Application of Two-Dimensional NMR for Quantitative Analysis of Viscosity in Medium–High-Porosity-and-Permeability Sandstones in North China Oilfields

Abstract

The viscosity of crude oil plays a pivotal role in the exploration and development of oil fields. The predominant reliance on laboratory measurements, which are constrained by manual expertise, represents a significant limitation in terms of efficiency. Two-dimensional nuclear magnetic resonance (NMR) logging offers a number of advantages over traditional methods. It is capable of providing faster measurement rates, as well as insights into fluid properties, which can facilitate timely adjustments in oil and gas development strategies. This study focuses on the loose sandstone reservoirs with high porosity and permeability containing heavy oil in the Huabei oilfield. Two-dimensional nuclear magnetic resonance (NMR) measurements and analyses were conducted on saturated rocks with different-viscosity crude oils and varying oil saturation levels, in both natural and artificial rock samples. This study elucidates the distribution patterns of different-viscosity crude oils within the two-dimensional NMR spectra. Furthermore, the T₁ and T₂ peak values of the extracted oil signals were employed to establish a model correlating oil viscosity with NMR parameters. Consequently, a criterion for determining oil viscosity based on two-dimensional NMR was formulated, providing a novel approach for estimating oil viscosity. The application of this technique in the BQ well group of the Huabei oilfield region yielded an average relative error of 15% between the actual oil viscosity and the computed results. Furthermore, the consistency between the oil types and the oil discrimination chart confirms the reliability of the method. The final outcomes meet the precision requirements for practical log interpretation and demonstrate the excellent performance of two-dimensional nuclear magnetic resonance (NMR) logging in calculating oil viscosity. The findings of this study have significant implications for subsequent exploration and development endeavors in the research area’s oilfields.

1. Introduction

The viscosity of crude oil is a function of the internal frictional resistance encountered during its flow. The viscosity of reservoir crude directly influences its ability to flow through subsurface pore media and pipelines [1,2]. An understanding of the viscosity characteristics of reservoir crude is of significant practical importance for devising development schemes, evaluating well productivity, studying flow mechanisms, and facilitating crude oil transportation [3]. Currently, the primary means of measuring viscosity is through laboratory experiments. This method of viscosity analysis places extremely high demands on the experiments, as the variation in viscosity parameters is influenced by numerous human factors [4]. In the early stages of field development, the lack of viscosity data for crude oil under reservoir conditions often prevents the provision of reliable recommendations for field development plans.

Nuclear magnetic resonance (NMR) logging is widely used in petroleum exploration and production to provide accurate porosity and identify fluid types. The T₁ and T₂ peak values are the most important parameters in nuclear magnetic logging, representing the maximum values of longitudinal relaxation time and transverse relaxation time [5,6,7,8]. Nuclear magnetic resonance logging uses the resonance phenomenon that occurs between hydrogen nuclei and an applied magnetic field to detect subsurface oil and gas reservoirs. It is the only logging method capable of providing information on crude oil viscosity [9]. When considered alongside traditional experimental approaches, nuclear magnetic resonance (NMR) stands out for its non-destructive nature and immunity to external influences, such as the composition of the rock matrix. Instead, it exclusively detects and characterizes hydrogen-containing compounds present within the sample, offering a distinct advantage in certain analytical contexts [10]. Nuclear magnetic resonance (NMR) logging offers unique advantages in measuring fluid component properties and reflecting pore structure, facilitating a qualitative assessment of reservoir characteristics [11]. It is commonly used in laboratory studies of fluid properties [12]. One-dimensional nuclear magnetic logging measures only the transverse relaxation time T₂ of formation pore fluids and has significant limitations in identifying and quantitatively evaluating oil, gas, and water. When T₂ is constant, two-dimensional nuclear magnetic resonance logging can differentiate oil, gas, and water by using different T₁ values [13].

In recent years, the development of two-dimensional nuclear magnetic resonance logging has become increasingly mature. Foreign scholars typically use two-dimensional nuclear magnetic resonance logging for rock physics experiments and the identification analysis of fluid components. Research has demonstrated that the T₁–T₂ technology of two-dimensional nuclear magnetic resonance is capable of effectively identifying fluid components and solid organic matter in various states within the pore space [14].

Xie used a 2 MHz NMR spectrometer to study the effect of temperature on the NMR relaxation characteristics of crude oil and provided a relationship formula for relaxation time with temperature and crude oil viscosity changes at 2 MHz. However, the viscosity estimation formula obtained under experimental conditions uses a homogeneous magnetic field, which has significant limitations [15].

He et al. performed variable echo interval (TE) NMR measurements on medium-to-high-viscosity saturated oil samples and established a relationship between crude oil viscosity and the transverse relaxation time T₂ peak of the oil sample. This relationship has been used to estimate subsurface crude oil viscosity using NMR logging, with good practical results. However, these studies only analyzed the one-dimensional NMR measurement results of oil sample fluids and did not consider the effect of the actual formation conditions on the measurement results [16].

Kadkhodaie used three steps to generate capillary pressure and relative permeability curves. First, a cluster analysis was used to classify reservoir rocks into six electrofacies (EFs), with the reservoir quality graded from EF1 to EF6. Secondly, the NMR T₂ distribution arrays were converted into synthetic drainage capillary pressure curves and the results were validated by available laboratory-measured mercury injection capillary pressure curves (MICP). Finally, relative permeability curves were generated from the NMR-derived MICP data using the Wyllie and Gardner equations for each individual electrofacies. A comparison of the results with laboratory data demonstrated the effective role of electrofacies control in the generation of highly accurate capillary pressure and relative permeability curves [17].

Li et al., based on laboratory studies of the NMR mechanism in saturated heavy oil cores, analyzed the difference spectra and migration spectra characteristics of P-type NMR data for heavy oil reservoirs. They summarized methods for identifying heavy oil reservoirs using NMR data, distinguished between heavy oil, medium-viscosity oil, and water layer NMR recording responses, and developed a new method for identifying heavy oil reservoirs. However, the detection depth of NMR logging is shallow, so it is necessary to integrate NMR data with conventional data during reservoir evaluation to accurately assess heavy oil reservoirs [18].

NMR technology provides relaxation times and diffusion coefficients that can be used to calculate porosity [19], permeability [20,21], and oil content [22,23], identify reservoir fluids [24,25], and delineate heavy oil reservoirs [26,27,28]. Shi and Cai proposed a “five-component” pore interval oil–water discrimination model. Building on this, the present paper further explores the method of assessing crude oil viscosity using two-dimensional NMR T₁–T₂ spectra under conditions of medium-to-high-porosity-and-permeability. The experimental requirements are clean lithology, good physical properties, and minimal interference from bound water [29].

Previous studies mainly focused on one-dimensional nuclear magnetic resonance in the laboratory, and only derived the trend of changes in crude oil viscosity with T₁ and T₂, or did not derive a specific formula for calculating crude oil viscosity using two-dimensional nuclear magnetic resonance, which is not very applicable. This article uses a laboratory nuclear magnetic resonance core analyzer with the same parameters to conduct two-dimensional nuclear magnetic resonance T₁–T₂ experimental measurements on crude oil samples, natural rock samples saturated with different viscosities of crude oil, and artificial rock samples. Full-diameter-core NMR measurements were also conducted on full-diameter-core and crude oil samples at the well site using a full-diameter-core NMR analyzer. By analyzing the characteristics of two-dimensional nuclear magnetic resonance T₁–T₂ spectra, the relaxation characteristics of different-viscosity crude oils and their distribution patterns in two-dimensional nuclear magnetic resonance spectra were elucidated. By utilizing the relationship between the extracted crude oil signal peaks T₁ and T₂ and crude oil viscosity, a two-dimensional NMR (T₁–T₂) rapid discrimination graph of crude oil viscosity was provided for the first time, and an innovative model for calculating crude oil viscosity using two-dimensional NMR was established. This provides a new method for estimating crude oil viscosity and reliable data for fluid evaluation and reservoir development.

2. Sample and Methods

2.1. Crude Oil Samples

To simulate the effect of the crude oil viscosity contained in the core porosity on the NMR response, actual oil samples from the North China Oilfield with viscosities of 70, 136, and 26,000 mPa·s were selected. These viscosities were determined by geochemical pyrolysis experiments. We extracted crude oil from rock samples by first crushing and grinding the samples, followed by centrifugal separation to remove water and solid particles from the crude oil. Pyrolysis experiments volatilized and crack the hydrocarbons in the samples at different temperatures. The hydrocarbon content and pyrolysis parameters of each component in the rock were measured by a detector. Based on the pyrolysis parameters and other analytical data, the viscosity of the crude oil was evaluated using an established chart. These samples were mixed with a low-viscosity base oil (1.5 mPa·s) in the laboratory to produce oil samples of different viscosities, namely 25, 61.7, 519, and 26,000 mPa·s, covering light, medium, and heavy oils. This sample preparation allowed a comprehensive evaluation of the effect of different oil viscosities on the NMR response.

Table 1. Basic information of crude oil samples.

Viscosity (mPa·s)	Density (g/cm3)	Boiling Point (°C)	Sulfur Content (% w/w)	Asphaltene Content (g)
1.5	0.83	200–300	0.1	4
25	0.85	300–400	0.3	5
75	0.88	400–500	0.5	5
519	0.92	500–600	1.0	6
26,000	1.02	600–700	3.0	10

shows the physical and chemical properties of crude oil samples (Table 1).

2.2. Natural and Artificial Rock Samples

Five representative natural rock samples from medium-to-high-porosity-and-permeability reservoirs in the North China Oilfield were selected for the experiments. The physical parameters of the rocks are shown in Table 1. The natural rock core samples indicated that all five samples were relatively loose, with porosity measured by gas displacement ranging from 15.5 to 28.3% and an average porosity of 23.24%. Permeability ranged from 23.39 to 500.16 × 10⁻³ μm², with an average permeability of 269.32 × 10⁻³ μm², classifying the samples as medium-to-high-porosity-and-permeability sandstone reservoirs (

Table 2. Basic information of natural rock samples.

Sample	Length (mm)	Diameter (mm)	Volume (mL)	Dry Weight (g)	Helium Porosity (%)	Helium Permeability (×10−3 μm2)
7A	42.52	25.06	20.98	47.01	15.5	23.39
9B	42.88	25.07	21.17	46.04	17.2	140.09
11A	42.31	25.12	20.97	39.26	27.8	305.90
13A	42.12	25.11	20.85	39.10	28.3	377.04
14B	41.66	25.13	20.66	39.49	27.4	500.16

).

To simulate the actual conditions of the formation, different pore structures in rock cores were mimicked using glass sand of different grain sizes. Three sets of artificial rock core experiments were designed using 60 mesh, 120 mesh, and 180 mesh cores corresponding to different sandstone grain sizes (

Table 3. Classification table of artificial rock samples by mesh size.

Mesh Size	Equivalent Diamete (μm)	Equivalent Pore Diameter (μm)	Corresponding Particle Grade
60 mesh	250	77	Medium Sand
120 mesh	125	39	Fine Sand
180 mesh	80	25	Silt Sand

).

2.3. Experimental Methods

This experiment involves nuclear magnetic resonance (NMR) testing of natural rock samples saturated with oil, artificial rock samples saturated with oil, and full-diameter rock cores containing oil at well sites. The objective is to perform a multi-dimensional comparative analysis of crude oil samples, natural rock samples, artificial rock samples, and full-diameter rock cores from well sites. The results will clarify the response characteristics of different viscous fluid components in the T₁–T₂ spectra and establish a crude oil viscosity calculation model.

2.3.1. Natural Rock Sample NMR Experiment

First, five groups of natural rock samples are selected for oil extraction. The extracted core samples are washed to remove salts and residual oil from the pore spaces. The cores are then dried in an oven at 70 °C for 24 h to determine their dry weight. Length and diameter measurements are taken, followed by porosity and permeability tests to obtain gas-measured porosity and permeability data.

Experimental oils of different viscosities (1.5, 70, 136, and 581 mPa·s) are prepared for free-state nuclear magnetic resonance (NMR) experiments. NMR one-dimensional T₂ spectra and two-dimensional T₁–T₂ spectra are obtained for each viscosity.

Finally, the rock samples saturated with oils of different viscosities, especially under bound water conditions, are subjected to NMR experiments. This is undertaken to confirm the positions of oils of different viscosities in the two-dimensional NMR T₁–T₂ spectra of natural rock samples and to establish a model for calculating crude oil viscosity.

2.3.2. Artificial Rock Sample NMR Experiment

First, artificial rock samples are prepared with different pore sizes, covering at least the ranges of medium sandstone, fine sandstone, and siltstone. These samples are saturated with oil and subjected to nuclear magnetic resonance (NMR) measurements.

The cylindrical samples are placed in an SVF rock core vacuum saturation apparatus (Xuan Yu Mechanical and Electrical Technology (Shanghai) Co., Ltd., Shanghai, China). Oil is introduced into the device and after 8 h of vacuum pumping to remove air, the device is sealed and pressurized to 1000 psi to ensure saturation with the crude oil. Pressure stability is maintained throughout this process. After one week, the cores are removed, cleaned on the surface, and subjected to NMR measurements of one-dimensional T₂ spectra and two-dimensional T₁–T₂ spectra.

Finally, the response characteristics of oils of different viscosities (viscosities of 1.5, 70, 136, and 445 mPa·s) in different pore spaces are analyzed. This analysis confirms the positions of oils of different viscosities in the two-dimensional T₁–T₂ NMR spectra of the artificial rock samples and establishes a model for calculating crude oil viscosity.

2.3.3. Full-Diameter Rock Core NMR Experiment

To prevent the escape of hydrocarbons, oil-bearing full-diameter-core samples are first wrapped for containment at the well site. These oil-bearing cores are then measured using a full-diameter nuclear magnetic resonance (NMR) logging tool to determine the position of the oil within the two-dimensional NMR T₁–T₂ spectrum of the full-diameter cores.

Finally, the NMR response characteristics of full-diameter cores saturated with crude oils of different viscosities (viscosities of 1.5, 25, 61.7, 136, 519, 26,000 mPa·s) in different pore spaces are analyzed by integrating the well test data. The aim of this analysis is to establish a model for the calculation of crude oil viscosity.

This approach provides detailed insight into how crude oils of different viscosities are distributed and behave within full-diameter cores in different pore spaces, thereby facilitating the development of a viscosity calculation model based on NMR data and well test analysis.

3. Results and Discussion

3.1. NMR Characteristics and Distribution Patterns of Crude Oil Samples with Different Viscosities

Figure 1 shows the T₁ and T₂ distributions (Figure 1). When observing Figure 1, it can be seen that the longitudinal relaxation time (T₁) and the transverse relaxation time (T₂) are approximately the same for the light crude oil. However, the transverse relaxation time (T₂) is significantly shifted back in Figure 1 for the heavy crude due to the increase in viscosity and decrease in visual hydrogen index, resulting in the NMR recorded response signal being indistinguishable from that of the bound fluid. Both the T₁ and T₂ relaxation times in the NMR measurements decrease significantly with increasing crude oil viscosity, but T₂ decreases more rapidly than T₁, suggesting that T₂ is more sensitive to information reflecting crude oil viscosity.

Figure 2 shows the two-dimensional nuclear magnetic maps (T₁–T₂) of crude oil samples measured using a laboratory NMR scanner (Figure 2). The white circles in the figure represent the peaks of T₁ and T₂. The three dashed lines from top to bottom represent T₁/T₂ values of 100, 10, and 1, respectively. These three lines can be used to observe the changes in T₁/T₂ near the dashed lines as the viscosity of the crude oil increases. From the six 2D NMR plots of different viscosities in Figure 2, it can be seen that the relaxation times T₁ and T₂ of the crude oil samples in the nuclear magnetic resonance (NMR) measurements are influenced by the viscosity and composition of the crude oil. Both T₁ and T₂ decrease with increasing crude oil viscosity, and the T₁/T₂ ratio increases from about 1 to about 10. In addition, T₂ shows a clear double- or multiple-peak feature in Figure 2, particularly in the differentiation of asphaltenes, heavy hydrocarbon fractions, and light fractions in the T₂ dimension. This suggests that higher concentrations of heavy hydrocarbon fractions correspond to larger T₁/T₂ ratios.

3.2. NMR Characteristics and Distribution Patterns of Natural and Artificial Rock Samples with Different Viscosities

Figure 3 illustrates the variation of the T₁ and T₂ NMR relaxation times in the natural cores saturated with oil of different viscosities (Figure 3). Figure 3 shows natural cores containing capillary-bound water, where the signals from the oil gradually overlap with those from the capillary-bound water as the viscosity of the oil increases.

The variation of the T₁ and T₂ NMR relaxation times in the artificial cores saturated with oils of different viscosities is shown in Figure 4, which shows that the position of the oil peaks in the artificial cores is less affected by changes in pore structure. Under the same oil viscosity condition, the oil peak positions are basically consistent at different oil saturations. This consistency suggests that accurate measurement of mobile oil signals in real samples can reliably estimate crude oil viscosity, provided the pore structures are similar. The patterns of the T₁ and T₂ relaxation time changes observed in natural and artificial cores saturated with oil of different viscosities are consistent with those observed for crude oil samples. In particular, the oil peaks move towards lower T₁ and T₂ values with increasing oil viscosity and recombinant content, and the T₁/T₂ ratio tends to increase.

3.3. Establishment of Crude Oil Viscosity Calculation Model

The samples used to establish the crude oil viscosity calculation model are as follows: Oil Sample (Lab)—actual crude oil from the Huabei Oilfield, blended with base oil in the laboratory to achieve different viscosities. Oil Sample (Xinghua)—actual crude oil samples taken from three wells in the Huabei oil field. Actual Sample (Lab)—five natural rock samples saturated with different viscosity crude oils in the laboratory. Lab (Field—Piston)—oil bearing core samples obtained by rotary coring at the well site. Lab (Full-Diameter Core)—oil-bearing full-diameter-core samples obtained by core drilling at the well site. Artificial Samples (60, 120, 180 mesh)—artificial rock samples of various grain sizes (pore sizes) injected with crude oils of various viscosities prepared in the laboratory.

3.3.1. Oil Sample (Laboratory)

In NMR logging, the relaxation times T₁ and T₂ are affected by molecular motion. An increase in viscosity means an increase in intermolecular friction, which affects the rotation and translation of the molecules, thereby affecting T₁ and T₂. This influence is related to the complexity of molecular motion, which usually manifests itself as a non-linear relationship, so we decided to use an exponential relationship to fit it. For the laboratory oil samples, Figure 5 shows the viscosity versus T₁/T₂ cross plot (Figure 5), from which the following formula (1) is derived to relate viscosity and two-dimensional NMR:

$$\mu =2.5649\ast ({\displaystyle \frac{T_1}{T_2}}{)}^{6.0156}$$

(1)

3.3.2. Field–Core Plug and Full-Diameter-Core Sample

For the oil-bearing rock cores obtained from the well site, Figure 6 shows the actual field oil-bearing rock viscosity versus T₁/T₂ cross plot (Figure 6), which leads to the following formula (2) for viscosity and two-dimensional NMR:

$$\mu =4.9646\ast ({\displaystyle \frac{T_1}{T_2}}{)}^{4.0752}$$

(2)

3.3.3. Discrimination Diagram for Crude Oil Viscosity Based on Two-Dimensional (T₁–T₂) Maps

The chemical composition of heavy oil, particularly the asphaltene and resin content, is relatively high. These components have complex molecular structures and contain a large number of aromatic and heterocyclic structures, which have a significant effect on the relaxation time of NMR. Due to the decrease in the apparent hydrogen index caused by asphaltenes in the oil sample during the NMR recording, we classified the oil into three categories: light oil, medium oil, and heavy oil, in order to improve the accuracy of the fitting formula as much as possible. Based on the analysis of the relationship between crude oil viscosity and T₁, T₂, a discrimination diagram for crude oil viscosity was constructed using two-dimensional (T₁–T₂) maps from both laboratory experiments and field cores. The laboratory artificial samples, natural rock samples, and field full-diameter-core measurements all show a pattern in which oil viscosity increases as T₂ decreases. As the heavy oil signals and heavy hydrocarbon components increase, there is a rapid increase in the T₁/T₂ ratio (Figure 7). This discrimination method can qualitatively distinguish light, medium, and heavy oils (the T₁ and T₂ data used in the graph are all oil peak readings).

3.3.4. Fitting Formulas Based on Crude Oil Viscosity Categories

After categorizing the experimental data by crude oil viscosity, fitting formulae were derived for each category. Figure 8 shows the cross plot of viscosity versus T₁/T₂ divided by crude oil viscosity, with different formulae derived for light, medium, and heavy oils (Figure 8).

For light oils, the relationship between viscosity and two-dimensional NMR is given by Formula (3):

$$\mu =1.7577\ast ({\displaystyle \frac{T_1}{T_2}}{)}^{5.7428}$$

(3)

For medium oils, the relationship is described by Formula (4):

$$\mu =2.1414\ast ({\displaystyle \frac{T_1}{T_2}}{)}^{5.7531}$$

(4)

For heavy oils, the relationship is given by Formula (5):

$$\mu =1.3277\ast ({\displaystyle \frac{T_1}{T_2}}{)}^{6.3536}$$

(5)

The main parameters in Equations (1)–(5) have the following meanings: u represents the viscosity of crude oil; T₁ and T₂ represent longitudinal and transverse relaxation peaks.

3.3.5. Classification of Crude Oil Viscosity Levels

The viscosity measurements in this experiment were carried out under standard temperature and pressure conditions (20 °C, 0.101 mPa·s). Based on experimental conditions, the crude oil viscosity levels in the high-porosity-and-permeability sandstone of the North China Oilfield are classified as shown in

Table 4. Classification standards for crude oil viscosity levels.

Crude Oil Types	μ (mPa·s)	T2 (ms)	Viscosity Formulas for Crude Oil
Heavy oil	μ ≥ 150 mPa·s	T2 < 20 ms	$\mu =1.3277\ast ({\displaystyle \frac{T1}{T2}}{)}^{6.3536}$
Medium oil	50 mPa·s < μ < 150 mPa·s	20 ms < T2 < 40 ms	$\mu =2.1414\ast ({\displaystyle \frac{T1}{T2}}{)}^{5.7531}$
Light oil	μ ≤ 50 mPa·s	T2 > 40 ms	$\mu =1.7577\ast ({\displaystyle \frac{T1}{T2}}{)}^{5.7428}$

for reference (Table 4).

This classification helps to understand the distribution of different types of crude oil in the field and provides a basis for further exploration and production strategies.

4. Applied Research

The BQ formation is a group of oil exploration wells in the Huabei oil field region. Figure 9 shows the comprehensive interpretation of the logging data from layer 21 of East Section 3 in BQ-20 well (Figure 9), which shows that the acoustic transit time is 293.0 μs/m, the resistivity is 9.7 Ω-m, the density is 2.19 g/cm³, the porosity is 25.2%, and the permeability is 657.8 mD. These characteristics classify it as a typical medium-to-high-porosity-and-permeability sandstone reservoir, conventionally interpreted as an oil-bearing formation.

To validate the reliability of the crude oil viscosity discrimination chart and Nuclear Magnetic Resonance (NMR) viscosity calculation formulae, these tools were applied to practical scenarios in the medium-to-high-porosity sandstone reservoirs of the North China Oilfield. Figure 10 shows that the results of the crude oil viscosity type identified by the viscosity discrimination chart are consistent with the known viscosity types (Figure 10).

The TPI is determined by a rock pyrolyzer and represents the ratio of the total amount of hydrocarbons produced by pyrolysis from an oil-bearing rock sample under specified conditions to the total organic carbon content. It is also referred to as the oil quality factor. This parameter demonstrates a negative correlation with viscosity and is useful in assessing the viscosity of crude oil. According to the crude oil viscosity discrimination chart and the corresponding NMR viscosity calculation formula, the NMR-calculated crude oil viscosity is negatively correlated with the Total Petroleum Index (TPI), which is consistent with the actual viscosity trend relative to the TPI. The analysis of

Table 5. Comparison of geochemical pyrolysis and NMR-calculated viscosity for BQ well group.

Layer Number/Well	Midpoint of Layer (m)	TPI	Measured Viscosity (mPa·s)	Crude Oil Type	Visual Identification	T1/T2	NMR Viscosity (mPa·s)
35/BQ-25	2193.6	0.52	25.99	Light oil	Light oil	1.57	23.48
36/BQ-25	2197.2	0.59	16.13	Light oil	Light oil	1.41	12.79
37/BQ-25	2198	0.57	27.35	Light oil	Light oil	1.55	22.12
14/BQ-10	1893.6	0.47	70	Medium oil	Medium oil	1.80	63.08
15/BQ-10	1897.2	0.44	75.6	Medium oil	Medium oil	1.87	80.07
16/BQ-10	1898	0.39	148	Medium oil	Medium oil	2.05	134.25
21/BQ-20	1989	0.31	502.3	Heavy oil	Heavy oil	2.44	390.7
22/BQ-20	1992	0.32	445	Heavy oil	Heavy oil	2.38	331.74
23/BQ-20	1999	0.32	581	Heavy oil	Heavy oil	2.50	450.11
24/BQ-20	2001	0.24	1268	Heavy oil	Heavy oil	2.87	1084.49

, which compares the geochemical pyrolysis and NMR-calculated viscosity for the BQ well group (Table 5), and

Table 6. Viscosity error analysis for BQ well group.

Layer Number/Well	Midpoint of Layer (m)	Crude Oil Type	Measured Viscosity (mPa·s)	NMR Viscosity (mPa·s)	Absolute Error	Relative Error
35/BQ-25	2193.6	Light oil	25.99	23.48	2.51	0.10
36/BQ-25	2197.2	Light oil	16.13	12.79	2.34	0.15
37/BQ-25	2198	Light oil	27.35	22.12	4.23	0.16
14/BQ-10	1893.6	Medium oil	70	63.08	6.92	0.10
15/BQ-10	1897.2	Medium oil	75.6	80.07	−4.47	−0.06
16/BQ-10	1898	Medium oil	148	134.25	13.75	0.09
21/BQ-20	1989	Heavy oil	502.3	390.7	111.60	0.22
22/BQ-20	1992	Heavy oil	445	331.74	113.26	0.25
23/BQ-20	1999	Heavy oil	581	450.11	130.89	0.23
24/BQ-20	2001	Heavy oil	1268	1084.49	183.51	0.14

, which presents the viscosity error analysis for the same group (Table 6), shows that the NMR-calculated crude oil viscosity is in close agreement with the measured viscosity for the light and medium crudes. Specifically, the average absolute error is 3.36, with an average relative error of 0.14, for light crude oil and 5.07, with an average relative error of 0.04, for medium crude oil, indicating high measurement accuracy.

However, for heavy crudes, the NMR viscosity calculation formula shows a smaller but noticeable error compared to the measured viscosity, with an average absolute error of 134.32 and an average relative error of 0.21. This lower measurement accuracy is attributed to the high concentration of heavy hydrocarbon components and wax content in heavy crude oil. In addition, the two-dimensional NMR spectrum is less sensitive to T₁/T₂ variations, which further affects the accuracy of the formula calculation.

5. Conclusions

This study addresses the challenge of limited reservoir data for early-stage crude oil viscosity determination in oil field development. Utilizing nuclear magnetic resonance (NMR) logging technology, a novel method using T₁ and T₂ for crude oil viscosity calculation is proposed. Using the BQ well group in the North China Oilfield as a case study, a comprehensive multidimensional analysis of NMR experimental data was conducted, leading to the development of a robust crude oil viscosity calculation model. The key findings include the following:

(1) As crude oil viscosity increases, there is a tendency for the T₁ and T₂ measurements of laboratory crude oil samples, artificially saturated rock samples containing crude oil, natural rock samples, and full-diameter-core measurements obtained at the well site to decrease, with a corresponding shift in the oil peak positions in the 2D NMR T₁–T₂ spectrum.

(2) Components such as resins and asphaltenes have a significant influence on crude oil viscosity, which is clearly evident in the 1D T₂ and 2D T₁–T₂ spectra, where increasing hydrocarbon content correlates with a marked increase in the T₁/T₂ ratios.

(3) Through an analysis of the experimental data, a discriminant chart for crude oil properties in high-porosity-and-permeability sandstone reservoirs in the North China Oilfield was developed, and specific relationships between crude oil viscosity and T₁–T₂ were fitted. The comparison with the actual well data confirmed the applicability of the crude oil property discriminant chart and the NMR viscosity calculation formula.

(4) A crude oil viscosity calculation method using 2D NMR was proposed as an alternative to pyrolysis for non-destructive assessment, thereby reducing development costs.

In summary, the results of this study are applicable to low-resistivity, high-porosity, and high-permeability water-bearing reservoirs and provide critical technical support for the characterization of fluid properties and production potential in heavy oil reservoirs.