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Classification and logging identification of reservoir space near the upper Ordovician pinch-out line in Tahe Oilfield

  • Debin Yang , Yanlin Jin , Juan Zhang , Runcheng Xie , Huaxin Chen , Shuai Yin and Wenli Cai EMAIL logo
Published/Copyright: April 4, 2024
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Open Geosciences
From the journal Open Geosciences Volume 16 Issue 1

Abstract

Real drilling near the upper Ordovician pinch-out line in the Tahe Oilfield shows that the drilling encountered karst reservoir. However, due to the transitional position between the denudation area and the overlying area and the special karst geological background, the existing drilling and completion data show that the reservoir space types in this area are complex and diverse. In this article, the classification of reservoir space near the Upper Ordovician pinch-out line and the extraction of logging response characteristics have been carried out based on drilling, logging, core, and crude oil quality data. Through this study, the classification scheme of karst reservoir space in the study area is proposed. The reservoir space types of karst reservoirs include fracture–cave, fracture–pore (light and low resistivity), fracture–pore (heavy and high resistivity), dissolved pore–pore (light and low resistivity), dissolved pore–pore (heavy and high resistivity), isolated pore (relatively isolated distribution of pores and fractures, weakly connected), and cave-type reservoir (sand and gravel filled or semi-unfilled). Furthermore, conventional logging parameters and five parameters sensitive to reservoir properties are extracted. The intersection maps based on the combination forms, fluid properties, and reservoir space effectiveness of different types of reservoir spaces are effective in distinguishing seven types of reservoir spaces and two types of stratified karst reservoirs. In this study, the reservoir space types and logging response characteristics of reservoirs near the Upper Ordovician pinch-out line are defined, which can provide a reliable geological basis for the quantitative identification and distribution evaluation of karst reservoirs.

Keywords: Tahe Oilfield; upper Ordovician pinch-out line; classification of reservoir space; logging response characteristics; sequence controlled karst

1 Introduction

Since the 13th Five-Year Plan period, the development of the Tahe Oilfield has been continuously advancing toward deeper layers. About 40 wells near the pinchout line of the Upper Ordovician in the shallow coverage area have encountered this type of karst reservoir and have achieved good evaluation results in both the upper and lower sections of the Yingshan Formation, especially in the oil wells that vertically connect the reservoir, with an average accumulated oil of over 50,000 tons [1]. The development of oil and gas reservoirs near the upper Ordovician pinch-out line in the Tahe Oilfield is closely related to karstification [2,3,4,5,6,7,8], which are usually called interstratified karst or bedding karst. Interlayer karst emphasizes the development of structure and karst cycle [9,10,11,12,13,14,15,16], such as the disconformity caused by tectonic movement and the dissolution along the sequence interface of T 7 4 and T 7 5 [17,18,19,20,21]. Stratified karst reservoir itself has a large development scale, complex diagenesis, multiple formation stages, and strong heterogeneity [22,23,24,25,26,27,28,29,30,31]. However, no matter what type of stratified karst reservoir, there are some differences and similar characteristics in the reservoir space [32,33,34,35,36,37]. Moreover, due to the combination form of pores, fractures, and caves and the large east–west regional span of the study area [38,39,40,41,42,43,44,45], the stratified karst reservoir space types are diverse and complex [46,47,48,49,50,51,52,53,54,55]. On the basis of previous knowledge of stratified karst reservoir, the reservoir space of a single well near the Upper Ordovician peak line is described in this article, and the division scheme of reservoir space types is proposed. Furthermore, the crossplot of conventional log parameters and sensitive parameters is established, and the log response characteristics of different types of reservoir spaces are defined. This study can provide geological basis for comprehensive quantitative identification and distribution evaluation of karst reservoirs and propose for the first time a plan to include karst reservoir types in the classification of carbonate reservoir spaces, providing a new approach for the classification of carbonate reservoir spaces.

The characteristics of the relevant formations in the study area are shown in Table 1, and the key focus of the article is on Yijianfang Formation and Yingshan Formation.

Table 1

Summary of strata in the study area

Formation Code Thickness (m) Lithological characteristics Sedimentary facies
Erathem System Strata Series
Paleozoic erathem Ordovician Upper Sangtamu formation O3s 0–900 Green gray, grayish green mudstone, gray silt-stone, and sandy mudstone interbedded with mudstone Mixed continental shelf facies
Lianggeliemu formation O3l 0–110 Brown gray mud microcrystalline limestone, powder-fine crystalline limestone, and brecciated bioclastic limestone Open platform facies
Qiaerbake formation O3q 0–25 Muddy limestone and nodular mudstone with dark brown-gray mudstone Guanghai continental shelf facies
Middle Yijianfang formation O2yj 0–120 Containing biological debris, bright sand debris limestone, mud microcrystalline limestone, and fine powder limestone Open platform-edge facies
Yingshan formation O1–2y 600–900 Mud fine limestone, fine powder crystal limes-tone, and bright crystal sand debris limestone
Lower Penglaiba formation O1p 250–400 Gray white dolomite limestone, gray dolomite, mud microcrystalline algae dolomite, gravel dolomite

Figure 1 shows a distribution map of well locations in the study area, which shows well-developed faults. The individual wells mentioned later in the article have been marked with boxes on the map.

Figure 1 Well location map of the research area.
Figure 1

Well location map of the research area.

2 Methods

Conventional logging data are mainly used to identify obvious fracture-cavity reservoirs in large sections of low-porosity formations. For fractured and pore-type reservoirs, due to the limitation of the resolution of logging data, micro-fractures and dissolved micro-pores in the reservoir can hardly be identified. In order to effectively reflect the characteristics and changes of pores, holes, and fractures in carbonate reservoirs, the sensitivity of response characteristics of log data is analyzed.

Five sensitive parameters, namely, porosity factor PACD, density ratio RDEN, relative neutron porosity RCNL, deep-shallow resistivity ratio, and deep-shallow resistivity difference, were constructed according to the principle of simultaneous amplification of log signals and the response characteristics of different log curves to fracture–cave reservoir theory.

2.1 Density ratio (RDEN)

Density logging (DEN) measures the volume density of rock and reflects the total porosity of formation. The density ratio is defined as a sensitive parameter (equation (1)) as the density value decreases with respect to the rock skeleton value when fractures, pores, and caves develop in the reservoir. The smaller the RDEN value is, the more developed the fractures and caves are

(1) RDEN = DEN ( 1 − V CL ) DEN DG ,

where DEN is the density logging value and DENDG is the rock skeleton density.

2.2 Relative neutron porosity (RCNL)

The neutron logging reflects the total porosity of the formation, so it can effectively identify the pore-type reservoir. Considering the influence of the muddy content on the neutron porosity, the neutron porosity is corrected in combination with the muddy content, and the neutron porosity information that is more sensitive to the pore information is obtained (equations (2) and (3))

(2) RCNL = CNL ⁎ ( 1 − V CL ) ,

(3) V CL = ( GR – GR min ) ( GR max – GR min ) ,

where CNL is the neutron logging value.

2.3 Porosity factor (PACD)

In addition to density and neutron porosity, acoustic wave time difference logging can also reflect the porosity of the rock matrix. By combining the neutron porosity, acoustic wave time difference, and density logs to amplify the same direction of pore signals, the pore development information in the presence of fractures and caves can be significantly amplified. The porosity factor is expressed as follows:

(4) PACD = ( AC ⁎ CNL ) / DEN ,

where CNL is the neutron logging value, AC is the logging value of acoustic wave time difference, and DEN is the log value of rock density.

2.4 Depth-to-shallow resistivity ratio (RD/RS)

Generally, the higher the depth-to-shallow resistivity ratio, the better the pore development and its connectivity will be.

2.5 Deep and shallow lateral resistivity amplitude difference (RD–RS)

In fracture-cave identification, the difference in lateral resistivity amplitude can reflect the development of the fracture-cave system. In general, when the deep and shallow lateral resistivity are more similar, it shows a small amplitude difference. With the increase of fracture inclination and until the fracture is vertical, the deep and shallow lateral resistivity show positive amplitude at the fracture.

In addition to constructing the above five sensitive logging parameters (Figure 2), we also carried out natural logarithm processing on the resistivity. This can reduce the impact of excessive value on sample data redundancy in the subsequent quantitative identification of reservoir space types. lnRD mentioned in the following represents the pair value of deep lateral resistivity, and lnRS represents the pair value of shallow lateral resistivity.

Figure 2 Relations and reflections of five sensitive logging parameters.
Figure 2

Relations and reflections of five sensitive logging parameters.

3 Results

Based on core observation and logging response, and considering the filling and oil-bearing properties of effective reservoir space, the reservoir space types of karst reservoirs in the study area are divided into seven types (Table 2, Figure 3): fracture–cave, fracture–pore (light and low resistivity), fracture–pore (heavy and high resistivity), dissolved pore–pore (light and low resistivity), dissolved pore–pore (heavy and high resistivity), isolated pore (relatively isolated distribution of pores and fractures, weakly connected), and cave-type reservoir (sand and gravel filled or semi-unfilled). For the study area, the fracture–cave, fracture–pore, and dissolved pore–pore-type reservoirs are the key reservoir space types for the formation of karst reservoirs.

Table 2

Classification scheme of reservoir space types of karst reservoirs

Reservoir space type Filling property Oil-bearing property Resistivity Stratified controled reservoir
Karst cave reservoir Unfilled or half-filled Reservoir identification without affecting Low resistance Bedding karst is dominant
Full filling of sand and gravel Reservoir identification without affecting Low resistance
Fracture-cave type Unfilled or half-filled Reservoir identification without affecting Relatively-low resistance Interlayer karst and bedding karst are developed to different degrees
Fracture-pore (light) Relatively light crude oil Low-medium resistance
Fracture-pore (heavy) Relatively heavy crude oil Relatively-high resistance
Dissolved pore-pore (light) Relatively light crude oil Medium resistance Bedding karst is dominant
Dissolved pore-pore (heavy) Relatively heavy crude oil High-very high resistance
Figure 3 Schematic diagram of the classification scheme of reservoir space types of karst reservoirs.
Figure 3

Schematic diagram of the classification scheme of reservoir space types of karst reservoirs.

Due to the differences in the properties of crude oil between the east and west of the study area, as well as the differences in planar and vertical sedimentary facies zones, the resistivity curves of the target reservoir sections in the east and west of the study area exhibit characteristics of being low in the east and high in the west, which poses great difficulties in identifying the reservoir space. Therefore, based on the comparison of core description and well response characteristics, this study proposes to subdivide the fracture pore and solution pore reservoirs into heavy and light types. However, this does not simply represent absolute differences in crude oil properties. It should be said that this is a comprehensive difference in reservoir electrical properties and oil-bearing properties. The heavy type reflects the characteristics of the reservoir being affected by the relative properties of heavy crude oil or the relative density of rocks, showing a relatively high resistivity of the same reservoir space type. The lightweight version is the opposite. Due to the significant impact of mud invasion during drilling, the resistivity of fracture pore type and karst cave reservoirs is relatively less affected by the properties of crude oil and sedimentary lithology. Therefore, this type of reservoir space has not been refined and identified. Isolated pores and fractures are a type of reservoir with poor storage and permeability. Due to their relatively poor connectivity in the reservoir space, the reservoir is relatively dense, and differences in oil content and sedimentary lithology have little impact on it. The resistivity is relatively high, but it is more preferred than the porosity of the surrounding rock. This type has not been further refined and identified this time.

4 Discussion

4.1 Logging response characteristics of different types of reservoir spaces

4.1.1 Fracture–cave-type reservoir

It represents a type of reservoir where fractures and caves communicate with each other to form a relatively high-quality reservoir. Cores are often broken (Figure 4), and a small amount of mud is lost when drilling. When drilling, the mud intrusion characteristics are obvious, the resistance will be significantly reduced, and the reduction range is lower than that of an unfilled karst cave. Low resistivity, low density, and relatively high porosity are the main conventional logging identification characteristics of this kind of reservoir (Figure 5).

Figure 4 Core from well S1. Fractures and caves are developed, and the core is broken.
Figure 4

Core from well S1. Fractures and caves are developed, and the core is broken.

Figure 5 Logging response characteristics of fracture–cave type reservoir. Notes: GR – natural gamma, CAL – caliper logging, DEN – density logging, AC – acoustic logging, CNL – compensated neutron logging, RD – deep lateral resistivity logging, RS – shallow lateral resistivity logging.
Figure 5

Logging response characteristics of fracture–cave type reservoir. Notes: GR – natural gamma, CAL – caliper logging, DEN – density logging, AC – acoustic logging, CNL – compensated neutron logging, RD – deep lateral resistivity logging, RS – shallow lateral resistivity logging.

According to the intersection diagram of acoustic wave time difference and deep lateral resistivity (Figure 6), the deep lateral resistivity of fracture–cave-type reservoirs is generally lower than 100 Ωm, and the acoustic wave time difference is mostly higher than 52 μs/ft. It has a relatively high acoustic wave time difference.

Figure 6 Crossplot of acoustic wave time difference and deep lateral resistivity.
Figure 6

Crossplot of acoustic wave time difference and deep lateral resistivity.

According to the crossplot of relative neutron porosity and density ratio (Figure 7), the density ratio of fracture–cave-type reservoirs is relatively low on the whole (mostly less than 0.9), and the main distribution range of relative neutron porosity is between 1.2 and 2.0. The resistivity has a better identification effect on fracture–cave-type reservoirs.

Figure 7 Crossplot of relative neutron porosity to density ratio.
Figure 7

Crossplot of relative neutron porosity to density ratio.

4.1.2 Fracture–pore-type reservoir

In this kind of reservoir, fractures are relatively developed with matrix pores and dissolution pores, and the reservoir types are mainly fracture–pore or pore–fracture type (pore size 1–2 mm). The cores are characterized by fractures + caves, fractures + dissolution pores, and network fractures. When the fracture is much developed, it usually appears as a broken core segment. This type of reservoir can be further subdivided according to the difference in oil-bearing property and reservoir resistivity (Figure 8): those with relatively light oil properties and relatively low resistance are named fracture–pore (light), and vice versa are fracture–pore (heavy).

Figure 8 Characteristics of fracture–pore-type reservoirs in cores.
Figure 8

Characteristics of fracture–pore-type reservoirs in cores.

From the crossplot of shallow and deep lateral resistivity (Figure 9), the fracture–pore type (light) and fracture–pore type (heavy) have obvious differences in resistivity. In particular, the shallow lateral resistivity of fracture–pore (light) type is in the range of 100–300 Ωm, which can be significantly different from other reservoir space types. On the whole, the shallow lateral resistance of the fracture–pore (heavy) type is widely distributed in the range of 200–2,000 Ωm. The fracture–pore (light) type has a lower resistance value than the fracture–pore (heavy) type. The comparison results show that the fracture-porosity reservoir of Well T4 in the western district has significantly higher deep and shallow dual lateral resistivity (Figure 10), while the fracture-porosity reservoir of Well S3 in the eastern district has a significantly lower deep and shallow dual lateral resistivity than that of the western district (Figure 11).

Figure 9 Crossplot of shallow side resistivity and deep side resistivity.
Figure 9

Crossplot of shallow side resistivity and deep side resistivity.

Figure 10 Logging response characteristics of fracture–pore (heavy) reservoir type in Well T4. Notes: GR – natural gamma, CAL – caliper logging, DEN – density logging, AC – acoustic logging, CNL – compensated neutron logging, RD – deep lateral resistivity logging, RS – shallow lateral resistivity logging.
Figure 10

Logging response characteristics of fracture–pore (heavy) reservoir type in Well T4. Notes: GR – natural gamma, CAL – caliper logging, DEN – density logging, AC – acoustic logging, CNL – compensated neutron logging, RD – deep lateral resistivity logging, RS – shallow lateral resistivity logging.

Figure 11 Logging response characteristics of fracture–pore (light) reservoir type in Well S3. Notes: GR – natural gamma, CAL – caliper logging, DEN – density logging, AC – acoustic logging, CNL – compensated neutron logging, RD – deep lateral resistivity logging, RS – shallow lateral resistivity logging.
Figure 11

Logging response characteristics of fracture–pore (light) reservoir type in Well S3. Notes: GR – natural gamma, CAL – caliper logging, DEN – density logging, AC – acoustic logging, CNL – compensated neutron logging, RD – deep lateral resistivity logging, RS – shallow lateral resistivity logging.

4.1.3 Dissolved pore–pore-type reservoir

In this type of reservoir, the dissolved pores or pores are relatively developed (the pore size is 1 –2 mm). In the core, there are layered dissolution pores, porphyritic dissolution pores, intercrystalline pores in dolomitic limestone, and dissolution pores in chemically filled small caves. Similar to the fracture–pore-type reservoir, the dissolved pore–pore-type reservoir is subdivided according to the difference of regional crude oil properties and the difference of reservoir resistance. The crude oil with relatively light properties and relatively low resistance is named as dissolution-pore (light), and the reverse is dissolution-pore (heavy). From the core observations, oil stains and oil spots in the dissolved pore-type reservoir in the eastern region are generally light in color, while those remaining in the dissolved pore in the western core are black, and asphaltene can be seen (Figure 12).

Figure 12 Characteristics of dissolved pore–pore-type reservoir in cores.
Figure 12

Characteristics of dissolved pore–pore-type reservoir in cores.

It can be seen from the crossplot between the natural pair value of the absolute difference of resistivity amplitude and the porosity factor that light and heavy dissolved pore–pore-type reservoirs are obviously different. The porosity factor of the dissolution pore–pore type (light) is lower than that of the dissolution pore–pore type (heavy). The porosity factor of the dissolution pore–pore type (light) is between 0 and 20, and that of the dissolution pore–pore porosity (heavy) is greater than 20 (Figure 13).

Figure 13 Crossplot of LN (absolute difference of resistivity amplitude) and porosity factor.
Figure 13

Crossplot of LN (absolute difference of resistivity amplitude) and porosity factor.

It can be seen from the crossplot of acoustic wave time difference and shallow lateral resistivity that the shallow lateral resistivity of dissolved pore (heavy) porosity is higher than that of dissolved pore (light) porosity, which is generally greater than 1,500 Ωm. The shallow lateral resistivity of the dissolved pore (light) porosity ranges from 200 to 1,000 Ωm. The acoustic wave time difference of the porosity of the light type is lower than that of the heavy type, but there is no obvious distinction between them. In addition, the acoustic wave time difference of dissolved pore (light) porosity is generally less than 52 μs/ft, and the acoustic wave time difference of dissolved pore (heavy) porosity is between 50 and 54 μs/ft (Figure 14).

Figure 14 Crossplot of acoustic wave time difference and shallow lateral resistivity.
Figure 14

Crossplot of acoustic wave time difference and shallow lateral resistivity.

It can be seen from the logging curve of the corresponding depth of the dissolved pore–pore-type reservoir described in the core that the resistivity of the solve-pore type (light) in Well T6 in the east is lower than that of the dissolved pore–pore type (heavy) in Well A7 in the west. The porosity factor of the dissolved pore–pore type (light) in the eastern region is significantly lower than that in the western region (Figures 15 and 16).

Figure 15 Logging response characteristics of dissolved pore–pore (light) reservoir type in Well T6. Notes: GR – natural gamma, CAL – caliper logging, DEN – density logging, AC – acoustic logging, CNL – compensated neutron logging, RD – deep lateral resistivity logging, RS – shallow lateral resistivity logging.
Figure 15

Logging response characteristics of dissolved pore–pore (light) reservoir type in Well T6. Notes: GR – natural gamma, CAL – caliper logging, DEN – density logging, AC – acoustic logging, CNL – compensated neutron logging, RD – deep lateral resistivity logging, RS – shallow lateral resistivity logging.

Figure 16 Logging response characteristics of dissolved pore–pore (light) reservoir type in Well A7. Notes: GR – natural gamma, CAL – caliper logging, DEN – density logging, AC – acoustic logging, CNL – compensated neutron logging, RD – deep lateral resistivity logging, RS – shallow lateral resistivity logging.
Figure 16

Logging response characteristics of dissolved pore–pore (light) reservoir type in Well A7. Notes: GR – natural gamma, CAL – caliper logging, DEN – density logging, AC – acoustic logging, CNL – compensated neutron logging, RD – deep lateral resistivity logging, RS – shallow lateral resistivity logging.

4.1.4 Isolated pore–fracture-type reservoir

In this type of reservoir, the dissolution pores, pores, or fractures are relatively isolated, and the reservoir space is relatively simple, which exists in the form of sutures, calcite-filled fractures, or chemical-filled small karst caves. The rock has poor permeability, which shows the characteristics of low natural gamma (GR) ray, high resistance-ultra-high resistance, high density, and low acoustic wave time difference in logging, and is less affected by the properties of crude oil and does not need to be subdivided (Figure 17).

Figure 17 Logging response characteristics of isolated dissolved pore–fracture reservoir type in Well T8. Notes: GR – natural gamma, CAL – caliper logging, DEN – density logging, AC – acoustic logging, CNL – compensated neutron logging, RD – deep lateral resistivity logging, RS – shallow lateral resistivity logging.
Figure 17

Logging response characteristics of isolated dissolved pore–fracture reservoir type in Well T8. Notes: GR – natural gamma, CAL – caliper logging, DEN – density logging, AC – acoustic logging, CNL – compensated neutron logging, RD – deep lateral resistivity logging, RS – shallow lateral resistivity logging.

According to the crossplot of GR ray and deep lateral resistivity, the resistivity of isolated pore and fracture-type reservoir is high, generally greater than 2,000 Ωm. The GR rays of isolated pore–fracture type are distributed around 10 API, and the GR values of isolated pore–fracture type are lower than those of dissolved pore–pore-type reservoirs (Figure 18). From the intersection analysis of acoustic wave time difference and density, isolated pore–fracture types have the characteristics of low acoustic wave time difference, which is lower than 50 μs/ft. There is little difference in density between isolated pore–fracture-type reservoir, but the density is generally high (Figure 19).

Figure 18 Crossplot of gamma-ray and deep lateral resistivity.
Figure 18

Crossplot of gamma-ray and deep lateral resistivity.

Figure 19 Crossplot of acoustic wave time difference and compensation density.
Figure 19

Crossplot of acoustic wave time difference and compensation density.

4.1.5 Karst cave-type reservoir

Unfilled karst cave: drilling will encounter blowout, and the logging curve shows ultra-low resistance, low-low GR, ultra-low density, and high acoustic wave time difference, which is easy to be identified in the logging. Sand and gravel-filled karst cave: the logging characteristics are high GR ray, low density, and low resistivity, and the scale of the karst cave is divided according to the logging amplitude or logging thickness. This study mainly identifies karst caves within 2 m. Such caves that are not fully filled or have a low degree of compaction can have certain reservoir properties, and the higher the density, the higher the degree of filling (Figures 20 and 21). This is a kind of reservoir and seepage complex formed by the communication of fractures and caves to form relatively high quality; the core is often broken, and a small amount of mud leakage occurs during drilling. When drilling mud intrusion is obvious, the resistance will be significantly reduced, and its reduction is less significant compared to the type of unfilled karst cave. Low resistance, low density, and relatively high hole are the main characteristics of conventional logging identification.

Figure 20 Crossplot of GR and deep lateral resistivity, density, and shallow lateral resistivity of karst cave type reservoir.
Figure 20

Crossplot of GR and deep lateral resistivity, density, and shallow lateral resistivity of karst cave type reservoir.

Figure 21 Logging response characteristics of karst cave reservoir. Notes: GR – natural gamma, CAL – caliper logging, DEN – density logging, AC – acoustic logging, CNL – compensated neutron logging, RD – deep lateral resistivity logging, RS – shallow lateral resistivity logging.
Figure 21

Logging response characteristics of karst cave reservoir. Notes: GR – natural gamma, CAL – caliper logging, DEN – density logging, AC – acoustic logging, CNL – compensated neutron logging, RD – deep lateral resistivity logging, RS – shallow lateral resistivity logging.

4.2 Semi-quantitative criteria for stratified karst types and well-logging identification

According to the stratified karst model and imaging logging, the logging response characteristics of interlayer karst and interlayer karst near the peak line are further discussed. The bedding karst reservoir space is stratified, and the interlayer karst is often communicated by fractures in the longitudinal direction. The distribution of stratified karst reservoir space is more complicated, and the occurrence is chaotic (Figure 22). According to the above characteristics, this study established the identification model of interlayer karst and stratified karst reservoirs (Table 3).

Figure 22 Stratified karst reservoir types near the pinch-out line in the Tahe area.
Figure 22

Stratified karst reservoir types near the pinch-out line in the Tahe area.

Table 3

Summary of logging response characteristics of different reservoir spaces and karst types

Reservoir space type Filling property Resistivity Stratified reservoir type Deep (shallow) lateral resistivity (Ωm) Acoustic wave time difference (μs/ft) Density ratio Neutron relative porosity Porosity factor Gamma (API) Deep/shallow lateral resistivity ratio Deep lateral–shallow lateral
Karst cave reservoir Unfilled or half-filled Low Stratified karst is dominant <10 Abnormal increase — — — <40 <10 <0
Sand and gravel fully-filled Low <100 Rather large — — — 40–90
Fracture–cave Unfilled or half-filled Relatively low Interlayer karst and stratified karst are developed to different degrees <100 >52 <0.9 1.2–2.0 — — >10 >0
Fracture–pore (light) Low–medium 100–300 — — — — —
Fracture–pore (heavy) Relatively high 200–2,000 — — — — —
Dissolved pore–pore (light) Medium Stratified karst is dominant >1,500 <52 — — 0–20 — <10 <0
Dissolved pore–pore (heavy) High–extremely high 200–1,000 50–54 — — >20 —
Isolated pore–fracture type Unfilled or half-filled High–extremely high — >2,000 <50 — — — — — —

The logging parameters of typical interlayer karst and stratified karst samples are extracted, and the identification models of these two strata-controlled karst reservoirs are established. According to the comparison results (Figures 23 and 24), the ratio of deep lateral resistivity to shallow lateral resistivity and the difference between deep and shallow lateral resistivity can be used to distinguish the karst reservoir types. When the ratio of deep and shallow lateral resistivity is greater than 10, it can be roughly judged as interlayer karst. The difference between deep and shallow lateral resistivity amplitude of stratified karst is generally less than 0, which is related to the occurrence distribution of bedding karst and similar to the characteristics of horizontal or low-angle fractures.

Figure 23 Relationship between GR and shallow lateral resistivity in stratified karst reservoirs.
Figure 23

Relationship between GR and shallow lateral resistivity in stratified karst reservoirs.

Figure 24 Intersection relationship between deep and shallow lateral resistivity and relative neutron porosity of stratified karst reservoir.
Figure 24

Intersection relationship between deep and shallow lateral resistivity and relative neutron porosity of stratified karst reservoir.

5 Conclusions

  1. Compared with previous studies, the classification of carbonate rock reservoir spaces often focuses on the combination of fractures, karst caves, and karst pores. In this article, the classification of reservoir space near the Upper Ordovician pinch-out line and the extraction of logging response characteristics have been carried out based on drilling, logging, core, and crude oil quality data.

  2. The new classification scheme of karst reservoir space in the study area is proposed. The reservoir space types of karst reservoirs include fracture–cave, fracture–pore (light and low resistivity), fracture–pore (heavy and high resistivity), dissolved pore–pore (light and low resistivity), dissolved pore–pore (heavy and high resistivity), isolated pore (relatively isolated distribution of pores and fractures, weakly connected), and cave-type reservoir (sand and gravel filled or semi-unfilled). Furthermore, conventional logging parameters and five parameters sensitive to reservoir properties are extracted.

  3. Based on the new method of reservoir space classification applied in this article, the intersection maps based on the combination forms, fluid properties, and reservoir space effectiveness of different types of reservoir spaces are effective in distinguishing seven types of reservoir spaces and two types of stratified karst reservoirs combining with core and logging data.

  4. The karst reservoirs in the research area are mainly concentrated in a room group and are concentrated 0–100 m below the T 7 4 surface.

  5. The development level of reservoir space has a certain impact on oil well productivity, and the exploration and development potential of three types of reservoir spaces: fracture–pore, fracture–cave, and dissolution pore is the most promising.

  1. Conflict of interest: Authors state no conflict of interest.

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Received: 2023-08-16
Revised: 2023-11-26
Accepted: 2024-01-24
Published Online: 2024-04-04

© 2024 the author(s), published by De Gruyter

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

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https://doi.org/10.1515/geo-2022-0608
Keywords for this article
Tahe Oilfield; upper Ordovician pinch-out line; classification of reservoir space; logging response characteristics; sequence controlled karst
Creative Commons
BY 4.0

Articles in the same Issue

  1. Regular Articles
  2. Theoretical magnetotelluric response of stratiform earth consisting of alternative homogeneous and transitional layers
  3. The research of common drought indexes for the application to the drought monitoring in the region of Jin Sha river
  4. Evolutionary game analysis of government, businesses, and consumers in high-standard farmland low-carbon construction
  5. On the use of low-frequency passive seismic as a direct hydrocarbon indicator: A case study at Banyubang oil field, Indonesia
  6. Water transportation planning in connection with extreme weather conditions; case study – Port of Novi Sad, Serbia
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  8. Monitoring of mangrove forests vegetation based on optical versus microwave data: A case study western coast of Saudi Arabia
  9. Microfacies analysis of marine shale: A case study of the shales of the Wufeng–Longmaxi formation in the western Chongqing, Sichuan Basin, China
  10. Multisource remote sensing image fusion processing in plateau seismic region feature information extraction and application analysis – An example of the Menyuan Ms6.9 earthquake on January 8, 2022
  11. Identification of magnetic mineralogy and paleo-flow direction of the Miocene-quaternary volcanic products in the north of Lake Van, Eastern Turkey
  12. Impact of fully rotating steel casing bored pile on adjacent tunnels
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  26. Permian tectonic switch of the southern Central Asian Orogenic Belt: Constraints from magmatism in the southern Alxa region, NW China
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  30. Classification and logging identification of reservoir space near the upper Ordovician pinch-out line in Tahe Oilfield
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  35. Detecting heterogeneity of spatial accessibility to sports facilities for adolescents at fine scale: A case study in Changsha, China
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  37. Vibration prediction with a method based on the absorption property of blast-induced seismic waves: A case study
  38. A new look at the geodynamic development of the Ediacaran–early Cambrian forearc basalts of the Tannuola-Khamsara Island Arc (Central Asia, Russia): Conclusions from geological, geochemical, and Nd-isotope data
  39. Spatio-temporal analysis of the driving factors of urban land use expansion in China: A study of the Yangtze River Delta region
  40. Selection of Euler deconvolution solutions using the enhanced horizontal gradient and stable vertical differentiation
  41. Phase change of the Ordovician hydrocarbon in the Tarim Basin: A case study from the Halahatang–Shunbei area
  42. Using interpretative structure model and analytical network process for optimum site selection of airport locations in Delta Egypt
  43. Geochemistry of magnetite from Fe-skarn deposits along the central Loei Fold Belt, Thailand
  44. Functional typology of settlements in the Srem region, Serbia
  45. Hunger Games Search for the elucidation of gravity anomalies with application to geothermal energy investigations and volcanic activity studies
  46. Addressing incomplete tile phenomena in image tiling: Introducing the grid six-intersection model
  47. Evaluation and control model for resilience of water resource building system based on fuzzy comprehensive evaluation method and its application
  48. MIF and AHP methods for delineation of groundwater potential zones using remote sensing and GIS techniques in Tirunelveli, Tenkasi District, India
  49. New database for the estimation of dynamic coefficient of friction of snow
  50. Measuring urban growth dynamics: A study in Hue city, Vietnam
  51. Comparative models of support-vector machine, multilayer perceptron, and decision tree ‎predication approaches for landslide ‎susceptibility analysis
  52. Experimental study on the influence of clay content on the shear strength of silty soil and mechanism analysis
  53. Geosite assessment as a contribution to the sustainable development of Babušnica, Serbia
  54. Using fuzzy analytical hierarchy process for road transportation services management based on remote sensing and GIS technology
  55. Accumulation mechanism of multi-type unconventional oil and gas reservoirs in Northern China: Taking Hari Sag of the Yin’e Basin as an example
  56. TOC prediction of source rocks based on the convolutional neural network and logging curves – A case study of Pinghu Formation in Xihu Sag
  57. A method for fast detection of wind farms from remote sensing images using deep learning and geospatial analysis
  58. Spatial distribution and driving factors of karst rocky desertification in Southwest China based on GIS and geodetector
  59. Physicochemical and mineralogical composition studies of clays from Share and Tshonga areas, Northern Bida Basin, Nigeria: Implications for Geophagia
  60. Geochemical sedimentary records of eutrophication and environmental change in Chaohu Lake, East China
  61. Research progress of freeze–thaw rock using bibliometric analysis
  62. Mixed irrigation affects the composition and diversity of the soil bacterial community
  63. Examining the swelling potential of cohesive soils with high plasticity according to their index properties using GIS
  64. Geological genesis and identification of high-porosity and low-permeability sandstones in the Cretaceous Bashkirchik Formation, northern Tarim Basin
  65. Usability of PPGIS tools exemplified by geodiscussion – a tool for public participation in shaping public space
  66. Efficient development technology of Upper Paleozoic Lower Shihezi tight sandstone gas reservoir in northeastern Ordos Basin
  67. Assessment of soil resources of agricultural landscapes in Turkestan region of the Republic of Kazakhstan based on agrochemical indexes
  68. Evaluating the impact of DEM interpolation algorithms on relief index for soil resource management
  69. Petrogenetic relationship between plutonic and subvolcanic rocks in the Jurassic Shuikoushan complex, South China
  70. A novel workflow for shale lithology identification – A case study in the Gulong Depression, Songliao Basin, China
  71. Characteristics and main controlling factors of dolomite reservoirs in Fei-3 Member of Feixianguan Formation of Lower Triassic, Puguang area
  72. Impact of high-speed railway network on county-level accessibility and economic linkage in Jiangxi Province, China: A spatio-temporal data analysis
  73. Estimation model of wild fractional vegetation cover based on RGB vegetation index and its application
  74. Lithofacies, petrography, and geochemistry of the Lamphun oceanic plate stratigraphy: As a record of the subduction history of Paleo-Tethys in Chiang Mai-Chiang Rai Suture Zone of Thailand
  75. Structural features and tectonic activity of the Weihe Fault, central China
  76. Application of the wavelet transform and Hilbert–Huang transform in stratigraphic sequence division of Jurassic Shaximiao Formation in Southwest Sichuan Basin
  77. Structural detachment influences the shale gas preservation in the Wufeng-Longmaxi Formation, Northern Guizhou Province
  78. Distribution law of Chang 7 Member tight oil in the western Ordos Basin based on geological, logging and numerical simulation techniques
  79. Evaluation of alteration in the geothermal province west of Cappadocia, Türkiye: Mineralogical, petrographical, geochemical, and remote sensing data
  80. Numerical modeling of site response at large strains with simplified nonlinear models: Application to Lotung seismic array
  81. Quantitative characterization of granite failure intensity under dynamic disturbance from energy standpoint
  82. Characteristics of debris flow dynamics and prediction of the hazardous area in Bangou Village, Yanqing District, Beijing, China
  83. Rockfall mapping and susceptibility evaluation based on UAV high-resolution imagery and support vector machine method
  84. Statistical comparison analysis of different real-time kinematic methods for the development of photogrammetric products: CORS-RTK, CORS-RTK + PPK, RTK-DRTK2, and RTK + DRTK2 + GCP
  85. Hydrogeological mapping of fracture networks using earth observation data to improve rainfall–runoff modeling in arid mountains, Saudi Arabia
  86. Petrography and geochemistry of pegmatite and leucogranite of Ntega-Marangara area, Burundi, in relation to rare metal mineralisation
  87. Prediction of formation fracture pressure based on reinforcement learning and XGBoost
  88. Hazard zonation for potential earthquake-induced landslide in the eastern East Kunlun fault zone
  89. Monitoring water infiltration in multiple layers of sandstone coal mining model with cracks using ERT
  90. Study of the patterns of ice lake variation and the factors influencing these changes in the western Nyingchi area
  91. Productive conservation at the landslide prone area under the threat of rapid land cover changes
  92. Sedimentary processes and patterns in deposits corresponding to freshwater lake-facies of hyperpycnal flow – An experimental study based on flume depositional simulations
  93. Study on time-dependent injectability evaluation of mudstone considering the self-healing effect
  94. Detection of objects with diverse geometric shapes in GPR images using deep-learning methods
  95. Behavior of trace metals in sedimentary cores from marine and lacustrine environments in Algeria
  96. Spatiotemporal variation pattern and spatial coupling relationship between NDVI and LST in Mu Us Sandy Land
  97. Formation mechanism and oil-bearing properties of gravity flow sand body of Chang 63 sub-member of Yanchang Formation in Huaqing area, Ordos Basin
  98. Diagenesis of marine-continental transitional shale from the Upper Permian Longtan Formation in southern Sichuan Basin, China
  99. Vertical high-velocity structures and seismic activity in western Shandong Rise, China: Case study inspired by double-difference seismic tomography
  100. Spatial coupling relationship between metamorphic core complex and gold deposits: Constraints from geophysical electromagnetics
  101. Disparities in the geospatial allocation of public facilities from the perspective of living circles
  102. Research on spatial correlation structure of war heritage based on field theory. A case study of Jinzhai County, China
  103. Formation mechanisms of Qiaoba-Zhongdu Danxia landforms in southwestern Sichuan Province, China
  104. Magnetic data interpretation: Implication for structure and hydrocarbon potentiality at Delta Wadi Diit, Southeastern Egypt
  105. Deeply buried clastic rock diagenesis evolution mechanism of Dongdaohaizi sag in the center of Junggar fault basin, Northwest China
  106. Application of LS-RAPID to simulate the motion of two contrasting landslides triggered by earthquakes
  107. The new insight of tectonic setting in Sunda–Banda transition zone using tomography seismic. Case study: 7.1 M deep earthquake 29 August 2023
  108. The critical role of c and φ in ensuring stability: A study on rockfill dams
  109. Evidence of late quaternary activity of the Weining-Shuicheng Fault in Guizhou, China
  110. Extreme hydroclimatic events and response of vegetation in the eastern QTP since 10 ka
  111. Spatial–temporal effect of sea–land gradient on landscape pattern and ecological risk in the coastal zone: A case study of Dalian City
  112. Study on the influence mechanism of land use on carbon storage under multiple scenarios: A case study of Wenzhou
  113. A new method for identifying reservoir fluid properties based on well logging data: A case study from PL block of Bohai Bay Basin, North China
  114. Comparison between thermal models across the Middle Magdalena Valley, Eastern Cordillera, and Eastern Llanos basins in Colombia
  115. Mineralogical and elemental analysis of Kazakh coals from three mines: Preliminary insights from mode of occurrence to environmental impacts
  116. Chlorite-induced porosity evolution in multi-source tight sandstone reservoirs: A case study of the Shaximiao Formation in western Sichuan Basin
  117. Predicting stability factors for rotational failures in earth slopes and embankments using artificial intelligence techniques
  118. Origin of Late Cretaceous A-type granitoids in South China: Response to the rollback and retreat of the Paleo-Pacific plate
  119. Modification of dolomitization on reservoir spaces in reef–shoal complex: A case study of Permian Changxing Formation, Sichuan Basin, SW China
  120. Geological characteristics of the Daduhe gold belt, western Sichuan, China: Implications for exploration
  121. Rock physics model for deep coal-bed methane reservoir based on equivalent medium theory: A case study of Carboniferous-Permian in Eastern Ordos Basin
  122. Enhancing the total-field magnetic anomaly using the normalized source strength
  123. Shear wave velocity profiling of Riyadh City, Saudi Arabia, utilizing the multi-channel analysis of surface waves method
  124. Effect of coal facies on pore structure heterogeneity of coal measures: Quantitative characterization and comparative study
  125. Inversion method of organic matter content of different types of soils in black soil area based on hyperspectral indices
  126. Detection of seepage zones in artificial levees: A case study at the Körös River, Hungary
  127. Tight sandstone fluid detection technology based on multi-wave seismic data
  128. Characteristics and control techniques of soft rock tunnel lining cracks in high geo-stress environments: Case study of Wushaoling tunnel group
  129. Influence of pore structure characteristics on the Permian Shan-1 reservoir in Longdong, Southwest Ordos Basin, China
  130. Study on sedimentary model of Shanxi Formation – Lower Shihezi Formation in Da 17 well area of Daniudi gas field, Ordos Basin
  131. Multi-scenario territorial spatial simulation and dynamic changes: A case study of Jilin Province in China from 1985 to 2030
  132. Review Articles
  133. Major ascidian species with negative impacts on bivalve aquaculture: Current knowledge and future research aims
  134. Prediction and assessment of meteorological drought in southwest China using long short-term memory model
  135. Communication
  136. Essential questions in earth and geosciences according to large language models
  137. Erratum
  138. Erratum to “Random forest and artificial neural network-based tsunami forests classification using data fusion of Sentinel-2 and Airbus Vision-1 satellites: A case study of Garhi Chandan, Pakistan”
  139. Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part I
  140. Spatial-temporal and trend analysis of traffic accidents in AP Vojvodina (North Serbia)
  141. Exploring environmental awareness, knowledge, and safety: A comparative study among students in Montenegro and North Macedonia
  142. Determinants influencing tourists’ willingness to visit Türkiye – Impact of earthquake hazards on Serbian visitors’ preferences
  143. Application of remote sensing in monitoring land degradation: A case study of Stanari municipality (Bosnia and Herzegovina)
  144. Optimizing agricultural land use: A GIS-based assessment of suitability in the Sana River Basin, Bosnia and Herzegovina
  145. Assessing risk-prone areas in the Kratovska Reka catchment (North Macedonia) by integrating advanced geospatial analytics and flash flood potential index
  146. Analysis of the intensity of erosive processes and state of vegetation cover in the zone of influence of the Kolubara Mining Basin
  147. GIS-based spatial modeling of landslide susceptibility using BWM-LSI: A case study – city of Smederevo (Serbia)
  148. Geospatial modeling of wildfire susceptibility on a national scale in Montenegro: A comparative evaluation of F-AHP and FR methodologies
  149. Geosite assessment as the first step for the development of canyoning activities in North Montenegro
  150. Urban geoheritage and degradation risk assessment of the Sokograd fortress (Sokobanja, Eastern Serbia)
  151. Multi-hazard modeling of erosion and landslide susceptibility at the national scale in the example of North Macedonia
  152. Understanding seismic hazard resilience in Montenegro: A qualitative analysis of community preparedness and response capabilities
  153. Forest soil CO2 emission in Quercus robur level II monitoring site
  154. Characterization of glomalin proteins in soil: A potential indicator of erosion intensity
  155. Power of Terroir: Case study of Grašac at the Fruška Gora wine region (North Serbia)
  156. Special Issue: Geospatial and Environmental Dynamics - Part I
  157. Qualitative insights into cultural heritage protection in Serbia: Addressing legal and institutional gaps for disaster risk resilience
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