Buried hill reservoir saturation evaluation method and medium

Abstract

The present application relates to buried hill reservoir saturation evaluation method, including: obtaining electrical imaging logging data, and preprocessing to generate electrical imaging dynamic and static image;The buried hill reservoir in the block corresponding to the electrical imaging logging data is analyzed, and the reservoir space of the buried hill reservoir is classified according to the electrical imaging dynamic and static image;The electrical imaging logging data is scaled, and the scaled electrical imaging logging data is obtained;Based on the electrical imaging logging data, the porosity spectrum corresponding to the electrical imaging logging data is calculated;According to the porosity spectrum cutoff value obtained by calculating the porosity spectrum, the secondary porosity is calculated;Based on the principle that the fracture porosity calculated by the dual lateral logging is equal to the secondary porosity, the flushing zone saturation of the buried hill reservoir is calculated.The present application can effectively evaluate the saturation of buried hill reservoir and identify oil and water layers, and has the advantages of strong practicability and convenience.

Description

Technical Field

[0001] This invention relates to the field of oil and gas development technology, specifically to a method and medium for evaluating the saturation of buried hill reservoirs. Background Technology

[0002] With the increasing global demand for oil and gas resources, significant progress has been made in the exploration technology of complex lithological oil and gas reservoirs.

[0003] For example, obtaining the porosity distribution characteristics of a reservoir is one aspect of oil and gas reservoir exploration technology. Wellbore microresistivity imaging logging, due to its high resolution, large coverage area, and intuitive processing results, can be used to quantitatively evaluate the porosity distribution characteristics of a reservoir. A porosity spectrum is a frequency histogram of porosity values. Electrical imaging logging tools measure the apparent conductivity of the wellbore, and through conversion, the apparent resistivity of the wellbore can be obtained. Using the Archie formula, the apparent resistivity matrix of the wellbore is converted into a porosity matrix. The porosity values ​​within a certain depth range are statistically analyzed across different porosity value intervals. The statistical results are then arranged from smallest to largest and plotted on a coordinate system to create a frequency histogram, thus obtaining the so-called porosity spectrum.

[0004] However, existing technologies lack methods for evaluating the saturation of buried hill reservoirs. The processes and calculation methods are relatively simple and complex, and they rely heavily on rock physics, which cannot meet the practical needs of efficient and convenient exploration activities. Summary of the Invention

[0005] To address the aforementioned problems, the present invention aims to provide a method and medium for evaluating the saturation of buried hill reservoirs, which can effectively evaluate the saturation of buried hill reservoirs and identify oil-water layers, and has the advantages of strong practicality and convenience.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] Firstly, this application provides a method for evaluating the saturation of buried hill reservoirs, including:

[0008] Acquire electrical imaging logging data and preprocess it to generate electrical imaging dynamic and static images;

[0009] The lithological analysis of buried hill reservoirs in the blocks corresponding to the electrical imaging logging data is performed, and the reservoir space of the buried hill reservoirs is classified according to the dynamic and static images of the electrical imaging.

[0010] The electrical imaging logging data is calibrated to obtain calibrated electrical imaging logging data.

[0011] Based on the electrical imaging logging data, the porosity spectrum corresponding to the electrical imaging logging data is calculated;

[0012] Calculate the secondary porosity based on the porosity spectrum cutoff value obtained from the porosity spectrum calculation;

[0013] Based on the fact that fracture porosity and secondary porosity are equal according to dual lateral logging, the saturation of the flushing zone in the buried hill reservoir is calculated.

[0014] In one implementation of this application, the calculated porosity spectrum corresponding to the electrical imaging logging data includes:

[0015] The porosity spectrum corresponding to the electrical imaging logging data was calculated based on the Archie formula.

[0016] In one implementation of this application, the formula for calculating the secondary porosity is:

[0017]

[0018] Where a and b are lithological correlation coefficients, R m R is the resistivity of the mud. xo To flush the resistivity, S xo denoted as saturation of the washing belt, m as cementation index, n as saturation index, and C as secondary porosity coefficient.

[0019] In one implementation of this application, the formula for calculating fracture porosity based on dual-lateral logging includes:

[0020]

[0021] Among them, C lld C lls For deep and shallow lateral conductivity, m f K is the crack porosity index. f This represents the crack distortion index.

[0022] In one implementation of this application, the formula for calculating the saturation of the flushing zone in the buried hill reservoir is as follows:

[0023]

[0024] Secondly, this application provides a computer-readable storage medium storing a computer program, wherein the computer program, when running, controls the device where the computer-readable storage medium is located to execute the buried hill reservoir saturation evaluation method described in the first aspect.

[0025] The present invention has the following advantages due to the adoption of the above technical solutions: 1. The present invention can effectively evaluate the saturation of buried hill reservoirs and identify oil and water layers; 2. The present invention uses electrical imaging to calculate secondary porosity and matches it with the secondary porosity calculated by dual lateral logging, and then fits and derives the corresponding calculation formula. Due to the high resolution of electrical imaging logging, the final calculation result has high accuracy; 3. The present invention can be applied to the entire well area and has the advantages of strong practicality, convenience and speed. Attached Figure Description

[0026] Figure 1 This is a 1:200 electro-optical imaging dynamic and static image of the buried hill reservoir in well K in one embodiment of this application;

[0027] Figure 2 This is a 1:200 logging map of the vertical lithology and reservoir space classification of the buried hill reservoir in well K in one embodiment of this application;

[0028] Figure 3 This is a resistivity contour map of K-well electrical imaging in one embodiment of this application;

[0029] Figure 4 This is a calibration diagram of the K-well scale curve and the medium resistivity curve in one embodiment of this application;

[0030] Figure 5 This is a comparison diagram of static and dynamic images and scale images of K-well electro-imaging in one embodiment of this application;

[0031] Figure 6 This is a comparison chart of the porosity spectrum cutoff values ​​calculated by different methods for well K in one embodiment of this application;

[0032] Figure 7 This is a comprehensive logging map of well K in one embodiment of this application, which includes the saturation of the buried hill reservoir calculated based on electrical imaging logging data.

[0033] Figure 8 This is a comparison chart of the calculated saturation results of the O-well buried hill top weathering zone in another embodiment of this application and the calculated saturation results of the dual-water model.

[0034] Figure 9 This is a comparison chart of the gas layer calculated based on the invention and the well logging interpretation of well O. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0036] One aspect of this application provides a method for evaluating the saturation of buried hill reservoirs, which includes the following steps:

[0037] (1) Acquire electrical imaging logging data and preprocess it to generate electrical imaging dynamic and static images;

[0038] Specifically, the electrical imaging logging data is preprocessed to correct values ​​such as acceleration, magnetic declination, and bad spots. Finally, the data is processed to generate dynamic and static electrical imaging images.

[0039] (2) Perform lithological analysis on the buried hill reservoirs in the blocks corresponding to the electrical imaging logging data, and classify the reservoir space of the buried hill reservoirs according to the electrical imaging dynamic and static images;

[0040] Specifically, based on conventional and electrical imaging logging data, geological data, core data, and analytical test data, the lithology of the buried hill reservoir in this block is determined, and the reservoir space of this buried hill reservoir is roughly classified by combining electrical imaging dynamic and static maps with core photographs and core thin sections.

[0041] (3) The electrical imaging logging data is calibrated to obtain calibrated electrical imaging logging data;

[0042] The accuracy of the calibration of electrical imaging images affects the calculation of fracture parameters and the evaluation of secondary porosity; therefore, precise calibration of electrical imaging logging data is essential. Finally, the quality of the calibration results is determined by the color difference between the static image and the calibrated image.

[0043] (4) Based on the electrical imaging logging data, calculate the porosity spectrum corresponding to the electrical imaging logging data;

[0044] Specifically, after processing the calibrated electrical imaging logging data, the porosity of the buried hill reservoir, interpreted by the shallow lateral resistivity and multi-mineral model, can be calculated using the Archie formula to obtain the porosity spectrum corresponding to the electrical imaging logging data.

[0045] (5) Calculate the secondary porosity based on the porosity spectrum cutoff value obtained from the porosity spectrum calculation;

[0046] Simultaneously with calculating the porosity spectrum, the porosity spectrum cutoff value can be calculated. By using a reasonable cutoff value, accurate secondary porosity can be obtained. Comparing the calculated secondary porosity VISO with the porosity PHIT AVE calculated during the calculation of the electro-imaging porosity spectrum yields the secondary porosity coefficient C, with the specific formula as follows:

[0047]

[0048] In the formula, VISO and PHIT_AVE represent the secondary porosity and average porosity calculated by electro-imaging, respectively. The formula for calculating secondary porosity can also be converted to:

[0049]

[0050] In the formula, a and b are lithological correlation coefficients, and R m R is the resistivity of the mud. xo To flush the resistivity, S xo denoted as saturation of the rinsing belt, m as the cementation index, and n as the saturation index.

[0051] (6) Based on the principle that the fracture porosity and secondary porosity calculated by dual lateral logging are equal, the saturation of the flushing zone of the buried hill reservoir is calculated.

[0052] Dual-lateral logging is most sensitive to fracture logging responses due to the instrument's strong current focusing capability. There is a good correlation between fracture development and resistivity changes. Currently, the main formula for calculating fracture porosity using dual-lateral logging is:

[0053]

[0054] The porosity calculated from dual lateral logging is fracture porosity, which is also secondary porosity. Therefore, by equalizing the two equations, we can obtain:

[0055]

[0056] In the formula, C lld C lls For deep and shallow lateral conductivity, m f K is the crack porosity index. f Crack distortion index

[0057] It can be seen that, through derivation, the saturation of the washing belt can be converted, and the formula is:

[0058]

[0059] The method described above will be illustrated below using a specific application scenario of this application, taking K-wells and O-wells in different blocks as examples.

[0060] In this application scenario, the above method is performed using Techlog software.

[0061] 1) Organize the electrical imaging logging data of the buried hill reservoir in Well K. Run the electrical imaging processing wizard in the Geology module of the Techlog platform to preprocess the electrical imaging logging data and ensure the quality of the processed dynamic and static images. The 1:200 electrical imaging dynamic and static images of the buried hill layer are shown below. Figure 1 .

[0062] 2) Based on conventional and advanced logging data, geological data, core data, and analytical test data of the buried hill reservoir in Well K, the logging response characteristics of different lithologies in the buried hill reservoir of Well K were analyzed. On this basis, dynamic and static electro-optical imaging maps were combined with core photographs and core thin sections to clarify the reservoir space type and approximate distribution of the buried hill reservoir in Well K.

[0063] 3) Run the Image calibration stream in the Processing module under the Geology module in the Techlog software, selecting the resistivity and plate data for the K-well. After selecting the corresponding plate data, click Run. Two windows will appear, respectively... Figure 3 Electrical imaging resistivity contour maps and Figure 4 Calibration curve and resistivity curve calibration diagram. By... Figure 3 The broken lines in the resistivity contour map should pass through as many regions of high resistivity density as possible, which can make... Figure 4 The two curves in the third-to-last track should overlap as much as possible. Save the data when the overlap is optimal to obtain the data after each electrode calibration. Then, combine the calibration data by running the Pad concatenation and orientation workflow in the Processing module. Selecting the calibrated electrode data will produce the combined image ARRAY_WBI_IMGCAL. The quality of the calibration image is mainly determined by comparing it with the electro-imaging static image; the closer the colors are, the better the processing quality. Figure 5 This is a comparison of the static and dynamic images and the scale image of the K-well electro-imaging system.

[0064] 4) Run the Porospect module in the Texture analysis module under the Geology module to process the porosity spectrum of the electrical imaging data of well K. Based on the rock electrical experiment of the buried hill reservoir in well K, the cementation index of the buried hill reservoir in well K can be obtained as 1.75. Therefore, change the Archie CementationExp parameter from 1.8 to 1.75, and leave the other parameters unchanged.

[0065] 5) When calculating the porosity spectrum, the cutoff value of the porosity spectrum and secondary porosity obtained using different methods are also calculated simultaneously. The Techlog software platform primarily uses the WN method, TSR method, and SDR method to calculate the porosity spectrum cutoff value. The Mannal method is a manual determination method and is generally not selected. Figure 6As shown, the porosity spectrum cutoff values ​​calculated by different methods differ to some extent. In well K, the WN method has a large error and is not considered, while the TSR method has large fluctuations and significant errors in some segments. Therefore, this invention selects the SDR method to calculate the porosity spectrum cutoff value and secondary porosity VISO in well K. In addition, the average porosity PHIT_AVE is also calculated when calculating the electro-imaging porosity spectrum. Combined with the secondary porosity VISO, the secondary porosity coefficient C can be obtained (see step 5 in the specific implementation).

[0066] 6) Based on the principle that the secondary porosity calculated from dual-lateral logging is equal to that calculated from electrical imaging porosity spectrum, the specific formula used is:

[0067]

[0068] Based on the rock electrical parameters of well K, the resistivity of the flushed zone of the buried hill reservoir was calculated. Where m f The fracture porosity index ranges from 1.3 to 1.8, and is set at 1.5 in well K; K f The crack distortion index was set to 1.1. The final interpretation of the flushing zone saturation is shown in [reference needed]. Figure 7 .

[0069] 7) Well 0 encountered buried hill reservoirs in different blocks, but its lithology differs from Well K, being predominantly gneiss. Within the weathering zone of the buried hill in Well 0, a sandstone-conglomerate reservoir is visible. Since the dual-water model is also applicable to sandstone-conglomerate reservoirs, the saturation results calculated using the dual-water model are compared with those calculated based on electrical imaging logging data. The results are shown in […]. Figure 8 The comparative results demonstrate that calculating buried hill saturation based on electrical imaging logging data is accurate and practical in sandstone and conglomerate sections. Figure 9 This is a comprehensive diagram of the gas reservoir in the lower section of the sandstone-conglomerate gneiss reservoir, showing the correspondence between the calculated saturation and the gas reservoir as interpreted by well logging.

[0070] Based on the application results of wells K and 0, it can be proven that this method is accurate and practical in calculating the saturation of buried hill reservoirs, and can be applied to buried hill reservoirs with different lithologies, thus having good potential for widespread application.

[0071] In the several embodiments provided by this invention, it should be understood that the disclosed methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0072] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0073] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for evaluating the saturation of buried hill reservoirs, characterized in that, include: Acquire electrical imaging logging data and preprocess it to generate electrical imaging dynamic and static images; The lithological analysis of buried hill reservoirs in the blocks corresponding to the electrical imaging logging data is performed, and the reservoir space of the buried hill reservoirs is classified according to the dynamic and static images of the electrical imaging. The electrical imaging logging data is calibrated to obtain calibrated electrical imaging logging data. Based on the electrical imaging logging data, the porosity spectrum corresponding to the electrical imaging logging data is calculated; Calculate the secondary porosity based on the porosity spectrum cutoff value obtained from the porosity spectrum calculation; Based on the equality of fracture porosity and secondary porosity calculated from dual lateral logging, the saturation of the flushing zone in the buried hill reservoir is calculated; wherein, the saturation of the flushing zone is obtained by equalizing the following two equations: In the formula, , These represent the deep and shallow lateral conductivity, respectively. The crack porosity index; denoted as the fracture distortion index; a and b are lithological correlation coefficients. The resistivity of the mud; For rinsing the resistivity; denoted as saturation of the flushing belt; m is the cementation index; n is the saturation index; and C is the secondary porosity coefficient.

2. The method for evaluating the saturation of buried hill reservoirs according to claim 1, characterized in that, The calculation of the porosity spectrum corresponding to the electrical imaging logging data includes: The porosity spectrum corresponding to the electrical imaging logging data was calculated based on the Archie formula.

3. The method for evaluating the saturation of buried hill reservoirs according to claim 2, characterized in that, The formula for calculating the secondary porosity is as follows: Where a and b are lithological correlation coefficients, The resistivity of the mud is... To flush the resistivity, denoted as saturation of the washing belt, m as cementation index, n as saturation index, and C as secondary porosity coefficient.

4. The method for evaluating the saturation of buried hill reservoirs according to claim 3, characterized in that, The formula for calculating fracture porosity based on dual-lateral logging includes: in, , For deep and shallow lateral conductivity, The crack porosity index, This represents the crack distortion index.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed, controls the device containing the computer-readable storage medium to perform the buried hill reservoir saturation evaluation method according to any one of claims 1 to 4.

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