Evaluation of fractures using conventional and FMI logs, and 3D seismic interpretation in continental tight sandstone reservoir

Xiangliang Qiu; Chengqian Tan; Yuanyuan Lu; Shuai Yin

doi:10.1515/geo-2022-0372

Article Open Access

Evaluation of fractures using conventional and FMI logs, and 3D seismic interpretation in continental tight sandstone reservoir

Xiangliang Qiu , Chengqian Tan , Yuanyuan Lu and Shuai Yin

Published/Copyright: June 2, 2022

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From the journal Open Geosciences Volume 14 Issue 1

Abstract

Due to the complex pore structures and strong heterogeneity of fractured reservoirs, it is a hot and difficult point in petroleum geology to identify fractures using logging principles. In this paper, taking the tight sandstone reservoir of the Chang 8 Member in the Huanjiang Oilfield as an example, field outcrops, cores, thin sections, and logging identification methods were used for quantitative description and fine logging evaluation of fractures. The research shows that high-angle, medium-low-angle, near-vertical, and horizontal fractures are developed in the Chang 8 Member of the Huanjiang Oilfield. The main ones are high-angle fractures, followed by horizontal fractures with a low degree of fillings. Under the constraints of core and imaging logging data, three fracture sensitivity logging parameters of acoustic wave time difference, natural gamma, and dual induction-octalateral resistivity were optimized, and a comprehensive fracture probability index was proposed. Seventy-nine fracture development intervals were identified based on log curve characteristics and fracture probability indexes. The coincidence rate of fracture logging identification results with the core observation and imaging logging interpretation is 80.6%. The research results can provide a theoretical basis for the efficient development of fractured continental tight sandstone reservoirs in similar areas.

Keywords: Huanjiang oilfield; Chang 8 reservoir; fracture characteristics; fracture interpretation using logging; imaging logging

1 Introduction

Tight sandstone oil and gas reservoir has become an important field of global oil and gas production growth, and it has huge reserve growth potential in the future [1,2,3,4,5]. A large number of oilfield practices showed that the degree of fracture development is the key factor for the high and stable production of tight sandstone reservoirs [6,7,8,9]. Fractures can significantly increase the matrix permeability of low-permeability tight reservoirs, thereby increasing the hydrocarbon charging efficiency [10,11,12,13,14]. For example, regional tectonic fractures increased the permeability of the tight sandstone reservoirs in the Mesaverde Basin in the United States by two orders of magnitude compared to matrix permeability; moreover, regional structural fractures increased the permeability of the Jurassic tight reservoirs in Dibei, Kuqa Depression, China by 1–3 orders of magnitude [15,16,17,18,19]. For tight reservoirs, fractures can improve the effective storage spaces and seepage properties of underground fluids, so fractures always control the distribution of sweet spots in hydrocarbon-enriched areas [20,21,22,23,24].

It is difficult for tight reservoirs to accumulate large-scale hydrocarbons without fractures [25,26,27]. Therefore, the production capacity of tight reservoirs has a strong dependence on natural fractures [28,29,30]. In recent years, with the urgent need for refined development of tight reservoirs, the quantitative characterization technology of fractures has been rapidly developed [31,32,33]. Seismic and logging data are often used for fracture predictions in tight reservoirs [34,35,36]. The advantage of the seismic method lies in its large coverage area and deep longitudinal detection range, but its disadvantage lies in its low longitudinal resolution, so core-scale fractures cannot be effectively identified [37,38,39]. Well, logs have high resolution and are often used to predict fractures in tight reservoirs. Fracture logging identifications usually require a combination of logging parameters, such as lithology, porosity, resistivity, and acoustic wave time difference and imaging logging [40,41,42]. A large number of field practices showed that the response characteristics of conventional logging curves corresponding to fracture-developed intervals are different, and this difference provides the possibility for fracture prediction using conventional logging data [43,44,45].

In this paper, considering the tight sandstone reservoir of the Chang 8 Member in the Huanjiang Oilfield as an example, field outcrops, cores, thin sections, and logging identification methods were used for quantitative description and fine logging evaluation of fractures. A comprehensive fracture probability index was proposed, which effectively improved the signal-to-noise ratio of fracture identifications using logging data.

2 Geological background

The Huanjiang Oilfield is located in the Tianhuan Sag in the western part of the Ordos Basin (Figure 1a and b). This area is developed on the west-dipping monoclinic structure of the basin, and the stratum is gentle. A series of strike-slip faults developed in the Chang 8 reservoir in the study area due to multi-phase tectonic stresses. The Chang 8 oil layer group in the study area belongs to the sedimentary front of the braided river delta, and microfacies such as underwater distributary river channel, inter-channel bay, and sheet sand are developed (Figure 1c). The multi-stage channels are superimposed and developed in a striplike distribution form, and the channel sand bodies are the dominant reservoirs. The lithologies of the reservoir are mainly gray, light gray fine-grained feldspar sandstone, and feldspar lithic sandstone, and the compositional maturity of the rock is low. In addition, the reservoir in this area has poor petrophysical properties, with an average porosity of 8.7% and an average permeability of 0.32 mD. The development of natural fractures can effectively improve the seepage capacity of the reservoir. Therefore, fractures play an important role in controlling the migration and accumulation of oil and gas [46,47,48,49,50,51,52].

Figure 1

The location, structure, and well distribution of the study area. (a) The location of the study area (modified after reference [52]); (b) contour map of the top surface of the Chang 8 Formation; and (c) histogram of stratigraphic units of the Yanchang Formation in the Huanjiang area.

The development practice in the research area has proved that some wells with poor reservoir properties but high production are closely related to natural fractures, such as Wells M119, B40, L306, and L152. In this paper, taking the tight sandstone reservoir of the Chang 8 Member in the Huanjiang Oilfield as an example, field outcrops, cores, thin sections and logging identification methods were used for quantitative description and fine logging evaluation of fractures. This study can provide scientific guidance for the scientific and rational development of tight oil reservoirs in similar areas.

3 Databases and methods

3.1 Core observation

In this paper, core observations and descriptions were conducted for 34 wells in the study area, with a total of 586.7 m in length, and 296 core photos were taken.

3.2 Thin section casting

The testing equipment for core casting thin sections was a LABORLUX-12POL polarizing microscope and a TIAS-2000 image analyzer. The test standard was based on SY/T 6103-2004, and the blue resin in the sheet was used for pore fillings. The experimental procedure for casting thin sections is as follows: Pigmented low-viscosity epoxy resins, casting agents, and dyes are poured into the pores of the rock; the curing reaction is then utilized to cross-link the linear epoxy resin into macromolecules of a network structure until it becomes a hard solid epoxy resin; finally, the production of casting thin slices is completed through procedures such as grinding and sticking.

3.3 Conventional and formation microscanner image (FMI) imaging logging

The equipment used for conventional logging tests is EILog, and the electrical imaging logging tool is MCI. The LEAD processing platform was used for the interpretation of conventional well log data. It used the cross-graph technique to analyze the lithological and physical parameters. MCI imaging data were processed and interpreted at the LEAD processing interface [43,44,45]. MCI electrical imaging was used for dynamic and static image generation, formation dip pickup, and fracture pickup. In fractured formations, the resistivity of fractures was significantly lower than that of surrounding rocks due to mud intrusion, which appears as dark streaks on imaging results [46,47,48,49,50,51,52]. Therefore, the fracture type, occurrence, and development degree could be identified using imaging logging data.

3.4 3D seismic interpretation

The discontinuity of seismic 3D data volume mainly reflects the changes in faults and lithology; the continuous data volume mainly reflects the homogeneity of lithology and the continuity of stratigraphy [43,44,45,46]. The 3D seismic data volume reflects the reflection of a regular network underground, and it calculates the similarity of waves in a certain time domain by the direction of the main and contact survey lines. When there are faults and discontinuous stratigraphic changes underground, some local seismic traces will show different reflection characteristics from adjacent seismic traces [47,48,49]. The extreme discontinuity in the correlation between tracks is the seismic dislocation generated by the fault. Using this principle, the structures of the target layer and the distribution of faults can be identified.

In addition, the 3D seismic data of Chang 8 Member were processed by the ant tracking algorithm. In this study, the inclination volume attributes after edge enhancement were used as input data, and Petrel software was used to extract the ant volume attributes of seismic data. Due to the poor quality of seismic data of frequently interbedded sand-mudstone formations, it will reduce the reliability of small faults identified in the ant data volume. Therefore, the ant data volume based on 3D seismic calculation was used as a reference for fracture prediction results.

4 Developmental characteristics of fractures

Based on observations of outcrops, cores, and imaging logging, the reservoirs in the Chang 8 Member of the Huanjiang Oilfield mainly develop structural and diagenetic fractures.

4.1 Outcrop fractures

The Jinsuoguan Profile is located in the northwest part of the Yintai District, Tongchuan City. Large-scale structural fractures are developed in the Chang 8 Member outcrops in this area, which mainly include near-vertical, high-angle, and horizontal bedding fractures (Figure 2). The horizontal spacing of two adjacent vertical fractures is usually between 4 and 15 m; bedding fractures are fractures formed by the fracture of rock mass along the bedding plane. The strike of the high-angle and vertical fractures of the Chang 8 Member is deflected to a certain extent with the change of the structural line; that is, it has a certain spatial twisting feature [50,51,52].

$Figure 2 Development characteristics of outcrop fractures in the Chang 8 Member of the Yanchang Formation in the Jinluoguan Profile, Tongchuan. The geographic coordinates are: 35°08′19.5″N, 108°53′10.9″E (a) and (b) are vertical fractures; and (c) and (d) are high angle fractures.$

Figure 2

Development characteristics of outcrop fractures in the Chang 8 Member of the Yanchang Formation in the Jinluoguan Profile, Tongchuan. The geographic coordinates are: 35°08′19.5″N, 108°53′10.9″E (a) and (b) are vertical fractures; and (c) and (d) are high angle fractures.

4.2 Core fractures

The core observation of the 34 wells shows that natural fractures are relatively developed in the Chang 8 reservoir in the Huanjiang area. The fracture surface in Figure 3 has certain scratches or steps, which are formed on the fracture surface during the rupture process, and the fracture surface is filled with argillaceous and secondary minerals. The fractures of the target layer are mainly high-angle structural fractures, accompanied by a small number of oblique fractures (Figure 3), followed by near-horizontal bedding fractures. A total of 19 wells with developed fractures are observed in the cores, with a coring length of 326.67 m, and a total of 98 natural fractures are identified. The average number of fractures in a single well is 5.2, and the fracture density is 0.3/m.

$Figure 3 Core images of fractures in the Chang 8 reservoir in Huanjiang Oilfield. (a) Well M120, 2681.06–2681.18 m, gray fine sandstone, high-angle fracture; (b) Well B162, 2618.00–2618.12 m, gray argillaceous siltstone, high-angle fracture; (c) Well B150, 2709.84–2709.95 m, gray fine sandstone, high-angle fracture; and (d) Well B150, 2709.13–2710.03 m, gray fine sandstone, vertical fracture.$

Figure 3

Core images of fractures in the Chang 8 reservoir in Huanjiang Oilfield. (a) Well M120, 2681.06–2681.18 m, gray fine sandstone, high-angle fracture; (b) Well B162, 2618.00–2618.12 m, gray argillaceous siltstone, high-angle fracture; (c) Well B150, 2709.84–2709.95 m, gray fine sandstone, high-angle fracture; and (d) Well B150, 2709.13–2710.03 m, gray fine sandstone, vertical fracture.

The distribution of the core fracture types is shown in Figure 4. High-angle fractures are the most common, with a total of 42 (42.9%); in addition, 17 medium-low-angle fractures (17.3%), 11 near-vertical fractures (11.2%), and 28 horizontal bedding fractures (accounting for 28.6%) were observed. According to the statistical results of fracture filling degrees (Figure 5), unfilled fractures accounted for 42.8%, mainly horizontal fractures (21.4%), followed by high-angle fractures; half-filled fractures accounted for 57.2%, which were dominated by high-angle and near-vertical fractures.

$Figure 4 Statistical results of distribution of core fracture types.$

Figure 4

Statistical results of distribution of core fracture types.

$Figure 5 Statistical results of filling degrees of core fractures.$

Figure 5

Statistical results of filling degrees of core fractures.

4.3 Micro-fractures

Through observation of thin sections, micro-fractures are widely developed in the Chang 8 Member of the study area. Their formation is related to regional tectonic activity, sedimentation, and diagenesis [53,54,55]. The scale of micro-fractures is generally micron level, and the Chang 8 Member mainly develops structural and diagenetic micro-fractures (mainly bedding micro-fractures). Structural micro-fractures generally form anywhere in the formation of rock formations, and are formed by one or more stress failures in the rock [56,57]. Their specific performance is through-cut bedding, and there are friction marks on the fracture surfaces [58,59]. The opening of the micro-fractures is between 0.1 and 100 μm and the length is between 0.1 and 10 mm. Statistics show that 70% of the structural micro-fractures are half-filled, and most of the filling minerals are quartz, calcite, dolomite, etc., and a small part is filled with argillaceous components (Figure 6a–d). The diagenetic micro-fractures are distributed along the bedding, and they will bypass and show the characteristics of gradually pinching out when encountering mineral particles. The openings of the diagenetic micro-fractures are distributed in the range of 0.1–5 μm, and the fracture lengths are between 0.1 and 10 mm. Most of the diagenetic micro-fractures are not filled, and the filling degrees of some semi-filled fractures are 20%. The fillings are argillaceous, iron or quartz, feldspar, calcite, and other minerals (Figure 6e and f).

$Figure 6 Photographs of cast thin sections of the Chang 8 reservoir in the study area. (a) Well L326, 2632.18 m, gray fine sandstone, a semi-filled micro-fracture; (b) Well B23, 2623.75 m, gray fine sandstone, a fully filled micro-fracture; (c) Well B19, 2728.97 m, gray fine sandstone, two unfilled micro-fractures; (d) Well L309, 2729.47 m, a semi-filled micro-fracture; (e) Well L146, 2675.38 m, muddy siltstone, one unfilled diagenetic micro-fracture; (f) Well B24, 2661.80 m, gray argillaceous siltstone, one unfilled diagenetic micro-fracture.$

Figure 6

Photographs of cast thin sections of the Chang 8 reservoir in the study area. (a) Well L326, 2632.18 m, gray fine sandstone, a semi-filled micro-fracture; (b) Well B23, 2623.75 m, gray fine sandstone, a fully filled micro-fracture; (c) Well B19, 2728.97 m, gray fine sandstone, two unfilled micro-fractures; (d) Well L309, 2729.47 m, a semi-filled micro-fracture; (e) Well L146, 2675.38 m, muddy siltstone, one unfilled diagenetic micro-fracture; (f) Well B24, 2661.80 m, gray argillaceous siltstone, one unfilled diagenetic micro-fracture.

5 Fracture logging identification in tight sandstone reservoirs

5.1 Responses of fractures in imaging logging

Imaging logging is currently the most effective fracture identification method, which can provide intuitive high-resolution 360° omnidirectional petrophysical and two-dimensional image information around the borehole wall [60,61,62]. In general, when the formation fractures are open, the drilling fluid invades the fracture network, and the fracture development section shows abnormal high electrical conductivity, and the fractures fluctuate in the form of sinusoidal waves in imaging logging [63,64]. When the fracture angle is low, the sinusoidal amplitude is low; when the fracture angle is high, the sinusoidal amplitude is high; vertical fractures appear as symmetrical dark lines (Figure 7). When the formation fractures are completely filled or half filled, the color of the sinusoidal curve displayed on the electric imaging and acoustic imaging images is different depending on the filling degrees and the differences of the fillings. The bedding fractures have limited development in the range of imaging logging and are often closed. The sinusoidal wave-like fluctuation on the static image log and the cross-domain of the peak-to-valley spacing of structural fractures are relatively large.

$Figure 7 Imaging log responses of fracture-developed intervals. (a) Section 2,675–2,679 m of Well L298; (b) Section 2,706–2,710 m of Well B150.$

Figure 7

Imaging log responses of fracture-developed intervals. (a) Section 2,675–2,679 m of Well L298; (b) Section 2,706–2,710 m of Well B150.

A comprehensive analysis of the imaging logging interpretation results of nine wells in the study area showed that the fracture strikes in the study area could be divided into three groups: NE, NW, and NSW strikes. The NE-trending fractures are widely distributed in the study area; the NNW-trending fractures are mainly concentrated in the western part of the study area, and the nearly NS-trending fractures are less developed and mainly distributed in the middle of the study area.

5.2 Identifications of fractures based on conventional logging

Compared with drilling coring, conventional logging data has the characteristics of strong data continuity and high resolution, so it has advantages in the process of reservoir fracture identification. For wells lacking imaging logging data, conventional logging data is the only way to identify fractures in the well. According to the different response degrees of logging sequences to fractures, the conventional logging data for identifying fractures generally include sonic logging, resistivity logging, nuclear logging, etc.

Conventional logging parameters are the comprehensive responses of features such as rocks, pores, fractures, and fluids around the wellbore. In this paper, the core and imaging logs were used as constraints, and the coupled conventional logs were used for fracture logging identifications.

5.2.1 Resistivity intrusion correction method

For cores developed with natural fractures, since the fluid resistivity is lower than that of the rock skeleton, the deep and shallow lateral resistivities both decrease, but the magnitude of the decrease is different [65,66]. When there are well-developed fractures, the mud filtrate has a greater impact on the shallow lateral but less on the deep lateral, so the decrease in the resistivity of the shallow lateral is greater. Cross plots of different types of resistivity parameters in fracture-developed sections showed that the influences of mud filtrate on deep induction and medium induction resistivity curves were greater (Figure 8).

$Figure 8 Cross plots of different types of resistivity parameters in fracture-developed intervals. (a) Relationship between deep induction and eight lateral resistivities; and (b) relationship between deep induction and medium induction resistivities.$

Figure 8

Cross plots of different types of resistivity parameters in fracture-developed intervals. (a) Relationship between deep induction and eight lateral resistivities; and (b) relationship between deep induction and medium induction resistivities.

Based on the calibration of core and imaging log data, the resistivity invasion correction factor (R _TC) was used to identify fractures. The calculation equation of R _TC is shown in equation (1).

(1) R TC = R t − R ILM R ILM ,

where R _TC is the resistivity intrusion correction index, R _t and R _ILM are the true and medium lateral resistivities of formation, respectively. When fractures are developed in the formation, R _TC > 0, when there are no fractures, R _TC ≈ 0. The applicable condition of this method is that the depth of mud filtrate intrusion along the fracture is within the detection range of induction logging.

5.2.2 Fractal dimension method of acoustic wave time difference

For conventional logging curves, the complexity or inhomogeneity of its shape, amplitude, and other characteristics reflects the change of formation characteristics, and this characteristic is called fractal [66,67,68]. For tight formations, the more complex the reservoir lithology, petrophysical and fluid-bearing properties, and fracture pore space structures, the greater the fluctuations of the logging curves, the more obvious the relative abnormality of shape amplitudes, and the more complex the fractal features of the logging curves.

The calculation of the fractal dimension of the logging curve needs to first intensify the plane grid where the logging curve is located. If the number of grids that the curve passes through in sequence are N(L ₁), N(L ₂),…, N(L _i), the statistical formula for the number of grids is

(2) N ( L i ) = ∑ j = 1 n INT V j + 1 − V j ( V max x − V min ) / L i .

In the formula, n is the number of curve data points in this interval, and L _i is the number of grids after the i-th grid refinement. 1 ≤ i ≤ j ≤ n, V is the logging value. V _j, V _j + 1 represent the j-th and j + 1st grid density additions, respectively, the acoustic wave time difference logging value; V _max and V _min represent the maximum and minimum values of the acoustic wave time difference curve in a certain interval, respectively. These data have a statistical relationship N(L) = CLD, and the logarithm on both sides has

(3) log N ( L ) = log C + D log L .

In the formula, D is the fractal dimension, and C is the undetermined coefficient. The equation is used to fit data series with different grid numbers L _i and the number of times the curve passes through the grid N(L _i), and then the fractal dimension D is obtained.

When fractures are developed in the reservoir, the heterogeneity of the reservoir increases; the acoustic wave time difference increases and it is prone to periodic jumps. Due to the small variation of the acoustic wave time difference of tight sandstone, the acoustic wave time difference amplitude R/S (R is the range and S is the standard deviation) and its fractal dimension were introduced to “amplify” the effective information of logging data. This makes it easier to identify the development of reservoir fractures. If the R/S curve deviates from the original straight line, that is, the fractal dimension increases, it means that the reservoir fractures are developed (Figure 9).

$Figure 9 Calculation results of R/S fractal of acoustic wave time difference in Well L298. (a) R/S fractal results of fracture-undeveloped section (2672.8–2674.3 m); (b) R/S fractal results of fracture development section (2676.5–2677.8 m).$

Figure 9

Calculation results of R/S fractal of acoustic wave time difference in Well L298. (a) R/S fractal results of fracture-undeveloped section (2672.8–2674.3 m); (b) R/S fractal results of fracture development section (2676.5–2677.8 m).

5.2.3 Comprehensive probability index method

The resistivity invasion correction difference ratio method and the time-difference shape dimension method cannot quantitatively describe the degree of fracture development in the reservoir. Therefore, this study introduces an index reflecting the abnormal change of fractures, which is used to quantitatively determine the development of reservoir fractures.

By comparing the logging parameters with the degree of fracture development, the logging parameters of acoustic wave time difference, natural gamma, dual-induction-eight lateral resistivity are optimized, and then the fracture index is constructed. First, the logging parameters are normalized:

(4) XI = X − X min X max − X min .

In the formula, X _max and X _min are the maximum and minimum values of the sandstone logging parameters of the target interval in the conventional logging curve, respectively; X is the value of the logging curve; and XI is the index of the curve reflecting the abnormal degree of fracture development.

Using a single parameter to identify fractures can lead to large errors. By analyzing the sensitivity of each logging parameter to fractures, multiple fracture-sensitive logging curves were weighted to obtain the fracture indication parameter (CWP_t):

(5) CWP t = 1 n ∑ i = 1 n A i × XI i .

In the formula, n is the number of characteristic parameters reflecting fractures. Since the preferred fracture-indicating parameters in this study are acoustic wave time difference, natural gamma, and dual-induction-octalateral logging, so n = 3. A _i is the weighting coefficient that reflects the value of characteristic parameters of fractures; XI_i is the abnormal variation index reflecting fractures; CWP_t is the arithmetically weighted fracture indicator parameter.

Mud components generally have the characteristics of high natural gamma, high acoustic time difference, and low resistance, ans they are close to the response characteristics of fractures. Therefore, the existence of mud components can easily interfere with the accuracy of fracture identification. In order to reduce the influence of shale in the formation on identifying fractures and improve the accuracy of fracture identification, a comprehensive probability index CWP is proposed, namely:

(6) CWP = CWP t × GR min GR 2 .

In the formula, CWP is the comprehensive fracture probability index; GR is the natural gamma curve value; GR_min is the minimum natural gamma value of the study interval.

The above treatments can effectively reduce the influence of reservoir shale components on fracture identification, reduce the interference of formation interface and shale laminae, and finally improve the accuracy of fracture identifications.

6 Case analysis

6.1 Analysis of fracture identification results

By comparing the results of imaging logging and core fracture observations, the fracture logging model proposed in this paper can identify fractures in Chang 8 reservoirs in the Huanjiang area with high precision. Comparing the results, the fractures identified by this method include high-angle fractures, near-vertical fractures, and horizontal fractures. Taking Well M110 as an example, the 2,520–2,534 m sandstone section is identified as a fracture-developed section under the calibration of core and imaging logging data (Figure 10). The corresponding resistivity intrusion correction difference ratio index RTC > 0, the fractal dimension of the acoustic transit time DFA ≈ 1, and the comprehensive fracture probability index CWP > 0.05 were obtained after filtering the shale content and the curve change rate (Figure 10a). Under the joint constraints of various parameters, No. 2 and No. 4 sand bodies were identified as fracture-developed intervals. And in this interval, multiple high-angle fractures were identified. Compared with the imaging logging data of this interval, fractures are relatively developed in the 2,518–2,531 m interval of Well M110 (Figure 10b), which further shows that the conventional logging fracture identification model proposed in this paper can accurately identify fractures.

$Figure 10 Comprehensive identification results of fractures in Well M110. (a) Identification results of fracture segments; (b) fracture identification results from imaging logs.$

Figure 10

Comprehensive identification results of fractures in Well M110. (a) Identification results of fracture segments; (b) fracture identification results from imaging logs.

Through logging identifications of fractures in 19 wells in the study area, 79 fracture development segments were identified under the constraints of core observations and imaging logging interpretations. Among them, 64 fracture development intervals were consistent with the fractures interpreted by imaging logging and 3D seismic, and the comprehensive fracture recognition rate was 80.6%. Therefore, the comprehensive fracture recognition rate of the method proposed in this paper was 80.6%.

The comprehensive fracture identification method can also be applied to other types of sedimentary rock reservoirs. However, affected by different mineral compositions, the logging response characteristics of fractures will change accordingly [65,66,67,68,69,70]. Therefore, if the method is applied to other types of reservoirs, it is necessary to re-analyze the correlation between the fracture characteristics of the reservoir and the logging curves and to re-establish the corresponding formulas and physical models [71,72,73,74,75].

6.2 Influence of fractures on single well productivity

The Chang 8 oil reservoir in the Huanjiang Oilfield of the Ordos Basin is adjacent to the Chang 7 source rocks. Natural fractures are the main storage space and seepage channel of tight sandstone reservoirs and play an important role in controlling hydrocarbon migration and accumulation in source rocks. In tight oil reservoirs, favorable oil and gas traps are likely to be formed in areas with natural fractures [76,77,78,79,80].

Based on fracture logging identification results and 3D seismic ant tracking results, favorable areas for natural fractures in the Chang 8 Member of the study area were demarcated (Figure 11). The fracture development areas of Chang 8 reservoir in the study area are mainly located in the H42 area in the southwest, the M110 area in the southeast, and the B55 and B40 areas in the middle part. High-yield wells are mostly located in natural fracture development areas, such as near wells M110, M119, and B40. High-yield wells are mostly located in natural fracture development areas, such as near wells M110, M119, and B40. The average daily oil production of these wells exceeds 10 tons. These high-yield fracturing sections mainly develop high-angle fractures with low filling degrees.

$Figure 11 Distribution of fracture development areas and high-yield wells in Chang 8 reservoirs in the study area. The bottom image is the result of the ant tracking algorithm based on 3D seismic data for the Chang 8 Member.$

Figure 11

Distribution of fracture development areas and high-yield wells in Chang 8 reservoirs in the study area. The bottom image is the result of the ant tracking algorithm based on 3D seismic data for the Chang 8 Member.

7 Conclusions

High-angle fractures, medium-low-angle fractures, near-vertical fractures and horizontal fractures were developed in the Chang 8 Member of the Huanjiang Oilfield. The main ones are high-angle fractures, followed by horizontal fractures with a low degree of fillings. The probability of fracture development is 55.9%.
Statistical analysis of imaging logging interpretation results showed that the target layer mainly develops fractures with NE, NW, and NSW strikes. The NE-trending fractures are widely distributed in the study area; the NNW-trending fractures are mainly concentrated in the western part of the study area; the nearly NS-trending fractures are less developed and mainly distributed in the middle of the study area.
Under the constraints of core and imaging logging data, the fracture sensitivity logging parameters of the acoustic wave time difference, natural gamma, and dual induction-octalateral resistivity were optimized, and a comprehensive fracture probability index were proposed. Seventy-nine fracture development intervals were identified based on log curve characteristics and fracture probability index. The coincidence rate of fracture logging identification results with core observation and imaging logging interpretation is 80.6%.
Most of the high-yield wells in the Chang 8 reservoir in the study area are located in areas where natural fractures are developed, and the daily oil production of a single well is more than 10 tons. The fracturing intervals in such wells usually develop high-angle and horizontal fractures with low filling degrees.

Conflict of interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Data availability statement: Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions.

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Received: 2022-01-08

Revised: 2022-03-19

Accepted: 2022-04-08

Published Online: 2022-06-02

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

Articles in the same Issue

https://doi.org/10.1515/geo-2022-0372

Keywords for this article

Huanjiang oilfield; Chang 8 reservoir; fracture characteristics; fracture interpretation using logging; imaging logging

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