Resistivity cutoff of low-resistivity and low-contrast pays in sandstone reservoirs from conventional well logs: A case of Paleogene Enping Formation in A-Oilfield, Pearl River Mouth Basin, South China Sea

Chenglin Liu; Zeyu Wang; Zhenyu Gu; Qiuhong Hu; Kaijin Zhou; Quanquan Liang

doi:10.1515/geo-2022-0520

Article Open Access

Resistivity cutoff of low-resistivity and low-contrast pays in sandstone reservoirs from conventional well logs: A case of Paleogene Enping Formation in A-Oilfield, Pearl River Mouth Basin, South China Sea

Chenglin Liu , Zeyu Wang , Zhenyu Gu , Qiuhong Hu , Kaijin Zhou and Quanquan Liang

Published/Copyright: September 20, 2023

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From the journal Open Geosciences Volume 15 Issue 1

Abstract

Deep sandstone reservoirs in the Paleogene Enping Formation of A-Oilfield, Pearl River Mouth Basin, are significant offshore petroleum targets for this basin. Compared with adjacent water zones and shales, the pays are of low resistivity and low contrast (LRLC). Without new unconventional well-logging techniques, conventional well-logging interpretation has encountered difficulties in these LRLC pays. In this article, based on the Simandoux water–oil saturation equation, influence factors of formation resistivity in sandstone LRLC pays are simulated theoretically. Resistivity cutoff is calculated at average reservoir conditions and proved by oil-testing results. It is applied to oil and water in LWD, and the possible genesis of LRLC is discussed. In sandstone LRLC pays, the resistivity of the oil zone is larger than the resistivity cutoff; the resistivity of the water–oil zone approximates the resistivity cutoff and the resistivity of the water zone is lower than this threshold. At average shale volume and porosity of pays in Enping Formation, the resistivity cutoff is referenced at 4.2 Ω·m overall. In sandstone reservoirs, low formation resistivity in the Enping Formation of A-Oilfield might result from high formation water salinity, complicated pore-throat structure, well physical properties, and the presence of conductive and clay minerals. The resistivity cutoff offers important information in real-time tracking of LWD. The proposed method is demonstrated to be beneficial for identifying hydrocarbons and monitoring trajectory in LRLC pays.

Keywords: resistivity cutoff; LRLC pays; Simandoux equation; conventional well logs; Enping Formation

1 Introduction

Low-resistivity and low-contrast (LRLC) reservoir is defined as pay that has a low-resistivity contrast between sand and adjacent shale due to the presence of conductive minerals, clay minerals, and thin beds of clay–sand sequences or freshwater [1,2,3,4,5,6,7]. As a type of unconventional reservoir, the problem of identifying LRLC pays by logging analysis has been acknowledged for over 50 years, and it was first reported in Texas and the Louisiana Gulf Coast of America [8]. The concept of low-resistivity pays encompassed commercial petroleum intervals with formation resistivity as low as 1–3 Ω·m originally, and even less than 0.5 Ω·m subsequently [2,9,10,11]. It is not known how many low-resistivity reservoirs have broken premature resistive bounds, and there is no universally acceptable resistivity cutoff of commercial zones today. Low-contrast sand was especially defined when resistivity is less than 1.5 times the resistivity of intraformational shales [3]. This definition allows the resistive limited range of higher than 10 Ω·m in some cases and this contrast specification is not universal as well [3,12]. The lack of reservoir understanding of LRLC zones is usually overlooked due to misinterpretation of abnormal resistivity responses in conventional well logs in the old oil and gas fields [6]. With the development of high-end well logs and comprehensive analysis techniques in recent years, such as nuclear magnetic resonance, image logs, spectral gamma ray, rock analysis, and other tools, commercial oil beds were discovered in LRLC pays around the world, which renewed mature and old oilfields [13,14,15,16,17,18,19,20,21,22]. The related subject areas, therefore, are both technical and commercial hotspots in petroleum exploration and development.

Unfortunately, it is difficult for these unconventional well-logging tools to work in complicated well conditions, such as high-angle deviated and horizontal wells in offshore oilfields. In A-Oilfield, Pearl River Mouth Basin of South China Sea, for example, normal resistivity reservoirs have been developed since 1993, and the water yielding ratio reached up to 97%. In order to penetrate the petroleum potential of deep sandstone reservoirs in the Paleogene Enping Formation, several pilot wells have been drilled in high-angle deviated, and producing wells had been drilled horizontally since 2009 [23,24]. Based on the wireline logs and LWD, the reservoirs are characterized in LRLC pays. Without unconventional well logging, identifying and quantifying hydrocarbons in LRLC pays encounter great challenges in conventional well-logging interpretations. The resistivity cutoff of LRLC pays is urgently warranted to identify water and oil zones quickly and intuitively in logging interpretations and LWD. Therefore, based on conventional well logs and LWD, the purpose of this article is to understand the resistivity cutoff of LRLC pays in Enping Formation of A-Oilfield.

2 Geological setting

Pearl River Mouth Basin, in NE–SW, is approximately parallel with the shoreline of south China continent. It is a significant and prospective offshore hydrocarbon basin for China. Several oilfield groups have been established since the 1980s of the last century. Controlled by the Eurasian, Pacific, and Indian Plate, three uplift zones (North Uplift Zone, Central Uplift Zone, and South Uplift Zone) and three depression zones (North Depression Zone, Central Depression Zone, and South Depression Zone) were developed during tectonic movements [25,26] (Figure 1a). In the North Depression Zone, A-Oilfield was found in the southeastern Huilu Low-Salient between Lufeng and Huizhou Sag [23,24] (Figure 1b). With the decrease of oil production in shallow sandstone reservoirs in the A-Oilfield, deep reservoirs of the Enping Formation, ∼430 m thickness, were drilled down gradually. In exploratory and development, for nearly 10 years, sandstones of the Enping Formation exhibited enormous petroleum potential, and about 11 pays were explored (Figure 1c).

Figure 1

Geological setting of the research area. (a) Tectonic features of the Pearl River Mouth Basin. Huilu Low-Salient is in the northeast of the basin, as indicated by the dotted rectangle. (b) Tectonic characteristics of the Huilu Low-Salient (modified from ref. [23]). A-Oilfield is in the southeast of the low-salient, as indicated by the dotted rectangle. (c) Formation characteristics of Enping and Zhuhai Formation, including open-hole conventional GR and mud logs in vertical Well A-1. Wenchang Formation is not drilled in this oilfield. Eleven potential pays have been discovered in braided river delta sandstones in Enping Formation.

In the depositional system, southeastern Huilu Low-Salient was the slope of Lufeng Sag in Paleogene. The Paleogene is divided into Wenchang, Enping, and Zhuhai Formation upwards. In the Wenchang Stage, southeastern Huilu Low-Salient accepted sands of braided river delta on the basement granite. With the fault depression of Lufeng Sag in Enping and Zhuhai Stages, the braided river delta filled the lake sag gradually, and alternate shales and sands were accumulated with less-pebbled sandstones and granules [27,28,29,30]. The dominant lithology is horizontal with massive bedding mudstone and siltstone, massive and cross-bedding sandstone, and pebbled sandstone with well sorting (Figure 2).

Figure 2

Cores in Enping Formation in the A-Oilfield, Well A-2P.

3 Database and workflow

3.1 Database

There are 14 wells that have been drilled into Enping Formation until now, including 2 vertical wells, 9 sidetracked wells, and 3 horizontal wells. Conventional well-logging suits were run in straight wells, including the borehole diameter, natural gamma ray, resistivity, acoustic time, neutron, and bulk density, and LWD were worked in high-angle deviated and horizontal wells including the borehole diameter, natural gamma ray, attenuation and phase resistivity in different frequencies, and bulk density. In this article, natural gamma ray (GR, API), bulk density (DEN, g/cm³), and undisturbed formation resistivity (RT, Ω·m) are significant logs used to calculate the shale volume, porosity, and resistivity cutoff, respectively. In addition, the lithology of drilled cuttings recorded by mud logs has contributed to an approximate understanding of the lithology (Figure 1c).

Without unconventional well logs in open holes, fundamental cores and analysis data are not abundant in Enping Formation. Only in Well A-2P, six runs were cored out in a 39.56 m drilling footage in Enping Formation. Based on these cores, 202 rock samples were used in the porosity–permeability experiment; 19 samples were used in the relative permeability experiment; 14 samples were tested in the mercury-injection capillary pressure experiment; 61 rock thin-sections were made; and 6 samples were used in the rock electrical experiment.

Based on the rock electrical experiment, the lithology coefficient a = 1.0266, the cementation factor m = 1.803, and the saturation exponent n = 1.968. Based on the porosity and permeability experiment, the effective porosities (φ _e) mainly range from 15 to 22% and permeabilities (K) are dominantly in the range of 10–1,000 mD. According to the relative permeability experiment, the bound water saturation (S _wb) ranges from 34.8 to 38.7% (average: 36.59%) and the residual oil saturation (S _or) ranges from 28.6 to 39.6% (average: 33.42%). On the basis of rock thin-sections, clay volumes (V _sh) mainly range from 3 to 5%, and even reached up to 30%. According to the water analysis of producing wells, the equivalent NaCl concentration averaged 31174.7 mg/L, from which it was inferred that the formation water resistivity (R _w) of Enping Formation is 0.062 Ω·m.

3.2 Workflow

The workflow for using conventional well logs to calculate and apply resistivity cutoff of LRLC pays in sandstone reservoirs of Enping Formation of A-Oilfield requires the knowledge of primary logging interpretation and comprises four stages in this article (Figure 3). The first stage, the preprocessing of well logging interpretation, involves standardizing conventional well logs and LWD, and correcting the depth of porosity and permeability with DEN in Well A-2P. This is essential work of logging interpretation. The second stage theoretically simulates influence factors on formation resistivity by the Simandoux equation, including the shale volume, formation water resistivity, water saturation (S _w), and porosity. The third stage actually calculates the resistivity cutoff of LRLC pays, including the shale volume and effective porosity. Comparing the formation resistivity with resistivity cutoff offers a convenient and fast way to identify water and oil zones qualitatively. The fourth stage discusses the application of the resistivity cutoff in real-time tracking of LWD and the possible genesis of LRLC in the Enping Formation of A-Oilfield.

Figure 3

Flowchart of this research.

4 Methods

4.1 Shale volume calculation

As most clay minerals are rich in potassium, they result in enhanced gamma-ray response. Therefore, the GR response corresponds to the volume of clay in the formation, which is estimated using the following equation [31]:

(1) SHI = GR − GR min GR max − GR min V sh = 2 c ·SHI − 1 2 c − 1 ,

where c is the empirical coefficient of formation, c = 3.7 in Paleogene; SHI is the GR index, which indicates the amount of shale in the formation; GR_min is the reading of the GR value in clean sandy formation; and GR_max is the reading of the GR value in pure shaly formation.

4.2 Porosity calculation

Effective porosity, defined by the connected pore space in the reservoir, is calculated by subtracting the volume of shale from rocks. The evaluated effective porosity can be derived from density logs as follows [32,33]:

(2) φ c = ρ ma − ρ ρ ma − ρ f − V sh ρ ma − ρ s ρ ma − ρ f ,

where ρ _ma is the density of the matrix, ρ _ma = 2.65 g/cm³; ρ _f is the fluid density, ρ _f ≈ 0.95 g/cm³; ρ _s is the density of shale, ρ _s = 2.15 g/cm³; ρ is the bulk density log; and φ _c is the evaluated effective porosity, which is calibrated with experimental data (Figure 4):

(3) φ e = 0 . 9506 × φ c + 0 . 6608 .

Figure 4

Scatter diagram of the calculated and laboratory effective porosity.

4.3 Resistivity cutoff calculation

Several popular logging interpretation models were used to quantify hydrocarbon volumes in reservoirs, such as classical Archie, Simandoux, dual-water, Indonesian water–oil saturation equation, and so on [34,35,36,37,38]. Specific to the geology and fundamental data of Enping Formation in A-Oilfield, the Simandoux equation is the most suitable. For the Archie equation, it is valid in clean, non-shaly reservoirs [34]; the dual-water model needs parameters for the cation exchange capacity experiment, which is absent in these target zones [36,37]; and the Indonesian model is appropriate for formation in low water salinity (<30,000 mg/L) [38]. The Simandoux equation basically introduces another conductivity source arising from shale, which is common in the sandstones of the Enping Formation. The Simandoux equation is applied as follows [35]:

(4) 1 RT = V sh S w R sh + φ e m S w n aR w ( 1 − V sh ) ,

where R _sh is the resistivity of shale, reading of adjacent shales. The a, m, and n values were taken from the previous section.

Due to the residual oil being adsorbed on particle surfaces, the highest water saturation of pays is 1 − S _or. It is the water saturation cutoff (S _wc) of pays and suggests that if water saturation is larger than this threshold, there would be no oil produced in pays. Therefore, in equation (4), considering S _w = S _wc, the resistivity cutoff (RT_c) of LRLC pays is defined as

(5) RT c = 1 V sh S wc R sh + φ e m S wc n aR w ( 1 − V sh ) .

In Enping Formation of A-Oilfield, S _wc = 1 − S _or = 1 − 33.42% = 66.58%.

5 Results

5.1 Reservoir characteristics

Some reservoir characteristics could be inferred from core analysis in limited quantity. Overall, the rock and mineral characteristics of the Enping Formation in A-Oilfield are not complicated. The primary mineralogical component is quartz, then felspar and lithic (Figure 5a). Reservoirs have well physical properties, with good primary and secondary porosities. The reservoir pore spaces are ubiquitous intergranular pores and intergranular dissolved pores (Figure 5b). The cementing materials include pyrite and chlorite (Figure 5c). The pays of Enping Formation in A-Oilfield are LRLC compared with adjacent shales and water zones: the resistivity of mudstone is 5–10 Ω·m and the resistivity of sandstone in the oil zone is 4–10 Ω·m, and that in the waterzone is 1–5 Ω·m (Figure 5d). Compared with mudstones, sandstones are of low resistivity; in contrast to water zones, oil zones are not very high resistive.

Figure 5

LRLC reservoir characteristics of Enping Formation in A-Oilfield. (a) The thin section of rocks in lithic arkose at Y166.22 m: well A-2P (+), quartz content 68%, felspar content 15%, and lithic content 17%. (b) The thin section of rocks at Y176.53 m: Well A-2P (−), pores are intergranular pores and intergranular dissolved pores. (c) The thin section of rocks of pyrite cementing material at Y173.98 m: Well A-2P (−). (d) In the oil–water system of Well A-3P, the resistivity of the water zone, the water–oil zone, and the oil zone is ∼3, ∼4, and ∼5 Ω·m, respectively.

5.2 Theoretical influence factors on formation resistivity

Theoretically, the resistivity of formation depends on the complicated conductive relationship among the constituents of the matrix, filled matters, cement materials, pore spaces, and fluids. In logging interpretation, water saturation calculation equations are almost used to calculate the water and oil saturation. They also in turn offer a readily comprehensible method to analyze resistive influence factors from the shale volume, formation water resistivity (water salinity), water saturation, and porosity in sandstone reservoirs [39]. In this article, three of these four parameters are set, and the remaining one is discussed in equation (5) and Figure 6.

Figure 6

Theoretical influence factors on the formation resistivity from the shale volume, formation water resistivity, water saturation, and porosity in sandstone reservoirs of Enping Formation. (a) The higher the shale volume, the lower the theoretical formation resistivity (R _w = 0.062 Ω·m, φ _e = 16%, R _sh = 5 Ω·m). (b) The lower the formation water resistivity, the lower the theoretical formation resistivity (V _sh = 5%, φ _e = 16%, R _sh = 5 Ω·m). (c) The higher the water saturation, the lower the theoretical formation resistivity (V _sh = 5%, R _w = 0.062 Ω·m, R _sh = 5 Ω·m). (d) The higher porosity, the lower the theoretical formation resistivity (V _sh = 5%, R _w = 0.062 Ω·m, R _sh = 5 Ω·m).

If the formation water resistivity, water saturation, and porosity are constant, the shale volume is higher and the theoretical formation resistivity is lower (Figure 6a); if the shale volume, water saturation, and porosity are constant, the formation water resistivity is lower (higher water salinity) and the theoretical formation resistivity is lower (Figure 6b); if the shale volume, formation water, and porosity are constant, the water saturation is higher and theoretical formation resistivity is lower (Figure 6c); and if the shale volume, formation water resistivity, and water saturation are constant, the porosity is higher and the theoretical formation resistivity is lower (Figure 6d). In summary, high formation water salinity, water saturation, shale volume, and porosity would reduce the formation resistivity.

5.3 Resistivity cutoff of LRLC pays

If φ _e = 16%, V _sh = 5%, R _sh = 5 Ω·m, R _w = 0.062 Ω·m, and S _wc = 66.58% in pays on average, then the referenced average resistivity cutoff is 4.2 Ω·m using equation (5) in sandstone reservoirs of Paleogene Enping Formation. In addition, the resistivity cutoff can be calculated at each deep point. First, in each deep point of one well, the shale volume is calculated using equation (1), and the effective porosity is calculated using equations (2) and (3). Figure 7 shows the calculated shale volume and porosity, which is compared with the results from core-derived porosity in the core interval in Well A-2P. There is a good correlation between the core and calculated porosity.

Figure 7

The calculated shale volume and porosity in the core interval in Well A-2P. The correlation between the core and calculated porosity is good. Sand volume, V _sa = 1 − V _sh–φ _e.

Second, based on the calculated shale volume and porosity, the resistivity cutoff of LRLC pays in each deep point can be computed using equation (5). Figure 8 shows the calculated result of the Y067 m–Y138 m interval in Well A-4P. Three intervals, Y070.1 m–Y083.2 m, Y104.1 m–Y108.8 m, and Y118.2 m–Y125 m, were perforated for oil testing. The calculated porosity, shale volume, and sand volume are shown in the sixth track. The resistivity cutoff of each deep point is shown in the seventh track. Comparing the resistivity cutoff with the formation resistivity, in oil zones (Y104.1 m–Y108.8 m), the resistivity cutoff is lower than the formation resistivity; in water zones (Y070.1 m–Y083.2 m), the resistivity cutoff is higher than the formation resistivity; in the water–oil-zone (Y118.2 m–Y125 m), the resistivity cutoff approximates the formation resistivity. Based on this comparison between both values in LRLC pays, an intuitive and qualitative method is proposed to identify water and oil zones: in the oil zone, formation resistivity > resistivity cutoff; in the water–oil zone, formation resistivity ≈ resistivity cutoff; and in the water zone, formation resistivity < resistivity cutoff.

Figure 8

The calculated shale volume, porosity, and resistivity cutoff of the perforated interval in Well A-4P. In the oil zone, the formation resistivity is higher than the resistivity cutoff; in the water–oil zone, the formation resistivity approximates the resistivity cutoff; and in the water zone, the formation resistivity is lower than the resistivity cutoff.

6 Discussions

6.1 Applying resistivity cutoff in LWD

Besides qualitatively identifying the water and oil zones in conventional well-logging interpretation in LRLC pays, understanding the resistivity cutoff has an important role in real-time tracking of horizontal wells by LWD [40]. In LWD, if the drilling reservoir resistivity is larger than this threshold, the well might penetrate the oil zones; if the drilling reservoir resistivity is close to this threshold, the well might penetrate the water–oil zones; and if the drilling reservoir resistivity is lower than this threshold, the well might penetrate the water zones.

Well A-5P is a high-angle-deviated pilot well for horizontal Well A-5H (Figure 9). When it was completed in pay A980, water and oil zones were identified fast using this proposed method. It could be inferred that upper A980 (Y026.64 m–Y036.63 m) is the oil zone; middle A980 is a nonreservoir (Y036.63 m–Y045.88 m) with higher GR; and lower A980 (Y045.88 m–Y051.76 m) is the water zone in vertical depth (Figure 10a). The nonreservoir interlayer obstructs the upper oil and low water system. Hence, upper A980 is a significant drilling target for horizontal production of Well A-5H. The resistivity cutoff of the oil zone is ∼3.9 Ω·m, calculated from Well A-5P (Figure 10a).

Figure 9

Trajectories of Well A-5P and A-5H are viewed from south to north. Well A-5H sidetracked drilling at a measured depth of Y365 m.

Figure 10

Resistivity cutoff application in real-time tracking of horizontal wells by LWD. (a) Resistivity cutoff of pay A980 in pilot Well A-5P in vertical depth. The resistivity cutoff of the upper oil zone is ∼3.9 Ω·m. (b) Resistivity cutoff used in real-time monitoring horizontal drilling of Well A-5H in measured depth. In pay A980, if the low-frequency attenuation resistivity of LWD is higher than 3.9 Ω·m, the bit would be in the oil zone and not the water zone.

Well A-5H is an open window and sidetracked drilling at a measured depth of Y365 m lead by LWD (Figure 9). In Well A-5P, during horizontal drilling in A980, from Y444.16 m, near-bit GR is low and low-frequency attenuation resistivity (RT) real-time reading of LWD is larger than 3.9 Ω·m, which suggested that the bit would be in the upper A980 oil zone (Y444.16 m–Y767.2 m in the measured depth) (Figure 10b). From a Y768.4 m depth, the GR increased, which indicated that the trajectory approached the middle nonreservoir. Then, the bit upregulated and entered the upper A980 oil zone again, where the GR is low and the resistivity is higher than 3.9 Ω·m again (Y782.96 m–Y819.96 m in measured depth) (Figure 10b). In this case, drilling into the oil–water boundary, the resistivity would be close to this threshold, and drilling should be upregulated immediately. At the rig site, the resistivity cutoff is beneficial for real-time monitoring of oil in LWD.

6.2 Possible genesis of LRLC

The limited experimental data were combined with recent literature studies and the possible genesis of LRLC in Enping Formation in A-Oilfield may be summarized as follows [41,42,43,44]. (ⅰ) High-formation water salinity: in a generalized case, high-formation water salinity implies low-formation water resistivity, which contributed to the low formation resistivity [45,46]. The formation water salinity of Enping Formation in A-Oilfield is 2.8 × 10⁴–3.6 × 10⁴mg/L, higher than the conventional 3.0 × 10⁴mg/L. (ⅱ) Complicated pore–throat structures: the pore–throat structure relates to the size and connectivity of the pore and throat. The complicated pore–throat structure suggests high bound water saturation and would result in low formation resistivity [47,48]. According to the mercury-injection capillary pressure experiment, the expulsion pressure is 0.005–0.309 MPa (average: 0.054 MPa); the maximum capillary radius is 2.43144.70 μm (average: 44.25 μm); the sorting of the pore–throat is 1.84–3.37 (average: 2.679); the skewness of pore–throat is 0.57–1.90 (average: 1.342); and the uniformity coefficient of the pore–throat is 4.5–10.14 (average: 7.336). These parameters demonstrate the complexity of the pore–throat structure (Figure 11). (ⅲ) Well physical properties: the well physical properties infer high water content and developed conductive network in the pore–throat [49,50]. For the intergranular pores and intergranular dissolved pores of wells, the reservoir has middle to high porosity and permeability, with effective porosities of more than 20% (Figure 4). (ⅳ) Conductive and clay minerals: the rocks of Enping Formation in A-Oilfield contain conductive ironstone minerals universally, such as pyrite, ferrite, hematite, and magnetite (Figure 5c). In addition, the clay volume is high occasionally in sandstone reservoirs and even reached up to 30%. These conductive and clay minerals are conducive to low formation resistivity [51]. The percentage of these conductive minerals even reaches up to 1% in rock bulk, as inferred from the observation of the rock’s thin section. In order to analyze the genesis and interpret the fluid of LRLC in the Enping Formation in A-Oilfield accurately, more means and technologies would be needed [52,53,54].

Figure 11

Fourteen results of mercury-injection capillary pressure experiment.

7 Conclusions

Considering the Paleogene Enping Formation in A-Oilfield of Pearl River Mouth Basin, South China Sea, for example, the resistivity cutoff of LRLC pays in sandstone reservoirs is proposed using the Simandoux water–oil saturation equation. Based on the Simandoux equation, the formation resistive influence factors from the shale volume, formation water resistivity, water saturation, and porosity are simulated theoretically.

At the average shale volume and porosity of LRLC pays, the resistivity cutoff in Enping Formation is overall referenced at 4.2 Ω·m. In each deep point, the resistivity cutoff can be also calculated after computing the shale volume and porosity. Confirmed by oil-test results, the resistivity cutoff is effective to identify water and oil zones fast and qualitatively: if the formation resistivity is higher than the resistivity cutoff, reservoirs might be oil zones; if the formation resistivity approximates the resistivity cutoff, reservoirs would be water–oil zones; and if the formation resistivity is lower than the resistivity cutoff, reservoirs should be water zones.

In LWD, the resistivity cutoff of LRLC pays offers important information in real-time tracking of horizontal wells in water and oil zones. High formation water salinity, complicated pore–throat structure, well physical properties, and conductive and clay minerals are the possible genesis of LRLC in the Enping Formation of A-Oilfield.

Acknowledgement

The authors are thankful to the anonymous reviewers for their constructive reviews on the manuscript, and the editors for carefully revising the manuscript.

Conflict of interest: The authors state no conflict of interest in this article.

References

[1] Stolper K. Identify potential low-resistivity pay using visual rock analysis. Houst Geol Soc Bull. 1994;7(4):32.Search in Google Scholar

[2] Boyd A, Darling H, Tabanou J, Davis B, Lyon B, Flaum C, et al. The lowdown on low-resistivity pay. Oil Rev. 1995;7(3):4–18.Search in Google Scholar

[3] Worthington PF. Recognition and evaluation of low-resistivity pay. Pet Geosci. 2000;6(1):77–92. 10.1144/petgeo.6.1.77.Search in Google Scholar

[4] Claverie M, Allen DF, Heaton N, Bordakov G. A new look at low-resistivity and low-contrast (LRLC) pay in clastic reservoirs. In SPE Annual Technical Conference and Exhibition. Florence, Italy; 2010. SPE Paper 134402.10.2118/134402-MSSearch in Google Scholar

[5] Okowi V, Ishola A, Onuoha V, Oifoghe S. Challenges in identifying and quantifying hydrocarbons in thinly bedded, laminated, and low-resistivity pay zones. In SPE Nigeria Annual International Conference and Exhibition. Lagos, Nigeria; 2014. SPE Paper 172834-MS.10.2118/172834-MSSearch in Google Scholar

[6] Iqbal MA, Salim AMA, Baioumy H, Gaafar GR, Wahid A. Identification and characterization of low resistivity low contrast zones in a clastic outcrop from Sarawak, Malaysia. J Appl Geophys. 2019;160:207–17 (in Chinese with English abstract). 10.1016/j.jappgeo.2018.11.013.Search in Google Scholar

[7] Shah JM, Dahlan NAM, Kamarulzaman MN, Razak MANCA, Jamaluddin J. The revelation of minor reservoir opportunity: Realizing low resistivity contrast reservoir play type in Baram Delta Basin East Malaysia, thru REM log enhancement and comprehensive water salinity analysis. In SPE Middle East Oil and Gas Show And Conference. Manama, Bahrain; 2019. SPE Paper 194917-MS.Search in Google Scholar

[8] Tixier MP, Morris RL, Connell JG. Log evaluation of low-resistivity pay sands in the Gulf Coast. Log Anal. 1968;9(6):3–20.Search in Google Scholar

[9] Murphy RP, Owens WW. A new approach for low-resistivity sand log analysis. J Pet Technol. 1972;24:1302–6. 10.2118/3569-PA.Search in Google Scholar

[10] Tripathi SN, Domangue EJ, Murdoch BT. Low-resistivity sand log evaluation with the chlorine log. In SPWLA 25th Annual Logging Symposium. New Orleans, Louisiana; 1984. SPWLA Paper SPWLA-1984-N.Search in Google Scholar

[11] Zemanek J. Low-resistivity hydrocarbon-bearing sand reservoirs. SPE Eval. 1989;4(4):515–21. 10.2118/15713-PA.Search in Google Scholar

[12] Chen J, Guo T, Zhu LQ. Evaluation of oil saturation for volcaniclastic rock reservoir with high shale content and low resistivity. J Chengdu Univ Technol (Sci Technol Ed). 2019;46(2):153–71 (in Chinese with English abstract). 10.3969/j.issn.1671-9727.2019.02.03.Search in Google Scholar

[13] Hassoun TH, Zainalabedin K, Minh CC. Hydrocarbon detection in low-contrast resistivity pay zones, capillary, pressure and ROS determination with NMR logging in Saudi Arabia. In SPE Middle East Oil Show. Bahrain; 1997. SPE Paper 37770.10.2118/37770-MSSearch in Google Scholar

[14] Fanini ON, Kriegshäuser BF, Mollison RA, Schön JH, Yu LM. Enhanced, low-resistivity pay, reservoir exploration and delineation with the latest multicomponent induction technology integrated with NMR, nuclear, and borehole image measurements. In SPE Latin American and Caribbean Petroleum Engineering Conference. Buenos Aires, Argentina; 2001. SPE Paper 69447.10.4043/13279-MSSearch in Google Scholar

[15] Hamada GM, Al-Blehed MS, Al-Awad MN, Al-Saddique MA. Petrophysical evaluation of low-resistivity sandstone reservoirs with nuclear magnetic resonance log. J Pet Sci Eng. 2001;29(2):129–38 (in Chinese with English abstract). 10.1016/S0920-4105(01)00095-X.Search in Google Scholar

[16] Simpson GA, Menke JY. Identifying low contrast-low resistivity pay zones with pulsed neutron capture logs in shaly sand Miocene Formations of South Louisiana. In SPWLA 51st Annual Logging Symposium. Perth, Australis; 2010. SPWLA Paper SPWLA-2010-99170.Search in Google Scholar

[17] Deng JM, Liu XP, He K, Hu XX. Novel methods of identifying hydrocarbon bearing formation with low resistivity contrast from conventional logs in tight sandstone reservoirs of North Ordos Basin. In SPE Middle East Unconventional Gas Conference and Exhibition. Muscat, Oman; 2013. SPE Paper 163952.10.2118/163952-MSSearch in Google Scholar

[18] Mashaba V, Altermann W. Calculation of water saturation in low resistivity gas reservoirs and pay-zones of the Cretaceous Grudja Formation, onshore Mozambique basin. Mar Pet Geol. 2015;67:249–26. 10.1016/j.marpetgeo.2015.05.016.Search in Google Scholar

[19] Si Y, Niu XB, Liang XW, Shi LC, Zhu YS. Main controlling factors of low resistivity and effective identification methods for the Chang 2 reservoir in the Jiyuan area, Ordos Basin. Geol Explor. 2019;55(3):882–90 (in Chinese with English abstract). 10.12134/j.dzykt.2019.03.020.Search in Google Scholar

[20] Xiao SD. Study on low-resistivity characteristics and genetic mechanism of Putaohua oil layers in Songliao Basin. Spec Oil Gas Reser. 2020;27(6):108–13 (in Chinese with English abstract). 10.3969/j.issn.1006-6535.2020.06.015.Search in Google Scholar

[21] Chen M, Sun DQ, Wu JB, Qu CW, Li SZ. Reservoir characteristics and cutoffs of low resistivity reservoir in Songtao Uplift, Qiongdongnan Basin. Mar Geol Front. 2023;39(2):17–27 (in Chinese with English abstract). 10.16028/j.1009-2722.2021.264.Search in Google Scholar

[22] Qi Y, Han DW, Du YY, Zhou WJ. Genesis of low⁃resistivity reservoirs in Sangonghe Formation, Mahu Sag. Xinjiang Pet Geol. 2023;44(2):151–60 (in Chinese with English abstract). 10.7657/XJPG20230204.Search in Google Scholar

[23] Luo DH, Liang W, Li XS, Zhou XB, Li B, Hou YM, et al. A breakthrough at Paleogene Enping Formation and its important significance in Lufeng 13-1 oilfield, Pearl River Mouth Basin. China Offshore Oil Gas. 2011;23(2):71–5 (in Chinese with English abstract). 10.3969/j.issn.1673-1506.2011.02.001.Search in Google Scholar

[24] Luo DH, Liu WX, Dai Z, Wang ZZ, Cao SY, Zhang W. Key techniques to evaluate deep reservoir and their application in the eastern Pearl River Mouth basin: A case of Paleogene Enping reservoir in LF13-1 oilfield. China Offshore Oil Gas. 2014;26(3):56–60 (in Chinese with English abstract).Search in Google Scholar

[25] Dai YD, Niu ZC, Wang XD, Wang XL, Xiao ZB, Zhang KD, et al. Differences of hydrocarbon enrichment regularities and their main controlling factors between Paleogene and Neogene in Lufeng sag, Pearl River Mouth Basin. Acta Pet Sin. 2019;40(S1):41–52 (in Chinese with English abstract). 10.7623/syxb2019S100.Search in Google Scholar

[26] Zhu XM, Ge JW, Wu CBJ, Zhang XT, Wang XD, Li XY, et al. Reservoir characteristics and main controlling factors of deep sandstone in Lufeng sag, Pearl River Mouth Basin. Acta Pet Sin. 2019;40(S1):69–80 (in Chinese with English abstract). 10.7623/syxb2019S1006.Search in Google Scholar

[27] Ge JW, Zhu XM, Wu CBJ, Zhang XT, Jia LK, Yi Z, et al. Sedimentary characteristics and genetic difference of braided delta: A case study of Enping Formation in Lufeng sag, Pearl River Mouth Basin. Acta Pet Sin. 2019;40(S1):139–52 (in Chinese with English abstract). 10.7623/syxb2019S1012.Search in Google Scholar

[28] Rui ZF, Deng HW, Yang XJ, Li X, Guo J. The channel stacking pattern under the control of sequence stratigraphic of Enping Formation in Lufeng area, Pearl River Mouth Basin. Nat Gas Geosci. 2019;30(5):701–11 (in Chinese with English abstract). 10.11764/j.issn.1672-1926.2019.01.011.Search in Google Scholar

[29] Wan QH, Liu WX, Wang H, Heng LQ, Wang YC, Gao YM. Research on sedimentary architecture pattern of braided river delta front of Lufeng Depression in Pearl River Mouth Basin. Nat Gas Geosci. 2019;30(12):1732–42 (in Chinese with English abstract). 10.11764/j.issn.1672-1926.2019.06.007.Search in Google Scholar

[30] Liu CL, Liu WX, Gu ZY, Liang QQ, Wang ZY, Gu R. Sand body configuration of shallow braided river delta of Enping Formation in Lufeng A Oilfield, Pearl River Mouth Basin. Mar Ori Pet Geol. 2022;27(3):236–48 (in Chinese with English abstract). 10.3969/j.issn.1672-9854.2022.03.002.Search in Google Scholar

[31] Rider MH, Kennedy M. The geological interpretation of well logs (3rd revised edition). Halsted Press, New York: Rider-French Consulting Ltd.; 2011.Search in Google Scholar

[32] Atlas W. Introduction to wireline log analysis. Houston, Texas: West Atlas Int. Inc.; 1995.Search in Google Scholar

[33] Ali M, Ma HL, Pan HP, Ashraf U, Jiang R. Building a rock physics model for the formation evaluation of the Lower Goru sand reservoir of the Southern Indus Basin in Pakistan. J Pet Sci Eng. 2020;194:107461. 10.1016/j.petrol.2020.107461 Search in Google Scholar

[34] Archie GE. The electrical resistivity log as an aid in determining some reservoir characteristics. Trans Am Ins Min Met Pet Eng. 1942;146:54–62.10.2118/942054-GSearch in Google Scholar

[35] Simandoux P. Dielectric measurements in porous media and application to shaly formations. Rev LĨnst Fran Petrolv. 1963;18(S1):193–215.Search in Google Scholar

[36] Waxman MH, Smits LJM. Ionic double-layer conductivity in oil-bearing shaly sands. Soc Pet Eng J. 1968;107–22. 10.2118/1863-A.Search in Google Scholar

[37] Waxman MH, Thomas EC. Electrical conductivities in shaly sands: Ⅰ. The relation between hydrocarbon saturation and resistivity index; Ⅱ. The temperature coefficient of electrical conductivity. J Pet Technol. 1974;26(3):213–25. 10.2118/4094-PA Search in Google Scholar

[38] Poupon A, Leveaux J. Evaluation of water saturation in shaly formations. Log Anal. 1971;12(4):3–8.Search in Google Scholar

[39] Yuan R, Liu CL, Gu ZY, Hu QH, Liang QQ, Zhou KJ, et al. Calculation and application of resistivity lower limit of pay zone of Enping Formation in Lufeng 13-1 Oilfield, Pearl River Mouth Basin. Mar Ori Pet Geol. 2023;28(1):105–12 (in Chinese with English abstract). 10.3969/j.issn.1672-9854.2023.01.011.Search in Google Scholar

[40] Kok J, DeJarnett J, Geary D, Vauter E. Successful geosteering in low resistivity contrast reservoirs of the Permian Basin. In SPE eastern regional meeting. Columbus, Ohio, USA; 2011. SPE Paper 149543.10.2118/149543-MSSearch in Google Scholar

[41] Yang CM, Zhou CC, Cheng XZ. Origin of low resistivity pays and forecasting of favorable prospecting areas. Pet Explor Dev. 2008;35(5):600–5 (in Chinese with English abstract).Search in Google Scholar

[42] Pan YW, Zuo XH, Zhang ST, Zhang YH. Genesis and identification of low resistivity of Chang 2 oil layer in Heshui Area, Ordos Basin. Xinjiang Pet Geol. 2020;41(3):253–60 (in Chinese with English abstract). 10.7657/XJPG20200301.Search in Google Scholar

[43] Liu ZD, Liu YC, Wang LG, Hu KL, Wu ZM, Kang YM. Genetic analysis of low resistivity in Yan 9 reservoir of Yanwu Oilfield, Ordos Basin. Fault-Block Oil Gas Field. 2021;28(4):498–503 (in Chinese with English abstract). 10.6056/dkyqt202104012.Search in Google Scholar

[44] Li F, Xu SP, Liu T, Lu ZM, Li X, Aini M. Genesis and fluid identification method of Cretaceous low⁃resistivity oil layers in WTK Oilfield. Xinjiang Pet Geol. 2022;43(2):241–51 (in Chinese with English abstract). 10.7657/XJPG20220217.Search in Google Scholar

[45] Ouyang J, Xiu LJ, Shi YJ, Li CX. Saturation evaluation and distribution of low-contrast oil reservoirs. China Pet Explor. 2009;14(1):38–52 (in Chinese with English abstract). 10.3969/j.issn.1672-7703.2009.01.007.Search in Google Scholar

[46] Lu YL, Xu SN, Zheng YY, Han ZL. Water saturation calculation method of low resistivity reservoir with clay additional conductivity correction. China Offshore Oil Gas. 2023;35(1):63–9 (in Chinese with English abstract). 10.11935/j.issn.1673-1506.2023.01.006.Search in Google Scholar

[47] Wang JM, Zhang S. Pore structure differences of the extra-low permeability sandstone reservoirs and the causes of low resistivity oil layers: A case study of Block Yanwumao in the middle of Ordos Basin, NW China. Pet Explor Dev. 2018;45(2):257–64 (in Chinese with English abstract). 10.11698/PED.2018.02.08.Search in Google Scholar

[48] Wang FT, Hou DM, Li YL, Quan B, Liu BW. Pore structure characteristics of C Oilfield in Bohai Sea and its influence on reservoir resistivity. J Xi’an Pet Univ (Sci Technol Ed). 2022;37(6):32–7 (in Chinese with English abstract). 10.3969/j.issn.1673-064X.2022.06.004.Search in Google Scholar

[49] Dong HM, Sun JM, Arif M, Zhang YH, Yan WC, Iglauer S, et al. Digital rock-based investigation of conductivity mechanism in low-resistivity gas hydrate reservoirs: Insights from the Muli area’s gas hydrates. J Pet Sci Eng. 2022;218:110988. 10.1016/j.petrol.2022.110988.Search in Google Scholar

[50] Chen SJ, Gao XJ, Yu J, Ma J, Huang H. An analysis of the causes of Chang 2 low resistivity in Middle-western Ordos Basin. J Southwest Pet Univ (Sci Technol Ed). 2017;39(2):1–8 (in Chinese with English abstract). 10.11885/j.issn.1674-5086.2015.06.11.01.Search in Google Scholar

[51] Feng Y, Qin K, Li ED, Zhang RY. Identification of low⁃resistivity oil layers based on clay mineral analysis. Xinjiang Pet Geol. 2020;41(2):237–42 (in Chinese with English abstract). 10.7657/XJPG20200217.Search in Google Scholar

[52] Liang W, Yan ZH, Yang Y, Huang YJ, Xiong Q, Dong YF. Study on identification and main controlling factors of low-resistivity oil reservoirs in Xijiang Oilfield, Eastern South China Sea. Spec Oil Gas Reser. 2022;29(1):10–4 (in Chinese with English abstract). 10.3969/j.issn.1006-6535.2022.01.002.Search in Google Scholar

[53] Liu X, Zhang RY, Sun Q, Sun YQ, Niu QW, Xu SY. Accurate identification method of low-resistance oil layers driven by big data. Editorial Dep Pet Geol Recov Effic. 2022;29(1):30–6 (in Chinese with English abstract). 10.13673/j.cnki.cn37-1359/te.2022.01.004.Search in Google Scholar

[54] Guo JZ, Li Y, Han BH, Zhang P. Conventional logging identification method of Jurassic low resistivity reservoir based on genetic analysis: a case study of Hedao area, Ordos basin. Prog Geophys. 2022;37(6):2381–94 (in Chinese with English abstract). 10.6038/pg2022FF0264.Search in Google Scholar

Received: 2022-12-16

Revised: 2023-06-20

Accepted: 2023-07-11

Published Online: 2023-09-20

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

https://doi.org/10.1515/geo-2022-0520

Keywords for this article

resistivity cutoff; LRLC pays; Simandoux equation; conventional well logs; Enping Formation

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