Method and apparatus for identifying an effective reservoir

Abstract

The present invention provides a method and device for identifying effective reservoirs. The method for identifying effective reservoirs includes: determining the color of the dense part in the core of the mottled bioturbated carbonate rock; calculating the area proportion of the dense part in the core according to the color; generating an identification model for effective reservoirs based on the logging response characteristics of the dense part and the color standard. By combining image recognition with logging interpretation, the present invention fills the blank in the method for identifying and calculating effective reservoirs in bioturbated heterogeneous carbonate rock oil and gas layers. Its calculation results are accurate and effective, providing more objective data for the calculation of reserves and asset evaluation of bioturbated heterogeneous carbonate rock oil and gas fields.

Description

Technical Field

[0001] The present invention belongs to the field of oil and gas field development, and particularly relates to a method and device for identifying effective reservoirs. Background Art

[0002] As is well known, bioturbation originally belonged to the research category of traditional ichnology. With the widespread discovery of bioturbation-induced reservoirs in places such as the waters in North America, the Middle East, and Norway, it has gradually been introduced into the petroleum industry. The tubular traces formed by trace-making organisms in sediments are called burrows, and the undisturbed surrounding rock part is the matrix. Multiple factors related to bioturbation, such as the geometric characteristics of burrows, bioturbation intensity, the permeability ratio of burrows to the matrix, and compositional differences, will all affect the heterogeneity of reservoirs. Compared with other types of oil and gas reservoirs, the most obvious feature of the heterogeneous oil and gas reservoirs related to bioturbation is the mottled feature observed in core samples, that is: the relationship between non-reservoirs and reservoirs in space is not an overlying distribution, but rather oil-bearing brown reservoirs formed by bioturbation are distributed among white oil-free dense reservoir masses.

[0003] The effective reservoir (effective thickness of the oil and gas reservoir) is an important factor affecting the calculation of oil and gas reserves by the volumetric method. According to the national standards GBn269-88 and GBn270-88 issued in April 1988, the petroleum reserve specification and the natural gas reserve specification require that the research on the physical property standards of the effective thickness of oil and gas reservoirs "should be based on core test data, starting from the basic factors controlling the oil production capacity of the reserves, and through the analysis and research of rock physical parameters, pore structure, and relative permeability relationships, determine the lower limits of porosity, permeability, and oil saturation of movable oil reservoirs". In general, during the research process, based on the single-well oil testing data, through sufficient experiments and research on the core data, the lower limits of lithology, physical properties, oiliness, and electrical properties of the effective thickness are formulated, and the effective thickness of the oil and gas reservoir is specifically determined in combination with logging curves and interpretation parameters.

[0004] Since the oil testing of bioturbation heterogeneous carbonate rock oil and gas reservoirs fully complies with the above standards for the effective thickness of oil and gas reservoirs. Therefore, at present, the above method is adopted for calculating the effective thickness of various oil and gas reservoirs including bioturbation heterogeneous carbonate rock oil and gas reservoirs. For example: Feng Jun's "Research on the Physical Property Standards of the Effective Thickness of Oil and Gas Reservoirs in Zhongyuan Oilfield", Song Ziqi's "Research on the Determination Method of the Effective Thickness of Ultra-Low Permeability Oil Reservoirs", Li Hongjuan's "Research on the Effective Thickness of Glutenite Reservoirs", and V. Mehdipoura, B. Ziaeea, H. Motieia.'s "Determination and distribution of petrophysical parameters (PHIE, Sw and NTG) of Ilam Reservoir in one Iranian oil filed" all determine the effective thickness using similar methods.

[0005] However, the above method has the following deficiencies:

[0006] (1) In bioturbated heterogeneous carbonate oil and gas reservoirs, the proportion of the space occupied by white oil-free dense reservoir masses is large, and if the effective reservoir volume is calculated based on well testing as the standard, the calculated effective reservoir volume is on the large side;

[0007] (2) If well testing data is lacking, the characteristics of bioturbated heterogeneous carbonate oil and gas reservoirs in well logging show as non-reservoirs, resulting in a smaller calculated effective reservoir volume;

[0008] (3) The proportion of white oil-free dense reservoir masses varies greatly, and it is difficult to accurately calculate the space volume they occupy. Summary of the Invention

[0009] In view of the problems in the prior art, the identification method and device for effective reservoirs provided by the present invention fill the gap in the identification and calculation methods of effective reservoirs in bioturbated heterogeneous carbonate oil and gas reservoirs by combining image recognition and well logging interpretation. The calculation results are accurate and effective, providing more objective data for the calculation of reserves and asset evaluation of bioturbated heterogeneous carbonate oil and gas fields.

[0010] To solve the above technical problems, the present invention provides the following technical solutions:

[0011] In the first aspect, the present invention provides an identification method for effective reservoirs, including:

[0012] In one embodiment, determine the color of the dense part in the core of the mottled bioturbated carbonate rock;

[0013] Calculate the area ratio of the dense part in the core according to the color;

[0014] Generate an identification model for effective reservoirs according to the well logging response characteristics of the dense part and the color standard.

[0015] In one embodiment, the determining the color of the dense part in the core of the mottled bioturbated carbonate rock includes:

[0016] Prepare a cast thin section of the core;

[0017] Determine the color of the dense part in the cast thin section.

[0018] In one embodiment, the calculating the area ratio of the dense part in the core according to the color includes:

[0019] Establish a color standard for the dense part in the core according to the color;

[0020] Calculate the area proportion of the dense part in the core according to the color standard.

[0021] In one embodiment, generating an identification model of an effective reservoir according to the logging response characteristics of the dense part and the color standard includes:

[0022] Obtain the logging response characteristics of the dense part;

[0023] Generate an identification model of the effective reservoir according to the logging response characteristics and the area proportion.

[0024] In one embodiment, establishing the color standard of the dense part in the core according to the color includes:

[0025] Extract the color characteristics of the core using the HSI model;

[0026] Establish the color standard according to the color characteristics.

[0027] In one embodiment, calculating the area proportion of the dense part in the core according to the color standard includes:

[0028] Using the method of fuzzy mathematics, determine the distribution area of the dense part in the core according to the color standard;

[0029] Calculate the area proportion according to the core and the distribution area.

[0030] In one embodiment, the identification method of the effective reservoir uses the identification model to identify the effective reservoir and calculate the proportion of the effective reservoir in the formation.

[0031] In a second aspect, the present invention provides an identification device for an effective reservoir, including:

[0032] A color determination unit, configured to determine the color of the dense part in the core of the mottled bioturbated carbonate rock;

[0033] An area proportion calculation unit, configured to calculate the area proportion of the dense part in the core according to the color;

[0034] An identification model generation unit, configured to generate an identification model of the effective reservoir according to the logging response characteristics of the dense part and the color standard.

[0035] In one embodiment, the color determination unit includes:

[0036] A casting thin section preparation module, configured to prepare a casting thin section of the core;

[0037] A color determination module, configured to determine the color of the dense part in the casting thin section.

[0038] In one embodiment, the area ratio calculation unit includes:

[0039] A color standard establishment module, configured to establish a color standard for the dense part in the core according to the color;

[0040] An area ratio calculation module, configured to calculate the area ratio of the dense part in the core according to the color standard.

[0041] In one embodiment, the recognition model generation unit includes:

[0042] A logging response feature acquisition module, configured to acquire the logging response features of the dense part;

[0043] A recognition model generation module, configured to generate a recognition model for the effective reservoir according to the logging response features and the area ratio.

[0044] In one embodiment, the color standard establishment module includes:

[0045] A color feature extraction module, configured to extract the color features of the core by using the HSI model;

[0046] A color standard establishment sub-module, configured to establish the color standard according to the color features.

[0047] In one embodiment, the area ratio calculation module includes:

[0048] A distribution area determination module, configured to determine the distribution area of the dense part in the core according to the color standard by using the device of fuzzy mathematics;

[0049] An area ratio calculation sub-module, configured to calculate the area ratio according to the core and the distribution area.

[0050] In one embodiment, the recognition device for the effective reservoir further includes: an effective reservoir ratio calculation unit, configured to recognize the effective reservoir by using the recognition model and calculate the ratio of the effective reservoir in the formation.

[0051] In a third aspect, the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of the method for recognizing an effective reservoir when executing the program.

[0052] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method for recognizing an effective reservoir are implemented.

[0053] As can be seen from the above description, for the method and device for identifying effective reservoirs provided in the embodiments of the present invention, first, the color of the dense part in the core of mottled bioturbated carbonate rocks is determined; then, the area ratio of the dense part in the core is calculated according to the color; finally, an identification model for effective reservoirs is generated based on the logging response characteristics and color criteria of the dense part. Compared with the existing methods for identifying and calculating effective reservoirs in bioturbated heterogeneous carbonate rock oil and gas layers, the present invention has the following beneficial effects:

[0054] (1) Image recognition is introduced into the white oil-free dense reservoirs in bioturbated heterogeneous carbonate rocks, and a quantitative formula for the development degree of oil-free dense reservoirs is established by using conventional logging curves;

[0055] (2) The existing methods do not deduct the white oil-free dense reservoir masses in bioturbated heterogeneous carbonate rocks, resulting in an overestimated reserve calculation. The present invention calculates the reserves of such reservoir oil pools more objectively, laying a foundation for objectively evaluating the economic value of the oil pool and formulating a reasonable development strategy;

[0056] (3) This method is based on the existing mature logging series and is easy to implement technically;

[0057] (4) This method can be widely applied to the heterogeneous evaluation of carbonate rock reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0059] Figure 1 Schematic flow chart of the method for identifying effective reservoirs in the embodiments of the present invention Figure 1 ;

[0060] Figure 2 Original schematic diagram of white patches in the embodiments of the present invention;

[0061] Figure 3 Schematic flow chart of step 100 in the embodiments of the present invention;

[0062] Figure 4 Schematic flow chart of step 200 in the embodiments of the present invention;

[0063] Figure 5 Schematic flow chart of step 300 in the embodiments of the present invention;

[0064] Figure 6Schematic flowchart of step 201 in the embodiment of the present invention;

[0065] Figure 7 Schematic flowchart of step 202 in the embodiment of the present invention;

[0066] Figure 8 Schematic flowchart of the method for identifying effective reservoirs in the embodiment of the present invention Figure 2 ;

[0067] Figure 9 Schematic flowchart of the method for identifying effective reservoirs in the specific application example of the present invention;

[0068] Figure 10 Schematic diagram of a cast thin section in the specific application example of the present invention;

[0069] Figure 11 Schematic diagram of the extraction result of white patches in the specific application example of the present invention;

[0070] Figure 12 Schematic diagram of the relationship between the patch ratio and the RHOB curve in the specific application example of the present invention;

[0071] Figure 13 Schematic diagram of the calculation curve (formula) of the effective reservoir of the oil and gas layer in the specific application example of the present invention;

[0072] Figure 14 Structural block diagram of the device for identifying effective reservoirs in the embodiment of the present invention Figure 1 ;

[0073] Figure 15 Structural block diagram of the color determination unit in the embodiment of the present invention;

[0074] Figure 16 Structural block diagram of the area ratio calculation unit in the embodiment of the present invention;

[0075] Figure 17 Structural block diagram of the identification model generation unit in the embodiment of the present invention;

[0076] Figure 18 Structural block diagram of the color standard establishment module in the embodiment of the present invention;

[0077] Figure 19 Structural block diagram of the area ratio calculation module in the embodiment of the present invention;

[0078] Figure 20 Structural block diagram of the device for identifying effective reservoirs in the embodiment of the present invention Figure 2 ;

[0079] Figure 21 Structural schematic diagram of the electronic device in the embodiment of the present invention. Detailed implementation manners

[0080] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0081] Those skilled in the art should understand that the embodiments of the present invention may be provided as a method, a system, or a computer program product. Therefore, the present invention may adopt the form of an all-hardware embodiment, an all-software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0082] It should be noted that the terms "comprising" and "having" in the description and claims of this application and any variations thereof in the above accompanying drawings are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0083] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0084] An embodiment of the present invention provides a detailed implementation manner of a method for identifying an effective reservoir. Refer to Figure 1 , and the method specifically includes the following contents:

[0085] Step 100: Determine the color of the dense part in the core of the mottled bioturbated carbonate rock.

[0086] It can be understood that biodisturbation refers to the strong agitation of organisms in sediments, such as the strong burrowing activities of organisms, forming repeated and interlaced burrow structures, etc. For example, refer to Figure 2 , Figure 2 the color of the dense part in the core of the mottled bioturbated carbonate rock in

[0087] is white. Step 200: Calculate the area ratio of the dense part in the core according to the color.

[0088] Specifically, according to the color of the dense part, circle the distribution area of the dense part in the core, and calculate the area proportion of this type of reservoir.

[0089] Step 300: Generate an identification model of the effective reservoir according to the logging response characteristics of the dense part and the color standard.

[0090] Preferably, extract the density, neutron, and acoustic triple porosity logging response characteristic parameters of the oil-free dense reservoir section, establish a two-dimensional planar graph of the area proportion of the oil-free dense reservoir and the logging response characteristic parameters, regress the linear relationship curve and calculate the correlation coefficient, and select the logging curve (possibly multiple) with the best correlation coefficient as the identification model of the effective reservoir of the oil and gas layer.

[0091] As can be seen from the above description, the identification method and device for the effective reservoir provided by the embodiments of the present invention first determine the color of the dense part in the core of the mottled bioturbated carbonate rock; then, calculate the area proportion of the dense part in the core according to the color; finally, generate an identification model of the effective reservoir according to the logging response characteristics of the dense part and the color standard. Specifically, by observing the thin sections and cores, circle the distribution range of the oil-free dense reservoir; then, according to the circled range, establish the HIS color system standard; according to the color standard, use the fuzzy clustering analysis method to identify the cores that meet the standard color, and calculate the distribution area and area proportion; extract the logging curve characteristic parameters of the oil-free dense reservoir section, establish the formula for the area proportion of the oil-free dense reservoir; establish the calculation formula for the effective reservoir of the oil and gas layer, and construct a logging curve of the effective reservoir proportion according to the formula. The invention fills the blank of calculating the effective reservoir space of bioturbated heterogeneous carbonate rocks, calculates the reserves of this type of reservoir oil reservoir more objectively, and lays a foundation for objectively evaluating the economic value of the oil reservoir and formulating development strategies.

[0092] In one embodiment, referring to Figure 3 , step 100 further includes:

[0093] Step 101: Prepare a cast thin section of the core;

[0094] Step 102: Determine the color of the dense part in the cast thin section.

[0095] The cast thin section is a thin section of the rock prepared by injecting colored liquid glue into the pore space of the rock under vacuum pressure and then grinding it after the liquid glue is solidified. Since the pores of the rock are filled with colored glue, it is very eye-catching and easy to identify under the microscope. The cast thin section provides an effective way to study the pore size, distribution, pore type, connectivity, combination characteristics, geometric morphology, average pore throat ratio, average pore radius, throat, coordination number, fracture length and width, fissure rate, etc.

[0096] In steps 101 and 102, first, a cast thin section of the core is made, and then, based on the cast thin section, the dense part is determined, and the color of this part is determined.

[0097] In one embodiment, referring to Figure 4 , step 200 further includes:

[0098] Step 201: Establish a color standard for the dense part in the core according to the color;

[0099] Specifically, the color standard in step 201 is established using the HIS color system. The HSI model includes three components: hue H, saturation S, and intensity I. The chromaticity H is the characteristic that distinguishes colors from each other and is determined by the wavelengths of the components in the visible light spectrum, that is, the so-called red, green, blue, etc.; the saturation S refers to the purity of the color and reflects the shade of the color. The saturation of pure white is zero, while the saturation of pure spectral colors is the highest; the intensity I refers to the intensity of light stimulation caused by light to the human eye and is related to the energy of light. The brightness of light is the degree of brightness of this color for humans.

[0100] Step 202: Calculate the area ratio of the dense part in the core according to the color standard.

[0101] In one embodiment, referring to Figure 5 , step 300 further includes:

[0102] Step 301: Obtain the logging response characteristics of the dense part;

[0103] Preferably, obtain the logging response characteristics of the three porosity logging curves corresponding to the dense part, that is, the logging response characteristics of the acoustic travel time, compensated neutron, and compensated density logging curves.

[0104] Step 302: Generate an identification model for the effective reservoir according to the logging response characteristics and the area ratio.

[0105] Specifically, establish a two-dimensional planar chart of the area ratio of the oil-free dense reservoir and the logging characteristic parameters, regress the linear relationship curve and calculate the correlation coefficient, and select the logging curve with the best correlation coefficient as the calculation formula for the effective reservoir of the oil and gas layer.

[0106] In one embodiment, referring to Figure 6 , step 201 further includes:

[0107] Step 2011: Extract the color characteristics of the core using the HSI model;

[0108] Step 2012: Establish the color standard according to the color characteristics.

[0109] In steps 2011 and 2012, specifically, calibrate the distribution of such reservoirs in the core according to the distribution of white oil-free tight reservoirs on the thin slice, extract the color features of such reservoirs, and establish a color standard for oil-free tight reservoirs.

[0110] In one embodiment, referring to Figure 7 , step 202 further includes:

[0111] Step 2021: Using the method of fuzzy mathematics, determine the distribution area of the tight part in the core according to the color standard;

[0112] Specifically, first, according to the HIS color system, make a component histogram distribution for each of the three components of H, I, and S for the extracted color, and its membership function can be specified as:

[0113]

[0114] where u 0 are the three components of H, I, and S; u mean is the expectation of the three components of H, I, and S in the extracted color, which can be replaced by the mean value in practical applications, and u fch is the variance of the sample.

[0115] Next, determine the weights of the three components in the HIS color space. In the HIS color space, I changes from black (0) at the bottom to white (1) at the top, which reflects the brightness information of the image. Again, determine the threshold of the color determination result. Finally, perform color recognition to determine whether the core image belongs to an oil-free tight reservoir.

[0116] Step 2022: Calculate the area ratio according to the core and the distribution area.

[0117] In one embodiment, referring to Figure 8 , the method for identifying effective reservoirs further includes:

[0118] Step 400: Use the recognition model to identify the effective reservoirs and calculate the proportion of the effective reservoirs in the formation.

[0119] It can be understood that the recognition model can identify the effective reservoirs in the mottled bioturbated carbonate rock formation. In addition, according to the formula constructed in step 302, a logging curve of the effective reservoir proportion can be constructed, that is, the effective reservoir of the oil and gas layer = reservoir thickness × effective reservoir proportion. Therefore, the proportion of the effective reservoir in the formation can also be calculated through this recognition model.

[0120] To further illustrate the present solution, the present invention also provides a specific application example of a method for identifying effective reservoirs, which specifically includes the following content. See Figure 9 。

[0121] Step S10: Observe the pore structure of the white oil-free and gas-free dense reservoir in the cast thin section.

[0122] It should be noted that in this specific application example, in the cast thin section, the dense part of the core is white. Specifically, a core sample including a white clump reservoir and an oil-bearing brown reservoir is selected to make a cast thin section, and the thin section is observed under a microscope. Observe the distribution range of the white oil-free dense reservoir, and circle the distribution range of the white oil-free dense reservoir. See Figure 10 。In addition, the purpose of observing the thin section under the microscope is to clarify the pore characteristics of the white oil-free dense reservoir under the microscope, and ensure that the circled white oil-free dense reservoir is a non-effective reservoir with no or very few connected pores.

[0123] Step S20: Establish a color standard for the white oil-free and gas-free dense reservoir.

[0124] Specifically, calibrate the distribution of this type of reservoir in the core according to the distribution of the white oil-free dense reservoir on the thin section, and extract the color characteristics of this type of reservoir to establish a color standard for the oil-free dense reservoir. The establishment of this color standard adopts the HIS color system. The HSI model includes three components: hue H, saturation S, and intensity I. The chromaticity H is the characteristic that distinguishes colors from each other, which is determined by the wavelengths of the components in the visible light spectrum, that is, what we usually call red, green, blue, etc.; the saturation S refers to the purity of the color, reflecting the shade of the color. The saturation of pure white is zero, while the saturation of pure spectral colors is the highest; the intensity I refers to the intensity of light stimulation caused by light to the human eye. Obviously, it is related to the energy of light, and the brightness of light is the brightness degree of this color for people.

[0125] The conversion of the same color from RGB to HSI is a non-linear change. For any three R, G, and B values within the range of [0, 1], the I, S, and H components of the corresponding HSI model can be calculated by the following formulas:

[0126]

[0127]

[0128]

[0129]

[0130] Step S30: Identify and count the area of the dense reservoir according to the color standard.

[0131] Specifically, the fuzzy clustering analysis method is adopted to identify the cores that meet the standard color, and calculate the distribution area and area proportion. First, for the extracted colors, according to the HIS color system, the component histograms are made for the three components of H, I, and S respectively, and their membership functions can be specified as:

[0132]

[0133] Among them, u 0 are the three components of H, I, and S; u mean are the expectations of the three components of H, I, and S in the extracted colors, which can be replaced by the mean value in practical applications, and u fch is the variance of the sample.

[0134] Secondly, determine the weights of the three components in the HIS color space. In the HIS color space, I changes from black (0) at the bottom to white (1) at the top, reflecting the brightness information of the image; the default weights of the three feature quantities (H, I, S) are set to 0.3, 0.4, 0.3.

[0135] Thirdly, determine the threshold of the color determination result. After actually running this system, it is found that the program can, after learning a certain number of acquisition points, achieve a difference degree of more than 85% when distinguishing different colors, and after setting the threshold to 0.90, the program can identify more than 80% of the colors at one time, and the recognition rate reaches more than 95%. Therefore, the threshold is defined as 0.90 here. Finally, perform color recognition to determine whether the core image belongs to the white oil-free tight reservoir. For each pixel point, the following formula is used for evaluation calculation:

[0136] A = 0.3×μ(H) + 0.4×μ(I) + 0.3×μ(S)

[0137] If A > 0.9, then the A pixel point belongs to the white oil-free tight reservoir, otherwise it does not belong to the white oil-free tight reservoir. For the recognition results of this specific application example, see Figure 2 and Figure 11 .

[0138] Step S40: Establish the relationship between the tight reservoir area and the logging curve.

[0139] Specifically, extract the density, neutron, and acoustic three-porosity logging characteristic parameters of the white oil-free tight reservoir section, establish a two-dimensional flat chart of the area ratio of the white oil-free tight reservoir and the logging characteristic parameters, regress the linear relationship curve and calculate the correlation coefficient, and select the logging curve with the best correlation coefficient as the calculation formula for the effective reservoir of the oil and gas layer; further, count the area ratios of the white oil-free tight reservoirs of multiple cores to make the statistical data have statistical laws. Extract the logging characteristic parameters such as density, neutron, and acoustic within the core depth range, and then establish a correlation relationship diagram between the area ratio of the white oil-free tight reservoir and each logging parameter X and regress the calculation formula for the effective reservoir Y of the oil and gas layer:

[0140] Y = aX + b

[0141] Specifically, the above formula established in this specific application example is: Y = 630.74X - 1413.8, as Figure 12 shown.

[0142] Step S50: Construct a calculation formula for the effective reservoir of the oil and gas layer.

[0143] According to the calculation formula for the effective reservoir of the oil and gas layer in step S40, calculate all wells to obtain the effective reservoir ratios of each well and each layer. Specifically, according to the calculation formula for the effective reservoir of the oil and gas layer, construct a logging curve of the effective reservoir ratio. The effective reservoir of the oil and gas layer = reservoir thickness × effective reservoir ratio. In this specific application example, the calculation formula of this logging curve is: NTG = (100 - Y) / 100 = (1313.8 - 630.74X) / 100. Construct a logging curve of the effective reservoir ratio according to the formula, as Figure 13 shown,[[]] Figure 13 which is the comprehensive logging chart of Well X, where NTG is the calculated effective reservoir thickness / formation thickness (sand-to-shale ratio).

[0144] Step S60: Identify the effective reservoir for each well and each layer according to the calculation formula in step S50 and calculate the storage volume of the effective reservoir.

[0145] Use the calculation formula for the effective reservoir of the oil and gas layer in step S50 to calculate all wells to obtain the effective reservoir ratios of each well and each layer.

[0146] Based on the same inventive concept, embodiments of the present application further provide an identification device for effective reservoirs, which can be used to implement the method described in the above embodiments, as in the following embodiments. Since the principle of the identification device for effective reservoirs to solve problems is similar to that of the identification method for effective reservoirs, the implementation of the identification device for effective reservoirs can refer to the implementation of the identification method for effective reservoirs, and the repeated parts will not be described again. As used hereinafter, the term "unit" or "module" can be a combination of software and / or hardware that can achieve a predetermined function. Although the systems described in the following embodiments are preferably implemented in software, implementations in hardware, or a combination of software and hardware are also possible and contemplated.

[0147] An embodiment of the present invention provides a specific implementation manner of an identification device for effective reservoirs that can implement the identification method for effective reservoirs. Refer to Figure 14 , and the identification device for effective reservoirs specifically includes the following:

[0148] A color determination unit 10, configured to determine the color of the dense part in the core of the mottled bioturbated carbonate rock;

[0149] An area ratio calculation unit 20, configured to calculate the area ratio of the dense part in the core according to the color;

[0150] An identification model generation unit 30, configured to generate an identification model for effective reservoirs according to the logging response characteristics of the dense part and the color standard.

[0151] In one embodiment, refer to Figure 15 , the color determination unit 10 includes:

[0152] A casting thin section preparation module 101, configured to prepare a casting thin section of the core;

[0153] A color determination module 102, configured to determine the color of the dense part in the casting thin section.

[0154] In one embodiment, refer to Figure 16 , the area ratio calculation unit 20 includes:

[0155] A color standard establishment module 201, configured to establish a color standard for the dense part in the core according to the color;

[0156] An area ratio calculation module 202, configured to calculate the area ratio of the dense part in the core according to the color standard.

[0157] In one embodiment, refer to Figure 17 , the identification model generation unit 30 includes:

[0158] A logging response characteristic acquisition module 301, configured to acquire the logging response characteristics of the dense part;

[0159] An identification model generation module 302, configured to generate an identification model of the effective reservoir according to the logging response characteristics and the area ratio.

[0160] In one embodiment, referring to Figure 18 , the color standard establishment module 201 includes:

[0161] A color feature extraction module 2011, configured to extract the color features of the core using the HSI model;

[0162] A color standard establishment sub-module 2012, configured to establish the color standard according to the color features.

[0163] In one embodiment, referring to Figure 19 , the area ratio calculation module 202 includes:

[0164] A distribution area determination module 2021, configured to use a fuzzy mathematics device to determine the distribution area of the dense part in the core according to the color standard;

[0165] An area ratio calculation sub-module 2022, configured to calculate the area ratio according to the core and the distribution area.

[0166] In one embodiment, referring to Figure 20 , the identification device for the effective reservoir further includes: an effective reservoir ratio calculation unit 40, configured to identify the effective reservoir using the identification model and calculate the ratio of the effective reservoir in the formation.

[0167] As can be seen from the above description, the identification device for the effective reservoir provided by the embodiment of the present invention first determines the color of the dense part in the core of the mottled bioturbated carbonate rock; then, calculates the area ratio of the dense part in the core according to the color; and finally generates an identification model of the effective reservoir according to the logging response characteristics and the color standard of the dense part. Compared with the prior art methods for identifying and calculating the effective reservoir of bioturbated heterogeneous carbonate rock oil and gas layers, the present invention has the following beneficial effects:

[0168] (1) Image recognition is introduced into the white oil-free dense reservoir in bioturbated heterogeneous carbonate rock, and a quantitative formula for the development degree of the oil-free dense reservoir is established using conventional logging curves;

[0169] (2) Existing methods do not deduct the white oil-free dense reservoir masses in bioturbated heterogeneous carbonate rock, resulting in an overestimated reserve calculation. The present invention calculates the reserve of this type of reservoir oil reservoir more objectively, laying a foundation for objectively evaluating the economic value of the oil reservoir and formulating a reasonable development strategy;

[0170] (3) This method is based on the existing mature logging series and is easy to implement technically;

[0171] (4) This method can be widely applied to the heterogeneous evaluation of carbonate reservoirs.

[0172] The devices, modules or units illustrated in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. A typical implementation device is an electronic device. Specifically, the electronic device can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0173] In a typical example, the electronic device specifically includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the steps of the above-mentioned effective reservoir identification method, and the steps include:

[0174] Step 100: Determine the color of the dense part in the core of the mottled bioturbated carbonate rock;

[0175] Step 200: Calculate the area ratio of the dense part in the core according to the color;

[0176] Step 300: Generate an identification model of the effective reservoir according to the logging response characteristics of the dense part and the color standard.

[0177] Next, refer to Figure 21 , which shows a schematic structural diagram of an electronic device 600 suitable for implementing the embodiments of the present application.

[0178] As Figure 21 shown, the electronic device 600 includes a central processing unit (CPU) 601, which can perform various appropriate operations and processes according to the program stored in the read-only memory (ROM) 602 or the program loaded from the storage part 608 into the random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the system 600 are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other through a bus 604. The input / output (I / O) interface 605 is also connected to the bus 604.

[0179] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as required. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is mounted on the drive 610 as required so that a computer program read therefrom is installed in the storage section 608 as required.

[0180] Specifically, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-described method for identifying an effective reservoir are implemented, and the steps include:

[0181] Step 100: Determine the color of the dense part in the core of the mottled bioturbated carbonate rock;

[0182] Step 200: Calculate the area ratio of the dense part in the core according to the color;

[0183] Step 300: Generate an identification model of an effective reservoir according to the logging response characteristics of the dense part and the color standard.

[0184] In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 609, and / or installed from the removable medium 611.

[0185] A computer-readable medium includes both permanent and non-permanent, removable and non-removable media and can implement information storage by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0186] For convenience of description, when describing the above devices, they are described separately as various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in one or more software and / or hardware.

[0187] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing device produce means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0188] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0189] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions for implementing the functions specified in one process or a plurality of processes and / or boxes Figure 1 one process or a plurality of processes and / or boxes Figure 1 steps for the functions specified in one box or a plurality of boxes.

[0190] It should also be noted that the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising an …" does not exclude the presence of additional identical elements in the process, method, commodity or device comprising such element.

[0191] The present application may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The present application may also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including storage devices.

[0192] Each embodiment in this specification is described in a progressive manner, and the same or similar parts among the embodiments may be referred to each other. Each embodiment focuses on the differences from other embodiments. In particular, for system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and the relevant parts may be referred to the partial description of method embodiments.

[0193] The above are only the embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A method for identifying effective reservoirs, applied to mottled bioturbated carbonate rock formations, characterized in that, it includes: Determine the color of the dense part in the core of the mottled bioturbated carbonate rock; Calculate the area ratio of the dense part in the core according to the color; Generate an identification model for effective reservoirs based on the logging response characteristics of the dense part and the area ratio, The generating an identification model for effective reservoirs based on the logging response characteristics of the dense part and the area ratio includes: Obtain the logging response characteristics of the dense part; Generate the identification model for effective reservoirs according to the logging response characteristics and the area ratio; the identification model is used to calculate the proportion of effective reservoirs in the formation.

2. The method for identifying effective reservoirs according to claim 1, characterized in that, The determining the color of the dense part in the core of the mottled bioturbated carbonate rock includes: Prepare a cast thin section of the core; Determine the color of the dense part in the cast thin section.

3. The method for identifying effective reservoirs according to claim 1, characterized in that, The calculating the area ratio of the dense part in the core according to the color includes: Establish a color standard for the dense part in the core according to the color; Calculate the area ratio of the dense part in the core according to the color standard.

4. The method for identifying effective reservoirs according to claim 3, characterized in that, The establishing a color standard for the dense part in the core according to the color includes: Extract the color features of the core using the HSI model; Establish the color standard according to the color features.

5. The method for identifying effective reservoirs according to claim 3, characterized in that, The calculating the area ratio of the dense part in the core according to the color standard includes: Using the method of fuzzy mathematics, determine the distribution area of the dense part in the core according to the color standard; Calculate the area ratio according to the core and the distribution area.

6. An identification device for effective reservoirs, applied to mottled bioturbated carbonate rock formations, characterized in that, it includes: A color determination unit for determining the color of the dense part in the core of the mottled bioturbated carbonate rock; An area ratio calculation unit for calculating the area ratio of the dense part in the core according to the color; An identification model generation unit for generating an identification model for effective reservoirs based on the logging response characteristics of the dense part and the area ratio; The identification model generation unit includes: A logging response feature acquisition module for acquiring the logging response characteristics of the dense part; An identification model generation module for generating the identification model for effective reservoirs according to the logging response characteristics and the area ratio; the identification model is used to calculate the proportion of effective reservoirs in the formation.

7. The identification device for effective reservoirs according to claim 6, characterized in that, The color determination unit includes: A cast thin section preparation module for preparing a cast thin section of the core; A color determination module for determining the color of the dense part in the thin section of the casting body.

8. The identification device for effective reservoir according to claim 6, wherein, the area ratio calculation unit includes: a color standard establishment module for establishing a color standard for the dense part in the core according to the color; an area ratio calculation module for calculating the area ratio of the dense part in the core according to the color standard.

9. The identification device for effective reservoir according to claim 8, wherein, the color standard establishment module includes: a color feature extraction module for extracting the color features of the core by using the HSI model; a color standard establishment sub-module for establishing the color standard according to the color features.

10. The identification device for effective reservoir according to claim 8, wherein, the area ratio calculation module includes: a distribution area determination module for determining the distribution area of the dense part in the core according to the color standard by using the device of fuzzy mathematics; an area ratio calculation sub-module for calculating the area ratio according to the core and the distribution area.

11. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein, when the processor executes the program, the steps of the identification method for effective reservoir according to any one of claims 1 to 5 are implemented.

12. A computer-readable storage medium, on which a computer program is stored, wherein, when the computer program is executed by the processor, the steps of the identification method for effective reservoir according to any one of claims 1 to 5 are implemented.

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