Carbonate rock dominant capacity reservoir identification method

Abstract

The invention provides a carbonate rock dominant productivity reservoir identification method. The carbonate rock dominant productivity reservoir identification method comprises the following steps: S1, acquiring element logging curve data, an electric imaging basic image, a conventional logging curve, core experiment data and gas logging data of a well in a research block; s2, by means of element logging curve data and conventional logging curve data, the mineral content of the region is obtained, and the lithofacies AL of the research region is determined; s3, a reservoir type parameter AE is given; solving an electric imaging porosity spectrum parameter by utilizing the electric imaging two-dimensional curve, and solving a porosity spectrum evaluation parameter AD; s4, analyzing the relationship between the conventional logging curve and the gas logging data, determining the boundary between the dominant gas production reservoir and the low-productivity reservoir, and constructing a productivity evaluation parameter AO; and S5, according to the calculated parameter values, respectively calculating evaluation standards and weights of different parameters, calculating the carbonate reservoir in the research area according to the standards and the weights to obtain a reservoir comprehensive quality index, and according to the reservoir comprehensive quality index, obtaining advantageous capacity reservoir distribution.

Description

Technical Field

[0001] The invention belongs to the technical field of carbonate gas reservoir geological exploration, and in particular to a method for identifying carbonate gas reservoirs with superior productivity. Background Art

[0002] Prediction of dominant reservoirs is a very important part of oil and gas exploration and development, and is one of the most important indicators before reservoir exploitation. Selecting a reasonable high-productivity reservoir before exploitation is crucial to the economic benefits of development. Carbonate reservoirs are highly heterogeneous and have large vertical differences. The selection of dominant reservoirs has more complex influencing factors than conventional reservoirs.

[0003] At present, most of the research on carbonate gas reservoirs adopts empirical methods, analytical methods, numerical simulation methods, etc. The empirical method relies on mining practice to fit the data; the analytical method relies on formula derivation; the numerical simulation uses seepage simulation analysis to carry out research.

[0004] However, due to the complexity of carbonate gas reservoirs in actual strata and their different regional characteristics in different regions, the above method is less practical in use and it is difficult to give practical suggestions based on local characteristics. In addition, it is difficult to comprehensively utilize a variety of cross-information when judging the dominant gas-producing layers in the formation. It takes a long time to identify the dominant reservoirs, and it is difficult to consider multiple aspects such as lithology, reservoir properties, and gas production performance. Summary of the invention

[0005] The purpose of the present invention is to solve the above problems in the existing technology and to propose a method for identifying carbonate reservoirs with superior productivity.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A method for identifying carbonate reservoirs with superior productivity, comprising the following steps:

[0007] S1. Obtain element logging curve data, basic electrical imaging images, conventional logging curves, core test data, and gas logging data of the wells in the study area; the gas logging data is the layer depth and gas production of the gas test layer at the mining site;

[0008] S2. Using element logging curve data and conventional logging curve data, calculate the regional mineral content and determine the lithofacies AL of the study area;

[0009] S3. Using the change value of conventional logging curve and the change form of electrical imaging reservoir space characteristics, determine the conventional logging curve demarcation point of different reservoir space types, and give the reservoir type parameter AE; using the electrical imaging two-dimensional curve to obtain the electrical imaging porosity spectrum parameter, and obtain the porosity spectrum evaluation parameter AD;

[0010] S4. Analyze the relationship between conventional well logging curves and gas logging data, determine the boundary between dominant gas-producing reservoirs and low-productivity reservoirs, and construct productivity evaluation parameters AO;

[0011] S5. According to the parameter values ​​obtained from the above calculations, the evaluation standards and weights of different parameters are calculated respectively. The comprehensive reservoir quality index of the carbonate reservoirs in the study area is calculated according to the standards and weights. The distribution of reservoirs with superior production capacity is obtained based on the comprehensive reservoir quality index.

[0012] Preferably, in step S1, the basic data obtained by formation element logging is data of various elements, including at least basic element data of calcium, magnesium, silicon, aluminum and iron. The formation basic element data and conventional logging curve data are used, and the element content and the conventional logging curve are used as input elements in cluster analysis. First, PCA principal component analysis is used to achieve dimensionality reduction processing of multiple input data, and then a cluster center algorithm is selected to divide the input data into different categories, that is, to obtain the formation lithofacies category;

[0013] The electrical imaging basic data is converted into a porosity image by using the Archie formula's flushing zone deformation formula. The mean and variance of the porosity spectrum are obtained using the obtained porosity image, and then the porosity spectrum evaluation parameter AD is constructed using the mean and variance.

[0014] Conventional logging curve data, i.e., raw logging data, at least include natural gamma curve, wellbore curve, compensated neutron curve, acoustic time difference curve, compensated density and deep and shallow lateral resistivity curve;

[0015] Core test data include information on mineral content, porosity and permeability of rocks at corresponding depths.

[0016] Preferably, in step S2, the main element contents in the original curve obtained by the element logging instrument are used, and the main rocks of the rock formation include: calcite, dolomite, part of siliceous, mudstone, potassium feldspar, plagioclase; calcium is used as the characteristic element of calcite, magnesium is used as the characteristic element of dolomite, silicon is used as the characteristic element of quartz or siliceous, and aluminum is used as the characteristic element of clay minerals, and combined with conventional logging curves;

[0017] Firstly, calcium-magnesium-silicon-aluminum and acoustic time difference, compensated density, deep lateral resistivity and natural gamma curves are taken as initial research curves. The PCA principal component analysis method is used to standardize the data of the initial research curves, calculate the correlation coefficient matrix of the sample data and solve the characteristic equation; the eigenvectors corresponding to the eigenvalues ​​are used to obtain different principal component curves, and the K-center clustering algorithm is used for the obtained principal component curves. Different principal component curves are input, and 8 clustering categories are selected. Finally, different clustering results are obtained. Combined with the actual core composition, the results are corrected, that is, the classification is repeatedly adjusted, and finally different lithofacies clustering results are obtained.

[0018] Preferably, a point existing in the current group is selected in the center point selection to avoid the influence of outliers. AC, DEN, RT, GR and silicon, calcium, magnesium and aluminum elements are used as inputs, and the absolute difference is used to evaluate the quality of the results. The calculation formula 1 is as follows:

[0019]

[0020] Where θ represents the absolute difference and p ij represents a point in the two-dimensional projection space, O ij Represents the center point of the cluster in space;

[0021] The results are divided into 8 categories: limestone, argillaceous limestone, dolomitic limestone, siliceous limestone, dolomitic and siliceous limestone, dolomitic limestone, argillaceous siliceous limestone, and gray dolomite. The lithofacies parameter AL is determined according to different classifications. Among them, the reservoirs where dolomitic limestone, dolomitic limestone, and gray dolomite are located are regarded as Class A productive reservoirs with a value range of 0.75-1, dolomitic and siliceous limestone, siliceous limestone, and argillaceous limestone are regarded as Class B productive reservoirs with a value range of 0.5-0.75, and the remaining categories are regarded as Class C productive reservoirs with a value range of 0-0.5.

[0022] Preferably, in step S3, a correlation is established between the logging curve and the electrical imaging image in stage 1, specifically, the mean value of the conventional logging curve at the reservoir position is obtained by using different reservoir spatial characteristics, the mean value of the conventional logging curve includes acoustic wave time difference, compensated density, natural gamma, compensated neutron, deep lateral resistivity, and the mean value data of conventional logging curves of multiple well positions are counted to enhance their representative ability in carbonate reservoirs. When there is no electrical imaging image of the reservoir, different types of reservoirs are quickly distinguished by the curve value combination table to preliminarily judge the gas production performance of the reservoir;

[0023] In stage 2, wellbore resistivity imaging logging is used to convert the apparent resistivity matrix of the wellbore into the porosity matrix of the wellbore using the Archie formula. The porosity values ​​within a certain depth range are statistically analyzed in different porosity value intervals. The statistical results are plotted as a frequency histogram in the coordinate system to obtain the porosity spectrum; the Archie formula of formula 2 is as follows:

[0024] S n =aR w / (Φ m R t ) (Formula 2)

[0025] When representing processed charged imaging images, it is assumed that S xo =1, b=1, n=1, Formula 2 is transformed into the following Formula 3:

[0026]

[0027] S in Formula 2 and Formula 3 n represents saturation, a represents formation factor, Φ represents porosity, R t represents the original formation resistivity, m represents the cementation index, R w Represents the formation water resistivity, R mf Represents the resistivity of mud filtrate, R xo It indicates the resistivity of the flushing zone;

[0028] Electrical imaging can reflect the characteristics of holes and cracks in the wellbore. The existence of holes and cracks has a significant impact on the reservoir productivity. Converting electrical imaging images into porosity spectra is an important means to evaluate complex carbonate formations. At the same time, the porosity spectrum is used to obtain the mean and variance of the porosity spectrum to further evaluate the relationship between holes, cracks and formations, through the following formula 4:

[0029]

[0030] And Formula 5:

[0031]

[0032] At the same time, the porosity spectrum evaluation parameter AD that can fully reflect the reservoir performance and connectivity is given by combining the deep and shallow lateral curves with the mean and variance of the porosity spectrum, as shown in Formula 6:

[0033]

[0034] In formulas 4, 5 and 6 represents the mean value of the electrical imaging porosity spectrum, Φ i represents the calculated electrical imaging porosity, represents the frequency of a porosity, It represents the square value of the porosity spectrum variance, RT represents the deep lateral resistivity value. Combined with the actual well data analysis, it is concluded that when AD<5.2, it is considered to have poor pore and fracture properties, which is a Class C productivity reservoir; when AD>14, it is considered to have excellent pore properties, which is a Class A productivity reservoir; when 5.2≤AD≥14, it is considered to have good pore properties, which is a Class B productivity reservoir.

[0035] Preferably, the reservoir space characteristics are specifically as follows: the porous reservoir has layered or eyelid-shaped structures on the electrical imaging, low natural gamma values, a "box-shaped" decrease in the resistivity curve under a high-resistance background, and the three porosities show an overall tooth-like increase or decrease; the reservoir space of the fracture-type reservoir is mainly composed of a series of fractures with different occurrences and low filling degrees, and the thickness is small; it appears as a series of black sinusoidal bands on the imaging logging map, and is manifested in conventional logging as a low GR value, a smaller DEN, an increased CNL or no response to DEN and CNL, an increased AC, a positive difference or no difference in the bilateral lateral direction, and a "U"-shaped decrease in resistivity under a high-resistance background; the reservoir space of the cave-type reservoir is mainly composed of caves, which are displayed as black spots with higher density on the imaging logging map, and are manifested in conventional logging as low natural gamma values, a decreased DEN, an increase in AC and CNL, an increase in the three porosities, a certain upward trend, a low value in the resistivity high-resistance background, and a positive difference or no difference in the bilateral lateral resistivity.

[0036] Preferably, in step S4, the boundaries of different productivity reservoirs on the scatter plot are analyzed. Since the mean value of the porosity spectrum is closely related to the fracture reservoir and permeability, a two-dimensional scatter plot of the conventional process curve parameters and the mean value of the porosity spectrum is constructed, and different points represent gas logging data at different depths; the boundaries of different conventional logging curves in productivity are respectively given in combination with the dry layer, medium productivity reservoir, high productivity reservoir and other reservoirs in the test gas layer. On the one hand, the reservoir productivity can be roughly and quickly judged in combination with the boundaries; on the other hand, the comprehensive productivity evaluation index can be constructed from the above parameters to accurately serve as one of the parameters for judging the reservoir productivity;

[0037] Combined with the mean porosity spectrum value proposed in step S3, the gas production limit of the reservoir reflected by the change of acoustic time difference and the change of porosity spectrum mean value, most of the high-quality gas-producing reservoirs are distributed in the range of acoustic time difference greater than 50us / ft; the total hydrogen content index of the rock is reflected by neutron porosity, which also reflects the changes of pores, cracks and caves from the side, and compensates for the gas production limit of the reservoir reflected by the neutron porosity and the mean porosity spectrum value. Most of the high-quality gas-producing reservoirs are distributed in the range of neutron porosity greater than 0%; the permeability is closely related to the quality of the reservoir. Combined with the mean porosity spectrum value and permeability, the permeability of high-quality gas-producing reservoirs is mainly concentrated in the range of greater than 0.05×10 -3 μm 2 range; deep lateral resistivity can reflect the change of original formation resistivity through current focusing, saturation can characterize the relationship between formation gas content and fluid, and the relationship between porosity spectrum mean and reservoir gas production capacity is analyzed. High-quality gas-producing reservoirs are concentrated in the range of gas saturation greater than 50% and deep lateral resistivity less than 5kΩ·m.

[0038] Preferably, in step S4, in combination with the relationships between acoustic travel time, compensated neutron porosity, compensated density porosity, permeability, deep lateral resistivity, gas saturation and reservoir gas production performance, the productivity evaluation parameter AO is given by the following formula 7:

[0039]

[0040] In the formula, AC represents acoustic travel time, CNL represents neutron porosity, K represents permeability, SO represents gas saturation, RT represents deep lateral resistivity, and DEN represents density porosity;

[0041] Combining the porosity change and the productivity evaluation parameter AO, the relationship between reservoir productivity change and the two is given. When AO > 1, the target reservoir is regarded as a Class A productivity reservoir; when 0.1 < AO < 10, the target reservoir is regarded as a Class B productivity reservoir; when AO < 1, the target reservoir is regarded as a Class C productivity reservoir.

[0042] Preferably, in step S5, the process of calculating the evaluation criteria and weights of different parameters is as follows: The required parameters are divided into categories A, B, and C respectively to correspond to Class A, Class B, and Class C productivities. The value range of Class A productivity parameters is defined as between 0.75 and 1, the value range of Class B productivity is defined as between 0.5 and 0.75, and the value range of Class C productivity is defined as between 0 and 0.5;

[0043] Furthermore, the parameter weights and values are defined for the lithofacies parameter AL, reservoir type parameter AE, porosity spectrum evaluation parameter AD, and productivity evaluation parameter AO respectively. Finally, by the following formula 8:

[0044]

[0045] In the formula, Dp is the reservoir comprehensive quality index, x i represents the weight of the i-th parameter, and the value ranges from 0 to 1; y i is the assignment of the i-th parameter, and the value ranges from 0 to 1; Calculate each parameter comprehensively to give the final evaluation value of the dominant productivity reservoir.

[0046] Preferably, in step S5, the weights and assignments of each parameter are as follows:

[0047] For the lithofacies AL, the weight is 0.2. The reservoirs where dolomitic limestone and siliceous limestone are located are regarded as Class A productivity reservoirs, and the assignment range is 0.75 - 1. The reservoirs with dolomite and silica-bearing limestone and dolomite-bearing limestone are regarded as Class B productivity reservoirs, and the assignment range is 0.5 - 0.75. The remaining types are regarded as Class C productivity reservoirs, and the assignment range is 0 - 0.5;

[0048] Reservoir type division is achieved by combining with the reservoir space discrimination pattern diagram. For reservoir type AE, its weight is 0.2. Fractured reservoirs and fractured-vuggy reservoirs are classified as Class A productive reservoirs, with an assignment range of 0.75 - 1. Among them, fractured reservoirs are assigned a value of 0.85 - 1 according to their status, and fractured-vuggy reservoirs are assigned a value of 0.75 - 0.85 according to their status; Fracture-porous reservoirs and vuggy reservoirs are classified as Class B productive reservoirs, with an assignment range of 0.5 - 0.75. Among them, fracture-porous reservoirs have an assignment range of 0.6 - 0.75, and vuggy reservoirs have an assignment range of 0.5 - 0.6. Porous reservoirs are classified as Class C productive reservoirs, and are divided into 0 - 0.5 according to the porosity size;

[0049] For the porosity spectrum evaluation parameter AD, its weight is 0.3. When AD < 5.2, the reservoir is regarded as a Class C productive reservoir, with an assignment range of 0 - 0.5. When 5.2 < AD < 14, the reservoir is regarded as a Class B productive reservoir, with an assignment range of 0.5 - 0.75. When AD > 14, the reservoir is regarded as a Class A productive reservoir, with an assignment range of 0.75 - 1;

[0050] For the productivity evaluation parameter AO, its weight is 0.3; When AO < 1, the reservoir is regarded as a Class C productive reservoir, with an assignment range of 0 - 0.5; When 1 < AO < 7, the reservoir is regarded as a Class B productive reservoir, with an assignment range of 0.5 - 0.75; When AO > 7, the reservoir is regarded as a Class A productive reservoir, with an assignment range of 0.75 - 1; After calculating the weights and assignments of each parameter, the numerical value of the comprehensive reservoir quality evaluation index Dp can be obtained by formula 8. For the comprehensive reservoir quality evaluation index, when Dp > 0.7, the reservoir is regarded as a Class A productive reservoir, that is, a superior productive reservoir. When 0.55 < Dp < 0.7, the reservoir is regarded as a Class B productive reservoir. When Dp < 0.55, the reservoir is regarded as a Class C productive reservoir.

[0051] Compared with the prior art, the method for identifying superior productive carbonate reservoirs has the following beneficial effects:

[0052] (1) The present invention uses conventional logging curves and element content changes to divide lithofacies changes at different depths, which can more prominently show lithology changes in carbonate reservoirs with strong heterogeneity.

[0053] (2) The present invention combines conventional logging curves to construct a comprehensive discrimination pattern for carbonate reservoir space. Based on the obtained curves, it can quickly and accurately judge the changes in actual formation reservoir types; The mean and variance of the porosity spectrum are introduced, and together with the deep lateral resistivity, a porosity spectrum evaluation parameter is constructed, which can more accurately and quickly reflect the reservoir storage performance and connectivity.

[0054] (3) The present invention analyzes the changes in reservoir capacity and conventional logging curves, and constructs reservoir capacity evaluation parameters using conventional logging curves and permeability and saturation parameters, thereby comprehensively analyzing the changes in reservoir capacity from the perspective of changes in conventional logging curves.

[0055] (4) The present invention establishes a set of processes for analyzing the advantageous productivity of carbonate reservoirs, which makes full use of logging data compared with traditional methods, and ultimately makes it more accurate and rapid to find advantageous productivity reservoirs.

[0056] In summary, the present invention makes full use of conventional logging curves and formation element information through cluster analysis, introduces lithofacies parameters as one of the excellent parameters for evaluating reservoirs, uses the relationship between conventional curves and reservoir types to provide a rapid reservoir type discrimination table to determine reservoir type parameters, and introduces electrical imaging porosity spectrum parameters as one of the excellent parameters for evaluating reservoirs. In addition, combined with the relationship between conventional logging curves and actual reservoir gas production, the productivity evaluation index is constructed using acoustic time difference, compensated neutron, permeability, saturation, density and deep lateral resistivity. Through the weights and assignments of different parameters, a comprehensive and rapid judgment of carbonate gas-producing reservoirs is achieved, which has a wide range of application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a flow chart of the method for identifying carbonate reservoirs with superior productivity of the present invention;

[0058] Figure 2 It is a flow chart of the K-center algorithm in cluster analysis of the present invention;

[0059] Figure 3 It is a relationship diagram of different productivity reservoirs of the present invention under the coordinates of acoustic wave time difference and porosity spectrum mean value;

[0060] Figure 4 It is a relationship diagram of different productivity reservoirs of the present invention under the coordinates of compensated neutron and porosity spectrum mean value;

[0061] Figure 5 It is a relationship diagram of different productivity reservoirs of the present invention under the coordinates of permeability and porosity spectrum mean value;

[0062] Figure 6 It is a relationship diagram of the productivity evaluation parameters and porosity coordinates of different productivity reservoirs of the present invention;

[0063] Figure 7 It is a schematic diagram of the conversion of the electrical imaging porosity spectrum of the present invention;

[0064] Figure 8 This is a diagram showing the identification results of the actual carbonate dominant gas-producing reservoir in the H area of ​​the present invention. DETAILED DESCRIPTION

[0065] The following are specific embodiments of the present invention and the accompanying drawings to further describe the technical solution of the present invention, but the present invention is not limited to these embodiments.

[0066] Embodiment 1

[0067] like Figure 1 As shown, the method for identifying carbonate reservoirs with superior productivity includes the following steps:

[0068] S1. Obtain element logging curve data, basic electrical imaging images, conventional logging curves, core test data, and gas logging data of the wells in the study area; the gas logging data is the layer depth and gas production of the gas test layer at the mining site;

[0069] In step S1, the basic data obtained by formation element logging is the data of various elements, including at least the basic element data of calcium, magnesium, silicon, aluminum and iron. The formation basic element data and conventional logging curve data are used, and the element content and the conventional logging curve are used as input elements in cluster analysis. First, PCA principal component analysis is used to realize the dimensionality reduction processing of multiple input data. The PCA principal component analysis is a common data analysis method in the prior art field. Then, a cluster center algorithm is selected, specifically a K-center clustering algorithm is used to divide the input data into different categories, that is, to obtain the formation lithofacies category;

[0070] The electrical imaging basic data is converted into a porosity image by using the Archie formula's flushing zone deformation formula. The mean and variance of the porosity spectrum are obtained using the obtained porosity image, and then the porosity spectrum evaluation parameter AD is constructed using the mean and variance.

[0071] Conventional logging curve data, i.e., raw logging data, at least include natural gamma curve, borehole diameter curve, compensated neutron curve, acoustic time difference curve, compensated density and deep and shallow lateral resistivity curve; the above curves are all original curve data measured by logging instruments and belong to the prior art, so their measurement process is not described in detail here. The relationship between conventional logging curves, reservoirs and productivity is analyzed by using the porosity spectrum mean and the scatter plot of conventional logging curves and using different productivity reservoirs as data points to fully reflect the characteristics of the dominant productivity reservoir.

[0072] The core test data includes the mineral content, porosity and permeability information of the rock at the corresponding depth, that is, the basic core information obtained from conventional rock tests, which belongs to the existing data measured in the prior art.

[0073] S2. Using element logging curve data and conventional logging curve data, calculate the regional mineral content and determine the lithofacies AL of the study area;

[0074] In step S2, the main element content in the original curve obtained by the element logging instrument is obtained by the element logging instrument. The original curve measured by the element logging instrument is the prior art, and the main element content is, for example, calcium, silicon, magnesium, and aluminum. The main rocks of the rock formation include: calcite, dolomite, part of siliceous, mudstone, potassium feldspar, and plagioclase; calcium is used as the characteristic element of calcite, magnesium is used as the characteristic element of dolomite, silicon is used as the characteristic element of quartz or siliceous, and aluminum is used as the characteristic element of clay minerals, and combined with conventional logging curves;

[0075] Firstly, calcium-magnesium-silicon-aluminum and acoustic time difference, compensated density, deep lateral resistivity and natural gamma curves are taken as initial research curves. The PCA principal component analysis method is used to standardize the data of the initial research curves, calculate the correlation coefficient matrix of the sample data and solve the characteristic equation; the eigenvectors corresponding to the eigenvalues ​​are used to obtain different principal component curves, and the K-center clustering algorithm is used for the obtained principal component curves. Different principal component curves are input, and 8 clustering categories are selected. Finally, different clustering results are obtained. Combined with the actual core composition, the results are corrected, that is, the classification is repeatedly adjusted, and finally different lithofacies clustering results are obtained.

[0076] Both PCA and K-MEANs are existing algorithms, so their calculation processes are not described in detail, and they belong to the application of existing technologies; here, the two algorithms are combined to solve different lithofacies.

[0077] like Figure 2 As shown in the K-center algorithm flow, in the selection of the center point, a point existing in the current group is used to avoid the influence of outliers. AC, DEN, RT, GR and silicon, calcium, magnesium and aluminum elements are used as inputs, and the absolute difference is used to evaluate the quality of the results. The calculation formula 1 is as follows:

[0078]

[0079] Where θ represents the absolute difference and p ij represents a point in the two-dimensional projection space, O ij Represents the center point of the cluster in space;

[0080] The results are divided into 8 categories: limestone, argillaceous limestone, dolomitic limestone, siliceous limestone, dolomitic and siliceous limestone, dolomitic limestone, argillaceous siliceous limestone, and gray dolomite. The lithofacies parameter AL is determined according to different classifications. Among them, the reservoirs where dolomitic limestone, dolomitic limestone, and gray dolomite are located are regarded as Class A productive reservoirs with a value range of 0.75-1, dolomitic and siliceous limestone, siliceous limestone, and argillaceous limestone are regarded as Class B productive reservoirs with a value range of 0.5-0.75, and the remaining categories are regarded as Class C productive reservoirs with a value range of 0-0.5.

[0081] S3. Using the change value of conventional logging curve and the change form of electrical imaging reservoir space characteristics, determine the conventional logging curve demarcation point of different reservoir space types, and give the reservoir type parameter AE; using the electrical imaging two-dimensional curve to obtain the electrical imaging porosity spectrum parameter, and obtain the porosity spectrum evaluation parameter AD;

[0082] The specific characteristics of reservoir space are as follows: the electrical imaging of porous reservoirs shows layered or eyelid-shaped structures, low natural gamma values, and a "box-shaped" decrease in the resistivity curve under high-resistance background, and the three porosities show an overall tooth-like increase or decrease; the storage space of fracture-type reservoirs is dominated by a series of fractures with different occurrences and low filling degrees, and the thickness is small; it appears as a series of black sinusoidal bands on the imaging logging map, and is manifested in conventional logging as low GR value, reduced DEN, increased CNL or no response of DEN and CNL, increased AC, positive or no difference in bilateral lateral directions, and a "U"-shaped decrease in resistivity under high-resistance background; the storage space of cave-type reservoirs is dominated by caves, which are displayed as black spots with higher density on the imaging logging map, and are manifested in conventional logging as low natural gamma values, reduced DEN, increased AC and CNL, and a certain upward trend in the three porosities, low values ​​under high-resistance background of resistivity, and positive or no difference in bilateral lateral resistivity.

[0083] In step S3, the association between the logging curve and the electrical imaging image is established in stage 1, specifically, the conventional logging curve mean at the reservoir position is obtained by using different reservoir spatial characteristics. The conventional logging curve mean includes acoustic wave time difference, compensated density, natural gamma, compensated neutron, and deep lateral resistivity. The mean data of conventional logging curves of multiple well positions are counted to enhance their representative ability in carbonate reservoirs. When there is no electrical imaging image of the reservoir, different types of reservoirs are quickly distinguished by the curve value combination table to preliminarily judge the gas production performance of the reservoir;

[0084] The core whole rock analysis is combined to further verify the correctness of the reservoir delineation by the electrical imaging image and the conventional logging curve. If there is a discrepancy, the whole rock analysis is used as the correct reservoir type. According to Table 1, the acoustic time difference data of the porous reservoir is between 55-75us / ft, and the compensated density value is between 2.6-2.75g / cm 3 , compensated neutron porosity is between 0-5%, natural gamma value is between 20-100 API, deep lateral resistivity data is between 10-1000Ω·m, fracture reservoir acoustic time difference data is between 55-65us / ft, and compensated density value is between 2.55-2.73g / cm 3 , compensated neutron porosity is between 0-7.1%, natural gamma value is between 30-60API, deep lateral resistivity data is between 200-2000Ω·m, sonic time difference data of pore-type reservoir is between 40-70us / ft, and compensated density value is between 2.2-2.7g / cm 3, compensated neutron porosity is between 0-2%, natural gamma value is between 20-65API, deep lateral resistivity data is between 50-1350Ω·m, fracture-cavity reservoir acoustic time difference data is between 45-55us / ft, and compensated density value is between 2.35-2.74g / cm 3 , compensated neutron porosity is between 0-9.6%, natural gamma value is between 15-60API, deep lateral resistivity data is >150Ω·m, fracture pore reservoir acoustic time difference data is between 55-65us / ft, compensated density value is between 2.5-2.7g / cm 3 , the compensated neutron porosity is between 1-10%, the natural gamma value is between 50-150API, and the deep lateral resistivity data is >60Ω·m. The above only describes one case of carbonate reservoir space type. For another case, see

[0085] Table 1 Reservoir space discrimination mode table:

[0086]

[0087] Table 1 Reservoir space discrimination pattern table

[0088] In stage 2, wellbore resistivity imaging logging is used to convert the apparent resistivity matrix of the wellbore into the porosity matrix of the wellbore using the Archie formula. The porosity values ​​within a certain depth range are statistically analyzed in different porosity value intervals. The statistical results are plotted as a frequency histogram in the coordinate system to obtain the porosity spectrum; the Archie formula of formula 2 is as follows:

[0089] S n =aR w / (Φ m R t ) (Formula 2)

[0090] When representing processed charged imaging images, it is assumed that S xo =0, b=1, n=1, Formula 2 is transformed into the following Formula 3:

[0091]

[0092] S in Formula 2 and Formula 3 n represents saturation, a represents formation factor, Φ represents porosity, R t represents the original formation resistivity, m represents the cementation index, R w Represents the formation water resistivity, R mf Represents the resistivity of mud filtrate, R xo It indicates the resistivity of the flushing zone;

[0093] Electrical imaging can reflect the characteristics of holes and cracks in the wellbore. The existence of holes and cracks has a significant impact on the reservoir productivity. Converting electrical imaging images into porosity spectra is an important means to evaluate complex carbonate formations. At the same time, the porosity spectrum is used to obtain the mean and variance of the porosity spectrum to further evaluate the relationship between holes, cracks and formations, through the following formula 4:

[0094]

[0095] And Formula 5:

[0096]

[0097] At the same time, the porosity spectrum evaluation parameter AD that can fully reflect the reservoir performance and connectivity is given by combining the deep and shallow lateral curves with the mean and variance of the porosity spectrum, as shown in Formula 6:

[0098]

[0099] In formulas 4, 5 and 6 represents the mean value of the electrical imaging porosity spectrum, φ i represents the calculated electrical imaging porosity, represents the frequency of a porosity, It represents the square value of the porosity spectrum variance, RT represents the deep lateral resistivity value. Combined with the actual well data analysis, it is concluded that when AD<5.2, it is considered to have poor pore and fracture properties, which is a Class C productivity reservoir; when AD>14, it is considered to have excellent pore properties, which is a Class A productivity reservoir; when 5.2≤AD≥14, it is considered to have good pore properties, which is a Class B productivity reservoir.

[0100] S4. Analyze the relationship between conventional well logging curves and gas logging data, determine the boundary between dominant gas-producing reservoirs and low-productivity reservoirs, and construct productivity evaluation parameters AO;

[0101] In step S4, the boundaries of different productivity reservoirs on the scatter plot are analyzed. Since the mean value of the porosity spectrum is closely related to the fracture reservoir and permeability, a two-dimensional scatter plot of conventional process curve parameters (acoustic time difference, compensated neutron porosity, compensated density porosity, permeability, deep lateral resistivity) and the mean value of the porosity spectrum is constructed. Different points represent gas logging data (gas test information) at different depths. In combination with the dry layer, medium productivity reservoir, high productivity reservoir and other reservoirs in the gas test layer, the boundaries of different conventional logging curves in productivity are given respectively. On the one hand, the reservoir productivity can be roughly and quickly judged in combination with the boundaries. On the other hand, the comprehensive productivity evaluation index can be constructed from the above parameters to accurately serve as one of the parameters for judging the reservoir productivity.

[0102] Combined with the porosity spectrum mean value proposed in step S3, Figure 3The figure shows the gas production limit of the reservoir reflected by the change in acoustic travel time and the average value of the porosity spectrum. It can be analyzed from the figure that most of the high-quality gas-producing reservoirs are distributed in the range where the acoustic travel time is greater than 50 μs / ft. Generally, the neutron porosity reflects the total hydrogen index of the rock, which also reflects the changes in pores, fractures, and karst caves from the side. For example, Figure 4 The figure shows the gas production limit of the reservoir reflected by the compensated neutron porosity and the average value of the porosity spectrum. It can be analyzed from the figure that most of the high-quality gas-producing reservoirs are distributed in the range where the neutron porosity is greater than 0%. The permeability is closely related to the quality of the reservoir. For example, Figure 5 As shown in the figure, by combining the average value of the porosity spectrum and the permeability, the permeability of high-quality gas-producing reservoirs mainly concentrates in the range greater than 0.05×10 -3 μm 2 The deep lateral resistivity can reflect the change in the resistivity of the virgin formation through current focusing, and the saturation can characterize the relationship between the gas content and fluid in the formation. By combining the average value of the porosity spectrum to analyze their relationship with the gas production capacity of the reservoir, high-quality gas-producing reservoirs concentrate in the range where the gas saturation is greater than 50% and the deep lateral resistivity is less than 5 kΩ·m.

[0103] In step S4, by combining the relationships between acoustic travel time, compensated neutron porosity, compensated density porosity, permeability, deep lateral resistivity, gas saturation, and the gas production performance of the reservoir, the productivity evaluation parameter AO is given as formula 7 below:

[0104]

[0105] In the formula, AC represents acoustic travel time, CNL represents neutron porosity, K represents permeability, SO represents gas saturation, RT represents deep lateral resistivity, and DEN represents density porosity.

[0106] For example, Figure 6 As shown in the figure, by combining the porosity change and the productivity evaluation parameter AO, the change in the reservoir productivity and their relationship are given. It can be analyzed from the figure that when AO > 1, the target reservoir is regarded as a Class A productivity reservoir; when 0.1 < AO < 10, the target reservoir is regarded as a Class B productivity reservoir; when AO < 1, the target reservoir is regarded as a Class C productivity reservoir.

[0107] S5. According to the numerical values of the parameters calculated above, calculate the evaluation criteria and weights of different parameters respectively, calculate the comprehensive quality index of the carbonate rock reservoir in the study area according to the criteria and weights, and obtain the distribution of the dominant productivity reservoirs based on the comprehensive quality index of the reservoir.

[0108] In step S5, the process of calculating the evaluation criteria and weights of different parameters is as follows: The required parameters are divided into categories A, B, and C respectively to correspond to Class A, Class B, and Class C productivities. Define the parameter value range of Class A productivity as between 0.75 and 1, the value range of Class B productivity as between 0.5 and 0.75, and the value range of Class C productivity as between 0 and 0.5.

[0109] Further, parameter weights and values are defined for the lithofacies parameter AL, reservoir type parameter AE, porosity spectrum evaluation parameter AD, and productivity evaluation parameter AO respectively. Finally, from the following formula 8:

[0110]

[0111] In the formula, Dp is the comprehensive reservoir quality index, x i represents the weight of the i-th parameter, with a value ranging from 0 to 1; y i is the assignment of the i-th parameter, with a value ranging from 0 to 1; Calculate each parameter comprehensively to give the final evaluation value of the reservoir with dominant productivity.

[0112] In step S5, the weights and assignments of each parameter are as follows:

[0113] For the lithofacies AL, the weight is 0.2. The reservoirs where dolomitic limestone and siliceous limestone are located are regarded as Class A productivity reservoirs, with an assignment range of 0.75 - 1. Dolomite-bearing and silica-bearing limestone and dolomite-bearing limestone are regarded as Class B productivity reservoirs, with an assignment range of 0.5 - 0.75. The remaining types are regarded as Class C productivity reservoirs, with an assignment range of 0 - 0.5;

[0114] Combined with the reservoir space discrimination pattern diagram to realize the reservoir type division. For the reservoir type AE, its weight is 0.2. Fractured and fractured-vuggy reservoirs are classified as Class A productivity reservoirs, with an assignment range of 0.75 - 1. Among them, the fractured reservoirs are assigned a value of 0.85 - 1 according to their status, and the fractured-vuggy reservoirs are assigned a value of 0.75 - 0.85 according to their status; Fracture-porous reservoirs and vuggy reservoirs are classified as Class B productivity reservoirs, with an assignment range of 0.5 - 0.75. Among them, the fracture-porous reservoirs are assigned a value range of 0.6 - 0.75, and the vuggy reservoirs are assigned a value range of 0.5 - 0.6. Porous reservoirs are classified as Class C productivity reservoirs, and are divided into 0 - 0.5 according to the porosity size;

[0115] For the porosity spectrum evaluation parameter AD, its weight is 0.3. When AD < 5.2, the reservoir is regarded as a Class C productivity reservoir, with an assignment range of 0 - 0.5. When 5.2 < AD < 14, the reservoir is regarded as a Class B productivity reservoir, with an assignment range of 0.5 - 0.75. When AD > 14, the reservoir is regarded as a Class A productivity reservoir, with an assignment range of 0.75 - 1;

[0116] For the production capacity evaluation parameter AO, its weight is 0.3; when AO < 1, the reservoir is regarded as a C-class production capacity reservoir, and the assignment range is 0 - 0.5; when 1 < AO < 7, the reservoir is regarded as a B-class production capacity reservoir, and the assignment range is 0.5 - 0.75; when AO > 7, the reservoir is regarded as an A-class production capacity reservoir, and the assignment range is 0.75 - 1; after calculating the weights and assignments of each parameter, the comprehensive evaluation index Dp value of the reservoir quality can be obtained by formula 8. For the comprehensive evaluation index of the reservoir quality, when Dp > 0.7, the reservoir is regarded as an A-class production capacity reservoir, that is, a dominant production capacity reservoir; when 0.55 < Dp < 0.7, the reservoir is regarded as a B-class production capacity reservoir; when Dp > 0.55, the reservoir is regarded as a C-class production capacity reservoir.

[0117] Example Two

[0118] The following is a practical case: The method for identifying the dominant production capacity reservoir of carbonate rocks obtains a flow chart, as Figure 1 , including the following steps:

[0119] Step 1, obtain the elemental logging curve data, electrical imaging basic images, conventional logging curves, core experiment data, and gas logging data of the wells in the study area;

[0120] Step 2, use the elemental logging curve data to obtain the regional mineral content and determine the lithofacies AL in the study area;

[0121] Step 3, use the change value of the conventional logging curve and the change form of the spatial characteristics of the electrical imaging reservoir to define the boundary points of the conventional logging curves of different reservoir spatial types, and give the reservoir type parameter AE; use the two-dimensional curve of the electrical imaging to obtain the electrical imaging porosity spectrum parameter and the porosity spectrum evaluation parameter AD;

[0122] Step 4, analyze the relationship between the conventional logging curve and the gas logging data, define the boundary between the dominant gas-producing reservoir and the low-production capacity reservoir, and construct the production capacity evaluation parameter AO;

[0123] Step 5, according to the parameter values calculated above, determine the evaluation criteria and weights of the calculation parameters, calculate the comprehensive quality index of the carbonate rock reservoir in the study area according to the criteria and weights, and obtain the distribution of the dominant production capacity reservoir according to the comprehensive quality index of the reservoir.

[0124] In the example of the present invention, in Step 1, collect the elemental logging curve data (proportion of basic element content) of 12 wells in Block H, perform multi-well standardization operations on the conventional logging curves. At the same time, combine the caliper curve and the deep lateral curve to delete the error points in the logging curves, collect the whole-rock analysis data (mineral composition, porosity, permeability, saturation, etc.) of the cores, and at the same time sort out the gas logging data (depth, gas production, etc.) and construct the work area.

[0125] In the embodiment of the present invention, the step 2 uses conventional logging curves and combines the lithofacies clustering results to input the initial parameters of the target well to obtain the lithofacies AL changes of each layer from layer 1 to layer 6 in the region. It can be concluded that layer number 1 is mainly limestone, partially containing dolomite, with a weight of 0.2 and an assignment of 0.65. The calculation result is 0.13; layer number 2 is mainly limestone, with less dolomite, with a weight of 0.2, an assignment of 0.6, and a calculation result of 0.12; layer number 3 is mainly dolomite, with a weight of 0.2, an assignment of 0.8, and a calculation result of 0.16; layer number 4 is mainly dolomite, with a weight of 0.2, an assignment of 0.8, and a calculation result of 0.8; layer number 5 is mainly siliceous limestone and dolomite, with a weight of 0.2, an assignment of 0.78, and a calculation result of 0.156; layer number 6 is mainly siliceous limestone, with a weight of 0.2, an assignment of 0.76, and a calculation result of 0.152.

[0126] In the embodiment of the present invention, the step three collects the core information and electrical imaging logging information of the H block, which includes spaces such as fractures, holes, and pores, and gives the comprehensive spatial identification result of the carbonate reservoir in the H zone in combination with the numerical changes of conventional logging curves; the numerical means of conventional logging curves at corresponding depths of different layers are statistically calculated, and the reservoir space identification is realized by using the reservoir space comprehensive identification pattern diagram, such as in the H zone, layer 1 is judged as a fracture type reservoir; layer 2 is judged as a fracture pore type reservoir; layer 3 is judged as a fracture type reservoir; layer 4 is judged as a pore type reservoir; layer 5 is judged as a fracture pore type reservoir; layer 6 is judged as a pore type reservoir. Combined with the actual curve state, the reservoir type parameter AE is calculated after weight assignment: Layer 1, the assignment interval is 0.85-1, the weight is 0.2, and the calculation result is 0.18; Layer 2, the assignment interval is 0.6-0.75, the weight is 0.2, and the calculation result is 0.14; Layer 3, the assignment interval is 0.85-1. The weight is 0.2, and the calculation result is 0.18; Layer 4, the assignment interval is 0-0.5, the weight is 0.2, and the calculation result is 0.06; Layer 5, the assignment interval is 0.6-0.75, and the calculation result is 0.12; Layer 6, the assignment interval is 0-0.5, and the calculation result is 0.04. Using formula 2 and formula, the electrical imaging image is converted into a porosity spectrum image, as shown in Figure 7, and use formula 4 and formula 5 to calculate the mean and variance of the porosity spectrum of layers 1 to 6 respectively, and use formula 6 to calculate the porosity spectrum evaluation parameter AD and assign values: layer 1, AD is assigned to 0.8, the weight is 0.3, and the calculation result is 0.24; layer 2, AD is assigned to 0.15, the weight is 0.3, and the calculation result is 0.045; layer 3, AD is assigned to 0.7, the weight is 0.3, and the calculation result is 0.21; layer 4, AD=0.95, the weight is 0.3, and the calculation result is 0.285; layer 5, AD is assigned to 0.6, the weight is 0.3, and the calculation result is 0.18; layer 6, AD is assigned to 0.85, the weight is 0.3, and the calculation result is 0.255;

[0127] In the embodiment of the present invention, the step four uses conventional logging curve data with the help of formula 7 to calculate the productivity evaluation parameter AO of layers 1 to 6 respectively: layer 1, AO=1.34, weight is 0.3, and the calculation result is 0.156; layer 2, AO=0.02, weight is 0.3, and the calculation result is 0.03; layer 3, AO=7.3, weight is 0.3, and the calculation result is 0.21; layer 4, AO=67, weight is 0.3, and the calculation result is 0.285; layer 5, AO=1.64, weight is 0.3, and the calculation result is 0.165; layer 6, AO=0.52, weight is 0.3, and the calculation result is 0.06.

[0128] In the embodiment of the present invention, in step 5, the reservoir quality evaluation index is used to calculate the results of layers 1 to 6 respectively, and the parameters thereof are as follows:

[0129]

[0130]

[0131] Table 2-Parameter table of well A in H area after parameter assignment

[0132] Combined with the discrimination criteria, when Dp>0.7, the reservoir is considered to be a Class A capacity reservoir, i.e., a superior capacity reservoir; when 0.55<Dp<0.7, the reservoir is considered to be a Class B capacity reservoir; when Dp<0.55, the reservoir is considered to be a Class C capacity reservoir. That is, the layers 1, 3, and 4 in Well A of Block H are superior capacity reservoirs. After the parameter calculation as shown in the figure above, Figure 8 The result diagram of identifying carbonate rock reservoirs with superior productivity can clearly show and identify carbonate rock reservoirs with superior productivity.

[0133] The specific embodiments described herein are merely examples of the spirit of the present invention. A person skilled in the art of the present invention may make various modifications or supplements to the specific embodiments described or replace them in a similar manner, but will not deviate from the spirit of the present invention or exceed the defined scope. Although the present invention is described and described in detail in the drawings and the foregoing description, such descriptions and descriptions are considered to be illustrative or exemplary rather than restrictive. It should be understood that within the scope of the following claims, changes and modifications may be made by a person of ordinary skill. Specifically, the present invention encompasses additional embodiments having any combination of features from the above-mentioned different embodiments. With respect to the use of the expression "generally" or "substantially", this patent application should be understood to disclose that these features and values ​​are also fully satisfied, i.e., there is no aforementioned characterization as "generally" or "substantially".

Claims

1. A method for identifying carbonate reservoirs with superior productivity, characterized in that: The following steps are involved: S1. Obtain element logging curve data, basic electrical imaging images, conventional logging curves, core experimental data, and gas logging data of wells in the study area; The gas measurement data include the depth and gas production of the test gas layer at the mining site; S2. Using element logging curve data and conventional logging curve data, calculate the regional mineral content and determine the lithofacies AL of the study area; S3. Using the change value of conventional logging curve and the change form of electrical imaging reservoir space characteristics, determine the conventional logging curve demarcation point of different reservoir space types, and give the reservoir type parameter AE; using the electrical imaging two-dimensional curve to obtain the electrical imaging porosity spectrum parameter, and obtain the porosity spectrum evaluation parameter AD; S4. Analyze the relationship between conventional well logging curves and gas logging data, determine the boundary between dominant gas-producing reservoirs and low-productivity reservoirs, and construct productivity evaluation parameters AO; S5. According to the parameter values ​​obtained from the above calculations, the evaluation standards and weights of different parameters are calculated respectively. The comprehensive reservoir quality index of the carbonate reservoirs in the study area is calculated according to the standards and weights. The distribution of reservoirs with superior production capacity is obtained based on the comprehensive reservoir quality index.

2. The method for identifying carbonate reservoirs with superior productivity according to claim 1, characterized in that: In step S1, the basic data obtained by formation element logging is the data of various elements, including at least the basic element data of calcium, magnesium, silicon, aluminum and iron. The formation basic element data and conventional logging curve data are used, and the element content and the conventional logging curve are used as input elements in cluster analysis. First, PCA principal component analysis is used to realize the dimensionality reduction processing of multiple input data, and then the cluster center algorithm is selected to divide the input data into different categories, that is, the formation lithofacies category is obtained; The electrical imaging basic data is converted into a porosity image by using the Archie formula's flushing zone deformation formula. The mean and variance of the porosity spectrum are obtained using the obtained porosity image, and then the porosity spectrum evaluation parameter AD is constructed using the mean and variance. Conventional logging curve data, i.e., raw logging data, at least include natural gamma curve, wellbore curve, compensated neutron curve, acoustic time difference curve, compensated density and deep and shallow lateral resistivity curve; Core test data include information on mineral content, porosity and permeability of rocks at corresponding depths.

3. The method for identifying carbonate reservoirs with superior productivity according to claim 1, characterized in that: In step S2, using the main element content in the original curve obtained by the element logging instrument, the main rocks of the rock formation include: calcite, dolomite, part of siliceous, mudstone, potassium feldspar, plagioclase; calcium is used as the characteristic element of calcite, magnesium is used as the characteristic element of dolomite, silicon is used as the characteristic element of quartz or siliceous, aluminum is used as the characteristic element of clay minerals, and combined with conventional logging curves; Firstly, calcium-magnesium-silicon-aluminum and acoustic time difference, compensated density, deep lateral resistivity and natural gamma curves are taken as initial research curves. The PCA principal component analysis method is used to standardize the data of the initial research curves, calculate the correlation coefficient matrix of the sample data and solve the characteristic equation; the eigenvectors corresponding to the eigenvalues ​​are used to obtain different principal component curves, and the K-center clustering algorithm is used for the obtained principal component curves. Different principal component curves are input, and 8 clustering categories are selected. Finally, different clustering results are obtained. Combined with the actual core composition, the results are corrected, that is, the classification is repeatedly adjusted, and finally different lithofacies clustering results are obtained.

4. The method for identifying carbonate reservoirs with superior productivity according to claim 3, characterized in that: In the selection of the center point, a point existing in the current group is used to avoid the influence of outliers. AC, DEN, RT, GR and silicon, calcium, magnesium and aluminum elements are used as inputs, and the absolute difference is used to evaluate the quality of the results. The calculation formula 1 is as follows: Where θ represents the absolute difference, p ij represents a point in the two-dimensional projection space, O ij Represents the center point of the cluster in space; The results are divided into 8 categories: limestone, argillaceous limestone, dolomitic limestone, siliceous limestone, dolomitic and siliceous limestone, dolomitic limestone, argillaceous siliceous limestone, and gray dolomite. The lithofacies parameter AL is determined according to different classifications. Among them, the reservoirs where dolomitic limestone, dolomitic limestone, and gray dolomite are located are regarded as Class A productive reservoirs with a value range of 0.75-1, dolomitic and siliceous limestone, siliceous limestone, and argillaceous limestone are regarded as Class B productive reservoirs with a value range of 0.5-0.75, and the remaining categories are regarded as Class C productive reservoirs with a value range of 0-0.

5.

5. The method for identifying carbonate reservoirs with superior productivity according to claim 1, characterized in that: In step S3, the association between the logging curve and the electrical imaging image is established in stage 1, specifically, the conventional logging curve mean at the reservoir position is obtained by using different reservoir spatial characteristics. The conventional logging curve mean includes acoustic wave time difference, compensated density, natural gamma, compensated neutron, and deep lateral resistivity. The mean data of conventional logging curves of multiple well positions are counted to enhance their representative ability in carbonate reservoirs. When there is no electrical imaging image of the reservoir, different types of reservoirs are quickly distinguished by the curve value combination table to preliminarily judge the gas production performance of the reservoir; In stage 2, wellbore resistivity imaging logging is used to convert the apparent resistivity matrix of the wellbore into the porosity matrix of the wellbore using the Archie formula. The porosity values ​​within a certain depth range are statistically analyzed in different porosity value intervals. The statistical results are plotted as a frequency histogram in the coordinate system to obtain the porosity spectrum; the Archie formula of formula 2 is as follows: S n = aR w / (Φ m R t ) (Formula 2) When representing processed charged imaging images, it is assumed that S xo =1, b=1, n=1, Formula 2 is transformed into the following Formula 3: S in Formula 2 and Formula 3 n represents saturation, a represents formation factor, Φ represents porosity, R t represents the original formation resistivity, m represents the cementation index, R w Represents the formation water resistivity, R mf Represents the resistivity of mud filtrate, R xo It indicates the resistivity of the flushing zone; Electrical imaging can reflect the characteristics of holes and cracks in the wellbore. The existence of holes and cracks has a significant impact on the reservoir productivity. Converting electrical imaging images into porosity spectra is an important means to evaluate complex carbonate formations. At the same time, the porosity spectrum is used to obtain the mean and variance of the porosity spectrum to further evaluate the relationship between holes, cracks and formations, through the following formula 4: And Formula 5: At the same time, the porosity spectrum evaluation parameter AD that can fully reflect the reservoir performance and connectivity is given by combining the deep and shallow lateral curves with the mean and variance of the porosity spectrum, as shown in Formula 6: In formulas 4, 5 and 6 represents the mean value of the electrical imaging porosity spectrum, φ i represents the calculated electrical imaging porosity, represents the frequency of a porosity, It represents the square value of the porosity spectrum variance, RT represents the deep lateral resistivity value. Combined with the actual well data analysis, it is concluded that when AD<5.2, it is considered to have poor pore and fracture properties, which is a Class C productivity reservoir; when AD>14, it is considered to have excellent pore properties, which is a Class A productivity reservoir; when 5.2≤AD≥14, it is considered to have good pore properties, which is a Class B productivity reservoir.

6. The method for identifying carbonate reservoirs with superior productivity according to claim 5, characterized in that: The reservoir space characteristics are as follows: In the pore-type reservoir, there are layered or eyelid-eye-shaped structures on the electrical image logging, low natural gamma value, and the resistivity curve shows a "box-shaped" decrease against a high-resistivity background, and the three porosity curves as a whole show a toothed increase or decrease; The reservoir space of the fracture-type reservoir is mainly a series of fractures with different occurrences and low filling degrees, and the thickness is small; On the imaging logging map, it is shown as a series of black sine bands, and on the conventional logging, it shows a low GR value, a decrease in DEN, an increase in CNL or no response in DEN and CNL, an increase in AC, and a positive difference or no difference in the dual laterolog, and the resistivity shows a "U-shaped" decrease against a high-resistivity background; The reservoir space of the vuggy-type reservoir is mainly vugs, which are shown as black spots with a relatively high density on the imaging logging map. On the conventional logging, it shows a low natural gamma value, a decrease in DEN, an increase in both AC and CNL, a certain upward trend in the three porosity curves, a low value against a high-resistivity background in resistivity, and a positive difference or no difference in the dual laterolog resistivity.

7. The method for identifying carbonate reservoirs with superior productivity according to claim 5, characterized in that: In step S4, analyze the boundaries of reservoirs with different productivities on the scatter plot. Since the mean value of the porosity spectrum is closely related to the fracture storage and permeability performance, construct a two-dimensional scatter plot of the conventional logging curve parameters and the mean value of the porosity spectrum. Different points represent the gas logging data at different depths; Combine the dry layers, medium-productivity reservoirs, high-productivity reservoirs, etc. in the tested gas layer positions to give the boundaries of different conventional logging curves in productivity. On the one hand, the productivity level of the reservoir can be roughly and quickly judged based on the boundaries. On the other hand, a comprehensive productivity evaluation index can be constructed from the above several parameters to accurately serve as one of the parameters for judging the reservoir productivity. Combined with the mean porosity spectrum value proposed in step S3, the gas production limit of the reservoir reflected by the change of acoustic time difference and the change of porosity spectrum mean value, most of the high-quality gas-producing reservoirs are distributed in the range of acoustic time difference greater than 50us / ft; the total hydrogen content index of the rock is reflected by neutron porosity, which also reflects the changes of pores, cracks and caves from the side, and compensates for the gas production limit of the reservoir reflected by the neutron porosity and the mean porosity spectrum value. Most of the high-quality gas-producing reservoirs are distributed in the range of neutron porosity greater than 0%; the permeability is closely related to the quality of the reservoir. Combined with the mean porosity spectrum value and permeability, the permeability of high-quality gas-producing reservoirs is mainly concentrated in the range of greater than 0.05×10 -3 μm 2 range; deep lateral resistivity can reflect the change of original formation resistivity through current focusing, saturation can characterize the relationship between formation gas content and fluid, and the relationship between porosity spectrum mean and reservoir gas production capacity is analyzed. High-quality gas-producing reservoirs are concentrated in the range of gas saturation greater than 50% and deep lateral resistivity less than 5kΩ·m.

8. The method for identifying carbonate reservoirs with superior productivity according to claim 7, characterized in that: In step S4, combine the relationships between the acoustic travel time, compensated neutron porosity, compensated density porosity, permeability, deep lateral resistivity, gas saturation and the gas production performance of the reservoir, and give the productivity evaluation parameter AO as formula 7 below: Where AC represents the acoustic travel time, CNL represents the neutron porosity, K represents the permeability, SO represents the gas saturation, RT represents the deep lateral resistivity, and DEN represents the density porosity; Combine the porosity change and the productivity evaluation parameter AO to give the relationship between the reservoir productivity change and the two. When AO > 1, the target reservoir is regarded as a Class A productivity reservoir. When 0.1 < AO < 1, the target reservoir is regarded as a Class B productivity reservoir. When AO < 1, the target reservoir is regarded as a Class C productivity reservoir.

9. The method for identifying carbonate reservoirs with superior productivity according to claim 1, characterized in that: In step S5, the process of calculating the evaluation criteria and weights of different parameters is as follows: The required parameters are divided into categories A, B, and C respectively to correspond to Class A, Class B, and Class C productivities. Define the Class A productivity parameter value to be between 0.75 and 1, the Class B productivity value to be between 0.5 and 0.75, and the Class C productivity value to be between 0 and 0.5; Furthermore, define the parameter weights and values for the lithofacies parameter AL, the reservoir type parameter AE, the porosity spectrum evaluation parameter AD, and the productivity evaluation parameter AO respectively. Finally, from formula 8 below: Where Dp is the comprehensive reservoir quality index, x i Indicates the weight of the i-th parameter, with a value between 0 and 1; y i is the value assigned to the i-th parameter, and its value is between 0 and 1; various parameters are calculated comprehensively to give the final evaluation value of the dominant productivity reservoir.

10. The method for identifying carbonate reservoirs with superior productivity according to claim 9, characterized in that: In step S5, the weights and assignments of each parameter are as follows: For lithofacies AL, the weight is 0.

2. The reservoirs where dolomitic limestone and siliceous limestone are located are regarded as Class A productive capacity reservoirs, with the assignment range of 0.75 - 1. Dolomite- and silica-bearing limestone and dolomite-bearing limestone are regarded as Class B productive capacity reservoirs, with the assignment range of 0.5 - 0.

75. The remaining types are regarded as Class C productive capacity reservoirs, with the assignment range of 0 - 0.5; Combined with the reservoir space discrimination pattern diagram to realize the classification of reservoir types. For reservoir type AE, its weight is 0.

2. Fractured reservoirs and fractured-vuggy reservoirs are classified as Class A productive capacity reservoirs, with the assignment range of 0.75 - 1. Among them, the fractured reservoirs are assigned a value of 0.85 - 1 according to their status, and the fractured-vuggy reservoirs are assigned a value of 0.75 - 0.85 according to their status; Fracture-porous reservoirs and vuggy reservoirs are classified as Class B productive capacity reservoirs, with the assignment range of 0.5 - 0.

75. Among them, the fracture-porous reservoirs are assigned a value range of 0.6 - 0.75, and the vuggy reservoirs are assigned a value range of 0.5 - 0.

6. Porous reservoirs are classified as Class C productive capacity reservoirs, which are divided into 0 - 0.5 according to the porosity size; For the porosity spectrum evaluation parameter AD, its weight is 0.

3. When AD < 5.2, its reservoir is regarded as a Class C productive capacity reservoir, with the assignment range of 0 - 0.

5. When 5.2 < AD < 14, its reservoir is regarded as a Class B productive capacity reservoir, with the assignment range of 0.5 - 0.

75. When AD > 14, its reservoir is regarded as a Class A productive capacity reservoir, with the assignment range of 0.75 - 1; For the productivity evaluation parameter AO, its weight is 0.3; When AO < 1, its reservoir is regarded as a Class C productive capacity reservoir, with the assignment range of 0 - 0.5; When 1 < AO < 7, its reservoir is regarded as a Class B productive capacity reservoir, with the assignment range of 0.5 - 0.75; When AO > 7, its reservoir is regarded as a Class A productive capacity reservoir, with the assignment range of 0.75 - 1; After calculating the weights and assignments of each parameter, the value of the comprehensive reservoir quality evaluation index Dp can be obtained by formula 8. For the comprehensive reservoir quality evaluation index, when Dp > 0.7, its reservoir is regarded as a Class A productive capacity reservoir, that is, a superior productive capacity reservoir. When 0.55 < Dp < 0.7, its reservoir is regarded as a Class B productive capacity reservoir. When Dp < 0.55, its reservoir is regarded as a Class C productive capacity reservoir.

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