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GEOPHYSICS, VOL. 74, NO. 2 MARCH-APRIL 2009 ; P. 1­XXXX, 15 FIGS. 10.1190/1.3076607

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Benefits of the induced polarization geoelectric method to hydrocarbon exploration

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Paul C. H. Veeken1, Peter J. Legeydo2, Yuri A. Davidenko2, Elena O. Kudryavceva2, Sergei A. Ivanov2, and A. Chuvaev3

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Delineation of hydrocarbon prospective areas is an important issue in petroleum exploration. The geoelectric method helps to identify attractive areas and reduces the overall drilling risk. For this purpose, we mapped induced polarization IP effects caused by the presence of epigenetic pyrite microcrystals in sedimentary rocks. These crystals occur in a shallow halo-shaped mineralogical alteration zone, assumed to overlie a deeper-seated hydrocarbon accumulation. Local enrichment in pyrite results from reducing geochemical conditions below an impermeable layer. The imperfect top seal of the accumulation permits minor amounts of hydrocarbons to escape and migrate through the overlying rocks to shallower levels. During migration, hydrocarbons encounter an impermeable barrier, forming an alteration zone. Induced polarization logging and coring in wells confirm this working model. Geoelectric data are acquired even in very shallow water, where some electromagnetic methods might be hampered by air-

wave energy. Geoelectric surveying visualizes anomalies in electric potential difference measured between receiver electrodes. The differentially normalized method DNME inverts the registered decay in potential differences, establishing a depth model constrained by seismic and petrophysical data. Diagnostic geoelectric attributes are proposed, giving a better grip on chargeability and resistivity distribution. Acquisition and processing parameters are adjusted to the target depth. Encouraging results are obtained in deeper 300 m as well as in very shallow water. Onshore, a grounded current transmitter is used. Geoelectric surveys cover different geologic settings with varying target depths. The success ratio for predicting hydrocarbon occurrences is high. So far, 40 successful wells have been drilled in Russia on mapped geoelectric anomalies. Out of 126 wells, the method produced satisfactory results in all but two cases. The technique reduces the risk attached to new hydrocarbon prospects and allows better ranking at a reasonable cost.

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Electrical methods initially encountered some problems in identification and delineation of hydrocarbon-containing reservoirs. Investigations, based on electromagnetic diffusion, can be carried out to delineate hydrocarbon reservoirs only when the contrast between the regional resistivity and that of the reservoir is sufficiently high. If not high enough, unwanted ambiguities will result in the analysis. Geophysicists soon realized the importance of the induced polarization IP effect and made serious attempts to characterize hydrocarbon accumulations by electrical methods e.g., Seigel, 1974; Dey

and Morrison, 1973; Allaud and Martin, 1977; Oehler and Sternberg, 1984; Sternberg, 1991 . The IP effect is captured and evaluated by relatively new electromagnetic attributes. Under the right circumstances, e.g., seal/reservoir leakage, as will be shown later, these attributes will help to solve some problems encountered in proper evaluation of the subsurface. We present two case histories from the Russian Federation, which illustrate very encouraging results. Schlumberger described the IP effect in 1920. The nature of the phenomenon is rather complex and still not understood completely after nearly 90 years. Nevertheless, IP well logging has become a valid option today e.g., Davydycheva et al., 2006 .

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Manuscript received by the Editor 7 August 2007; revised manuscript received 23 July 2008. 1 Wintershall Russia, Moscow, Russia. E-mail: [email protected] 2 SGRDC Llc, Irkutsk, Russia. E-mail: [email protected]; [email protected]; [email protected]; [email protected] 3 Lukbeloil Ltd, Saratov, Russia. E-mail: [email protected] © 2009 Society of Exploration Geophysicists. All rights reserved.


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2 Veeken et al. There are three main causes for the IP effect Olhoeft, 1985 : · Some sort of electrochemical processes at the interface of metallic minerals, such as pyrite or magnetite, and the pore fluid. These processes often are exploited to reveal the presence of ore deposits Mikhailov et al., 1973 . · Exchange reactions in clay and shaley sands Klein and Sill, 1982; Ulrich and Slater, 2004 . These prove very useful in hydrogeologic applications Komarov, 1980; Slater and Glaser, 2003 . · Reactions involving organic materials. Pirson 1982 assumes that during the migration of hydrocarbons, the mineralized pore water changes its chemical composition and acquires alkaline properties. Thus, a reduction zone appears in the area above oil or gas reservoirs that are characterized by a remarkable increased intensity of electrochemical reactions. Many authors propose empirical models for describing the IP effect, e.g., the ColeCole model, Dias model, Debye model, Warburg model, DavidsonCole model, and Wait model. All models imply a complex electric conductivity depending on frequency, particularly at low frequencies from 0.1 through 1000 Hz. The empirical model for electric conductivity, proposed by Cole and Cole 1941 , was applied by Pelton et al. 1978 to the IP effect: From its deployment for mineral reconnaissance in the 1950s and 1960s Seigel et al., 2007 , substantial progress in geoelectric surveying was made in the 1990s with direct application for hydrocarbon exploration. Commercial acquisition started in Russia about 10 years ago. In other countries, the electromagnetic method has made great advances in solving specific exploration and production problems He et al., 2007 . Different geologic settings are covered by the investigations, and the results look very encouraging. Data were acquired in the Arctic region, Siberia, Caucasus, Tatarstan, China, the Baltic Sea, and the Caspian Sea 300-m water depth . Out of 126 wells in Russia, when the geoelectric behavior at the surface was analyzed, only two wells contradicted the prediction made by the method. A relation between the measured anomaly and presence of micropyrite crystals is evident. A direct link to the presence of commercial hydrocarbons is not proved in all cases, but this still represents a useful working hypothesis to be verified on a caseto-case basis. Given its high prediction value, the geoelectric method warrants the keen attention of geoscientists around the world. Unfortunately, sophisticated investigation methods are required when the difficult problem of finding hydrocarbons must be addressed. Recently, the geochemical and geoelectric methods have proved very effective in this context. The geochemical "sniffing" method exploits subtle but measurable effects of leaking hydrocarbons to the surface cf. Veeken, 2007 . These techniques are basically complementary; both add substantial value to the hydrocarbon exploration chain. Geoelectric investigations represent a convenient and cost-effective methodology. Three major issues play a role in determining the efficiency of geoelectric studies: 1 signal-to-noise ratio; 2 effects of 3D geobodies, although modeling usually is done in 2D sections; and 3 separation of IP from other electromagnetic coupling effects. The methodology described here ensures a good signal-to-noise ratio in all working environments. The proposed time-domain technology is robust with good repeatability of results. This is enhanced by conscientious data conditioning and the application of a sensitive inversion scheme to interpret results. Consistency of the line intersections is high, and an increased integrity of the data set is ensured. Moreover, the acquisition of control segments resurveying will augment the degree of confidence for the reliability of geoelectric field measurements.

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where is conductivity 1/resistivity at infinity frequency; is the IP coefficient, also known as the intrinsic chargeability of sedimentary rocks; is the time decay constant of the IP potential; and c is the relaxation constant. Usually, c ranges from zero through one, but the constant for sedimentary rocks does not exceed 0.1. The time decay constant varies in the range of seconds and dozens of seconds in the presence of electro-conductive rocks ore deposits, basalts . However, for ionic-conductive rocks, it usually does not exceed a few tenths of a second Komarov, 1980 . Omega is the circular fre1. quency, and i is the imaginary number An alternating low-frequency electromagnetic field spreads into the formation because of the electromagnetic and IP effects. This occurs not only because of diffusion currents, described by the real part of the conductivity model, but also via currents stemming from IP. Thus, because of the IP effect, polarized beds behave similarly to a giant condenser. They accumulate electric potential energy when the energizing current is turned on, and then slowly release it again during the turnoff time. This is why the electric field decay time after the current shuts down is much slower in polarizable media than in nonpolarizable media Dey and Morrison, 1973 . Displacement currents appear for only very high frequencies. Most IP effects are caused by distortion of double-charged layers or chemical reactions at least for decays during multisecond time lengths, according to a reviewer . When measuring potential differences in the field, this slowly decaying behavior can be observed directly. Long time constant decays often are observed over highly polarizable targets. The response appears to be related to the depth and properties of the responding beds. Therefore, the ability of an electric survey to image formation properties using a low-frequency pulse current is far greater than traditionally thought possible e.g., Nedra, 1989 .

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In geoelectric surveying, the response of the subsurface is studied with the help of an electrical transmitter-receiver setup. An axial dipole-dipole configuration is deployed for that purpose Leygeydo et al., 1990; Mandelbaum et al., 2002 . Figure 1 shows the basic acquisition layout of a marine geoelectric survey and illustrates the main parameters that are recorded. A high-amperage current with a power of 140 kW as high as 400­500 A is introduced into the earth via two input electrodes A and B, which typically are situated 200­500 m apart. The receiver assembly X1-X7 is positioned 1­3 km behind the input electrodes A and B along the measured trajectory. The subhorizontal input electrodes are normally positioned 1­2 m below the water surface. The receiver cable is towed somewhat closer to the surface. The speed of the assembly in the water helps to prevent negative effects of gas release when the strong cur-

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curve; this is not exploited in the DNME method. Proper forward rent is put into the water column. Several potential differences are modeling ensures that the airwave energy is adequately taken care monitored in time during the subsequent switch-off stage of the elecof. tric current. A more detailed description of the physical and electroThe direct task solution takes into account all factors of influence, magnetic processes, when a layered medium conducts an electric including airwave energy. The basic calculation in case of a layered current, is given by Davydycheva et al. 2006 , who also provide a medium is given by Vanyan 1965 . The Maxwell equations incorconcise overview of the differentially normalized electromagnetic porate the airwave effect. It also is taken into account when doing the method DNME . Cole-Cole modeling. The switch-off response and digital ADC filThe polarity of the electric current is changed before the next meatering are not ideal, however, and a 10-ms delay is implemented, surement cycle is started. The current is transmitted during a 4-s time starting from the transmitter turning-off point. Reduction of the inperiod and then turned off for 4 s. During this turnoff phase, the defluence of airwave energy is obtained by 1 proper data conditioncay of the potential difference in the receiver electrodes is recorded ing, 2 adopted inversion methodology, and 3 computation of spewith a time-sampling step of 0.25 ms. Several diagnostic parameters cially designed geoelectric parameters. or attributes are measured and computed on the decay function. The An additional advantage of the geoelectric method is its rather sudden turn-on of the current is not registered instantaneously in the simple acquisition setup. It makes use of electrodes of manhandling receiver assembly, but the initial phase of the power turn-on shows a size, resulting in a reduced exposure to health/safety/environment typical upbuilding trend that is monitored carefully. HSE hazards compared with other electromagnetic techniques. Sensitivity of the receiver device is approximately 1 10 6 V For example, there is an electromagnetic surveying technique that 1 microvolt with a gain factor of one. The input resistance is not deploys a huge conductor setup for field operations. This device is less than 4 108 ohms. Gain factors range from 1 through 128. The used to prevent gas-escape problems when the electrical current is equipment allows detecting signals as low as 1 10 6 V introduced into the water column. The cylindrical conductor is hoist1 microvolt for static observation and in the order of 2 10 5 V ed overboard by a big crane installed on an oceangoing vessel. Han20 microvolts for moving observations. The relative parameters dling this type of oversized equipment introduces an additional HSE are measured with an accuracy of 0.001 units under real field condirisk to the operations, along with increased costs. tions. The timing of the emitting direct current DC and length of the Some basic processing and smoothing are done to stabilize the quiet recording interval is adjusted to the expected target depth. For readings cf. Strack et al., 1989; Buselli and Cameron, 1996 . In the first place, the measured response is influenced by petrochemical alterations and/or the presence of micropyrite crystals in the subsurface. The alteration minerals get polarized easily and retain their polarization when the current is a) turned off, with a delayed slow return to their initial neutral state decay . This IP decay effect is visualized by the geoelectric surveying method. The effect is measurable on land as well as under b) marine conditions. The geoelectric response in shallow water depth of the beach transition zone is rather good. This stands in contrast to some other seabed logging or controlled-source electromagnetic CSEM methods. The latter are contaminated in such domain by energy traveling along the sea/air c) interface, making reliable observations for the deeper subsurface difficult. The airwave is represented by downgoing components that have been refracted and reflected by the sea surface, because of extreme contrast between conductive seawater and the highly resistive air Johansen et al., 2007 . Some upgoing multiple energy also is present Kong et al., 2008; M. Darnet, personal communication, 2008 . Proper airwave elimination or modeling is crucial in electromagnetic surveying MacGregor et al., 2006; Andreis and MacGregor, 2008 to preFigure 1. The setup is shown for a marine geoelectric survey applying the differentially vent the introduction of artifacts that cause erronormalized IP method. The positive electric current pulse between input electrodes A and B is turned on for 4 s. The response is measured on the receiver electrodes located neous interpretation of results cf. Dell'Aversana, 1 to 3 km away from the source X1-X7 . Subsequently, the current is turned off during 2007; Nordskag and Amundsen, 2007 . The air4 s, and the decay of the potential difference on the receiver assembly is monitored and wave is an atmospheric coupling effect, which recorded with a 0.25-ms time-sampling step. Several attributes are computed on the regleads to a decrease in sensitivity for a certain class istered decay function. The vessel moves on, and the next measuring cycle starts with a of resistors in the subsurface. Airwave energy is flipped negative polarized pulse in electric current. Three configurations for the potential present mainly in the frontal part of the decay difference U1 and U2 on the receiver electrodes are indicated MO and ON .

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4 Veeken et al. ically that a significant enrichment in pyrite often is related to hydrocarbon occurrences at deeper levels e.g., Sternberg, 1991 . The in-situ pyrite generation process is evaluated by a computer modeling program based on theoretical backgrounds developed by O. F. Puticov from the Saint Petersburg Mining Institute Puticov, 2000 . The seal of the hydrocarbon accumulation often is not perfect. Minor amounts of hydrocarbons along with hydrogen sulfide can escape and are dissolved in the surrounding pore waters. They migrate upward through the overlying rock column until they encounter an effective regional seal. Here, an alteration zone is formed. Retention and sufficient time are needed for the critical reactions to take place. This alteration zone is characterized by various mineralizations, among which pyrite enrichment is of particular interest for the geoelectric investigation method. The presence of zones with pyrite enrichment is registered in exploration wells, where IP logging was conducted Moiseev, 2002 . The alteration zone often forms a sort of halo above the hydrocarbon accumulation, and the distance to the reservoir varies from 1 through 5000 m cf. Karus, 1986 . Small microcrystals of pyrite hydrocarbon-related alteration zone show a decay of the polarized charge in the 10-ms range, whereas larger crystals basalts, dolerite dykes show a much longer decay profile 1­10 s . The pyrite, as it occurs in isolated grains, has only a minor influence on the resistivity Nedra, 1989 because the grains are physically separated from each other. This property can be used to distinguish hydrocarbon-related alteration zones from igneous-rock-related anomalies.

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instance, for a deeper target, a larger 8-s time interval can be used instead of a 2-s measurement period. In addition, the strength of the input current can be increased 200­300 kW. The distance between the source and receiver can be changed to augment the penetration depth, but more cumbersome operations in the field prevent the latter from being done. Special processing of the recorded field data helps to get a better resolution at greater depth, whereby the amplitude of the signal is optimized and/or increased. The source-receiver assembly, which is towed behind the recording vessel, normally has a speed of 7­8 km per hour. This setup allows a production in geoelectric surveying of some 100 km per day under normal operating conditions. The marine surveying equipment is shown in Figure 2. Surveying can be done now at as much as 300-m water depth, but with special design it can be increased in the near future. A smaller vessel is used in the shallow transition zone. The costs are comparable to those of seismic acquisition. The speed of surveying during the collection of onshore data is much reduced and ranges from 2 through 15 km per day. Normally, it is done with a station step between 0.2 and 1 km.


Micropyrite is formed in the subsurface under reducing condi276 277 tions, when free iron and sulphur are available. An inorganic reac278 tion takes place:


H 2S


2OH FeS2

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280 It is stimulated by the following reaction between freely dissolved 281 ions in the pore water to form carbonate: 282 283 284 CH4 Ca2 SO2 CaCO3 4 H2S H2O.

Some special processing is needed to improve the quality of the field data. It helps when proper care is taken during the acquisition phase to register the best possible signal cf. Macnae et al., 1984 . In addition, biodegradation and bacterial activity might give rise to Bad records are rejected, or their influence is reduced, by applying a an organic origin of pyrite in the sedimentary pile. It is proved empirclustering technique. Normalization of the electrical signal is done during the power-on phase of the acquisition cycle. It is performed by division by the potential difference. The potential difference is taken during the power-on phase of the acquisition cycle. Spikes caused by atmospheric disturbances are removed. Distortions in data section caused by drastic changes of the electric circuitry are taken into account during the next data inversion step. Industrial background noise is removed by applying a 50-Hz notch filter. Some smoothing of the decay data via the M-estimation algorithm Hampel et al., 1986 gives a better-stabilized response. Basic quality control is performed in the time as well as in the frequency domain Figure 3 . Flip of the measured signal phase is performed to obtain the same polarity at all observation points. Low-pass filtering using a 2D sliding window further augments the quality of the output Figure 4 . The M-estimation algorithm is used in the 2D sliding window, which is more effective than averaging when atmospheric spikes are present in records. The 2D sliding window allows using more input data for getting more robust statistics. Periodic noise also is suppressed because an averFigure 2. Marine geoelectric surveying in Russian waters and the recording vessel layout. aging effect takes place. Quality control and some basic data processing steps are conducted onboard.

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The decay value, measured at a specific XY location, is visualized along the trajectory and displayed in the sampled time windows Figure 5 . Important aspects of the decay function are the current turnoff point and the half decay value as well as the delay time. Note that the decay profile of the potential difference is sampled in a 0.25-ms time step. The curve now is resampled in flexible intervals and the average value with some weighting applied to obtain a stable output signal. The resampled time interval or time windows have a logarithmic step. Weights in the time windows are defined using Hampel's psi function. For each point at which the measurement is taken, several decay-attribute values are computed and stored for later display. The attribute is computed for discrete time intervals. It is convenient to plot the different iso-time decay values vertically along the surveyed section. The values of the decay are less for larger iso-times; hence it is useful to plot one under another Figure 6 . The profile representation allows rapid ident1ification of the lateral changes in electric potential response at various locations. The recorded data is arranged in so-called pickets. Data from overlapping pickets form the trajectory along the surveyed geoelectric traverse Figure 7 . The potential difference responses of time traces are plotted vertically below each other for the increasing time periods. The natural decay with time of the registered signal is neatly exploited in this way. Later, the geoelectric data is used to constrain the model construction and perform an inversion of the data set to obtain a depth model of the investigated subsurface area.


Several potential differences are measured in the receiver electrodes Legeydo et al., 1990, 1996; Legeydo et al., 1997; Davydycheva et al., 2006; Davidenko et al., 2008 . Three configurations are acquired simultaneously by varying the distances between the receiver electrodes. Figure 3. Data recorded on the receiver axial dipole-dipole array system. Curves for various differential potentials DU are plotted in the graph. The mathematical definition of the various geoelectric parameters is given in the main text body. The gradual decline of the potential differential in time is evident. The delay in relaxation is a result of the total field, which is composed of IP and electromagnetic coupling processes. Some minor noise is present on the registered trace, which is filtered easily. The filtered data also are represented in the frequency domain.

Figure 4. The decay function for subsequent geoelectric measurements along a trajectory are displayed in a 3D plot. Individual decay curves belong to a specific field position where the transient electrical measurement was performed. On the left, the raw data are shown. On the right, the filtered and smoothed data are plotted.

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Various diagnostic parameters are computed and registered. The physical meaning of the measured potential differentials U and 2 U across the receiver electrodes are shown graphically in Figure 1. Use of the Greek symbol delta indicates a measurement data value, whereas the normal D represents a more complex computed entity.





U0 /


U0 U0



t t

2 2




U0 / U


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U / D S



U1 U1

U2 U2

2 I tS PS 10 3 4 5 6 7

The parameter U0 is the difference in potential measured when the current is on; PS is a parameter mainly related to the electrodynamic process; It is IP; S is a parameter that is a function of time. Figure 8 shows the response of various geoelectric parameters. The response values are investigated for every measuring station along the geoelectric line. This data also is presented in map form. Local variation in the relaxation response can be outlined in this way. Subsequently, an inversion procedure is used to link the observed with the calculated relaxation response, and a depth model is computed. Figure 5. Receiver electric potential-time plot showing a typical decay function at a measurement station. The inset depicts a blowup blue ellipse . An important parameter is the delay time for the system to return to the base condition after the current in the input electrodes is switched off. The shape of the relaxation curve is diagnostic for the presence or absence of resistors that exhibit an IP effect.


DU D 2U P0 P1


U/ U0


U/ U0

U0 / U0


U/ U

Figure 6. Distance-time plot of data showing the change of the decay function along the geoelectric line. The decay curve is sampled in a 0.25-ms time step. Here, the potential difference values, coming from several decay functions, are plotted for discrete iso-time-sample values blue time lines . The iso-time curves are conveniently plotted below each other because of the naturally decreasing decay values. This permits rapid identification of zones with an anomalous behavior. Anomalies, caused by deeper-seated resistors, take more time to arrive at the receiver assembly.

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An inversion scheme, developed in collaboration with I. Pesterev in Irkutsk, is used to build a model that best explains the observed geoelectric response Figure 9 . Careful data preconditioning results in good repeatability even for lines acquired in different surveying campaigns . At the same time, it ensures that line intersections are consistent. The depth model is shown in the right-hand side of Figure 9. Output parameters of the inversion are the resistivity, polarization coefficient, relaxation time, relaxation spectrum width, and thickness of the layer. A range in values is specified, and the program searches for an optimal solution. The difference between observed and computed response is minimized by various methods, e.g., simulated annealing, global minimum, Nelder-Mead simplex method, principle axis, and genetic algorithm. The computed inversion results are shown as the thicker line on the left-hand side of the inversion plot. The observed parameter values are plotted as the thinner line behind it.

The depth model is updated iteratively until the difference between observed and calculated response model error reaches the minimum threshold value. The threshold value depends also on the accuracy of the observation defined as the mean convergence between ordinary and control measurements. The errors on the parameters P0, P1, PS, D S, and DU are calculated on a routine basis for quality-control purposes. Standard deviations convergence between ordinary and control measurements are related, for parameters P0, P1, PS, D S, and DU, to their threshold values of the inversion. The typical errors, shown in relative units, are DU%e 0.763, P1e 0.006, D e 0.006, PSe 0.002, and P0e 0.002. The inversion is done in a 1D mode using the Cole-Cole model, already presented in equation 1. Hence, 1D models of section are used, although in the actual terrain experiment, a 3D electromagnetic field source is deployed. Chargeability and resistivity depth sections are generated, whereby anomalous zones are identified easily Figure 10 . An important distinction can be made, especially when taking into account various inverted parameters at the same time. For exFigure 7. The data in the distance-time plots are subdivided in overlapping pickets. In the picket, a certain averaging is done to stabilize the output. Time lines are labeled with incremental numbers.

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Figure 8. Various geoelectric attributes are computed at each measurement station. The attribute can be displayed for each picket. The following attributes are computed: DU, D2U, PS, P1, P0, and D S. See main text for the definition of the geoelectric parameters.

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ample, the difference in response for a dolerite dike or basalt is detected easily by the strong resistivity of the igneous rocks, although the hydrocarbon-related alteration zone has a much lower resistance contrast with the surrounding host rock Figure 11 . The problem with every inversion is the nonuniqueness of its solution, i.e., several depth models can give similar results in the forward modeling Veeken and Da Silva, 2004 . Information from other techniques therefore is needed to support the retained solution. Hence, it seems less appropriate to attempt assessing the value of the geoelectric method on a stand-alone basis in each newly explored basin. The results can be much better when an integrated approach for the geoelectric studies is chosen from the start. Support from other geophysical techniques helps to fully exploit the discrimination power of the method and facilitates the interpretation of recorded anomalies. It is reassuring to see that integrated inversion schemes are adopted today to circumvent some of the ambiguities in electromagnetic studies cf. Colombo and De Stefano, 2007; Strack and Pandey, 2007 .


The geoelectric survey on the Severo-Guljaevskaya oil and gas field Barents Sea, northern Russian Federation is shown here as a case study Figure 12 . Resistivity logs in the 1-SG well and structural maps allow construction of an input model for the geoelectric evaluation. Structural maps for various geologic horizons were compiled using the seismic reflection data set. The inversion of the recorded data set and computation of geoelectric sections are based on a rather simple input model. An example of a geologic input section that forms the initial starting point for further parameterization in resistance is presented in Figure 13. During the modeling, it was decided to combine the layers in the lower part of the section. This was justified because the layered sequence at the depth interval of 1000­3500 m can be replaced by one equivalent layer with an averaged resistivity without a significant deterioration of the convergence of the observed and modeling curves. Subdivision of this interval leads to equivalence between re-

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Figure 9. The geoelectric data set is inverted and a depth model established. The response for the model is computed thick line and compared with the measured values thinner line . The depth model is perturbed until the error between the observed and computed values reaches a minimum. The vertical scale to the left belongs to P1, PS, and D parameters; on the right, the DU scale is shown. The black line is the measured first differential potential in the receiver electrodes during current-off period DU , blue is ratio of the second to the first differential potential during current-off period P1 , green is ratio of the second to the first differential potential in receiver electrodes during current-on period PS , red is combination of space and time derivative D , and the dotted line is ratio of the second to the first differential potential at stationary value in pulse P0 .

Figure 10. A simple depth model of the subsurface is obtained from the geoelectric response via data inversion. An iterative approach allows the establishment of a depth model that best explains the observed geoelectric response. The depth model permits the visualization of the behavior of the polarization coefficient and resistivity along the trajectory of the sailed geoelectric traverse. The input model is refined with the aid of information provided by other geophysical and petrophysical analysis techniques.

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sistances of separate layers within it, and hence can be safely ignored. A sharp local increase of in the fourth layer, from 2%­6% to 11%­13%, is present on the polarizability geoelectric sections in areas situated directly above the oil and gas field Figure 14 . It should be noted that the fourth geoelectric layer is located at an approximate depth of 400 m, i.e., on the geochemical permeability barrier depth. In addition, the polarizability for the fourth layer is shown. Within the closed contour area, the values of polarizability are seen to be rather high and come as high as 9% or more, compared with the 3%­6% value for the normal background value of the sedimentary deposits. The values of the other mapped parameters do not change as rapidly when the closed area border is crossed. The northwest and northeast anomaly boundaries correlate relatively well with the mapped limiting contour of oil and gas accumulation. The discrepancy does not exceed 1­1.5 km. However, the southeast and southwest limits of the anomaly are displaced with respect to the closing contour by 3 to 4 km in a southward direction. This probably is connected to a lateral shift in hydrocarbon migration leakage, caused by the fact that some tectonic faulting controls the migration path, as can be seen on the seismic data. In addition, the inaccuracy of the position of the mapped closed contour limiting the hydrocarbon accumulation might play a role. The geoelectric results suggest that the structural interpretation should be revised because it is based on 2D seismic data interpreted more than 20 years ago. Interestingly enough, the zone with increased resistivity values for layer 5 are in fact confined to the area where the hydrocarbon-bearing deposits are present on the deeper level of layer 6. The southeast limit of the region with increased resistivity is tectonic in nature, as it coincides with a postulated fault zone. The southwest limit is structurally tectonic determined, whereas the northwest and northeast flanks are controlled by a structural dip. It is postulated that the geologic cause for the occurrence of an increased resistivity area over the Severo-Guljaevskaya accumulation is connected partially with epigenetic processes above the hydrocarbon-bearing deposits. For instance, this could be a diagenetic effect caused by calcitization/carbonization of the rock. In any case, the appearance of the area with relatively high resistance values can be viewed here as an additional hydrocarbon-prospecting indicator. Geoelectric data acquisition was performed also in the mid-Volga region onshore Russia . The result of the analysis of , , and c polarizing parameters shows that the polarizability coefficient is a fundamental interpretation parameter, allowing oil and gas forecast in this research area.

Figure 11. The inverted geoelectric data set makes it possible to visualize the change in geoelectric parameters. Here, the behavior of the chargeability, relaxation time, and resistivity are shown. Notice the difference in behavior for the hydrocarbon reservoir and volcanic rocks. The volcanic rocks have a high resistivity, although the sedimentary cover has relatively low values.

Figure 12. The Severo-Guljaevskaya case study is located in the frozen Barents Sea, offshore Siberia. Two zones with increased IP response correspond to areas with an augmented concentration in micropyrite in the sedimentary rocks. Highest concentrations are measured in a halo just above the hydrocarbon-bearing interval. A regional seal confines the reducing environment created by the migration of leaking hydrocarbons from the reservoir, and determines the location of geochemical alteration zones. The IP anomaly is a powerful diagnostic attribute for determining the presence of potential hydrocarbon accumulations.

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10 Veeken et al. by the differentially normalized IP method. The mapped closure of the eastern structure at top reservoir level was drilled before the geoelectric work was carried out. The well in the closure to the west was drilled in a later phase upon recommendations made after the geoelectric survey was conducted and its inversion results became available. The geoelectric response is classified in a color code reflecting the strength/reliability of the anomaly red high, magenta intermediate . The new well confirmed the positive geoelectric forecast and actually proved the presence of a hydrocarbon-filled reservoir. Often, the seismic information is difficult to interpret and the distribution of carbonate reservoir is very hard to predict. Geoelectric data provide an additional means for outlining prospects under such circumstances and might serve also as support to update the timedepth conversion model. The new velocities give a better grip on the volumetrics and actual shape of the mapped hydrocarbon accumulations. This also allows better ranking of prospects and helps in the de-risking of drilling targets cf. MacGregor et al., 2007 .

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The c parameter is the relaxation spectrum width. Although there is no direct correlation between the resistivity and parameters, polarizability anomalies are found to be limited to a diffuse halo above the hydrocarbon-containing deposits. These anomalies are confined to the third geoelectric layer and located at a depth of 60 through 100 m. The anomalies correlate well with the location of the deeper reservoirs that contain hydrocarbons. The sensitivity of definition in this particular area does exceed the depth of 1600 m in the modeled inversion results. The inversion is rather straightforward, and the obtained information speaks for itself. Figure 15 shows a part of a polarizability coefficient section along a recorded geoelectric line. The intensity of the anomaly is in this case sufficiently high, between 14%­16% in comparison with 12% outside the closing contour of the known hydrocarbon accumulation. The comparison of the geoelectric data map with the structural contours of the seismic reflection marker horizon nC1up also is presented. The position of various depth closures from the seismic data correlate with the location of the geoelectric anomalies determined

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The geoelectric method quantifies the presence of IP effects in the subsurface. These effects are caused by mineralogical changes in the rock column. In sedimentary rocks, these changes often are related to an alteration zone with typical epigenetic micropyrite crystals. The relationship between the documented geoelectric anomalies and occurrence of hydrocarbons is indirect. It is postulated that leaking hydrocarbons are the main reason for an alkaline environment in the pore fluid of overlying rocks. This gives rise to specific conditions under which authigenic pyrite crystals can grow. A distinct IP anomaly corresponds often with the presence of a deeper hydrocarbon accumulation. It is obvious that this particular relation needs to be verified in individual study areas before it can be used as a working hypothesis. It should be realized that if the seal of the trap is perfect, no hydrocarbons will migrate into the overburden and no shallow IP effect will be detected. Therefore, absence of a geoelectric anomaly does not necessarily indicate that there is no commercial hydrocarbon accumulation underlying the investigated area. When the relation between anomaly and hydrocarbons is proved in a basin, the method becomes very effective in predicting potential hydrocarbon occurrences, and hence the ranking of drillable prospects is much facilitated. Based on experience with the method in various parts of Russia and the larger region of the Union of Soviet Socialist Republics USSR , the success ratio of the method for predicting the presence of hydrocarbons is high, e.g., a positive prediction in 124 out of 126 study cases. Other areas with encouraging results for geoelectric investigations are Peru, China, and the Baltic Sea. Forty successful wells have been proposed and drilled on IP DNME anomalies. Confidentiality reasons do not permit reproduction of any specific evidence concerning the validation of these statistics. The quality of the geoelectric parameter inversion is improved by using detailed subsurface information stemming from complementary geoscience study techniques. An integrated approach enhances reliability of the output results. The mapped geoelectric anomalies provide useful support for the maturation of prospects in known hydrocarbon provinces, as well as in greenfield exploration areas. The method provides an additional cost-effective means to identify quickly areas of increased interest. It allows more reliable outlining

Figure 13. Resistivity section of Severo-Guljaevskaya oil and gas field. The first layer is seawater with fixed 0.26 ohm-m; thickness is defined by an echo sounding device. The resistance in the second layer could change within wide limits, from 1 through 20 ohm -m, because electric logging data are absent, but as a rule, it should not exceed the range of 7 through 15 ohm-m. The third layer is relatively conductive, and its ranges between 1 and 8 ohm-m. The fourth layer is relatively highly resistive, between 8 and 20 ohm-m. The fifth layer has again a lower specific resistance between 1 and 8 ohm-m. In the sixth layer strata alternation with different , the increase of resistance is set in the range of 7 through 60 ohm-m usually 8 through 25 ohm-m . The nonconducting basement tentatively is placed in the base of the section.

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Geoelectrics and HC exploration 11 Figure 14. Geoelectric section of polarization coefficient distribution in the Severo-Guljaevskaya area Barents Sea . The IP anomaly is marked in red. Distribution of polarization coefficient in the fourth geoelectric layer is based on the top of the structure of carbonate complex C2-3-P1. Tectonic disturbances are shown as a red line and the contour of the water-oil contact as a blue line. The IP anomaly is indicated in orange colors.

of mapped prospects, especially in poor seismic data zones e.g., chimney effect . The recognition of potential stratigraphic traps is facilitated greatly. Geoelectric anomalies also are helpful for fine-tuning the depth conversion model of mapped hydrocarbon traps. The method provides an independent extra constraint on the subsurface velocity model, giving a better grip on the volumetrics and shape of the investigated hydrocarbon occurrence.

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Geoelectric surveying is an innovative tool to help the geoscientist in the ranking of hydrocarbon prospects. The technique measures the electric potential difference between receiver electrodes and exploits changes in IP. In sedimentary rocks, these changes often reflect the presence of a mineralogical alteration zone overlying a hydrocarbon accumulation. Application of the method in deeper and shallow water, along with the efficiency of the acquisition setup and inversion processing, make it an attractive proposition in the prospect evaluation procedure. Diagnostic DNME geoelectric attributes are computed and monitored in time. Inversion of the geoelectric data set allows the establishment of a relevant depth model, with basic geoelectric parameters such as the polarization coefficient and resistivity. Geoelectric data sets cover different geologic settings and have varying target depths. The method already has proved its added value under a wide range of working conditions. It is cost-ef-

Figure 15. Geoelectric section illustrating the polarization coefficient distribution in the mid-Volga basin. The IP anomaly is marked in yellow and is slightly offset with respect to the structural closure shown in the corresponding map. The productive borehole is represented by a red triangle, and a nonproductive borehole is a blue triangle. Wells are projected to the geoelectric traverse. The comparison is made between the geoelectric data and a structural map of the reflecting horizon nC1up. The IP response is classified by various color representations along the profiles. The productive borehole is indicated by a red circle; the nonproductive borehole is marked by a blue circle. The well, which was drilled taking into account geoelectric data, is shown as a red triangle. Outline of depth contours could be adjusted to get a better correspondence between the geoelectric and the mapped structurally closed area. In addition, slanted leaking of hydrocarbons to shallower levels could explain the offset of the alteration zone in respect to the mapped accumulation. Green reflects a weaker anomalous response, red intermediate, and magenta is a higher anomalous geoelectric response. The red curve along the nonproductive borehole is impedance log response, and the yellow curve is the modeled receptivity of section. Electrical characteristics of section assigned in compliance with logging. Similarity of the modeled and measured well-log curves makes clear that the geoelectric results have a high degree of confidence in this case.

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12 Veeken et al.

Kong, F. N., S. E. Johnstad, T. Rosten, and H. Westerdahl, 2008, A 2.5D finite-element-modeling difference method for marine CSEM modeling in stratified anisotropic media: Geophysics, 73, no. 1, F9­F19. Legeydo, P., M. M. Mandelbaum, and N. I. Rykhlinski, 1997, Results of differential-normalized electrical prospecting in the Central part of the Nepa arch on the Siberian platform In Russian : Russian Geology and Geophysics, 38, 1707­1713. Legeydo, P. Y., M. M. Mandelbaum, and N. I. Rykhlinski, 1990, Application of differentially adjusted electric exploration of the Nepa Dome In Russian : Soviet Geology and Geophysics, 31, 86­91. ----­, 1996, The differentially normalized method of geoelectrical prospecting: Methodical manual textbook In Russian : SGP. MacGregor, L. M., D. Andréis, J. Tomlinson, and N. Barker, 2006, Controlled-source electromagnetic imaging on the Nuggets-1 reservoir: The Leading Edge, 25, 984­992. MacGregor, L., A. Overton, S. Moody, and D. Rockhopper, 2007, Derisking exploration prospects using integrated seismic and electromagnetic data -- A Falkland Islands case study: The Leading Edge, 26, 356­359. Macnae, J. C., Y. Lamontagne, and G. F. West, 1984, Noise processing techniques for time-domain EM systems: Geophysics, 48, 934­948. Mandelbaum, M. M., E. B. Ageenkov, P. Y. Legeydo, P. Y. Pesterev, and N. I. Rykhlinski, 2002, Normalized-differential electrical measurements in hydrocarbon exploration: The state of the art and prospects for future In Russian : Russian Geology and Geophysics, 43, 1085­1143. Mikhailov, G. N., I. P. Yurgens, and B. V. Yagovkin, 1973, Manual on induced polarization method In Russian : Nedra. Moiseev, V. S., 2002, Ground-well electro prospecting while contouring hydrocarbon deposits using cased well: Nedra. Nedra, 1989, Electro prospecting, handbook for the geophysicist parts 1 and 2 : Nedra. Nordskag, J. I., and L. Amundsen, 2007, Asymptotic airwave modeling for marine controlled-source electromagnetic surveying: Geophysics, 72, no. 6, F249­F255. Oehler, D. Z., and B. K. Sternberg, 1984, Seepage-induced anomalies, "false" anomalies, and implications for electrical prospecting, AAPG Bulletin, 68, 1121­1145. Olhoeft, G. R., 1985, Low-frequency electrical properties: Geophysics, 50, 2492­2503. Pelton, W. H., S. H. Ward, P. C. Hallof, W. R. Sill, and P. H. Nelson, 1978, Mineral discrimination and removal of inductive coupling with multifrequency IP: Geophysics, 43, 588­603. Pirson, S. J., 1982, Progress in magneto-electric exploration: Oil and Gas Journal, 80, 216­228. Puticov, O. F., 2000, "Stream" dispersion haloes under hydrocarbon deposits in heterogeneous rocks In Russian : Russian Geophysics, no. 1, 52­56. Schlumberger, C., 1920, Étude sur la prospection electrique du sous-sol: Gauthier-Villars. Seigel, H. O., 1974, The magnetic induced polarization MIP method: Geophysics, 39, 321­339. Seigel, H., M. Nabighian, D. S. Parasnis, and K. Vozoff, 2007, The early history of the induced polarization method: The Leading Edge, 26, 312­321. Slater, L. D., and D. R. Glaser, 2003, Controls on induced polarization in sandy unconsolidated sediments and application to aquifer characterization: Geophysics, 68, 1547­1558. Sternberg, B. K., 1991, A review of some experience with the induced-polarization/resistivity method for hydrocarbon surveys: Success and limitations: Geophysics, 56, 1522­1532. Strack, K. M., T. H. Hanstein, and H. N. Eilenz, 1989, LOTEM data processing for areas with high cultural noise levels: Physics of the Earth and Planetary Interiors, 53, 261­269. Strack, K. M., and P. B. Pandey, 2007, Exploration with controlled-source electromagnetics under basalt cover in India: The Leading Edge, 26, 360­363. Ulrich, C., and D. Slater, 2004, Induced polarization measurements on unsaturated, unconsolidated sands: Geophysics, 69, 762­771. Vanyan, L. L., 1965, Electromagnetic sounding principles: Nedra. Veeken, P. C. H., 2007, Seismic stratigraphy, basin analysis and reservoir characterisation, in K. Helbig and S. Treitel, eds., Handbook of geophysical exploration, vol. 37: Elsevier Science Publishing. Veeken, P. C. H., and M. Da Silva, 2004, Seismic inversion methods and some of their constraints: First Break, 22, 47­70.

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fective and basically complementary to other hydrocarbon exploration techniques. For some Russian oil companies, geoelectric surveying already is a part of the standard investigation toolbox. So far, the prediction capabilities are very promising, and the IP geoelectric method surely warrants the attention of geoscientists worldwide.


Gratitude goes to Siberian Geophysical Research Development owner of patent 2301431 and ISO9001-2000 certificate , the Megatron consortium, Wintershall Russland, Lukoil, and Schlumberger for giving permission to publish this article. Ing I. Pesterev is acknowledged for compiling the Cole-Cole inversion software program. S. Benko and P. Thiede are thanked for help in the preparation of some figures. G. Kobzarev Lukoil , M. Minning Wintershall , Graham Clark Wintershall , C. Hanitzsch Wintershall , and M. Watts Geosystem participated in stimulating discussions on various electromagnetic investigation topics. The editors are thanked for their comments.


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