

Spring 2011 47

inputs from multiple frequencies can be performed at different DOIs (above).

To facilitate integration of dielectric data with other logging tool data, engineers have developed the Dielectric Pro dielectric disper-sion interpretation software. Full data process-ing and interpretation are available using porosity, resistivity and saturation analysis from conventional tools. Conductivity and permittiv-ity at multiple frequencies can be computed. Crossplots of the data provide insight into dis-persion for both textural analysis and shaliness. Various interpretation models are incorporated into the workflows and provide alternative methods of analyzing the data. Radial process-ing can derive variations in formation conduc-tivity and permittivity for anisotropy analysis. But, the real test of dielectric logging comes from downhole applications.

Research to ReservoirPetroleum Development Oman (PDO) tested the Dielectric Scanner tool in several wells. PDO objectives included evaluating laminated sand-shale sequences, heavy-oil carbonates, shaly sands and ultrahigh-salinity carbonates.26 For one of the test wells, the objectives were to quantify the volume of residual oil—oil that has not been flushed by invading mud filtrate—independent of resistivity measurements and to integrate dielec-tric data with a full suite of openhole logging tools. PDO evaluated the tool’s ability to detect oil mobility and provide textural information in this test. The selected well was in a carbonate reser-voir. The mud filtrate salinity was approximately 180,000 parts per million (ppm) NaCl.

Because the dielectric tool measures the water-filled portion of the porosity, the difference between density-neutron crossplot porosity and dielectric porosity is the residual oil saturation. In this case, the difference was large, clearly

24. Longitudinal and transverse acquisition compare to endfire and broadside modes from the older generation EPT tools, which are modes that required completely separate sets of hardware.

25. Heger K, Uematsu M and Franck EU: “The Static Dielectric Constant of Water at High Pressures and Temperatures to 500 MPa and 550°C,” Berichte der Bunsengesellschaft für physikalische Chemie 84, no. 8 (August 1980): 758–762.

26. Mude J, Arora S, McDonald T and Edwards J: ”Wireline Dielectric Measurements Make a Comeback: Applications in Oman for a New Generation Dielectric Log Measurement,” Transactions of the SPWLA 51st Annual Logging Symposium, Perth, Western Australia, Australia (June 19–23, 2010), paper GG.

> The CRI method versus the dispersion textural model. The Dielectric Scanner tool has four operating frequencies and multiple transmitter-receiver spacings. For the CRI method (left), the inputs consist of total porosity, φT, matrix permittivity, εmatrix, temperature and pressure. The inversion takes the real permittivity measurement and the dielectric conductivity and outputs water saturation, water conductivity and dielectric constant for any combination of frequency and transmitter-receiver spacing. Shown is the shallow (SH) measurement. For reference and quality

control, the measurement uncertainty of the inputs can be computed and applied to the outputs as well. Inputs for the dispersion model (right) are similar but permittivity and conductivity at multiple frequencies are required for processing. Outputs include water saturation, conductivity, dielectric constant and textural parameters. The data can be inverted for different depths of investigation, which are functions of transmitter-receiver spacing and formation properties. (Adapted from Seleznev et al, reference 19.)

Mud

acak

e

Deep invaded zoneShallow invaded zone

Mud

acak

e

Deep invaded zoneShallow invaded zone

Water modelDielectric model

Dielectric

Input uncertainty

r, SH, F3water, SH

Parameter uncertaintyDielectric constantSH

SW, SH

Inversion

CRI Method Dispersion Model

Water modelDispersion model

Input uncertainty

Dielectric, SH, F0r, SH, F0,

Dielectric, SH, F1r, SH, F1, Dielectric, SH, F2r, SH, F2,

Dielectric, SH, F3r, SH, F3,

water, SH

SW, SH

SH, F3

SH, F3

water, SH

Parameter uncertainty

Dielectric constantSH

SW, SH

Textural parametersInversion

SH, F0

SH, F1

SH, F2

SH, F3

SH, F0,

SH, F1,

SH, F2,

SH, F3,

Deep, F0

Deep, F1

Deep, F2

Deep, F3

Deep, F0,

Deep, F1,

Deep, F2,

Deep, F3,

ε

matrix, temperature and pressureT,ε matrix, temperature and pressureT,ε

εεεε

εεεεε

εεεε

σ

σ

σ

σ

σσ

σσσσ

σσσσ

σσσ

φ φ

48 Oilfield Review

indicating the presence of considerable unmoved hydrocarbon (left). This quantification of resid-ual oil, independent of the resistivity measure-ment, met PDO’s first objective of the test.

To achieve the second objective, analysts com-puted the dielectric textural output for use in Archie’s water saturation equation. The dispersion analysis indicated that the cementation exponent, m, varied from 1.5 to 2.5 across the interval in question. PDO attributed the variability of m to textural and facies differences in the carbonate. The use of a more accurate m-parameter resulted in more precise hydrocarbon saturation determi-nation. General practice is to use a constant value for m, which, based on these findings, would yield inaccurate results.

Next, the dielectric data were integrated in an analysis and compared to water saturation com-puted from inputs that are typical for the field. In the upper section, where a high Archie saturation parameter, or n-value, is commonly used, there is good agreement between the two methods. This fixed value for n was obtained from a nearby field and is appropriate for oil-wet rocks (next page). Across a transition from an oil to a water zone, there is a difference between the output using this constant n-value and from that derived from dielectric measurements. This is most likely because the rock is less oil-wet in this zone than the oil-bearing zone. Rather than using the high n-value used in the upper section to compute water saturation with Archie’s equation, log ana-lysts learned that they should use a lower value.

Saturation SolutionShallow, heavy-oil reservoirs, which include some of the few areas where dielectric tools are in use today, can be found in a number of regions around the globe. Canada, the USA, Mexico, Indonesia and Venezuela are among a number of places with vast heavy-oil reserves.27 In California, USA, heavy-oil production has been underway since the 1890s. Most of this heavy oil is found at depths of less than 3,000 ft [1,000 m].

These shallow heavy-oil reservoirs are beset with interpretation difficulties associated with freshwater. Interpretation is further complicated because many of the reservoirs have been under steam- or waterflood for more than 50 years.28 The fluids encountered by newly drilled wells in these reservoirs may little resemble those originally in place, or may change drastically across seemingly homogeneous reservoir sections because of dif-ferences in operational histories.

>Middle East carbonate test. Log analysts incorporated Dielectric Scanner data with those from a LithoDensity–Array Porosity–HRLA logging suite. The porosity analysis (Track 5) includes total porosity (black) and dielectric porosity (blue). The difference between the porosities (green shading) represents residual hydrocarbons. The dielectric conductivity, converted to resistivity (Track 4, blue), was presented alongside the HRLA resistivities (red and black) and the shallow resistivity from the LithoDensity tool (green). Water saturation was computed from the dielectric data (Track 2, black) and Archie’s equation (red), which was corrected for variations in the m-exponent (Track 1, blue) derived from the dielectric data. Dispersion effects can be visualized by comparing the permittivity and conductivity differences computed from pairs of frequencies (Track 6). The difference between frequency responses is color coded (cyan, blue and red).

X10

X20

X30

X40

Lithology

Resistivity

Porosity

DispersionEffects

Array Laterolog

Perm

ittiv

ity

Cond

uctiv

ity

Total Porosity

Invaded Zone

Dielectric ScannerInvaded Zone

Dielectric ScannerWater-Filled PorosityHRLA TruePorosity

Residual OilSaturation

DielectricScanner

Saturation

ArchieSaturation

Archie Inputs, m = nDepth,

m

Caliper

in.6 16

0.2 2,000ohm.m

0.2 2,000ohm.m

0.2 2,000ohm.m

0.2 2,000ohm.m

0100 %

0 0100 100% % 050 %

050 %

0 3.5

Difference

Oil

Bound Water

Oil

WaterDolomite

Calcite

Illite

HydrocarbonF3–F2

F2–F1

F2–F3

F0–F1

F0–F1

F1–F2

Spring 2011 49

Beginning in the mid-1980s, petrophysical analysis of shallow, heavy-oil reservoirs in California often included the EPT tool to esti-mate hydrocarbons in place, and the use of the tool became routine in the 1990s. The tool mea-sured flushed-zone water-filled porosity. An added benefit of using dielectric tools in these

reservoirs, where there is little invasion from drilling mud filtrate and where the oil is virtually immobile, is that the information reflects that of the virgin zone. Whereas the EPT tool was ini-tially developed to analyze reservoirs where the formation water was known to be fresh, today dielectric tools are also used where formation

water salinity is unknown because of the altera-tions caused by injection of fluids for enhanced oil recovery.

Obtaining quality data from wells in California heavy-oil reservoirs has been problematic. In many reservoirs, the sand grains are held together by the viscous oil originally in place. Depleted zones often exhibit rugose wellbores because they become unstable after some of the oil is removed. The mandrel design of the EPT pad often resulted in measurements that were compromised by borehole rugosity. The articu-lated pad of the Dielectric Scanner tool was designed to improve contact with the borehole wall when the hole is in less than ideal condition.

Interpretation of EPT measurements is also influenced by changes in downhole conditions created by steamflooding. The temperature profile of steamflooded wells does not follow a typical linear gradient, which is assumed for interpretation of dielectric measurements. Because the EPT tool lacks an external tempera-ture sensor, it cannot correct the raw data for temperature, thus introducing errors in the mea-surement. To overcome this limitation and pro-vide additional environmental corrections, the Dielectric Scanner tool incorporates pressure, temperature and mudcake sensors in its articu-lated pad.

Chevron U.S.A. Inc. tested the Dielectric Scanner tool in its heavy-oil steamflood opera-tion in the Cymric field, located in the south-west margin of the San Joaquin Valley, California. One of the main producing intervals is the Tulare Formation, which is Pliocene to Pleistocene in age and mostly poorly consoli-dated fluvio-deltaic sandstone deposits bounded by shales. Producing sands are at depths from 50 ft to 1,600 ft [15 m to 490 m]. Average poros-ity is 34%, permeability is 2,000 to 3,000 mD and oil saturation averages 55% to 65%. The oil is 9 to 14 API gravity. Production commenced in the early 1900s and steamflooding was first introduced in the 1970s. Water saturation calcu-lations from resistivity data are challenging at Cymric because of alterations in the original formation water salinity caused by years of steam injection.

Chevron ran a Platform Express triple-combo logging suite along with the Dielectric Scanner tool in the Cymric well. The logging suite included an EPT tool so the company could compare leg-acy measurements with those from the new tool. Sidewall cores were taken throughout the pro-ducing interval.

27. Alboudwarej H, Felix J, Taylor S, Badry R, Bremner C, Brough B, Skeates C, Baker A, Palmer D, Pattison K, Beshry M, Krawchuk P, Brown G, Calvo R, Cañas Triana JA, Hathcock R, Koerner K, Hughes T, Kundu D, López de Cárdenas J and West C: “Highlighting Heavy Oil,” Oilfield Review 18, no. 2 (Summer 2006): 34–53.

> Improved water saturation computation. In this Middle East carbonate, standard inputs were used to compute water saturation (Track 5). A constant n-value, obtained from offset core data, was used in Archie’s water saturation equation. Water saturation was also computed from dielectric data (Track 6). There is good agreement in the upper interval (light-green shaded zone), confirming the n-value. The dielectric water saturation in the lower interval (light-blue shaded zone), which includes a zone that transitions from oil to water, is lower—indicating more oil—than that using the n-value appropriate for the upper interval. Results such as these can affect oil-reserve estimates, which impact equipment requirements and field development.

X25

X75

X50

Depth,m

CaliperArray Neutron

Porosity

Bulk DensityDielectricPorosity

DensityPorosity

Archie WaterSaturationGamma Ray

in.6 0.02 45 -15%2,000ohm.m

0.02 2,000 1.95 01000 40%

0 40%

% 0100 %2.95ohm.m

16

gAPI0 60

Dielectric WaterSaturation

g/cm3

Rxo HRLA Tool

Rt HRLA Tool

28. Little JD, Julander DR, Knauer LC, Aultman JT and Hemingway JL: “Dielectric Dispersion Measurements in California Heavy Oil Reservoirs,” Transactions of the SPWLA 51st Annual Logging Symposium, Perth, Western Australia, Australia (June 19–23, 2010), paper D.

50 Oilfield Review

The well intersected the oil/water contact at a depth of 830 ft [253 m] (above). Below that depth, the dielectric porosity closely matched the crossplot porosity from the LithoDensity pho-toelectric density and neutron porosity tools, which indicated the formation is filled predomi-nantly with water.

The sidewall cores were analyzed for porosity, permeability and fluid saturation. Log-derived water saturations from the shallow-reading dielectric tool matched the saturations from the sidewall cores. Although sidewall samples and dielectric log measurements represent the flushed

zone immediately around the wellbore, fluid satu-rations obtained from both methods are gener-ally equivalent to saturations in the virgin zone for this field.

The Dielectric Scanner tool’s articulated pad helps compensate for hole rugosity and washouts. The EPT tool is a mandrel device, meaning the pad is fixed on the tool body. A comparison was made between the two devices in the Cymric heavy-oil well. The caliper curve indicated rugos-ity and washouts and the articulated pad handled the borehole irregularities better than the man-drel design did.

The Platform Express flushed-zone resistiv-ity measurement appeared to indicate the pres-ence of mudcake. Mudcake builds up as filtrate displaces oil and pushes fluids deeper into the formation, which makes it an indicator of per-meability and oil mobility. But, in heavy-oil res-ervoirs, it is possible to use the multiple depths of investigation from the Dielectric Scanner tool to look for evidence of mobile oil. If all four depths of investigation deliver the same water-filled porosity, evidence of oil mobility is lack-ing. If they differ, then the data suggest oil mobility in the reservoir—a potential comple-tion target.

The porosity measurement from the EPT tool should overlay the Dielectric Scanner porosity, and this was the case in most of the intervals. However, in the two rugose sections, the EPT tool sensed higher water-filled porosity, which equated to 23 saturation units lower than the Dielectric Scanner results. If this difference was not from oil mobility, it could have been attrib-uted to preferential water or steam breakthrough. Data from the Dielectric Scanner tool did not indicate invasion or oil mobility.

Based on the caliper reading, the borehole at the zones in question was enlarged. Unconsoli-dated sands, such as in this well, may slough off and mud solids have a tendency to build up along the borehole wall. Hole instability and rugosity caused the conflicting results, not mudcake from invasion or the presence of formation water.

These zones could have been misinterpreted as containing movable hydrocarbons due to vis-cosity variations in the oil column, having lower oil saturations or experiencing early water break-through. The error in water saturation, which equates to 23% less hydrocarbon in place, might have caused an operator to bypass both potential pay zones. Increased confidence in the dielectric measurements helped Chevron make informed completion decisions.

Moved OilVenezuela’s Orinoco Belt contains the largest deposit of heavy-oil reserves in the world. The operator, PDVSA, found that the region had a complex depositional setting where thick homo-geneous intervals could rapidly transform into thin, discontinuous layers. The complex geology was further complicated by significant differ-ences in sand quality, which made log interpreta-tion more difficult.

Early water production convinced engineers of the need for greater understanding of the res-ervoir. The identification and elimination of zones with high water production potential were

> Overcoming rugosity. The articulated pad of the Dielectric Scanner tool, which follows the contours of the borehole, compensates for hole rugosity and washouts. The EPT tool is a mandrel device, meaning the pad is fixed in place; Chevron wanted to compare data from the two tools in their Cymric heavy-oil well. After logging the well, engineers observed an apparent mudcake (Depth Track, light-blue shaded zone) in the zone from 780 ft to 820 ft from the LithoDensity tool’s microlog sensor (olive gray shading). Mudcake, if present, can indicate permeability and moved oil. The water-filled porosity from the EPT tool (Track 5, red) from 810 ft to 820 ft was higher than it was in other intervals such as from 540 ft to 605 ft. This could indicate filtrate replacing original oil, and the engineers might have assumed primary production was possible in this zone. However, the improved design of the Dielectric Scanner pad overcame hole rugosity effects and the water-filled porosity (Tracks 4 and 5, blue) showed no increase across this interval. The log response from the LithoDensity tool indicating mudcake was attributed to refilling of a slumping formation with circulated cuttings.

% %%

Dielectric ScannerWater Saturation

Dielectric ScannerWater Saturation

Core Water Saturation

Core Porosity

Total Porosity

DensityStandoff Dielectric

Invaded Zone Resistivity

Quartz

Hydrocarbon

ResidualHydrocarbon

ResidualHydrocarbon

ResidualHydrocarbon

ResistivityStandoff

DensityStandoff Irreducible

Water Volume

Carbonate

Depth, ft

600

800

Clay

Water

100100

2.5

0.5 50ohm.m 5,000

0in.

00 0

%50 0

%50 0% 1000

Dielectric ScannerWater-Filled Porosity

Dielectric ScannerWater-Filled Porosity

%50 0

Invaded Zone Resistivity

0.5 ohm.m 5,000

2-ft Array InductionResistivity Deep

0.5 ohm.m 5,000ResistivityStandoff

2.5 0in.

Caliper8.5 18.5in.

%50 0

EPT Porosity

%50 0

Total Porosity

800

Spring 2011 51

crucial for proper development of the region. Formation resistivity is often used to identify water-producing zones, but engineers discovered that this method was not reliable because of sand quality variability, the presence of freshwater and previously flushed layers that contained sig-nificant quantities of immovable residual oil in conjunction with movable water.

This environment is ideal for incorporating dielectric propagation measurements with stan-dard logging suites, but operators were reluctant to use the tools because of frequent adverse bore-hole conditions, complicated mud-filtrate inva-sion effects and complex interpretation issues. PDVSA recognized the design differences of the new Dielectric Scanner tool and actively partici-pated in the field testing of the device.29

Early in the testing process, engineers observed that filtrate invasion from the water-base mud could complicate interpretation of dielectric data. In the heavy-oil reservoirs of the Orinoco Belt, invasion is usually shallow, on the order of a few inches. Engineers modeled the invasion response of the dielectric tool by creating synthetic logs with typical well charac-teristics: 35% porosity sandstone with simulated virgin to fully flushed conditions. Inputs for the simulation included 5 porosity units (pu) of irreducible water-filled porosity in the virgin zone compared to 15 pu of water-filled porosity in the fully-flushed zone.30 Mud-filtrate salinity for the simulation was 5,000 ppm.

The CRI model, used to compute the tool response, was applied to the four frequencies available from the Dielectric Scanner tool along with nine separate transmitter-receiver spacings. The simulation provided 36 apparent dielectric permittivities and 36 apparent conductivity mea-surements and generated a step-profile with increments that were approximately 1 ft long by 0.1 in. deep [30 cm by 0.25 cm].

Analysis of the synthetic logs generated for one of the lower frequencies showed that when there was no invasion, the apparent permittivity and conductivity were the same as those of the virgin zone. As filtrate pushed deeper into the formation, the deepest DOI values approached those of the shallowest reading. For the highest frequency, the situation was extremely complex. Apparent permittivities and conductivities lost linearity and DOI was not uniform (right).

29. Mosse L, Carmona R, Decoster E, Faivre O and Hizem M: “Dielectric Dispersion Logging in Heavy Oil: A Case Study from the Orinoco Belt,” Transactions of the SPWLA 50th Annual Logging Symposium, The Woodlands, Texas (June 21–24, 2009), paper AAA.

30. In this simulation, 5 pu of water represents a water saturation of 14%. After the formation is flushed by 15 pu of filtrate, this represents a water saturation of 43%.

>Modeling dielectric response. PDVSA’s Orinoco Belt has complex lithology and difficult interpretation issues. PDVSA and Schlumberger tested the Dielectric Scanner tool by first modeling the response to invasion in conditions anticipated in Orinoco wells. A total of 36 sets of attenuation–phase shift measurements using nine spacings and four frequencies (F0 to F3) were used in the study. For the analysis, each 1 ft [30 cm] of log interval represented 0.1 in. [0.25 cm] of invasion (inset). For simplicity, synthetic apparent dielectric permittivity and conductivities are shown for frequency F1 (top) and for F3 (bottom). There are two sets of permittivity and conductivity curves: longitudinal polarization (left set) and transverse polarization (right set). The modeled responses are for the longest spacing (red curves) to the shortest spacing (blue curves). For frequency F1 (top left), when the invasion depth is zero, shown at the top of each log, permittivity curves read the deep zone value (dashed black line). As the simulated invasion pushes into the formation and filtrate replaces oil, the permittivity curves from the longitudinal polarization eventually converge to the fully flushed reading, shown at the bottom of the log; however, the transverse data do not converge and only the shortest spacing data approach the flushed zone value. For the highest frequency, F3 (bottom left), the permittivity from both longitudinal and transverse polarizations initially read the deep zone value and, as the simulated invasion pushes deeper, the transverse measurements converge on the flushed zone value while the longitudinal permittivities exhibit an oscillatory response. Regardless of the direction of polarization, conductivity data behave better for F1 frequency (top right). At the outset, longitudinal and transverse data reflect the value of no invasion and converge at the flushed value at the bottom of the log. This is not the case for conductivity data from F3 (bottom right), where oscillatory responses are seen for both polarizations. These results do not lend themselves to quicklook analysis; however, a response model was created from this analysis to correct data from Orinoco wells. (Adapted from Mosse et al, reference 29.)

5

0

10

20

30

Sim

ulat

ed d

epth

, ft

Sim

ulat

ed d

epth

, ft

Permittivity, F1

Longitudinal Polarization

No invasion

Fully invaded

No invasion

Fully invaded

No invasion

Fully invaded

No invasion

Fully invaded

Transverse Polarization

Conductivity, F1

Longitudinal Polarization Transverse Polarization

40

15

25

35

45

5

0

10

20

30

40

15

25

35

45

0.1 in.

1 ft

Permittivity, F3

Longitudinal Polarization Transverse Polarization

Conductivity, F3

Longitudinal Polarization Transverse Polarization

52 Oilfield Review

Lessons learned from the simulation were applied to permittivity and conductivity data acquired in an Orinoco well. These results closely resembled the simulated logs, providing a petro-physical inversion scheme that could be applied to the well data. Based on these results, PDVSA used the Dielectric Scanner tool on other wells.

The results from one well in particular showed the benefit of using the dielectric mea-surement in conjunction with other logging tools. An appraisal well was drilled in an area that was first explored in the 1980s and had relatively poor well control. PDVSA expected to encounter thick reservoir sections with low resistivity. Based on previous experience, such intervals were often interpreted as having heavy residual oil flushed with movable water. Log analysts expected these zones to produce mainly water.

The logging program included a Platform Express suite with an HRLA high-resolution

laterolog array tool and an MR Scanner expert magnetic resonance service. In other wells in the region, geologists had observed high resistivity in the oil-bearing interval, but resistivity values deeper in the interval were not as high. This well encountered similar intervals exhibiting high and low resistivity.

Conventional interpretation of oil mobility relied on comparing deep and shallow resistivity measurements. In this case the results were inconclusive because of similarities in the forma-tion water and filtrate salinities. In the high-resistivity upper interval, the NMR log showed a bimodal distribution with a strong oil signature. With increasing depth, the apparent porosity and the resistivity were reduced, and the NMR data appeared to indicate no movable oil. Log analysts called on the Dielectric Scanner data to validate this interpretation.

Although the caliper log indicated significant borehole rugosity, the pad of the Dielectric Scanner tool maintained good contact with the formation. The dielectric data resolved the uncertainty associated with the deeper reservoir section (left). In contrast to the NMR data indi-cating little oil mobility across two intervals, a total 150 ft [45 m] of low-resistivity pay with significant movable oil was indicated. PDVSA included this new information in their produc-tion plan and reserves calculations. The inter-pretation based on dielectric data was later confirmed from sidewall core samples.

Because water production is such a major con-cern in the Orinoco Belt development program, it was important to identify and avoid water-produc-tive zones. The dielectric measurement not only revealed the zones that contained movable oil, but was helpful in also identifying zones where only water was mobile. Resistivity and spontaneous potential (SP) techniques, commonly used to iden-tify such zones, require some contrast between the resistivities of the filtrate and formation water. In this case, there was no contrast and it would not have been possible to confirm water and oil mobility without integration of the dielectric data.

The analysis was further confirmed by sam-pling the various intervals. From the deepest interval, only water was produced. Oil and water came from the transition zone. From both the low- and high-resistivity intervals, oil was produced. This matched the interpretation from the dielec-tric measurements. PDVSA reservoir engineers were able to determine the best intervals for both production and additional field development.

Final AnalysisDielectric measurements from downhole tools have been available to petrophysicists since the early 1980s. Recognized benefits from the infor-mation were overshadowed by measurement complexity and tool limitations.

The introduction of the Dielectric Scanner tool has combined better tool design with new process-ing techniques. The dielectric information provides clear benefits for carbonate reservoir interpreta-tion, shaly-sand analysis, heavy-oil reservoir evalua-tion and any formation where the water is fresh or the water salinity is unknown.

Sometimes it takes a while for a technology to evolve and find its niche. Just as not every kitchen in the world has or needs a microwave oven, not every oil well interpretation requires dielectric data. But in certain situations, and for the right environments, zapping a formation with micro-waves may offer just that extra bit of information the log analyst needs. —TS

> Applying the model. Armed with the information from the dielectric modeling exercise, PDVSA logged an Orinoco Belt well with Platform Express–HRLA, MR Scanner and Dielectric Scanner tools. Conventional methods of interpretation relied on differences in shallow and deep resistivity measurements to indicate oil mobility. These data (Track 5) are not conclusive, even when dielectric resistivities from different depths of investigation (red and blue curves) are included in the analysis. NMR data (Track 7) show a bimodal distribution, indicative of possible oil mobility, across much of the upper interval but not below X,650 ft. The differences between NMR data at the two blue-shaded zones are significant. The lower interval could be interpreted as containing nonmovable oil. Data from the Dielectric Scanner tool indicated a distinct difference between the deep and shallow porosity measurements (Track 6), corresponding to the moved oil (gold shading). The interpretation suggests a total of 150 ft [46 m] of low-resistivity movable oil. This was later confirmed with production tests after casing was in place. (Adapted from Mosse et al, reference 29.)

Caliper

SP

in.8

–100 0

0.2 2,000ohm.m

0.2 2,000ohm.m

0.2 2,000ohm.m

0.2 2,000ohm.m

0.2 2,000ohm.m

0.2 2,000ohm.m

01 in.

01 in.

01 in.

0 100%

0 100%

50 0 0.5 ms 5,000%

50 0%

50 0%

mV

18

Moved Oil

Water

Lith

olog

y

Residual Oil

ResistivityStandoff

8-in. Invaded Zone Resistivity

Dielectric Scanner Shallow Resistivity

Dielectric Scanner Shallow Water-Filled Porosity

Dielectric Scanner Deep Water-Filled Porosity

Total Porosity T1 Cutoff

T1 DistributionDielectric Scanner Deep Resistivity

HRLA True Resistivity

Array Laterolog Resistivity

Invaded Zone Resistivity

DensityStandoff

DielectricScannerMudcakeThickness

DielectricScanner ShallowWater Saturation

DielectricScanner Deep

Water Saturation

Depth,ft

X,450

X,500

X,550

X,600

X,650

X,700

X,750

X,800

Moved Oil

Residual Oil