Low resistivity pays (LRPs) are widely developed in sandstone and carbonate reservoirs in major petroliferous basins in Mexico, Venezuela, the United States, India and China, with a proportion up to 50% ^{[1,2,3,4,5,6,7]}. In the Gulf of Mexico, especially, LRPs contribute 100 t/d to the production from sandstone reservoirs. As such an important type of pay zones, the identification, evaluation and saturation interpretation of LRPs are significant to correctly understand the oil-water relation, reasonably evaluate geological reserves and eventually realize the overall development and balanced production of reservoirs ^[8,9].

Logging identification, especially quantitative saturation interpretation, of LRPs is difficult due to their low resistivity and low log correlation ^{[10,11,12,13]}. LRPs are mostly originated from complex mechanisms, such as high immobile water saturation, low-amplitude structure, thin interbed, salinity difference between oil layers and water layers, additional conductivity of minerals, natural fractures, and drilling fluid intrusion ^{[14,15,16,17,18,19,20,21,22,23,24]}. For these mechanisms, systematic saturation evaluation methods have been developed, including the non-resistivity techniques, such as J-function oil column height method, C/O logging and RST (reservoir saturation testing), and the resistivity techniques, such as the modified Archie Formula, W-S model, Gulf Coast, thin interbed, and empirical formula^{[21, 25-29]}.

Oilfield A in East Rub Al Khali Basin, the Middle East, contains typical carbonate microporous LRPs ^[28], which have the pyrite content of 2%-4% and no fractures and encrusted particles. The carbonate microporous LRPs were formed in medium-low energy depositional environment. They are characterized by small pore throats, and the resistivity of 0.4-0.7 Ω•m, which is equivalent to or even slightly lower than the water layer below. These LRPs are estimated with water saturation of 33%-70%, but could be produced with little or almost no water for 6-8 years. The carbonate microporous LRPs are similar to the LRPs originated from high immobile water saturation (hereinafter referred to as high-IWS LRPs), but different in conductive path and conductivity mechanism. The methods for LRPs of other genesis are not applicable to the carbonate microporous LRPs. The Dean Stark experiment in coring wells and RST casing logging in production wells have been effectively applied in identification of oil layers. However, the accuracy of saturations calculated by nuclear magnetic resonance (NMR) logging, oil column height method and the methods for conventional reservoirs is low. Moreover, conventional logging is primarily adopted in most of production wells during reservoir development, which actually needs the support of fine saturation interpretation techniques based on conventional logging ^{[4, 30]}. There have been no reports on what are the differences between carbonate microporous LRPs and high-IWS LRPs in genesis of low resistivity and conductivity mechanism, what are the geological conditions (which also serves as an evaluation index, because the evaluation based on logging alone shows low coincidence) of the carbonate microporous LRPs formation, and whether the Archie Formula is applicable to saturation calculation for the carbonate microporous LRPs, and how the carbonate microporous LRPs are different from conventional pays in saturation interpretation parameters. In this paper, the genesis of low resistivity and special conductive properties of carbonate microporous LRPs and their differences from conventional oil layers and LRPs are analyzed. Then, a saturation interpretation technology based on conventional resistivity logging of open-hole is presented to rapidly and accurately evaluate the oil saturation. The study results are expected to facilitate the production and also provide a technical support for enriching the theory of LRP.

The Cretaceous Shuaiba Formation in the East Rub Al Khali Basin, the Middle East, is a set of carbonate ^[31] (Fig. 1). In the Cretaceous, several secondary basins such as Bab were developed in the Arab Basin, and typical gentle slope or rimmed carbonate strata (up to 2000 m thick) were formed in the periphery ^[32]. In the 1950s-1980s, large structural reservoirs such as Bu Hasa, Bab and Asab and structural-lithologic reservoirs such as Margham and Musallim were successively found around the Bab Basin.

The Shuaiba Formation carbonate is composed of four sedimentary facies: open platform, platform margin, platform margin slope, and basin, and 6 subfacies: intra-platform shoal, inter-shoal sea, platform margin reef, upper ramp, lower ramp and basin ^{[31, 33]}. The study area is developed in open platform and platform margin facies belt. Layer X is found with three lithofacies (Fig. 2) ^[34]: (1) Ooids Bacinella Grainstone is developed in the intra- platform shoal, associated with algal fragments, oolitic particles, foraminifera, bivalves and echinoderms, the matrix between algal fragments is composed of spherical particles to mud particles, and the particles are cemented in situ by synaptic cements surrounding echinoderm debris; (2) Peloid Burrows Packstone is developed in the shallow-water inner lagoon where algae cannot grow, and it is rich in peloid and small bivalves, foraminifera and echinoderms, with biological disturbance commonly seen; and (3) Bacinella Floatstone is developed in the outer lagoon, with algal fragments, in symbiosis with bivalve shellfish, thick shell clams and echinodermsm, and also with low sedimentary energy for deposition and high content of lime-mud. Specifically, spheroidal burrow packstone and bioclastic floating rock are developed in low sedimentary energy and are potential reservoirs to form LRPs. In the lithofacies of Layer W, Orbitolinid Skeletal Packstone and Algal Skeletal Peloid Floatstone are also potential reservoirs for LRP.

The carbonate in the study area is simple in mineral composition, but complex in rock texture and pore structure ^{[35,36,37,38]}. High-energy sedimentary grainstones are characterized by "big pores and small throats", whereas low-energy sedimentary packstones or wackestones are characterized by "small pores and micro throats". Because the additional conductivity of clay minerals is rare, according to the principle of least resistance conductive path" ^[39], its conduction mode is different from that of sandstone. Fig. 3 compares the cast thin sections and conductive paths of two rock samples from Well A14, where sample a is grainstone and sample b is packstone. Table 1 shows and contrasts the reservoir parameters of the two core samples. It can be seen that samples a and b have roughly the same porosity, but sample a exhibits more complex conductive path and also higher resistivity under the same conditions (Fig. 3, Table 1). After saturated with water (Fig. 3c, 3d), the resistivity ratio of sample a to sample b is 1.9. When the oil saturation increases, the conductive path is gradually controlled by the bound water in pore throats and pore walls, and the conductive path of sample a becomes even more complicated (Fig. 3e, Fig. 3f). Given the water content of 50% and in bound water state, the resistivity ratio of sample a to sample b is 3.4 and 2.8, respectively. Saturated oil complicates the conductive path of sample a, but not so much to sample b.

To further highlight the influence of conductive path on rock conductivity, the conductive path tortuosity is introduced, with the formula as ^[40]:

For the carbonate in the Middle East, which has a high conductivity of bound water, the conductive path tortuosity is not completely equal to the pore tortuosity. Since there is no non-resistivity calculation method, in this paper, the conductive path tortuosity is calculated by using the data derived from rock resistivity experiment and verified with the average pore throat radius and porosity. As shown in Fig. 4, the conductive path tortuosity increases with the increase of average pore throat radius determined by mercury injection experiment and decreases with the increase of porosity obtained in conventional experiment. This good correlation shows that the conductive path tortuosity is controlled by rock grain size, pore structure and porosity, which is consistent with the aforesaid understanding. Thus, the calculation result is proved reasonable. The positive correlation between the conductive path tortuosity and the resistivity of water-saturated rock shows that the tortuosity is an important factor controlling the rock conductivity (Fig. 5).

Next, the calculation result is further verified by displacement resistivity experiment. According to Fig. 6 and Table 1, the average pore throat radius, permeability, conductive path tortuosity and rock resistivity under the same saturation conditions of sample a are higher than those of sample b, which is what differentiates it from sandstone.

To sum up, the genesis of LRPs is complex ^[41,42,43]. With high porosity, the carbonate microporous reservoirs in the study area exhibit low conductive path tortuosity and high conductivity. In bimodal or multimodal packstones, movable oil is preserved predominantly in isolated pores with medium to large size, whereas the bound water is enriched in micro-pores or on pore walls. When the oil charging pressure is not enough to drive the crude oil into the microscopic pore throats, the rock conductive path is less susceptible to oil, and the rock resistivity is equivalent to that of pure water layers, leading to the formation of LRPs.

The definition criteria for LRPs are clarified firstly. According to the oil testing and production data of 8 wells in the study area, it is determined that the resistivity of LRP is not greater than 1 Ω•m, which is taken as the first definition criterion. As mentioned above, the irreducible water saturation in the study area is 33%-70% ^[28], so the second definition criterion is determined as the resistivity of rock with water saturation of 70% is not greater than 1 Ω•m. Since the resistivity index of LRP is usually less than 2, the resistivity of water-saturated rock no greater than 0.5 Ω•m is taken as the third definition criterion. In addition, taking the characteristics of microporous reservoir as the auxiliary definition criterion, the pore throat structure data revealed by rock thin section, mercury injection and NMR experiments are used for screening and verification. Using the above definition criteria, the samples of rock resistivity experiment are divided into three types: potential LRP samples, non-LRP samples, and low porosity (ϕ<15%) samples, which are comparatively analyzed.

According to Griffiths, Gyllensten, et al. ^{[30, 39]}, the classic Archie Formula is applicable to the saturation calculation of carbonate reservoirs. It is unclear whether the Archie Formula is applicable to LRPs and how LRPs are different from conventional reservoirs in model parameters, which are essential for saturation interpretation of LRPs. According to Worthington and Ayadiuno, the reservoir conditions for using Archie Formula to calculate saturation include ^{[13, 16]}: (1) reservoir is homogeneous, with simple mineral composition, and low content of clay minerals, mud or silt; (2) it is water wet, with high salinity of electrolyte and low resistivity in formation water; (3) it contains a single pore throat system dominated by intergranular pores; and (4) it is free of conductive minerals or impacts by conductive minerals.

As to minerals, the reservoir in the study area contains dominantly calcite, basically no clay minerals, and pyrite below 4%, which has little effect on rock conductivity. The reservoir space is dominated by intergranular pores, and that of the LRP is dominated by intergranular micropores, all characterized by simple pore throat system. All LRPs are water wet, with the formation water salinity of over 170 mg/g and water resistivity of about 0.013 Ω•m. In the displacement resistivity experiment shown in Fig. 7, the potential LRP samples, non-LRP samples and low porosity samples all meet the law of Archie Formula. In other words, the Archie Formula is applicable for saturation calculation of LRPs.

The Archie's Formula for calculating water saturation of LRP is as follows:

where m and n are the most important model parameters.

Petrophysical researches and related experiments are the basis for establishing saturation interpretation model^[44,45]. Firstly, the influencing factors of water-saturated rock resistivity (R_o) are analyzed. As shown in Fig. 8, R_o has a good correlation with porosity and decreases with the increase of the porosity. For high porosity reservoirs with porosity greater than 15%, R_o of them is less than 1 Ω•m and R_o of the potential LRP samples is less than 0.5 Ω•m (Fig. 8a). R_o of high porosity reservoirs has good correlation with the average pore throat radius and increases with the increase of the average pore throat radius. The average pore throat radius of potential LRP samples is less than 0.7 μm (Fig. 8b). It is speculated that the conductive path becomes complex with the increase of pore throat size. R_o of high porosity reservoirs has good correlation with permeability and increases with the increase of the permeability. The permeability of potential LRP samples is less than 4×10^-3 μm² (Fig. 8c). Wettability has a great influence on R_o. In high-porosity reservoirs, R_o of oil-wet sample is greater than 0.5 Ω•m, while R_o of water-wet sample is less than 0.5 Ω•m. To sum up, the formation of carbonate microporous LRPs in the Middle East must meet the basic geological conditions such as high porosity, low permeability, small pore throat, and water wet.

Then, the variation law and value range of m for LRPs are analyzed. As shown in Fig. 9, the m value is less correlated to the porosity, and varies greatly in the high porosity reservoirs, being less than 1.93 for the potential LRP samples (Fig. 9a). The m value has good correlation with permeability and average pore throat, and it increases with the increase of them, which is consistent with the understanding on the low conductive path tortuosity of LRPs as mentioned above (Fig. 9b, 9c). However, when the permeability is greater than 1000×10^-3 μm², the reservoir becomes a high permeability strip (abbr. HPS), and its m value becomes slightly lower, which is related to the better free water conductivity of the reservoir. When the permeability is lower than 0.5×10^-3 μm², the m value is slightly high, which is related to reduced conductivity due to smaller pore throats.

PG, i.e. petrophysical group, is a classification method for petrophysical research on carbonate reservoirs in the Middle East. It uses capillary pressure curves and the method proposed by Thomeer and Baker, etc. to classify rocks ^[34]. In the study area, reservoirs are divided into 6 types of PGs, of which PG1 represents a large-scale pore throat system, and the remaining PGs decrease in an order of pore throat size (Fig. 9c). Most LRPs are PG4-PG5.

Among rock samples with different wettability, the m value of water-wet sample is obviously low, which is consistent with the understanding of simple oil-water distribution and low conductive path tortuosity.

In conclusion, the m value of potential LRP samples is 1.77-1.93, which is generally smaller than that of non-LRP samples (2.00-2.14) and low porosity samples (1.96-2.02). This further proves that microporous LRPs have low conductive path tortuosity and high conductivity. It is a universally adopted method to determine the saturation interpretation parameters according to the reservoir types^[46]. In practice, the m value is determined by taking the average value according to the PGs classification. In the study area, the m value is 1.85 for LRP, and 2.2 for PG1, 2.1 for PG2, 1.96 for PG3 and PG4, 1.77 for PG5, and 1.93 for PG6. Taking the LRP as an example, and taking the formation water resistivity (R_w) as 0.013 Ω•m, n as 1.9, porosity as 0.2, rock resistivity (R_t) as 0.7 Ω•m, and when m is taken as 1.77 (lower limit), 1.85 (average) and 1.93 (upper limit) respectively, the calculated water saturation (S_w) is 55%, 59% and 63% correspondingly. If m is taken as the average value, the maximum error is about 4%, and it is reasonable and controllable.

The displacement resistivity experiment is applied to analyze the influence of oil-water distribution on rock resistivity. Fig. 10 shows an experiment on oil-wet sample, which includes 8 steps:

Step 1: Samples saturated by the brine with salinity of 170 mg/g to simulate the original formation.

Steps 1-2: The primary drainage cycle displacing water with oil under low-pressure to simulate the early stage of reservoir formation, with formation water as membrane bound water and connected free water.

Steps 2-3: The primary drainage cycle displacing water with oil under increasing pressure to simulate the stage of reservoir formation, with membrane bound water.

Steps 3-4: Wettability restoration, with a part of water film being changed to oil film.

Steps 4-5: Spontaneous brine imbibition to simulate the initial stage of reservoir recovery or damage, with dispersed and connected free water.

Steps 5-6: Brine imbibition cycle under negative pressure displacing oil with brine to simulate the process of reservoir recovery or damage, with connected free water developed gradually.

Steps 6-7: Spontaneous oil imbibition to simulate the initial stage of secondary reservoir formation, with connected free water in dominance.

Steps 7-8: The secondary drainage cycle displacing water with oil again to simulate secondary reservoir formation, with dispersed and connected free water in dominance.

The experiment reveals the significant variation of oil-water distribution in the process of reservoir formation and recovery. Also, the Amott-Harvey index and relevant standards are used to determine the wettability of rock samples ^[47]. As shown in Fig. 10a, the Amott water and oil indices of the sample are 0.144 and 0.443 respectively, and so the Amott-Harvey indices is -0.3. Accordingly, the wettability of the rock sample is determined to be oil wet. After wettability restoration of oil wet core samples, some membrane bound water disappears and the oil-water distribution of water in oil becomes normal in them. The case is different for water-wet samples, where the membrane bound water exists for long time, and the oil-water distribution of oil in water is normal.

Fig. 11 shows the test results of two types of rock samples, in which the sample shown in Fig. 11a is an oil-wet sample of grainstone, with porosity of 20.2% and permeability of 114.0×10^-3 μm², while the sample shown in Fig. 11b is a water-wet sample of wackestone, with porosity of 19.5% and permeability of 1.7×10^-3 μm². Comparison of the two figures reveals that:

① When saturated with brine, the resistivity of water-wet sample reaches the criterion of LRP.

② At the initial stage of the primary drainage cycle, the oil inlet pressure is low for the oil-wet sample and high for the water-wet sample.

③ After the primary drainage cycle, the irreducible water saturation of oil-wet sample is about 13%, while that of water-wet sample is 20%.

④ After wettability restoration, the resistivity of both samples rises to the highest value, but the increase of water-wet sample is low.

⑤ After spontaneous brine imbibition, the decrease of oil saturation is 6% for oil-wet sample and 27% for water-wet sample.

⑥ After brine imbibition cycle, the saturation of both samples is roughly the same.

⑦ After spontaneous oil imbibition, the increase of oil saturation is 17% for oil-wet sample and none for water-wet sample.

⑧ After the secondary drainage cycle, the saturation of two samples is the same.

The change trend of rock resistivity is analyzed by comparing the resistivity value under the same saturation. The resistivity of the oil-wet sample is the lowest in the primary drainage cycle and the highest after wettability restoration, and remains in between in the secondary drainage cycle. For the water-wet sample, the change trend of resistivity remains unchanged and decreases slightly only in the secondary drainage cycle. This indicates that the oil-water distribution of oil-wet sample is gradually complicated in the displacement process, while that of the water-wet sample is more stable due to maintaining membrane bound water.

Next, the variation law of saturation index n of different rock samples is analyzed in the experiment (Fig. 12). ① The oil-water distribution becomes complex during the experiment, and the n value usually increases, with the maximum change of 0.85, indicating that the oil-water distribution has a great impact on rock conductivity. ② The n value of oil-wet sample in the exploitation stage is higher than that in the reservoir forming stage, and it increases with the increase of water saturation and tends to be stable in the secondary reservoir forming stage. ③ The overall change of n value of water-wet sample is small, which may be related to the stable distribution of bound water film and oil-water relation. ④ The potential LRP samples are similar to water-wet sample, with the n value relatively lower in the process of reservoir formation.

The saturation evaluation is mainly conducted on the original reservoir before exploitation. Therefore, the variation of n value is analyzed based on the displacement resistivity experiment simulating the reservoir formation stage. As shown in Fig. 13, the n value decreases with the increase of porosity, and increase with the increase of permeability, pore throat size and better PGs. The n value of potential LRP samples is 1.82-2.03, generally lower than that of non-LRP samples, and included in the range of low porosity samples. Similarly, the n value is determined with the average method of PGs classification. In the study area, the n value is determined to be 1.9 for potential LRP, 2.10 for PG1 and PG2, 2.00 for PG3 and PG4, and 1.85 for PG5 and PG6. Taking the LRP as an example, and given the same reservoir parameters in Section 4.1, when m is 1.85 and n is 1.82, 1.90 and 2.03 respectively, the calculated S_w value is 57%, 59% and 61%, correspondingly. The calculation error of saturation is about 2% at most when taking average n comparing with the upper limit n and lower limit n, which is reasonable and controllable.

Based on the above experimental analysis, and the genetic mechanism, definition criteria and forming conditions of LRPs in the study area, the LRP is qualitatively identified in single well. Then, the reservoir saturation is quantitatively calculated by using the LRP saturation interpretation technology is used to quantitatively calculate, and the interpretation results are verified by Dean Stark, RST (reservoir saturation testing), dynamic test and actual production data (Figs. 14 and 15).

The qualitative identification results are shown in the column 8 of Fig. 14. The LRP in Well A is developed at 2998.6-3002.3 m, mainly composed of Bacinella Floatstone (BF). The lower adjacent layer is a high-permeability belt of Ooids Bacinella Grainstone (OBG), with a water avoidance height of only 1.5 m. However, the formation is separated from the aquifer by a thin interlayer (Fig. 14a). The LRPs of Well B are developed at 3033.4-3034.0 m and 3034.9-3035.6 m. They are composed of BF, with a water avoidance height of 3.4 m. The physical properties of the reservoir between LRP and the aquifer are poor (Fig. 14b).

The quantitative saturation interpretation results are shown in column 7 of Fig. 14. The oil saturation of LRP interpreted is 30%-50% with the method introduced by this article, which is about 15% higher than that interpreted by conventional method, but is basically consistent with the aforementioned irreducible water saturation revealed by field production ^[28]. Considering that the oil column height of LRP is small and the reservoir is controlled by micro pores, the calculation result is believed reasonable.

Validation is made using Dean Stark data. The Dean Stark saturation data is available in Well B, but no oil saturation data is collected. It can be seen from Column 7 in Fig. 14b that the change of water saturation calculated by logging is consistent with that analyzed by experiment (light blue bar in the figure). The S_w value calculated by logging is obviously higher than that measured from core, which is related to the measurement loss of experimental data (usually (S_w+S_o)<100%). In the LRP, the oil saturation calculated by the new method is higher than that calculated by the conventional method. The difference between the results of the new interpretation method and the experimental data is reduced to less than 15%, and the interpretation performances are improved.

Validation is made using RST data. Well A has two RST logs. Due to the influence of borehole conditions and fluids, the two test results are quite different (Column 7 in Fig. 14a). In the upper half of LRP, the interpretation results of conventional technology are closer to the second RST, and the interpretation results of the new method are between the two RSTs. In the lower half of LRP, two RSTs show low oil saturation, and the interpretation results of conventional methods are equivalent to RST. The oil saturation interpreted by the new method is significantly higher than the test results. Analyzing the electric properties and oil-water relationship of the upper and lower sections of LRP, it is considered that the interpretation result of the new method is relatively more reasonable.

Validation is made using well test data. The LRP and its below HPS in Well A were perforated in April 2003 (Column 10 in Fig. 14a). Trial productions were conducted by using oil nozzle of 127 mm. With bottom-hole fluid pressure of 33.3 MPa, the well has daily oil production of 717 t with no water produced. It is confirmed that this section is not an aquifer though its resistivity is low to only 0.4-0.7 Ω•m, In well B, the related section is not perforated. But according to the oil-water relationship of the whole reservoir, adjacent wells and the vertical layers of well B itself, it is confirmed that the 3033.4-3034.0 m and 3034.9-3035.6 m sections are all low resistivity pays instead of aquifers.

Validation is made using production data. Fig. 15 shows the production curve of Well A. After the well was put into operation in 2006, the daily output of crude oil was 272 t, with no water production until 2008. The analysis shows that the high yield of crude oil and later water production are related to the HPS, so the HPS was closed and the lower part at 3004.0-3007.5 m was perforated in 2012 (Column 12 in Fig. 14a). At the initial stage of the test, the daily liquid production was 180 m³, with water cut 10%. The stable production was maintained to 2013. Then the water cut increased to 40%-50%, and the crude oil production was reduced to 68 t/d. It is further confirmed that the lower perforated section is an aquifer and the upper perforated section is a LRP.

The new method is extensively deployed in the study area. After oil testing and production verification of 15 wells, the coincidence rate is found to be more than 90%, which further verifies the feasibility of this method. The research results lay a foundation for quantitative evaluation and large-scale effective development of LRPs in the study area.

Different from sandstone and mudstone LRPs, the Cretaceous carbonate LRPs in the Middle East are developed in medium-to-low-energy depositional environment, their reservoir spaces are controlled by microscopic pore throats, and their conductive paths are affected by bound water film, the conductive path tortuosity is lower than that of medium to microporous reservoirs. Their resistivity is lower than that of other reservoirs under the same saturation condition, so they belong to typical microporous LRPs.

The resistivity of LRPs in the study area is 0.4-0.7 Ω•m, only 1/3-1/2 of that of conventional reservoirs, equivalent to or even slightly lower than that of water layer, so the logging correlation of LRPs is low. The formation conditions and identification criteria of LRPs include high porosity (greater than 15%), low permeability (less than 4×10^-3 μm²), low pore throat radius (less than 0.7 μm), water-wet reservoir and high salinity of formation water.

The Archie Formula is applicable to the saturation interpretation of marine carbonate microporous LRPs in the Middle East, with the m and n values generally lower than those of conventional reservoirs. The m values of LRPs in the study area vary at 1.77-1.93 and are controlled by pore structure and conductive path tortuosity, but they change slightly during recovery. The n values of LRPs vary at 1.82-2.03 and are subject to the impacts of oil/water distribution, so they may change dramatically during recovery. Conventional reservoirs, especially oil-wet reservoirs, are more susceptible to oil-water distribution than LRPs. Therefore, in practice, the m and n values of LRPs are commonly determined by taking average directly, i.e. 1.85 and 1.90 respectively in the study area, and the m and n values of other pays are determined by taking average according to PGs classification. The maximum calculation error is about 4%, which is reasonable and controllable.

The oil saturation of LRPs in the study area calculated by the new method is 30%-50%, which is about 15% higher than that calculated by the conventional method. It is verified to be reasonable by Dean stark, RST, oil testing and production data. The new method has been applied extensively in the study area, showing an interpretation coincidence rate exceeding 90% for 15 wells.

a—ithology coefficients related to lithology, dimensionless;

b—constants related to lithology, dimensionless;

F—stratigraphic factors, dimensionless;

GR—natural gamma, API;

m—cementation index, dimensionless;

n—saturation index, dimensionless;

R—multiple correlation coefficient, dimensionless;

R_LLD—deep lateral resistivity, Ω•m;

R_LLS—shallow lateral resistivity, Ω•m;

R_o—resistivity of water-saturated rock, Ω•m;

R_t—resistivity of oil-bearing rock, Ω•m;

R_w—formation water resistivity, Ω•m;

S_o—oil saturation, %;

S_w—water saturation, %;

S_wLRP—calculated water saturation considering LRP, %;

S_wRST1—water saturation in the first RST calculation, %;

S_wRST2—water saturation in the second RST calculation, %;

ϕ—porosity, %;

ϕ_CNL—Neutron porosity, %;

ρ—density, g/cm³;

τ—conductive path tortuosity, dimensionless.