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In order to understand the research situation of saturation interpretation models, and to offer some basic information for the well logging reservoir evaluation, this paper summarizes saturation interpretation models frequently used throughout the world, discusses the characteristics and development tendency of the saturation interpretation model research, and offers some advice for the future research. Based on theoretical background, the saturation interpretation models are divided into four types: classical Archie formula, saturation interpretation models considering shale effects, saturation interpretation models considering the effects of conductive mineral and multi-porosity, general saturation interpretation models based on the network conduction theory in heterogeneous rock. Except for the classical Archie formula, there arc a variety of saturation models in the other types. The saturation interpretation models considering shale effects are subdivided into three kinds based on argillaceous equivalent volume, argillaceous cationic exchange capacity, and effective medium theory.

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... The Archie formula is the main method for calculating gas saturation systematically [10]. However, that equation has mostly been used for conventional reservoirs and has limitations in the interpretation of tight sandstone gas saturation [11][12][13]. The sealing coring technique is an accurate method to obtain S g , which provides data for the systematic study of tight sand gas-bearing properties. ...

Gas saturation (Sg) is an important parameter for studying the gas-bearing properties of tight sandstone; however, there has been limited research on gas-bearing properties based on sealing coring. This study determines the controlling factors of the gas-bearing properties of tight sandstone in the Permian He8 Member of the Sulige Gas Field, Ordos Basin, Northern China, based on sealing coring, logging, drilling, gas testing, and laboratory analysis. The He8 Member Sg distribution is 17.9–63.8% (main range: 30–45%) and shows a downward trend from bottom to top. The Ro and hydrocarbon generation intensity of the source rock, reservoir porosity, and permeability tend to control the Sg in stages. When these parameters are less than 1.8%, 17 × 108 m3/km2, 10%, and 0.5 × 10–3 μm2, respectively, Sg increases significantly. When each parameter is greater than the aove boundary value, the change in Sg is not obvious. Four types of gas-bearing patterns of tight sandstone can be observed according to the distribution of reservoir and source rock conditions: “upper and lower constant,” “upper low–lower high,” “upper high–medium low–lower high,” and “upper high–lower low”; these patterns are mainly controlled by high maturity source rocks, reservoir physical properties, reservoir physical properties and structure, and structure, respectively. The corresponding gas test results reveal the existence of pure gas, pure gas and gas–water, gas–water, and gas-bearing water and pure water layers, respectively.

... Over the years, many methods [1][2][3][4][5] represented by Archie formula for calculating reservoir water saturation have been formed, and some models for evaluating reservoir resistivity have been formed, such as the three-water model [6,7]. However, the research on the conductive mechanism of sandstone resistivity and the derivation of the calculation formula are quite complicated, the theoretical derivation and proof logic are not rigorous, and the practical application is often inconsistent. ...

... Unlike cementation exponent, for which a substantial understanding has been obtained through various investigation methods, including theoretical and numerical modeling approaches as well as fractal theories (e.g., Sen et al. 1981;Glover 2009;Han et al. 2015;Tang et al. 2015;Wei et al. 2015;Cai et al. 2017;Yue 2019), there is still no well-accepted physical interpretation for the saturation exponent (Yue et al. 2004(Yue et al. , 2009Glover 2017). The saturation exponent is commonly interpreted qualitatively as some measure of the efficiency for the electrical flow to take place within the water occupying a partially saturated rock (e.g., Sun and Chu 1994;Sun 2008;Li et al. 2012Li et al. , 2013Chen et al. 2016). Recently, based on the generalized Archie's law, Glover (2017) provided a new theoretical interpretation for the saturation exponent in terms of the rate of change of the fractional connectedness with saturation and connectivity within the reference phase. ...

Saturation exponent is an important parameter in Archie's equations; however, there has been no well-accepted physical interpretation for the saturation exponent. We have theoretically derived Archie's equations from the Maxwell-Wagner theory on the assumption of homogeneous fluid distribution in the pore space of clay-free porous rocks. Further theoretical derivations showed that the saturation exponent is in essence the cementation exponent for the water-air mixture and is quantitatively and explicitly related to the aspect ratio of the air bubbles in the pores. The results have provided a theoretical backup for the empirically obtained Archie's equations and have offered a more physical and quantitative understanding of the saturation exponent.

... Unlike cementation exponent, for which a substantial understanding has been obtained through various investigation methods, including theoretical and numerical modeling approaches as well as fractal theories (e.g., Sen et al. 1981;Glover 2009;Han et al. 2015;Tang et al. 2015;Wei et al. 2015;Cai et al. 2017;Yue 2019), there is still no well-accepted physical interpretation for the saturation exponent (Yue et al. 2004(Yue et al. , 2009Glover 2017). The saturation exponent is commonly interpreted qualitatively as some measure of the efficiency for the electrical flow to take place within the water occupying a partially saturated rock (e.g., Sun and Chu 1994;Sun 2008;Li et al. 2012Li et al. , 2013Chen et al. 2016). Recently, based on the generalized Archie's law, Glover (2017) provided a new theoretical interpretation for the saturation exponent in terms of the rate of change of the fractional connectedness with saturation and connectivity within the reference phase. ...

Saturation exponent is an important parameter in Archie's equations; however, there has been no well-accepted physical interpretation for the saturation exponent. We have theoretically derived Archie's equations from the Maxwell-Wagner theory on the assumption of homogeneous fluid distribution in the pore space of clay-free porous rocks. Further theoretical derivations showed that the saturation exponent is in essence the cementation exponent for the water-air mixture and is quantitatively and explicitly related to the aspect ratio of the air bubbles in the pores. The results have provided a theoretical backup for the empirically obtained Archie's equations and have offered a more physical and quantitative understanding of the saturation exponent.

... Similar to the calculation of porosity distribution, for a given image frame, distribution of apparent formation water resistivity can be calculated. According to the method given below, in deep window for a given length, all pixels can be used to work out an apparent formation water resistivity value which can be counted by frequency histogram to generate frequency distribution curve [4,5]. According to their distribution, we can understand distribution of apparent formation water resistivity size in correspondence with the window. ...

... Based on the saturation model of the shale content and distribution, the area has been studied using two research methods: one that only considers the shale content and one that considers both the shale content and shale distribution (Simandoux, 1963;Patnode and Wyllie, 1950;Winsauer and Mccardell, 1953;Poupon and Leveaux, 1971;Clavier et al., 1984;Waxman and Smits, 1968;Silva and Bassiouni, 1985). The second idea is to create a modified Archie model (Sun et al., 2008;Shi et al., 2008;Mo et al., 2001;Zhang and Shi, 2005;Xie et al., 2006;Li et al., 2009Li et al., , 2017, such as a high-precision cementation index model (Li et al., 2014;Stalheim and Eidesmo, 1996). The third is based on the Baojie Si Chuan and Central Sichuan Basin sandstone reservoirs, which have complex pore structures (Yin et al., 2008;Ge et al., 2018) and have mainly been studied using saturation models considering the influence of pore structure Ge et al., 2015), such as the equivalent rock elements model (EREM) (Shang et al., 2003;Ge et al., 2011), the pore geometry conduction theory model (Herrick and Kennedy, 1993;Song et al., 2012), a double porosity conducting model (Raiga-Clemenceau et al., 1984;Li et al., 2012a), the "double water" model (Zeng, 1991), and the "three water" model. ...

... The water saturation is an important index to evaluate the oil-bearing reservoir and one of the essential parameters for quantitative interpretation of well logging, it is also important to the evaluation of low resistivity reservoir [1,2]. Due to the diversity of complex sandstone, the pore structure and wettability of it are different from those of pure sandstone and its lithology, electrical property, oil-bearing property also shows 'atypical Archie' phenomenon [3,4]. ...

According to core data, this paper studies variation of resistivity in different pore structures and wettability conditions. The results show that with the increase of pore structure index m, the resistivity will increase significantly when the saturation is constant. Similarly, with increasing saturation index n, the resistivity will also increase even with the same saturation. With fixed m and n, the calculated formation water saturation will be very high, resulting in hydrocarbon reservoir being ignored. This variation characteristic is significant for the identification of hidden reservoir with atypical Archie formula.

... In view of the characteristics of conglomerate reservoirs, i.e. complex lithology and severe heterogeneity, and based on detailed analysis of the meaning of the parameters and their determination in various models for calculation of water saturation (such as the Archie equation, revised Archie equation, Simandoux equation and multiple linear regression equation) (Sun et al, 2008;Shen et al, 2008), and using the coring data of sealed coring wells, the parameters value for model calculation of characteristic conglomerate reservoir are eventually determined. The accuracy of various models in calculation of the water saturation of a conglomerate reservoir was analyzed (see Fig. 3) and a multiple linear regression equation was selected as the model for calculation of water saturation of the Kexia Group conglomerate reservoir. ...

The rapid changing near source, multi-stream depositional environment of conglomerate reservoirs leads to severe heterogeneity,
complex lithology and physical properties, and large changes of oil layer resistivity. Quantitative evaluation of water-flooded
layers has become an important but difficult focus for secondary development of oilfields. In this paper, based on the analysis
of current problems in quantitative evaluation of water-flooded layers, the Kexia Group conglomerate reservoir of the Sixth
District in the Karamay Oilfield was studied. Eight types of conglomerate reservoir lithology were identified effectively
by a data mining method combined with the data from sealed coring wells, and then a multi-parameter model for quantitative
evaluation of the water-flooded layers of the main oil-bearing lithology was developed. Water production rate, oil saturation
and oil productivity index were selected as the characteristic parameters for quantitative evaluation of water-flooded layers
of conglomerate reservoirs. Finally, quantitative evaluation criteria and identification rules for water-flooded layers of
main oil-bearing lithology formed by integration of the three characteristic parameters of water-flooded layer and undisturbed
formation resistivity. This method has been used in evaluation of the water-flooded layers of a conglomerate reservoir in
the Karamay Oilfield and achieved good results, improving the interpretation accuracy and compliance rate. It will provide
technical support for avoiding perforation of high water-bearing layers and for adjustment of developmental programs.
Key wordsWater-flooded layer-quantitative evaluation-conglomerate reservoir-lithology identification-decision tree-characteristic parameters

Cementation factor and saturation exponent are important indexes for oil reservoirs evaluation. In order to facilitate the quantitative analysis of oil reservoirs accurately, a method of calculating cementation factor and saturation exponent by resistivity was proposed. Taking the silty clay (cohesive soil) samples in Changchun as an example, the resistivity expressions of cohesive soil was derived. Combined with the moisture content, porosity, and resistivity of the samples, the cementation factor and saturation exponent were calculated by multivariate fitting of resistivity formula of silty clay from two boreholes in Changchun. Results revealed that the dispersion of cementation factor and saturation exponent calculated by resistivity method is very small, and R2 of the independent analysis are all above 0.95. The comprehensive analysis showed that it is accurate and simple to determine the cementation factor and saturation exponent by the derived resistivity expression and multiple regression analysis, which provides a theoretical basis for further calculation of oil reservoirs.

Because of the particularity of neutral and basic volcanic reservoir, it is difficult to compute its gas saturation by the traditional electric conduction model. Through the study on the type and feature of pore and on the litho-electric experimental data of neutral and basic volcanic reservoir, the paper finds that the main pore types are blowhole, corrosion hole and micro-crack, which is of extremely high pore throat radius ratio; there is non-electric conduction pore in the conductive background. Because of this, through the analysis of Maxwell conductivity model, the total porosity can be divided into conductive porosity and non-conductive porosity. By introducing communicating pore factor, the paper gets the conductive porosity. With the principle of conductivity superposition, the model of the reservoir gas saturation based on the conductive pore is obtained. Comparing with the saturation from the analysis of capillary pressure data, the gas saturation by this model is of less than 3% mean absolute error, and agrees with the well testing data.

Because of various types of minerals, complex pore structure and generally high resistivity of volcanic reservoir, using conventional neutron-density- logging curve intersection and high-low of resistivity to identify reservoir fluid type is not viable. Through the analysis of characteristics of natural gas logging response, the paper uses porosity combination, the ratio of transverse wave and longitudinal wave, nuclear magnetic resonance and integrated parameter to identify fluid type. Results show that these methods can only identify gas-bearing reservoir, and the result of identifying reservoirs which contain gas and water is not good. Resistivity is sensitive to the changing of reservoir fluid types, but is not so for acid volcanic reservoir. Starting from the microscopic pore structure of reservoir, the paper studies the influence factors of volcanic reservoir resistivity. Results show that complicated pore structure is one of the reasons of high resistivity of acidic volcanic reservoir. On this basis, the paper develops the fluid type gradual identification method based on the pore structure. Using this method, the paper identified the fluid type of 49 wells in Xushen gas field. Through the test of 69 layers, identification accuracy is 94. 1 The accuracy has increased nearly 15 percent.

The enhanced diffusion theory of NMR to identify the type of reservoir fluid utilizes the obvious relaxation signal located on the right of the diffusion relaxation time of water in the transverse relaxation time spectrum as the direct indicator of oil existence in reservoirs. The result of NMR experiments by changing TEs in the uniform magnetic field is greatly different from that in the gradient field. Only the latter can simulate the result of MRIL-Prime tools and guide well logging interpretation. According to foreign successful NMR experiments with multiple TEs in a gradient magnetic field, we discuss the application range of this method and point out that it's better to use timely logging data in order to decrease the effect of drilling mud invasion. Preliminary applications prove it very effective, as a valuable supplement of the resistivity logging method, in the determination of fluid type in complex reservoirs of the Bohai Bay Basin.

In mature oil fields, vertical resolution of early log curves is not high enough for the characterization of thin beds and doesn't meet the demand of delicate geologic research. Aimed at this problem, the process of blind deconvolution for log curves is developed. The algorithm of this method is: form linear equations by transforming the problem of deconvolution into an extreme value problem between the expected and actual output signals, and then get the deconvolution factor by solving the equations. By this process, the influences of surrounding rocks are wiped, the energy of high frequency band in frequency spectrum is improved, the new curve is more sensitive to the variance of curve slope, low amplitude variances of curves are zoomed in and the characteristics of sedimentary surfaces are more distinct. The identification ability of processed log curves in thin beds interpretation, especially in alternating beds with single beds about 0.5 m thick, is improved after the deconvolution process. The geological rationality of deconvolution process is verified by research with cored well data.

C/O logging is a main logging method that can determine oil saturation. The oil saturation of initial formation can be determined by C/O according to the inelastic gamma spectroscopy in LWD (logging while drilling). The inelastic gamma spectrum were simulated using the Monte Carlo method under the condition of different lithology, porosity, saturation, borehole size, mud and mud invasion, water salinity, shale content, hydrocarbon and source-receiver spacing in horizontal wells. The response of C/O and porosity and saturation was studied, and the effects of several factors on C/O values were given. It is concluded that the relationship of C/O value and oil saturation is linear. C/O values are high when with oil-based mud in boreholes or in limestone formation. The smaller the hole size, the higher the formation water salinity, the more the shale content in the matrix and the bigger the spacing, the greater the formation C/O value. C/O values are affected differently as mud invasion depth, lithology, porosity and drilling mud change. It is favorable to identify formation fluid by using C/O spectroscopy logging under the condition of small mud invasion depth, small borehole size, high porosity and heavy oil.

In order to understand the current situation of carbonate reservoir water saturation models, and to offer basic information for saturation evaluation in heterogeneous reservoir, this paper summarizes the current saturation interpretation models. According to study objects, conductive mechanism and study methods, the carbonate reservoir saturation interpretation models are divided into four types: Archie and its extended empirical saturation models, saturation models of dual and triple porosity system based on different porosity types, saturation models of dual and triple porosity system based on different porosity size, and saturation models based on effective medium theory. The conductive mechanism, assumption and application of different saturation interpretation models have been discussed. It points out that the Archie formula is concise, and always equivalent in the numerical sense after scaled by the data of experimental analysis and the triple-model will be the most conventional one that we expected currently, and based on the discussion we suggests that rock conductive mechanism, petrophysics experiment and the calculation of model parameters need further research, and that the pore network model based on digital core is an important research subject in carbonate reservoir evaluation.

The effect of drilling fluid invasion on the resistivity of oil-bearing zones during the period from penetrating the zone to completion well logging was studied using intergraded logging while drilling (LWD) and wire-line log data. The results indicate that resistivity change during invasion responds to some important factors such as porosity, oil saturation, pressure differential between drilling mud column and formation, mud filtrate salinity and invasion time. It increases as an exponential function of porosity, a logarithmic function of pressure differential, and a power function of invasion time and oil saturation. Based on the LWD and MDT data, the corrected resistivity equation subject to the drilling fluid invasion was acquired. With the equation, the oil saturation (So) increases by 6.3%–20.0%, averaging at 10.2%.

A saturation equation is derived from effective-medium theory (the Hanai-Bruggeman equation) for calculating water saturation from resistivity and porosity measurements. That saturation equation is then incorporated into dispersed-clay and laminated-shale models. The five basic variables needed for the saturation formula include whole-rock porosity, true formation resistivity, water resistivity, cementation exponent, and grain resistivity. In the dispersed-clay model, whole-rock porosity, true formation resistivity, and water resistivity are calculated by standard log-analysis methods. Next, cementation exponent and grain resistivity are calculated for the whole rock. These five variables are then used in the saturation equation to calculate whole-rock saturation that is, in turn, used to calculate effective saturation. Intermediate variables used in calculation include clay volume and effective porosity in addition to sand and shale counterparts for porosity, cementation exponent, and grain resistivity. In the laminated-shale model, shale resistivity is subtracted from the whole-rock properties by resistors-in-parallel treatment and effective saturation is then calculated directly from the saturation equation using only sand input variables. The shaly sand models are proved accurate and stable by calculations on some published log data, including low-resistivity, low-contrast examples. Saturations can be determined from standard log suites. The variables used are calculated in a straightforward manner, while the calculation sequence is flexible to allow for unusual conditions such as nonclay microporosity.

The recently introduced S-B conductivity model for water-bearing shaly formations is presented and its ability to accurately predict shaly sand conductivities is evaluated. The basis of the evaluation is a comparison between calculated conductivity and accurate laboratory data available for water saturated rocks. The model's performance is also compared to that of the W-S and D-W models. The S-B model appears to be the best compromise for predicting water bearing sand conductivities in the range of shaliness and water conductivities expected in shaly sand interpretation.

Published in Petroleum Transactions, AIME, Volume 204, 1955, pages 103–110.
Abstract
A review is given of the principles on which recently proposed methods of electric log interpretation in shaly sands are based and of the evidence brought up in support of the theoretical derivations. It is pointed out that the qualitative techniques suggested to date have a tendency to be too pessimistic in the prediction of the presence of commercial hydrocarbon accumulation.
In the quantitative treatment, a new concept is introduced, namely that of strongly reduced activity of the double layer counter ions which are present near the negatively charged rock surfaces. This lemma when applied to the calculation of the equilibrium concentrations of interstitial waters yields a set of very simple relations. The resulting expressions for electrochemical potentials across rock samples appear in satisfactory agreement with laboratory experiments. The formulae obtained for the electrical conductivity of shaly formations are of the same form as those arrived at empirically by previous workers. Combination of the expressions for potentials and conductivities gives a direct proof of the Tixier relation which states that for shaly water sands the product of apparent formation water resistivity and apparent formation factor equals the resistivity of the sand 100 per cent saturated with formation water and which was verified by the work of Wyllie and Perkins and their co-workers.
The relations for the resistivities and spontaneous potentials have been extended to the case of hydrocarbon bearing shaly formations, thus laying a formal basis forthe quantitative interpretation of electric logs in shaly oil sands.
Introduction
The presence of disseminated clays in porous rocks saturated with electrolytic solutions has a strong influence on the transference of ions taking place under electrical or chemical potential gradients. Both the electrochemical emf's in evidence on the SP curves of electric logs and the electrical conductivity measured on resistivity logs are directly dependent on the ionic transference in the interstitial waters of the formations traversed by boreholes.

CRMM (conductive rock matrix model) is a phenomenological model of electrical conductance in a porous rock. The model differs from the many shaly-sand models in its formulation and application in that the problem is approached primarily through the resistivity index rather than the formation factor. The model assumes the Archie formation factor and resistivity index equations to be valid for the fluid-filled pore network, and it treats the parallel matrix conductance strictly as an electrical property independent of any specific conductive mechanism. The CRMM model correctly describes the basic features of a productive low-contrast resistivity (LCR) formation that by Archie analysis appears to be very wet.

Published in Petroleum Transactions, Volume 207, 1956, pages 65–72.
Abstract
In quantitative interpretation of electrical logs the presence of clay minerals introduces an additional variable which further complicates an already complex problem. Although recognizing the difficulties introduced as a result of the heterogeneity of natural sediments and despite the present incomplete state of knowledge regarding electrochemical behavior of shades, disseminated clay minerals and concentrated electrolytes, it was felt that useful empirical correlations might be obtained from experimental investigation.
Six typical sandstone formations, having a wide variety of petrophysical properties, were selected for the study. Approximately 45 samples from each formation were selected to satisfactorily represent the range of pore size distribution within the particular formation. As a matter of general interest, four limestone formations were also included in the investigation.
Previously proposed equations relating to resistivity, SP and interrelationship of the two phenomena have, where possible, been tested with data obtained in this investigation. These equations do not satisfactorily describe experimental behavior of samples through all degrees of shaliness or throughout the range of brine solution resistivities normally encountered in logging practice.
An empirical equation has been developed which quantitatively relates formation resistivity factor to saturating solution resistivity, porosity, and "effective clay content." This relation is indicated to be uniformly applicable to clean or shaly reservoir rocks.
It is shown that both the SP and resistivity phenomena of shaly samples are related to the sample cation exchange capacity per unit pore volume. The independent chemical determination of this parameter is thus a means of determining the "effective clay content" of samples.
Some implications regarding theory and electric log interpretation of shaly sands are discussed.
Introduction
The use of electrical resistivity logs as a means for estimating formation porosity is based upon the original work of Archie.

The abnormal conductivity found in shaly reservoir rocks containing anelectrolyte is shown to be a consequence of the electrical double layer in thesolution adjacent to charged clay surfaces. This increased conductivity resultsfrom a higher concentration of ions in the double layer than in the solution inequilibrium with the double layer. It is shown that the magnitude of theincreased conductivity of a shaly reservoir material is influenced by theconcentration and type of ions in the equilibrium solution as well as by thecolloidal nature of the rock.
Introduction
An important factor in the quantitative interpretation of the electric logis the resistivity factor of reservoir rock. The resistivity factor is definedas the resistivity of the rock when completely saturated with an electrolytedivided by the resistivity of the electrolyte itself. In the normal case, theresistivity factor for a particular rock sample is independent of theresistivity of the electrolyte and reflects the pore geometry of the rock.
In 1949, Patnode and Wyllie showed that in some cases the resistivity factoris not constant, but instead varies with the resistivity of the electrolyticsolution. It was shown that clean sands behave normally - i.e., that theresistivity factor does not vary with resistivity of the electrolyte - but thatsands con taining shale and clay may exhibit abnormally low resistivity factors when thesolution used to saturate them is of fairly high resistivity. At lowresistivities of the electrolyte, the resistivity factor for a shaly sandappeared to approach a normal behavior. It is evident that the resistivityfactor of a shaly sand is dependent upon factors other than pore geometry whenthe solution used to saturate it is of high resistivity.
Patnode and Wyllie, and later de Witte, assumed that the conductivity of ashaly sand saturated with an electrolyte could be represented as the sum of twoquantities. One of these was assumed, in effect, to be the conductivity whichwould be expected if the sample were a clean sand, and the other was assumed tobe a conductivity inherent in the sample itself. The latter was supposed to beconstant for a given rock sample regardless of the solution used to saturateit. The source of this added conductivity was ascribed to "conductivesolids."
In actual practice, the effect of the abnormal behavior of shaly sands is ofminor importance except when the resistivity of a formation such as a sand ishigh. Thus the effect is more important when the sand is saturated with adilute brine than when it contains a more concentrated electrolyte. The effectis also more important when the sample contains oil or gas than when itcontains only saline water.
T.P. 3565

A laboratory study was made on the relation of wettability and wetting equilibrium to electrical resistivity, particularly under dynamic conditions. Teflon cores and synthetic fluids as well as reservoir cores and fluids wereused. Resistivities were measured by a four-electrode system. Under static conditions and with the wetting equilibrium prevailing, exponents as high as 9 were calculated from Archie's Equation when the interstitial conductive liquid was the non-wetting phase. When it was the wetting phase, saturation exponents ranged from 2 to 3 for the same core. This means that in laboratory measurements of Archie's saturation exponents on reservoir cores, the corewettability must represent the wettability of the reservoir. Otherwise, thecalculated connate water saturations will be in error. Under dynamic conditions, wetting equilibrium prevails only for very low displacement rates. At high flow rates the rate has an important bearing on the measured resistivity, indicating non-equilibrium fluid distribution in some experiments, the porous medium was saturated with the non-wetting liquid first. It was shown that, in these tests, the wetting equilibrium is either notobtained at all or it is obtained extremely slowly. These laboratory resultsare explained in terms of fluid distribution in porous media. A qualitative discussion is given on the implications of this work in electrical loginterpretation and the recovery of crude oil by chemical waterfloods.
INTRODUCTION
HYDROCARBON-BEARING FORMATIONS are generally identified through themeasurement and interpretation of their electrical properties by well logging. The empirical equation used in the estimation of the hydrocarbon saturations from resistivity data is: {See equation in full paper},
In this equation, S, is connate water saturation, R. is the resistivity ofthe 100 per cent water-saturated formation, Rt is the resistivity of the formation containing both water and hydrocarbon, and n is the saturation exponent. The true resistivity, R, of the un-invade zone and R. are obtained from the logs; n is found from laboratory measurements on reservoir cores. Implicit in the above approach are the assumptions that:the saturation-resistivity relationship is unique,laboratory-derived n valuesapply to the reservoirn is constant for a given porous medium andall of the water contributes to the flow of the electric current.
Numerous studies on the electrical resistivities of cores partly saturated with sodium chloride solution have shown that the above assumptions are notnecessarily invalid. For the same saturations, the resistivity of cores hasbeen found to vary significantly with wetting conditions, clay content, textureand salinity. To find the effect of wettability on resistivity, some studieshave been performed on natural or synthetic cores in which the wettability waschanged through a chemical reaction, i.e. hot solvent extraction, oxidation or coating the surfaces with a chemical. There are some complicating factors insuch an approach; namely, the resulting core wettability is not uniform, and cannot be described or measured, and exchangeable ions from natural mineral scan change the resistivity of the electrolyte filling the pores. Also, the porosity of the core, particularly the effective porosity, can change.

Simple qualitative methods are explained for identifying those shaly sands in a well that are most likely to contain oil. A need for more precise measurement of the variables that enter shaly sand analysis is indicated. Field examples are given to illustrate the methods.
A theoretical discussion of the quantitative interpretation of shaly sands is given as a basis for discussion and as a guide for the future. While not generally capable of practical application at the present time because of the lack of sufficient accuracy of the electric log data, these methods may become more feasible in the future as the result of the improved logging methods now being introduced.
Introduction
Experience has shown that for porous formations containing only a negligible amount of clayey material, reliable information on the fluid saturation and porosity of the reservoir rocks can usually be derived from the electrical logs. The interpretation is based on empirical formulae relating the true resistivity of a porous formation to its lithologic character, to the resistivity of the interstitial water, and to the proportions of water and hydrocarbons in the pores. If the resistivity of the interstitial water is not known, its approximate value can be derived from the SP curve. The porosity can usually be determined to a good approximation from a MicroLog, or a MicroLaterolog.
When the reservoir rocks contain an appreciable percentage of clayey material, an additional factor is introduced into the analysis. In a clean formation, the matrix is an electrical insulator, so that the ability of the formation to conduct current is due only to the conductivity of the electrolytes in the pores; in a shaly formation, the shale constitutes a part of the rock matrix able to conduct current, and influences the resistivity of the formation.