Well logging qualitative identification method for water flooded layer of medium-low permeability sandstone reservoir

Abstract

The invention relates to a medium-low permeability sandstone reservoir water flooded layer logging qualitative identification method. The method mainly solves the problem that an existing logging curve analysis method does not conduct qualitative interpretation on a water flooded layer. The method is characterized by comprising the steps of S1, determining an oil layer type according to relative gravity center parameters of a density curve; s2, establishing water logging identification parameters of a deep lateral resistivity curve and a density curve; and S3, in combination with the steps S1 and S2, respectively determining a homogeneous rhythm oil layer, and establishing water logging models of a positive rhythm oil layer, a negative rhythm oil layer and a composite rhythm oil layer. According to the medium-low permeability sandstone reservoir water flooded layer logging qualitative identification method, by overlapping the resistivity curve and the density curve, qualitative identification can be carried out on the sandstone reservoir water flooded layer, and judgment on oil-gas-bearing property or water-bearing property information of a reservoir stratum is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of oilfield logging, and specifically to a qualitative identification method for watered-out zones in medium and low permeability sandstone reservoirs by logging. Background Art

[0002] After years of development, domestic old oilfields have entered the second and third oil recovery stages. Most reservoirs are in a watered-out state to varying degrees. The accurate and rapid identification of watered-out zones will provide technical support for "water control and potential tapping" in the later stage of the oilfield.

[0003] Qualitative logging interpretation of watered-out zones refers to qualitatively interpreting watered-out zones by analyzing logging data and judging the types and degrees of watered-out zones. Common qualitative logging interpretation methods for watered-out zones include: Natural gamma logging: Judging the location and scope of watered-out zones through natural gamma logging curves. When there is an obvious upward trend in the natural gamma logging curve, it indicates the presence of watered-out zones. Resistivity logging: Judging the types and degrees of watered-out zones through resistivity logging curves. In watered-out zones, the resistivity is usually low and shows a flat or downward trend. Acoustic logging: Analyzing the lithology and porosity of watered-out zones through acoustic logging curves. When the porosity of the watered-out zone increases, the acoustic logging curve will show a downward trend. Porosity logging: Judging the degree of watered-out zones through the change of porosity in the logging curve. When the degree of the watered-out zone is large, the porosity logging curve will show a large downward trend. The above methods usually need to be comprehensively analyzed in combination with the actual situation to improve the accuracy and reliability of qualitative logging interpretation of watered-out zones. On the premise that the "four properties" relationship of the reservoir is consistent, the complexity of the differential morphological waveform between the porosity and resistivity curves of the reservoir contains information about the hydrocarbon-bearing property of the reservoir. At present, some technicians have conducted in-depth research on the principle and application of the overlapping method of resistivity and porosity logging curves. The main application focuses include logging evaluation of source rocks, logging sequence stratigraphic division, estimation of water saturation, discrimination of oil, gas and water layers, etc.; some people have studied the formation mechanism of low-resistivity oil layers in Qudi Oilfield and used the curve morphology recognition method to overlap the spontaneous potential and resistivity curves to judge oil and water layers. However, neither of these two methods qualitatively identifies watered-out zones, so watered-out zones cannot be clearly identified. Summary of the Invention

[0004] In order to overcome the problem that the existing logging curve analysis methods do not qualitatively interpret watered-out zones, the present invention provides a qualitative identification method for watered-out zones in medium and low permeability sandstone reservoirs by logging. This qualitative identification method for watered-out zones in medium and low permeability sandstone reservoirs by logging can qualitatively identify watered-out zones in sandstone reservoirs by overlapping resistivity and density curves, and realize the judgment of the hydrocarbon-bearing or water-bearing information of the reservoir.

[0005] The technical solution of the present invention is: A qualitative identification method for watered-out zones in medium and low permeability sandstone reservoirs by logging, comprising the following steps:

[0006] S1. Determine the oil layer type according to the relative center-of-gravity parameter of the density curve;

[0007] S2. Establish the waterflood identification parameters for the deep lateral resistivity curve and the density curve;

[0008] S3. Combine steps S1 and S2 to determine the waterflood models for homogeneous rhythm oil layers, positive rhythm oil layers, reverse rhythm oil layers, and composite rhythm oil layers respectively.

[0009] Further, in step S1, when the relative center-of-gravity parameter RCG of the density curve > 0.5, it is a positive rhythm oil layer; when the relative center-of-gravity parameter RCG of the density curve < 0.5, it is a reverse rhythm oil layer; when the relative center-of-gravity parameter RCG of the density curve = 0.5, it is a homogeneous rhythm oil layer or a composite rhythm oil layer.

[0010] Further, in step S2, the waterflood identification parameters include the ellipticity EDP of the density curve, the ellipticity EDR of the deep lateral resistivity curve, and the fullness PC of the deep lateral resistivity curve, where:

[0011] EDP = (b 1 - b 2 ) / a 1 (1)

[0012] In the formula, b 1 is the short axis below the ellipse of the density curve, b 2 is the short axis above the ellipse of the density curve, and a 1 is the semi-major axis of the ellipse of the density curve;

[0013] EDR = (b 3 - b 4 ) / a 2 (2)

[0014] In the formula, b 3 is the short axis above the ellipse of the deep lateral resistivity curve, b 4 is the short axis below the ellipse of the deep lateral resistivity curve, and a 2 is the semi-major axis of the ellipse of the deep lateral resistivity curve;

[0015] PC = C 2 / C 1 (3)

[0016] In the formula, C 1 is the coefficient of the upper curve, and C 2 is the coefficient of the lower curve.

[0017] Further, C 1 = tan(α), C 2 = tan(β),

[0018] Where α is the angle between the line connecting the upper shale value point and the maximum resistivity point and the parallel line of the upper and lower shale interfaces; β is the angle between the line connecting the lower shale value point and the maximum resistivity point and the parallel line of the upper and lower shale interfaces.

[0019] Furthermore, the water flooding model of the homogeneous rhythm oil reservoir is as follows: in the deep lateral resistivity curve, b 3 <b 4 , C 2 <C 1 , EDP = 0, EDR < 0, PC < 1.

[0020] Furthermore, the water flooding model of the positive rhythm oil reservoir is as follows: the short axis b on the ellipse of the deep lateral resistivity curve 3 decreases, and the short axis b under the ellipse 4 increases; at the same time, the curve coefficient C in the middle and lower parts of the resistivity curve 2 decreases, reducing the saturation PC.

[0021] Furthermore, the water flooding model of the reverse rhythm oil reservoir is as follows: the short axis b on the ellipse of the deep lateral resistivity curve 3 increases, and the short axis b under the ellipse 4 decreases; at the same time, the curve coefficient C in the middle and lower parts of the resistivity curve 1 increases, reducing the saturation PC.

[0022] Furthermore, the water flooding model of the composite rhythm oil reservoir: in the resistivity curve, b 3 = b 4 , C 1 = C 2 , and the saturation PC = 1.

[0023] The present invention has the following beneficial effects: By adopting the above-mentioned scheme, the method eliminates the influence of porosity and shale additional conductivity on the overlapping curve spacing. Through the description of three parameters, namely relative centroid, ellipticity, and saturation, it depicts the morphological changes of the logging curves in the water flooding state, qualitatively shows the water flooding state under different sedimentary rhythms, and realizes the judgment of the oil and gas or water content information of the reservoir. The present invention provides an effective method for the qualitative identification of water flooded layers in medium and low permeability sandstone reservoirs, and also improves the accuracy of comprehensive judgment of oil layer water flooding by multiple logging curves. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is the flow chart of the present invention;

[0025] Figure 2 In [figure reference], a is the homogeneous rhythm oil reservoir, b is the positive rhythm oil reservoir, c is the reverse rhythm oil reservoir, and d is the composite rhythm oil reservoir;

[0026] Figure 3 In it, a is the density curve of the oil layer, and b is the deep lateral resistivity curve of the oil layer;

[0027] Figure 4 It is the saturation schematic diagram of the deep lateral resistivity curve;

[0028] Figure 5 In it, a is the water flooding model of the homogeneous rhythm oil layer, b is the water flooding model of the positive rhythm oil layer, c is the water flooding model of the reverse rhythm oil layer, and d is the water flooding model of the composite rhythm oil layer. Specific implementation manners

[0029] The present invention will be further described below with reference to the accompanying drawings:

[0030] As Figure 1 shown, a qualitative identification method for water flooded layers of medium and low permeability sandstone reservoirs includes the following steps:

[0031] S1. Observe the density logging curve in the logging interpretation result diagram, and determine each oil layer type according to the relative centroid parameter of the density curve. When the centroid of the oil layer density curve is biased downward, the relative centroid parameter RCG>0.5, which is a positive rhythm oil layer, see Figure 2 (b); when the centroid of the density curve is biased upward, the relative centroid parameter RCG<0.5, which is a reverse rhythm oil layer, see Figure 2 (c); when the centroid of the density curve is in the middle, the relative centroid parameter RCG = 0.5, which is a homogeneous rhythm oil layer or a composite rhythm oil layer, see Figure 2 (a) and (d). Therefore, the oil layers can be divided into three categories according to the density curve.

[0032] S2. Establish water flooding identification parameters for the deep lateral resistivity curve and the density curve. The water flooding identification parameters include the ellipticity EDP of the density curve, the ellipticity EDR of the deep lateral resistivity curve, and the saturation PC of the deep lateral resistivity curve.

[0033] S2.1. First, intercept an oil layer density curve on the density curve, regard the oil layer density curve as an elliptical curve, the maximum horizontal distance a at the horizontal vertex of it 1 is the horizontal semi-axis of the ellipse of the density curve. Taking the straight line where a 1 is located as the center, b 1 is the minor axis of the ellipse of the density curve above, b 2 is the minor axis of the ellipse of the density curve below, see Figure 3 (a), then there is

[0034] EDP=(b 1 - b 2 ) / a 1 (1).

[0035] S2.2. Similarly, intercept the resistivity curve of an oil layer on the deep lateral resistivity curve and regard it as an elliptical curve. The maximum horizontal distance a at the lateral vertex of the curve is 2 the semi-major axis of the ellipse of the resistivity curve. With the straight line where a 2 is located as the center, b 3 is the minor axis on the upper part of the ellipse of the resistivity curve, and b 4 is the minor axis on the lower part of the ellipse of the resistivity curve. See Figure 3 (b). Then there is

[0036] EDR = (b 3 - b 4 ) / a 2 (2).

[0037] S2.3. Establish a coordinate system with the center O of the lateral vertex in the deep lateral resistivity curve. The Y-axis of the coordinate system is the formation depth, and the X-axis is the resistivity value. Connect the upper and lower shale value points with point O (i.e., the resistivity maximum point). The angles between the connecting lines and the X-axis are α and β respectively. See Figure 4 . Then the fullness PC of the deep lateral resistivity curve is

[0038] PC = C 2 / C 1 (3)

[0039] where C 1 is the upper curve coefficient, C 1 = tan(α); C 2 is the lower curve coefficient, C 2 = tan(β).

[0040] S3. Combining steps S1 and S2, determine the water flooding models for homogeneous rhythm oil layers, positive rhythm oil layers, reverse rhythm oil layers, and composite rhythm oil layers respectively.

[0041] The water flooding model for the homogeneous rhythm oil layer is as follows: After water flooding, in the deep lateral resistivity curve, b 3 < b 4 , C 2 < C 1 , the ellipticity EDP of the density curve = 0, the ellipticity EDR of the resistivity curve < 0, and the fullness PC of the deep lateral resistivity curve < 1. See Figure 5 (a).

[0042] The water flooding model for the positive rhythm oil layer is as follows: After water flooding, the relative centroid of the resistivity curve moves upward and approaches 0.5. The minor axis b 3 on the upper part of the ellipse of the deep lateral resistivity curve decreases, and the minor axis b 4 on the lower part of the ellipse increases, making b 3 close to b 4 ; At the same time, the lower curve coefficient C of the resistivity curve2 decreases, reducing the plumpness PC, see Figure 5 (b).

[0043] The anti-rhythm oil reservoir water-flooding model is as follows: after water-flooding, the relative center of gravity of the resistivity curve moves downward and approaches 0.5, and the minor axis b of the ellipse of the deep lateral resistivity curve 3 increases, and the minor axis b of the ellipse 4 decreases, making b 3 close to b 4 ; meanwhile, the curve coefficient C of the lower part of the resistivity curve 1 increases, also reducing the plumpness PC, see Figure 5 (c).

[0044] The composite rhythm oil reservoir water-flooding model: after water-flooding, the relative center of gravity of the resistivity logging curve remains around 0.5, and in the resistivity logging curve, b 3 = b 4 , C 1 = C 2 , the ellipticity EDR is still close to 0, and the plumpness PC = 1, see Figure 5 (d).

[0045] The qualitative identification method of water-flooded layers proposed by the present invention eliminates the influence of porosity and shale additional conductivity on the overlapping curve spacing. Through the description of three parameters, namely the relative center of gravity, ellipticity, and plumpness, it depicts the morphological changes of the logging curve in the water-flooded state, qualitatively shows the water-flooded state under different sedimentary rhythms, realizes the judgment of the oil and gas or water content information of the reservoir, and also improves the accuracy of comprehensively judging the water-flooding of oil layers by multiple logging curves.

[0046] Example:

[0047] Well X1 is a closed coring well. From the analysis results of physical properties and water washing data, the water-flooding degree of this oil reservoir in this well is relatively high, and the better the lithology and physical properties of the reservoir, the higher the possibility of water-flooding. Figure 5 This is the comprehensive logging analysis diagram of a section of this well.

[0048] For the overall SⅡ81 sub-layer, it can be regarded as an inverse rhythm layer. From the logging curves, it can be seen that the porosity and resistivity logging curves are in a funnel shape. The relative center of gravity of the curves is less than 0.5. The minor axis of the upper ellipse is less than that of the lower ellipse, b1 < b2, b3 < b4. The curve coefficient C1 > C2. Therefore, the ellipticity EDP and EDR of the logging curves are both less than 0, and the fullness PC of the resistivity curve is less than 1. After the inverse rhythm oil layer is flooded, the relative center of gravity of the resistivity curve moves downward and approaches 0.5. The minor axis of the upper ellipse increases and the minor axis of the lower ellipse decreases, making b3 and b4 close, and EDR approaches 0. At the same time, the curve coefficient C1 of the upper part of the resistivity curve increases, also making the fullness PC decrease. It is judged as strong water washing, which is consistent with the conclusion of the sealed coring analysis.

[0049] For the SⅡ822 sub-layer, it can be regarded as a positive rhythm layer. Generally, when it is not flooded, the density and resistivity logging curves are in a bell shape. The relative center of gravity of the curves is greater than 0.5. The minor axis of the upper ellipse is greater than that of the lower ellipse, b1 > b2, b3 > b4. The curve coefficient C1 < C2. Therefore, the ellipticity EDP and EDR of the logging curves are both greater than 0, and the fullness PC of the resistivity curve is greater than 1. When it is flooded, the relative center of gravity of the resistivity curve moves upward and approaches 0.5. The minor axis of the upper ellipse decreases and the minor axis of the lower ellipse increases, making b3 and b4 close, and EDR approaches 0. At the same time, the curve coefficient C2 of the lower part of the resistivity curve decreases, making the fullness PC decrease. The conclusion of the sealed coring analysis is strong water washing.

[0050] For the SⅡ12 sub-layer, when the homogeneous rhythm oil layer is not flooded, the density and resistivity logging curves are in a box shape. The relative center of gravity of the curves is about 0.5. The minor axes of the upper and lower parts of the ellipse are similar to the curve shape, that is, b1 = b2, b3 = b4, C1 = C2. Therefore, the ellipticity EDP and EDR of the logging curves are both close to 0, and the fullness PC of the resistivity curve is close to 1. After the homogeneous rhythm oil layer is flooded, the amplitude of the resistivity curve decreases and the maximum value rises, that is, the major axis of the ellipse of the resistivity curve decreases, the minor axis of the upper part decreases, the minor axis of the lower part increases and the fullness degree decreases. There is b3 < b4, C2 < C1, then EDP = 0, EDR < 0, PC < 1. From the analysis of the oil displacement efficiency, the water washing ratio of the lower part is higher than that of the upper part.

[0051] For the SⅡ13 small layer, the density and resistivity logging curves of the composite rhythm oil layer show a finger shape. The relative center of gravity of the curve is about 0.5. The minor axes of the upper and lower ellipses are similar to the curve shape, that is, b1 = b2, b3 = b4, C1 = C2. Therefore, the ellipticity EDP and EDR of the logging curve are both close to 0, and the fullness PC of the resistivity curve is close to 1. After the composite rhythm oil layer is flooded, the relative center of gravity of the resistivity logging curve still remains around 0.5, and the minor axes of the upper and lower ellipses change little, while the major axes decrease. However, the curve coefficients C1 and C2 of the resistivity curve change almost proportionally. Therefore, b3 = b4, C1 = C2. The ellipticity EDR of the resistivity logging curve is still close to 0, and the fullness PC of the resistivity curve is close to 1, which is interpreted as a strongly water-washed layer.

Claims

1. A qualitative logging identification method for water-flooded layers in medium and low permeability sandstone reservoirs, characterized in that it includes the following steps: S1. Determine the oil layer type according to the relative centroid parameter of the density curve; S2. Establish water-flooding identification parameters for the deep lateral resistivity curve and the density curve; S3. Combine steps S1 and S2 to determine the water-flooding models for homogeneous rhythm oil layers, positive rhythm oil layers, reverse rhythm oil layers, and composite rhythm oil layers respectively.

2. The qualitative logging identification method for water-flooded layers in medium and low permeability sandstone reservoirs according to claim 1, characterized in that: In step S1, when the relative centroid parameter RCG of the density curve > 0.5, it is a positive rhythm oil layer; when the relative centroid parameter RCG of the density curve < 0.5, it is a reverse rhythm oil layer; when the relative centroid parameter RCG of the density curve = 0.5, it is a homogeneous rhythm oil layer or a composite rhythm oil layer.

3. The qualitative logging identification method for water-flooded layers in medium and low permeability sandstone reservoirs according to claim 2, characterized in that: In step S2, the water-flooding identification parameters include the ellipticity EDP of the density curve, the ellipticity EDR of the deep lateral resistivity curve, and the fullness PC of the deep lateral resistivity curve.

4. The qualitative logging identification method for water-flooded layers in medium and low permeability sandstone reservoirs according to claim 3, characterized in that: In step S2, EDP = (b 1 - b 2 ) / a 1 (1) where b 1 is the minor axis of the ellipse of the density curve, b 2 is the lower minor axis of the ellipse of the density curve, a 1 is the semi-major axis of the ellipse of the density curve; EDR=(b 3 - b 4 ) / a 2 (2) where b 3 is the minor axis of the ellipse of the deep lateral resistivity curve, b 4 is the lower minor axis of the ellipse of the deep lateral resistivity curve, a 2 is the semi-major axis of the ellipse of the deep lateral resistivity curve; PC = C 2 / C 1 (3) where C 1 is the coefficient of the upper curve, and C 2 is the coefficient of the lower curve.

5. The qualitative logging identification method for water-flooded layers in medium and low permeability sandstone reservoirs according to claim 4, characterized in that: The said C 1 = tan(α), C 2 = tan(β), where α is the angle between the line connecting the upper mudstone value point and the resistivity maximum value point and the parallel line of the upper and lower mudstone interfaces; β is the angle between the line connecting the lower mudstone value point and the resistivity maximum value point and the parallel line of the upper and lower mudstone interfaces.

6. The qualitative logging identification method for water-flooded layers in medium and low permeability sandstone reservoirs according to claim 5, characterized in that: The homogeneous rhythm oil reservoir water flooding model is as follows: b in the deep lateral resistivity curve 3 <b 4 , C 2 <C 1 , EDP = 0, EDR < 0, PC < 1.

7. The qualitative logging identification method for water-flooded layers in medium and low permeability sandstone reservoirs according to claim 5, characterized in that: The positive rhythm oil reservoir water flooding model is as follows: the short axis b of the ellipse of the deep lateral resistivity curve 3 decreases, and the short axis b of the lower ellipse 4 increases; at the same time, the curve coefficient C in the middle and lower parts of the resistivity curve 2 decreases, resulting in a decrease in the saturation PC.

8. The qualitative logging identification method for water-flooded layers in medium and low permeability sandstone reservoirs according to claim 5, characterized in that: The anti-rhythm reservoir water flooding model is as follows: the short axis b on the ellipse of the deep lateral resistivity curve 3 increases, and the short axis b at the lower part of the ellipse 4 decreases; at the same time, the curve coefficient C in the middle and lower parts of the resistivity curve 1 increases, causing the saturation PC to decrease.

9. The qualitative logging identification method for water-flooded layers in medium and low permeability sandstone reservoirs according to claim 5, characterized in that: The described composite rhythm oil reservoir water flooding model: b in the resistivity curve 3 = b 4 , C 1 = C 2 , and the saturation PC = 1.

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