Deep high-quality sandstone reservoir identification method based on well logging curve fusion

Abstract

The application provides a deep high-quality sandstone reservoir identification method based on logging curve fusion, comprising the following steps: step 1, establishing a natural gamma and lithology correlation chart according to the drilling and logging data obtained by drilling; step 2, establishing an oil test conclusion and reservoir porosity correlation chart according to the oil test and core physical property analysis data; step 3, counting the acoustic time difference value of the sandstone development layer section, fitting the function relationship between the acoustic time difference and the porosity, and calculating the acoustic time difference value corresponding to the lower limit of the porosity; step 4, reconstructing the acoustic time difference curve, fusing the natural gamma curve and the reconstructed acoustic time difference curve, obtaining the identification curve of the high-quality reservoir, and identifying the high-quality reservoir. The deep high-quality sandstone reservoir identification method based on logging curve fusion is beneficial to guiding the exploration and development of oil and gas reservoirs, the identification method is simple and effective, and is suitable for the identification and prediction of deep high-quality sandstone reservoirs.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of well logging, in particular to a deep high-quality sandstone reservoir identification method based on well logging curve fusion. BACKGROUND

[0002] In the continental basin depression zone of western China, the buried depth of oil-bearing formations is generally greater than 5000 meters, the strata are subjected to stable subsidence and compaction, and the reservoirs generally have the characteristics of low porosity, but in some areas and sections, the high-quality reservoirs with good physical properties are developed under the control of overpressure and dissolution, which are beneficial to the enrichment and accumulation of oil and gas, so the identification of high-quality reservoirs is the key to the exploration of deep oil and gas reservoirs.

[0003] At present, the identification of high-quality sandstone reservoirs is mainly carried out by using longitudinal wave impedance and transverse wave impedance to construct attribute factors, but in deep layers with large burial depth, the strata are subjected to strong compaction, the velocity relationship of sandstone and mudstone is mixed, and it is difficult to effectively distinguish sandstone and high-quality reservoirs by using wave impedance.

[0004] In the Chinese patent application with application number CN201810170342.6, a tight sandstone reservoir effective reservoir identification method is disclosed, which is characterized by the following steps: identifying the lithology of the reservoir; determining the lithology type of the effective reservoir; obtaining the effective reservoir discrimination curve; and dividing the reservoir according to the effective reservoir discrimination curve to determine the effective reservoir. The invention can overcome the drawbacks of misjudgment and low identification accuracy of conventional well logging methods in identifying effective reservoirs of tight sandstone reservoirs, and can accurately and quickly distinguish effective reservoirs, greatly improving the accuracy of identifying effective reservoirs of tight sandstone reservoirs.

[0005] In the Chinese patent application with application number CN201710303114.7, a calcareous sandstone reservoir pore type identification method is disclosed, which includes the following steps: identifying the lithology of calcareous sandstone, selecting the calcareous sandstone dry layer section, obtaining the acoustic porosity curve and density porosity curve, correcting the density porosity curve and acoustic porosity curve through the porosity correction curve, and identifying the pore type that meets the proportion condition in the calcareous sandstone reservoir by using the difference between the density porosity and the acoustic porosity. The lithology identification standard and the corresponding relationship between the difference and the pore type that meets the proportion condition are summarized through experiments. After the establishment of the lithology identification standard and the corresponding relationship, no more samples need to be collected, and the method has good adaptability to calcareous sandstone reservoirs with strong heterogeneity, overcoming the difficulty of identifying or dividing the pore type of calcareous sandstone in the past due to the lack of core and thin section data, large number of wells, and large amount of rock selection, widening the identification area, and being easy to master.

[0006] In the Chinese patent application with the application number CN201410601495.3, a method for identifying fractures in tight sandstone reservoirs is disclosed, which belongs to the field of logging technology for identifying fractures by using conventional logging data. By analyzing the relationship between the characteristic parameters of single-well fracture logging response, the correlation characteristic parameters of single-well fracture identification are established, and then the weighted arithmetic mean method is used to construct the fracture comprehensive evaluation model parameter, i.e. the fracture comprehensive index, so as to identify the fractures in the single-well fracture-identified tight sandstone reservoir section by analyzing the size of the fracture comprehensive index. The method for identifying fractures in tight sandstone reservoirs can accurately and reliably identify the fracture section, thereby providing a basis for the reasonable and effective development of tight sandstone oil and gas reservoirs.

[0007] The above prior art is quite different from the present application, and cannot solve the technical problems we want to solve. Therefore, we have invented a new method for identifying deep high-quality sandstone reservoirs based on logging curve fusion. SUMMARY

[0008] The purpose of the present application is to provide a method for identifying deep high-quality sandstone reservoirs based on logging curve fusion, which is based on the logging response characteristics of deep sandstone favorable reservoirs, and optimizes two curves of natural gamma and acoustic time difference. Through curve fusion, the development section of high-quality reservoirs can be accurately identified.

[0009] The purpose of the present application can be achieved by the following technical measures: a method for identifying deep high-quality sandstone reservoirs based on logging curve fusion, which comprises:

[0010] Step 1: According to the drilling and logging data obtained by drilling, a natural gamma and lithology correlation chart is established.

[0011] Step 2: According to the oil testing and core physical property analysis data of drilling, an oil testing conclusion and reservoir porosity correlation chart is established.

[0012] Step 3: The acoustic time difference values of the sandstone development section are counted, and the functional relationship between the acoustic time difference and the porosity is fitted, and the acoustic time difference value corresponding to the lower limit of the porosity is calculated.

[0013] Step 4: The acoustic time difference curve is reconstructed, the natural gamma curve and the reconstructed acoustic time difference curve are fused, and the discrimination curve of high-quality reservoirs is obtained to identify the high-quality reservoirs.

[0014] The purpose of the present application can also be achieved by the following technical measures:

[0015] In step 1, according to the drilling and logging data obtained by drilling, the natural gamma values of sandstone and mudstone development sections are counted, the normal distribution curve of the natural gamma values of sandstone and mudstone is calculated by using histogram analysis method, and the lithology division chart is established.

[0016] In step 1, according to the natural gamma and lithology correlation chart, the limit value of the corresponding natural gamma of sandstone and mudstone is determined, and the drilling sandstone and mudstone development section is divided.

[0017] In step 2, according to the drilling oil test and core physical property analysis data, the reservoir porosity is classified and counted according to the oil test conclusion, and the oil test conclusion and reservoir porosity correlation chart is established.

[0018] In step 2, the reservoir with oil layer, oil and gas layer, oil and water layer, gas and water layer, oil water layer, gas water layer and water layer is high quality reservoir, and the reservoir with poor oil layer and dry layer is dense reservoir, according to the oil test conclusion and reservoir porosity correlation chart, the porosity limit value of high quality reservoir and dense reservoir is determined.

[0019] In step 3, the acoustic time difference value of sandstone development section is counted, and the function relationship between acoustic time difference and porosity is fitted, as follows:

[0020] D = f (Φ)

[0021] In the formula, Φ is the porosity of sandstone, unit is %, and D is the acoustic time difference value, unit is microsecond / foot.

[0022] In step 3, the acoustic time difference value corresponding to the lower limit of porosity determined in step 2 is calculated.

[0023] In step 4, according to the minimum value of acoustic time difference curve of sandstone section, the minimum value is assigned to mudstone section, and a reconstructed acoustic time difference curve is obtained.

[0024] In step 4, the natural gamma curve and the reconstructed acoustic time difference curve are fused to obtain the discrimination curve of high quality reservoir, and the fusion model of the high quality reservoir discrimination curve is as follows:

[0025]

[0026] In the formula, Z is the dimensionless value of high quality reservoir discrimination curve, GR is natural gamma, unit is API, GR min is the minimum value of natural gamma data, GR max is the maximum value of natural gamma data, SAC is the reconstructed acoustic time difference, unit is microsecond / foot, SAC min is the minimum value of reconstructed acoustic time difference data, SAC max is the maximum value of reconstructed acoustic time difference data, K is the limit value of corresponding natural gamma of sandstone and mudstone, and Φ is the lower limit value of high quality reservoir porosity.

[0027] In step 4, the sandstone section with discrimination curve less than Z is high quality reservoir, and the sandstone section with discrimination curve greater than Z is dense reservoir.

[0028] The deep high-quality sandstone reservoir identification method based on logging curve fusion in the present invention optimizes the natural gamma ray and acoustic wave time difference curves based on the logging response characteristics of favorable deep sandstone reservoirs. Through curve fusion, the development layer of high-quality reservoirs can be accurately identified.

[0029] Compared with the existing technology, the advantages of the present invention are: using the natural gamma curve to determine the sandstone development layer, establishing a functional relationship between sandstone porosity and acoustic wave time difference, determining the acoustic wave time difference threshold value of high-quality reservoirs and tight reservoirs based on the lower limit of the porosity of high-quality reservoirs, reconstructing the acoustic wave time difference curve, and fusing the natural gamma and reconstructed acoustic wave time difference curves according to the model to obtain the discrimination curve of the high-quality reservoir, overcoming the problem that it is difficult to effectively distinguish high-quality reservoirs using wave impedance, which is beneficial to guiding the exploration and development of oil and gas reservoirs. The high-quality reservoir identification method provided by the present invention is simple and effective, and is suitable for the identification and prediction of deep high-quality sandstone reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a flow chart of a specific embodiment of the method for identifying deep high-quality sandstone reservoirs based on well logging curve fusion of the present invention;

[0031] Figure 2 This is a relationship diagram between natural gamma and lithology of a well in Yongjin Oilfield in the central depression zone of Junggar Basin in a specific embodiment of the present invention;

[0032] Figure 3 This is a relationship diagram between the oil test results of a well in the Yongjin Oilfield in the central depression zone of the Junggar Basin and the reservoir porosity in a specific embodiment of the present invention;

[0033] Figure 4 This is a graph showing the relationship between acoustic wave transit time and porosity of a well in the Yongjin Oilfield in the central depression zone of the Junggar Basin in a specific embodiment of the present invention;

[0034] Figure 5 This is a diagram showing the identification effect of high-quality reservoirs in a well of the Yongjin Oilfield in the central depression zone of the Junggar Basin in a specific embodiment of the present invention. DETAILED DESCRIPTION

[0035] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0036] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations and / or combinations thereof.

[0037] like Figure 1 As shown, Figure 1 The flowchart of the method for identifying deep high-quality sandstone reservoirs based on well logging curve fusion of the present invention is as follows:

[0038] Step 101: Based on the well logging and well logging data obtained during drilling, the natural gamma values ​​of the sandstone and mudstone development intervals are calculated to create a natural gamma and lithology correlation chart.

[0039] Read the natural gamma limit values ​​corresponding to sandstone and mudstone, and divide the drilling sandstone and mudstone development intervals.

[0040] Step 102: Based on the drilling oil test and core physical property analysis data, the reservoir porosity is classified and counted according to the oil test conclusion, and a correlation chart between the oil test conclusion and the reservoir porosity is established.

[0041] Read the porosity limit values ​​of high-quality reservoirs and tight reservoirs.

[0042] Step 103: Count the acoustic transit time values ​​of the sandstone development interval and fit the functional relationship between the acoustic transit time and porosity as follows:

[0043] D=f(Φ)

[0044] Where, Φ is the porosity of sandstone, in %; D is the acoustic time difference value, in microseconds / foot;

[0045] According to the porosity lower limit determined in step 102, the acoustic wave time difference value corresponding to the porosity lower limit is calculated.

[0046] Step 104: read the minimum value of the acoustic time difference curve of the sandstone layer segment, assign it to the mudstone layer segment, and obtain a reconstructed acoustic time difference curve;

[0047] The natural gamma curve and the reconstructed acoustic time difference curve are fused to obtain the high-quality reservoir discrimination curve. The fusion model of the high-quality reservoir discrimination curve is as follows:

[0048]

[0049] Where Z is the dimensionless value representing the high-quality reservoir discrimination curve, GR is the natural gamma, the unit is API, GRmin is the minimum value of natural gamma data, GR max is the maximum value of the natural gamma data, SAC is the reconstructed acoustic wave time difference, the unit is microsecond / foot, SAC min To reconstruct the minimum value of the acoustic time difference data, SAC max To reconstruct the maximum value of the acoustic time difference data, K is the limit value of natural gamma corresponding to sandstone and mudstone, and Φ is the lower limit value of the porosity of high-quality reservoirs.

[0050] The following are several specific embodiments of the present invention.

[0051] Example 1

[0052] In a specific embodiment 1 of the present invention, the method for identifying deep high-quality sandstone reservoirs based on well logging curve fusion includes the following steps:

[0053] Step 1: Based on the logging and well data obtained during drilling, the natural gamma values ​​of the sandstone and mudstone development intervals are counted. The normal distribution curves of the natural gamma values ​​of the sandstone and mudstone are calculated using the histogram analysis method. A lithologic division chart is established, the natural gamma limit values ​​corresponding to the sandstone and mudstone are read, and the drilling sandstone and mudstone development intervals are divided.

[0054] By establishing a lithologic identification chart for a well in Yongjin Oilfield in the Central Depression of Junggar Basin, China, it is determined that the natural gamma value less than 72API is sandstone, and the natural gamma value greater than 72API is mudstone (such as Figure 2 shown).

[0055] Step 2: Based on the drilling oil test and core physical property analysis data, the reservoir porosity is classified and counted according to the oil test conclusions, and a correlation chart between the oil test conclusions and the reservoir porosity is established.

[0056] In this step, the reservoirs whose oil test conclusions are oil layer, oil and gas layer, oil and water in the same layer, gas and water in the same layer, oil-water layer, gas-water layer, water layer are high-quality reservoirs, and the reservoirs whose oil test conclusions are poor oil layer and dry layer are tight reservoirs, and the porosity limit values ​​of high-quality reservoirs and tight reservoirs are read.

[0057] By establishing a relationship chart between the oil test results of a well in Yongjin Oilfield in the central depression of Junggar Basin, China and the reservoir porosity, it is determined that the porosity limit value between high-quality reservoirs and tight reservoirs is 9.2% (e.g. Figure 3 shown).

[0058] Step 3: Based on the division of sandstone and mudstone layers in step 1, the acoustic transit time values ​​of the sandstone development layers are counted, and the functional relationship between the acoustic transit time and porosity is fitted as follows:

[0059] D=f(Φ)

[0060] Where, Φ is the porosity of sandstone, in %; D is the acoustic time difference value, in microseconds / foot;

[0061] According to the porosity lower limit determined in step 2, calculate the acoustic wave time difference value corresponding to the porosity lower limit.

[0062] By establishing a relationship diagram between the acoustic transit time and porosity of a well in the Yongjin Oilfield in the central depression of the Junggar Basin, China, it was determined that the acoustic transit time value corresponding to the lower limit of porosity is 68 microseconds / foot (e.g. Figure 4 shown).

[0063] Step 4: read the minimum value of the acoustic time difference curve of the sandstone layer, assign it to the mudstone layer, and obtain a reconstructed acoustic time difference curve;

[0064] The natural gamma curve and the reconstructed acoustic time difference curve are fused to obtain the high-quality reservoir discrimination curve. The fusion model of the high-quality reservoir discrimination curve is as follows:

[0065]

[0066] Where Z is the dimensionless value representing the high-quality reservoir discrimination curve, GR is the natural gamma, the unit is API, GR min is the minimum value of natural gamma data, GR max is the maximum value of the natural gamma data, SAC is the reconstructed acoustic wave time difference, the unit is microsecond / foot, SAC min To reconstruct the minimum value of the acoustic time difference data, SAC max To reconstruct the maximum value of the acoustic time difference data, K is the limit value of natural gamma corresponding to sandstone and mudstone, and Φ is the lower limit value of the porosity of high-quality reservoirs.

[0067] It is determined that the sandstone section with a discriminant curve less than 0.7 is a high-quality reservoir, and the sandstone section with a discriminant curve greater than 0.7 is a tight reservoir (such as Figure 5 shown).

[0068] Example 2

[0069] In the specific embodiment 2 of the application, by establishing a lithology identification chart of a well in the Mosizhuang oilfield in the central depression zone of the Junggar Basin in China, it is determined that the sandstone has a natural gamma value less than 76 API and the mudstone has a natural gamma value greater than 76 API; by establishing a chart of the relationship between the oil test conclusion and the reservoir porosity of the well, it is determined that the porosity limit value of the high-quality reservoir and the tight reservoir is 9.6%; by establishing a chart of the relationship between the acoustic time difference and the porosity of the well, it is determined that the acoustic time difference value corresponding to the lower limit of the porosity is 69.5 microseconds / foot; the minimum value of the acoustic time difference curve of the sandstone section is read and is assigned to the mudstone section to obtain a reconstructed acoustic time difference curve; on this basis, the natural gamma curve and the reconstructed acoustic time difference curve are fused to obtain a discrimination curve of the high-quality reservoir, and it is determined that the sandstone section with a discrimination curve less than 0.74 is a high-quality reservoir and the sandstone section with a discrimination curve greater than 0.74 is a tight reservoir.

[0070] Embodiment 3

[0071] In the specific embodiment 3 of the application, by establishing a lithology identification chart of a well in the Xingshacun area in the central depression zone of the Junggar Basin in China, it is determined that the sandstone has a natural gamma value less than 74 API and the mudstone has a natural gamma value greater than 74 API; by establishing a chart of the relationship between the oil test conclusion and the reservoir porosity of the well, it is determined that the porosity limit value of the high-quality reservoir and the tight reservoir is 8.8%; by establishing a chart of the relationship between the acoustic time difference and the porosity of the well, it is determined that the acoustic time difference value corresponding to the lower limit of the porosity is 67 microseconds / foot; the minimum value of the acoustic time difference curve of the sandstone section is read and is assigned to the mudstone section to obtain a reconstructed acoustic time difference curve; on this basis, the natural gamma curve and the reconstructed acoustic time difference curve are fused to obtain a discrimination curve of the high-quality reservoir, and it is determined that the sandstone section with a discrimination curve less than 0.68 is a high-quality reservoir and the sandstone section with a discrimination curve greater than 0.68 is a tight reservoir.

[0072] Finally, it should be noted that: the above only describes the preferred embodiments of the application and is not used to limit the application, although the application has been described in detail with reference to the foregoing embodiments, and for those skilled in the art, the technical solutions recorded in the foregoing embodiments can still be modified or some technical features can be replaced equivalently. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the application should be included in the protection scope of the application.

[0073] In addition to the technical features described in the specification, they are known to those skilled in the art.

Claims

1. A method for identifying deep high-quality sandstone reservoirs based on well logging curve fusion, characterized by: The deep high-quality sandstone reservoir identification method based on well logging curve fusion includes: Step 1: Create a natural gamma and lithology correlation chart based on the logging and well data obtained during drilling; Step 2: Based on the drilling test and core physical property analysis data, a correlation chart between the test results and reservoir porosity is established; Step 3: Count the acoustic transit time values ​​of the sandstone development interval, and fit the functional relationship between the acoustic transit time and porosity to calculate the acoustic transit time value corresponding to the lower limit of porosity; Step 4: reconstruct the acoustic time difference curve, fuse the natural gamma curve and the reconstructed acoustic time difference curve, obtain the discrimination curve of the high-quality reservoir, and identify the high-quality reservoir; In step 4, the minimum value of the acoustic time difference curve of the sandstone layer is assigned to the mudstone layer to obtain a reconstructed acoustic time difference curve; In step 4, the natural gamma curve and the reconstructed acoustic time difference curve are fused to obtain a high-quality reservoir discrimination curve. The fusion model of the high-quality reservoir discrimination curve is as follows: ； Where Z is the value of the high-quality reservoir discrimination curve, GR is the natural gamma, the unit is API, GR min is the minimum value of natural gamma data, GR max is the maximum value of natural gamma data, SAC is the reconstructed sound wave time difference, the unit is microsecond / foot, SAC min To reconstruct the minimum value of the acoustic time difference data, SAC max To reconstruct the maximum value of the acoustic time difference data, K is the boundary value of natural gamma corresponding to sandstone and mudstone, and Φ is the porosity of sandstone.

2. The method for identifying deep high-quality sandstone reservoirs based on well logging curve fusion according to claim 1, characterized in that: In step 1, the natural gamma values ​​of the sandstone and mudstone development intervals are statistically analyzed based on the logging and well data obtained during drilling. The normal distribution curves of the natural gamma values ​​of the sandstone and mudstone are calculated using the histogram analysis method to establish a lithologic classification chart.

3. The method for identifying deep high-quality sandstone reservoirs based on well logging curve fusion according to claim 2, characterized in that: In step 1, the natural gamma ray boundary values ​​corresponding to sandstone and mudstone are determined based on the natural gamma ray and lithology correlation chart, and the drilling sandstone and mudstone development intervals are divided.

4. The method for identifying deep high-quality sandstone reservoirs based on well logging curve fusion according to claim 1, characterized in that: In step 2, based on the drilling oil test and core physical property analysis data, the reservoir porosity is classified and counted according to the oil test conclusions, and a correlation chart between the oil test conclusions and the reservoir porosity is established.

5. The method for identifying deep high-quality sandstone reservoirs based on well logging curve fusion according to claim 4 is characterized in that: In step 2, the reservoirs whose oil test conclusions are oil layer, oil and gas layer, oil and water layer, gas and water layer, oil-water layer, gas-water layer, water layer are high-quality reservoirs, and the reservoirs whose oil test conclusions are poor oil layer and dry layer are tight reservoirs. The lower limit of the porosity of the high-quality reservoir is determined based on the correlation chart between the oil test conclusions and the reservoir porosity.

6. The method for identifying deep high-quality sandstone reservoirs based on well logging curve fusion according to claim 1, characterized in that: In step 3, the acoustic transit time values ​​of the sandstone development interval are counted, and the functional relationship between the acoustic transit time and porosity is fitted as follows: D=f(Φ) Where Φ is the porosity of sandstone, in units of %; D is the acoustic time difference value, in units of microseconds / foot.

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