A method for predicting deliverability of a tight sandstone reservoir containing igneous rock fragments

Abstract

This invention discloses a method for predicting the productivity of igneous conglomerate tight sandstone and conglomerate reservoirs. The method involves classifying the reservoir into lithofacies, determining the lithofacies identification index for each lithofacies, performing grain size analysis, constructing a grain size identification index calculation model for the igneous conglomerate tight sandstone and conglomerate, obtaining the grain size identification index for perforated and fractured sections, calculating the porosity and permeability of the perforated and fractured sections, and performing depth-weighted processing on the logging parameters of the perforated and fractured sections to determine the fracturing engineering index, perforation section index, and fractured section index values. This establishes a productivity prediction model for igneous conglomerate tight sandstone and conglomerate reservoirs, and calculates the comprehensive productivity index to predict the productivity of a single well. This invention integrates the influence of lithofacies, grain size, reservoir properties, construction design, perforation, and fracturing on single-well productivity, achieving accurate prediction of the productivity of igneous conglomerate tight sandstone and conglomerate reservoirs.

Description

Technical Field

[0001] This invention relates to the field of exploration and development technology of tight sandstone and conglomerate, and specifically to a method for predicting the production capacity of tight sandstone and conglomerate reservoirs containing igneous conglomerate. Background Technology

[0002] Tight sandstone and conglomerate formations are characterized by complex grain sequences, intricate lithology, low or ultra-low porosity and permeability, and strong vertical and horizontal heterogeneity, making it difficult to directly predict the productivity of tight sandstone and conglomerate reservoirs using well logging data. Furthermore, when the gravel in tight sandstone and conglomerate formations is composed of igneous rocks, the heterogeneity of the reservoir is further amplified, making it even more difficult to accurately determine the response relationship between well logging curves and reservoir parameters. This significantly increases the difficulty of well logging evaluation, failing to meet the field requirements for efficient production and development of tight oil reservoirs, and greatly impacting the efficient exploration and development of these reservoirs.

[0003] Currently, given the numerous factors influencing the productivity of conventional tight sandstone and conglomerate fractured wells and the complex mechanisms by which these factors affect productivity, domestic and international scholars have conducted targeted research on productivity prediction methods for tight sandstone and conglomerate reservoirs. The research content mainly focuses on the following two aspects:

[0004] 1) From the perspective of well logging and geology, by classifying the lithology and diagenetic facies of the strata, and based on the hydrocarbon-bearing properties, fluid properties, and pore types, establish hydrocarbon identification charts or establish a comprehensive production prediction index influenced by multiple factors to achieve production prediction.

[0005] 2) From an engineering and development perspective, obtain the construction and technical parameters of fracturing stimulation, reservoir engineering factors, and well drainage systems. By analyzing the relationship between different engineering factors and production capacity during fracturing construction, select the factor with the best correlation as the main control factor to achieve production capacity prediction.

[0006] While the aforementioned methods have been used to predict the productivity of conglomerate reservoirs from the perspectives of well logging, geology, and engineering, they are difficult to apply to conglomerate reservoirs containing igneous boulders. Due to the strong heterogeneity of conglomerate containing igneous boulders, downhole measurement parameters are subject to significant errors, affecting reservoir physical properties obtained from well logging and leading to substantial errors in productivity prediction. Furthermore, the presence of igneous boulders makes grain order identification of conglomerate formations even more difficult.

[0007] Therefore, there is an urgent need to propose a production capacity prediction method applicable to igneous conglomerate tight sandstone reservoirs, so as to achieve accurate prediction of the production capacity of igneous conglomerate tight sandstone reservoirs. Summary of the Invention

[0008] This invention addresses the current difficulty in accurately predicting the productivity of igneous conglomerate tight sandstone reservoirs by proposing a method for predicting the productivity of such reservoirs. This method comprehensively considers the influence of lithofacies, grain order, reservoir properties, construction design, perforation, and fracturing on the single-well productivity of igneous conglomerate tight sandstone reservoirs, thus achieving accurate prediction of the productivity of such reservoirs.

[0009] The present invention adopts the following technical solution:

[0010] A method for predicting the production capacity of igneous conglomerate tight sandstone reservoirs, based on the fracturing engineering index, perforation interval index, and fracturing interval index of a single well, specifically includes the following steps:

[0011] Step 1: Based on the logging, core sampling, and well logging data of the igneous conglomerate tight sandstone reservoir, determine the gravel composition in the igneous conglomerate tight sandstone reservoir. According to the gravel composition, classify the lithofacies of the igneous conglomerate tight sandstone reservoir and obtain the oil-bearing occurrence and lithofacies identification index of each lithofacies.

[0012] Step 2: Based on the grain size analysis data of the igneous conglomerate tight sandstone and conglomerate reservoir, establish a grain size identification index calculation model for the igneous conglomerate tight sandstone and conglomerate, and use the grain size identification index calculation model to calculate the grain size identification index of the perforated and fractured sections in the igneous conglomerate tight sandstone and conglomerate reservoir.

[0013] Step 3: Based on the lithofacies division of the igneous conglomerate tight sandstone reservoir according to the gravel composition, determine the porosity calculation model and permeability calculation model for each lithofacies, and calculate the porosity and permeability of the perforated and fractured sections in the igneous conglomerate tight sandstone reservoir.

[0014] Step 4: Based on the depth of the perforation and fracturing intervals of a single well in the igneous conglomerate tight sandstone reservoir, obtain the logging data of the perforation and fracturing intervals of the igneous conglomerate tight sandstone reservoir, determine the logging parameter values ​​of each perforation and fracturing interval, and perform depth-weighted processing on each logging parameter to obtain the depth-weighted logging parameter values ​​of the perforation and fracturing intervals.

[0015] Step 5: Based on the fracturing engineering parameters, perforation intervals, and depth-weighted logging parameters, porosity, and permeability of the igneous conglomerate tight sandstone and conglomerate reservoir, a comprehensive production capacity evaluation parameter set is obtained. The impact of lithofacies, grain order, reservoir properties, construction design, perforation, and fracturing on the production capacity of a single well in the igneous conglomerate tight sandstone and conglomerate reservoir is considered, and the fracturing engineering index, perforation interval index, and fracturing interval index of a single well in the igneous conglomerate tight sandstone and conglomerate reservoir are determined.

[0016] Step 6: By performing multiple linear regression analysis on the fracturing engineering index, perforation interval index, fracturing interval index and single-well productivity of igneous conglomerate tight sandstone reservoir, a productivity prediction model for igneous conglomerate tight sandstone reservoir is established. The comprehensive productivity index of igneous conglomerate tight sandstone reservoir is calculated, and the single-well productivity of igneous conglomerate tight sandstone reservoir is predicted.

[0017] Preferably, the logging data includes sonic transit time logging curves, neutron logging curves, density logging curves, natural gamma logging curves, and deep resistivity logging curves, and the logging parameters include sonic transit time values, neutron porosity values, density values, natural gamma values, resistivity values, porosity values, and permeability values.

[0018] Preferably, in step 1, based on well logging data and well logging data, K-means clustering and principal component analysis are used to analyze the lithofacies of the igneous conglomerate tight sandstone reservoir. Core samples are obtained by combining the core data of each lithofacies. By analyzing the core samples, the gravel components and oil occurrence of each lithofacies in the igneous conglomerate tight sandstone reservoir are determined. The oil occurrence of each lithofacies in the igneous conglomerate tight sandstone reservoir is sorted in order of superiority to obtain the order of oil occurrence of all lithofacies in the igneous conglomerate tight sandstone reservoir, and the lithofacies identification index of each lithofacies is determined.

[0019] Preferably, in step 2, based on the core data of the igneous conglomerate tight sandstone reservoir, grain size analysis is performed on the core data to determine the median grain size of each lithofacies. By performing correlation analysis between the median grain size of each lithofacies and each logging curve in the logging data, the two logging curves with the highest and second highest correlation with the median grain size are selected to establish a grain size identification index calculation model, determine the grain size range of each lithofacies, and use the grain size identification index calculation model to calculate the grain size identification index of the perforated and fractured sections in the igneous conglomerate tight sandstone reservoir.

[0020] The particle size identification index calculation model is as follows:

[0021] (1)

[0022] In the formula, The particle size distribution index is expressed in mm. The measured value of the logging curve that has the highest correlation with the grain size median. The measured value of the logging curve with the second highest correlation to the grain size median. , , All are coefficients calculated using the order recognition index.

[0023] Preferably, in step 3, the porosity and permeability values ​​of each rock facies are obtained by conducting core property experiments on core samples of each rock facies in the laboratory.

[0024] For each lithofacies in the igneous conglomerate tight sandstone reservoir, the experimental porosity values ​​were fitted with the measured values ​​of the lithofacies in the well logging data to construct the relationship between porosity and each well logging parameter, and to establish a porosity calculation model for the lithofacies, as shown in formula (2):

[0025] (2)

[0026] In the formula, Porosity, expressed as % This is the sound wave time difference value, in µs / ft; Neutron porosity value, in percentages (%) This is the density value, in g / cm³. 3 ; , , and All are porosity calculation coefficients;

[0027] For each lithofacies in the igneous conglomerate tight sandstone reservoir, the experimental permeability values ​​were fitted with the measured values ​​of the lithofacies in the well logging data to construct the relationship between permeability and each well logging parameter, and to establish a lithofacies porosity calculation model, as shown in formula (3):

[0028] (3)

[0029] In the formula, Permeability, in md; , All are permeability calculation coefficients.

[0030] Preferably, in step 4, the depth-weighted calculation formula for the logging parameters is:

[0031] (4)

[0032] In the formula, These are the weighted logging parameters. For logging parameters The measurement value in the i-th layer segment, The thickness of the i-th segment is in meters, where i is the segment number and n is the total number of segments in a single well in a reservoir containing igneous conglomerate tight sandstone and conglomerate. Let be the average depth of the i-th segment. , Let be the depth of the top interface of the i-th segment, in meters. The depth of the bottom interface of the i-th segment, in meters; For the logging parameters within the i-th segment The average value, This represents the total thickness of the layer segment, in meters (m).

[0033] Preferably, in step 5, the comprehensive production capacity evaluation parameters include fracturing engineering parameters and physical property parameters of the perforated section and the fracturing section. The fracturing engineering parameters include the injection fluid volume, fracturing fluid usage, proppant addition, half-fracture length, half-fracture height, fracturing section thickness, perforated section thickness, and particle size distribution index. The physical property parameters of the perforated section and the fracturing section are depth-weighted values ​​of acoustic transit time, neutron porosity, density, natural gamma ray, resistivity, porosity, and permeability.

[0034] For the fracturing engineering index, perforation thickness, fracturing thickness, injection fluid volume, fracturing fluid volume, proppant volume, half-fracture length, and half-fracture height are selected as analysis parameters for the fracturing engineering index. The relationship between the analysis parameters of the fracturing engineering index and the productivity of the igneous conglomerate tight sandstone reservoir is determined. A fracturing engineering index calculation model is established, and the fracturing engineering index of a single well in the igneous conglomerate tight sandstone reservoir is calculated using the fracturing engineering index calculation model.

[0035] The calculation model for the fracturing engineering index is shown in formula (10):

[0036] (10)

[0037] In the formula, This refers to the fracturing engineering index value; The thickness of the perforation is in meters (m). The amount of sand added is expressed in meters (m). 3 ; This refers to the fracturing fluid usage, in cubic meters (m³). 3 ; The length of half a seam is in meters (m). The height is half the seam height, in meters (m).

[0038] For the perforation interval index, the lithofacies identification index, grain size identification index, depth-weighted sonic transit time, depth-weighted neutron porosity, depth-weighted density, depth-weighted natural gamma value, depth-weighted resistivity, depth-weighted porosity, and depth-weighted permeability are selected as the perforation interval index analysis parameters. The relationship between the perforation interval index analysis parameters and the productivity of igneous conglomerate tight sandstone reservoirs is determined. A perforation interval index calculation model is established, and the perforation interval index of a single well in an igneous conglomerate tight sandstone reservoir is calculated using the perforation interval index calculation model.

[0039] The calculation model for the perforation segment index is shown in formula (11):

[0040] (11)

[0041] In the formula, The perforation interval index is dimensionless. The acoustic time difference value after deep weighting is dimensionless. The porosity value is a dimensionless value after depth-weighted processing. The permeability value is a dimensionless value after deep weighting. The density value is a dimensionless value after depth weighting. The lithofacies identification index is a dimensionless index obtained after deep weighting. The order recognition index is dimensionless.

[0042] For the fracturing segment index, the following parameters were selected for analysis: lithofacies identification index, grain size identification index, depth-weighted sonic transit time, depth-weighted neutron porosity, depth-weighted density, depth-weighted natural gamma value, depth-weighted resistivity value, depth-weighted porosity value, and depth-weighted permeability value. The relationship between the fracturing segment index analysis parameters and the productivity of igneous conglomerate tight sandstone reservoirs was determined. A fracturing segment index calculation model was established, and the fracturing segment index of a single well in igneous conglomerate tight sandstone reservoirs was calculated using the fracturing segment index calculation model.

[0043] The fracturing segment index is shown in formula (12):

[0044] (12)

[0045] In the formula, This represents the index value of the fracturing section.

[0046] Preferably, in step 6, the productivity prediction model for igneous conglomerate tight sandstone reservoirs is as follows:

[0047] (8)

[0048] In the formula, This is a comprehensive index of production capacity. This is the fracturing engineering index value. This represents the perforation layer index value. This represents the index value of the fracturing section. , , , All of these are capacity forecast coefficients.

[0049] The present invention has the following beneficial effects:

[0050] From the perspective of production practice, this invention addresses the complex lithology and heterogeneity of conglomerate reservoirs containing igneous gravels. It classifies lithofacies and identifies order parameters based on gravel composition, and obtains reservoir physical properties based on this classification. Combined with depth-weighted logging parameters, a comprehensive productivity evaluation parameter set is constructed. This reduces the impact of igneous gravels on the prediction of productivity in tight conglomerate reservoirs, providing sufficient and reliable data support for productivity prediction in tight conglomerate reservoirs containing igneous gravels.

[0051] This invention comprehensively considers the impact of lithofacies, grain size, reservoir properties, construction design, perforation, and fracturing on the single-well productivity of igneous conglomerate tight sandstone and conglomerate reservoirs. By utilizing multiple influencing factors, it determines the fracturing engineering index, perforation interval index, and fracturing interval index of a single well, and constructs a productivity prediction model for igneous conglomerate tight sandstone and conglomerate reservoirs. This model achieves accurate prediction of single-well productivity in igneous conglomerate tight sandstone and conglomerate reservoirs, meets the field requirements for efficient production and development of tight oil reservoirs, and is of great significance for guiding the exploration and development of igneous conglomerate tight sandstone and conglomerate reservoirs in actual production. Attached Figure Description

[0052] Figure 1 This is a flowchart of a method for predicting the productivity of tight sandstone and conglomerate reservoirs containing igneous rocks, according to the present invention.

[0053] Figure 2 This is a statistical chart of the lithological oil-bearing occurrence of the X oilfield.

[0054] Figure 3 This is a cross-plot of porosity values ​​between unclassified and classified lithofacies.

[0055] Figure 4 This is a cross-plot of permeability values ​​between unclassified and classified lithofacies.

[0056] Figure 5This is a schematic diagram of the fracturing engineering index calculation model.

[0057] Figure 6 This is a schematic diagram of the perforation layer index calculation model.

[0058] Figure 7 This is a schematic diagram of the fracturing segment index calculation model.

[0059] Figure 8 This is a schematic diagram of a model for predicting the productivity of tight sandstone and conglomerate reservoirs containing igneous conglomerate. Detailed Implementation

[0060] The specific implementation of the present invention will be further explained below with reference to the accompanying drawings and seven fractured production wells in the igneous conglomerate tight sandstone and conglomerate reservoir of the X oilfield:

[0061] This invention proposes a method for predicting the production capacity of igneous conglomerate tight sandstone and conglomerate reservoirs. Based on the fracturing engineering index, perforation interval index, and fracturing interval index of a single well, the production capacity of the igneous conglomerate tight sandstone and conglomerate reservoir is predicted. Figure 1 As shown, the specific steps include:

[0062] Step 1: Based on well logging, core sampling, and other data from the igneous conglomerate tight sandstone-conglomerate reservoir, sonic transit time curves, neutron porosity curves, density curves, natural gamma curves, and resistivity curves related to lithology were used as input curves. K-means clustering and principal component analysis were employed to analyze and obtain six lithofacies. The lithofacies classification results in this embodiment are shown in Table 1. By analyzing the core samples, the gravel composition and oil-bearing occurrence of each lithofacies in the igneous conglomerate tight sandstone-conglomerate reservoir were determined, and a statistical chart of lithological oil-bearing occurrence was drawn, as shown in Table 1. Figure 2 As shown in Table 1, the oil-bearing occurrences of each lithofacies in the igneous conglomerate tight sandstone reservoir were sorted in order of superiority to obtain the order of oil-bearing occurrences of all lithofacies in the igneous conglomerate tight sandstone reservoir, and the lithofacies identification index (HRA) of each lithofacies was determined.

[0063] Table 1 Correspondence between lithofacies and conventional logging curve ranges

[0064]

[0065] Step 2: Based on the grain size analysis data of the igneous conglomerate tight sandstone-conglomerate reservoir, grain size analysis was performed on the core data to determine the median grain size measurements from the core samples of seven production wells. The natural gamma ray curve and neutron porosity curve, which have the highest correlation with grain size, were selected. Correlation analysis was then performed between the median grain size of each lithofacies and the logging curves in the well logging data. The two logging curves with the highest and second-highest correlation with the median grain size were selected to establish a grain size identification index calculation model. In this embodiment, the grain size identification index calculation model is as follows:

[0066] (9)

[0067] In the formula, The particle size distribution index is expressed in mm. The natural gamma value, This represents the neutron porosity value.

[0068] Based on the results of the grain size identification index calculation, the relationship between the grain size identification index and gravelly sandstone, sandstone and conglomerate was analyzed and determined. The grain size range of different lithologies is shown in Table 2.

[0069] Table 2. Statistical table of median grain size for different lithofacies

[0070]

[0071] Step 3: Based on the lithological classification of gravel composition in the igneous conglomerate tight sandstone reservoir, and on the basis of gravel composition lithological classification and core depth correction, the porosity calculation model and permeability calculation model for each lithological facies are determined respectively. The experimentally measured porosity and permeability are fitted with conventional logging curves to obtain the porosity and permeability calculation models, as shown in Tables 3 and 4.

[0072] Table 3 Summary of rock facies porosity models with different gravel compositions

[0073]

[0074] In the porosity calculation model in Table 3, This is the calculated porosity value at the current depth. This is the density logging value at the current depth. This represents the acoustic time difference logging value at the current depth.

[0075] Table 4 Summary of permeability models for different gravel compositions in rock facies

[0076]

[0077] In the permeability calculation model in Table 4, This is the calculated porosity value at the current depth. This is the calculated permeability value at the current depth.

[0078] Analysis of Tables 3 and 4 shows that the correlation between the porosity calculation model and the permeability calculation model after facies division is improved compared with that without facies division. Figure 3 This is a cross-plot of porosity values ​​between unclassified and classified lithofacies. Figure 4 This is a cross-plot of permeability values ​​between unclassified and classified lithofacies. Figure 3 and Figure 4 As can be seen, the calculation accuracy of porosity was improved to a certain extent after the lithofacies were divided, and the calculation accuracy of permeability was significantly improved after the lithofacies were divided.

[0079] Step 4: Using formula (4), the acoustic transit time (AC), neutron porosity (CNL), density (DEN), natural gamma (GR), porosity (POR), and permeability (K) values ​​of the perforated and fractured sections of the production well in the igneous conglomerate tight sandstone reservoir are depth-weighted to obtain the depth-weighted acoustic transit time, neutron porosity, density, natural gamma, porosity, and permeability values.

[0080] Step 5: Based on the fracturing engineering parameters of the igneous conglomerate tight sandstone-conglomerate reservoir, the depth-weighted logging parameters of the perforated and fractured sections, porosity, and permeability, a comprehensive production capacity evaluation parameter set is obtained. This parameter set includes fracturing engineering parameters and the physical properties of the perforated and fractured sections. The fracturing engineering parameters include the injection fluid volume, fracturing fluid usage, proppant addition, half-fracture length, half-fracture height, fractured section thickness, perforated section thickness, and graded particle identification index. The physical properties of the perforated and fractured sections are obtained by depth-weighted logging. The depth-weighted values ​​of acoustic transit time, neutron porosity, density, natural gamma, resistivity, porosity, and permeability were analyzed. These factors, along with the combined influence of lithofacies, grain size, reservoir properties, construction design, perforation, and fracturing on the single-well productivity of igneous conglomerate tight sandstone reservoirs, were used to determine the fracturing engineering index, perforation interval index, and fracturing interval index for single wells in igneous conglomerate tight sandstone reservoirs.

[0081] For the fracturing engineering index, the perforation thickness, fracturing thickness, injection fluid volume, fracturing fluid volume, proppant volume, half-fracture length, and half-fracture height were selected as the analysis parameters of the fracturing engineering index. The relationship between the analysis parameters of the fracturing engineering index and the productivity of tight sandstone and conglomerate reservoirs containing igneous boulders was determined, as shown in Table 5.

[0082] Table 5 Summary of Fracturing Engineering Index Analysis Parameters and Single Well Productivity Single Correlation Model

[0083]

[0084] Establish a calculation model for fracturing engineering indices, such as Figure 5 As shown, the fracturing engineering index of a single well in a tight sandstone-conglomerate reservoir containing igneous conglomerate is calculated using the fracturing engineering index calculation model.

[0085] The fracturing engineering index calculation model in this embodiment is as follows:

[0086] (10)

[0087] In the formula, This refers to the fracturing engineering index value; The thickness of the perforation is in meters (m). The amount of sand added is expressed in meters (m). 3 ; This refers to the fracturing fluid usage, in cubic meters (m³). 3 ; The length of half a seam is in meters (m). The height is half the seam height, in meters (m).

[0088] For the perforation interval index, the lithofacies identification index, grain size identification index, depth-weighted sonic transit time, depth-weighted neutron porosity, depth-weighted density, depth-weighted natural gamma value, and depth-weighted resistivity, porosity, and permeability values ​​of the perforation interval were selected as the analysis parameters for the perforation interval index. The relationship between the analysis parameters of the perforation interval index and the productivity of tight sandstone and conglomerate reservoirs containing igneous conglomerate was determined, as shown in Table 6.

[0089] Table 6 Summary of relevant models for perforation interval index analysis parameters based on depth weighting

[0090]

[0091] Establish a calculation model for the perforation interval index, such as Figure 6 As shown, the perforation interval index of a single well in a tight sandstone-conglomerate reservoir containing igneous conglomerate is calculated using a perforation interval index calculation model.

[0092] In this embodiment, the calculation model for the perforation segment index is shown in formula (11):

[0093] (11)

[0094] In the formula, The perforation interval index is dimensionless. The acoustic time difference value after deep weighting is dimensionless. The porosity value is a dimensionless value after depth-weighted processing. The permeability value is a dimensionless value after deep weighting. The density value is a dimensionless value after depth weighting. The lithofacies identification index is a dimensionless index obtained after deep weighting. This is the order recognition index, which is dimensionless.

[0095] For the fracturing segment index, the lithofacies identification index, grain size identification index, depth-weighted acoustic transit time, depth-weighted neutron porosity, depth-weighted density, depth-weighted natural gamma value, depth-weighted resistivity value, depth-weighted porosity value, and depth-weighted permeability value of the fracturing segment were selected as the fracturing segment index analysis parameters. The relationship between the fracturing segment index analysis parameters and the productivity of the tight sandstone and conglomerate reservoir containing igneous conglomerate was determined, as shown in Table 7.

[0096] Table 7 Summary of relevant models for depth-weighted fracturing segment index analysis parameters

[0097]

[0098] Establish a calculation model for the fracturing interval index, such as Figure 7 As shown, the fracturing interval index of a single well in a igneous conglomerate reservoir is calculated using a fracturing interval index calculation model.

[0099] The fracturing segment index is shown in formula (7):

[0100] (12)

[0101] In the formula, This represents the index value of the fracturing section.

[0102] Step 6: By conducting multiple linear regression analysis on the fracturing engineering index, perforation interval index, fracturing interval index, and single-well productivity of igneous conglomerate tight sandstone reservoirs, a productivity prediction model for igneous conglomerate tight sandstone reservoirs is established, such as... Figure 8 As shown, the comprehensive productivity index of igneous conglomerate tight sandstone reservoirs is calculated, and the single-well productivity of igneous conglomerate tight sandstone reservoirs is predicted.

[0103] In this embodiment, the productivity prediction model for igneous conglomerate tight sandstone reservoirs is as follows:

[0104] (13)

[0105] In the formula, This is a comprehensive index of production capacity. This is the fracturing engineering index value. This represents the perforation layer index value. This represents the index value of the fracturing section.

[0106] Of course, the above description is not intended to limit the present invention, and the present invention is not limited to the examples given above. Any changes, modifications, additions or substitutions made by those skilled in the art within the scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. A method for predicting the productivity of tight sandstone and conglomerate reservoirs containing igneous conglomerate, characterized in that, Predicting the productivity of igneous conglomerate-bearing tight sandstone reservoirs based on the fracturing engineering index, perforation interval index, and fracturing interval index of a single well includes the following steps: Step 1: Based on the logging, core sampling, and well logging data of the igneous conglomerate tight sandstone reservoir, determine the gravel composition in the igneous conglomerate tight sandstone reservoir, classify the lithofacies of the igneous conglomerate tight sandstone reservoir according to the gravel composition, and obtain the oil-bearing occurrence and lithofacies identification index of each lithofacies. Step 2: Based on the grain size analysis data of the igneous conglomerate tight sandstone and conglomerate reservoir, establish a grain size identification index calculation model for the igneous conglomerate tight sandstone and conglomerate, and use the grain size identification index calculation model to calculate the grain size identification index of the perforated and fractured sections in the igneous conglomerate tight sandstone and conglomerate reservoir. Step 3: Based on the lithofacies division of the igneous conglomerate tight sandstone reservoir according to the gravel composition, determine the porosity calculation model and permeability calculation model for each lithofacies, and calculate the porosity and permeability of the perforated and fractured sections in the igneous conglomerate tight sandstone reservoir. Step 4: Based on the depth of the perforation and fracturing intervals of a single well in the igneous conglomerate tight sandstone reservoir, obtain the logging data of the perforation and fracturing intervals of the igneous conglomerate tight sandstone reservoir, determine the logging parameter values ​​of each perforation and fracturing interval, and perform depth-weighted processing on each logging parameter to obtain the depth-weighted logging parameter values ​​of the perforation and fracturing intervals. Step 5: Based on the fracturing engineering parameters, perforation intervals, and depth-weighted logging parameters, porosity, and permeability of the igneous conglomerate tight sandstone and conglomerate reservoir, a comprehensive production capacity evaluation parameter set is obtained. The impact of lithofacies, grain order, reservoir properties, construction design, perforation, and fracturing on the production capacity of a single well in the igneous conglomerate tight sandstone and conglomerate reservoir is considered, and the fracturing engineering index, perforation interval index, and fracturing interval index of a single well in the igneous conglomerate tight sandstone and conglomerate reservoir are determined. Step 6: By performing multiple linear regression analysis on the fracturing engineering index, perforation interval index, fracturing interval index and single-well productivity of igneous conglomerate tight sandstone reservoir, a productivity prediction model for igneous conglomerate tight sandstone reservoir is established. The comprehensive productivity index of igneous conglomerate tight sandstone reservoir is calculated, and the single-well productivity of igneous conglomerate tight sandstone reservoir is predicted. In step 2, based on the core data of the igneous conglomerate tight sandstone and conglomerate reservoir, grain size analysis is performed on the core data to determine the median grain size of each lithofacies. By performing correlation analysis between the median grain size of each lithofacies and each logging curve in the logging data, the two logging curves with the highest and second highest correlation with the median grain size are selected to establish a grain size identification index calculation model, determine the grain size range of each lithofacies, and use the grain size identification index calculation model to calculate the grain size identification index of the perforated and fractured sections in the igneous conglomerate tight sandstone and conglomerate reservoir. The particle size identification index calculation model is as follows: （1） In the formula, The particle size distribution index is expressed in mm. The measured value of the logging curve that has the highest correlation with the grain size median. The measured value of the logging curve with the second highest correlation to the grain size median. , , All are coefficients used in the particle size identification index calculation. In step 4, the depth-weighted calculation formula for the logging parameters is as follows: （4） In the formula, These are the weighted logging parameters. For logging parameters The measurement value in the i-th layer segment, The thickness of the i-th segment is in meters, where i is the segment number and n is the total number of segments in a single well in a reservoir containing igneous conglomerate tight sandstone and conglomerate. Let be the average depth of the i-th segment. , Let be the depth of the top interface of the i-th layer segment, in meters. The depth of the bottom interface of the i-th segment is expressed in meters (m). For the logging parameters within the i-th segment The average value, This represents the total thickness of the layer segment, in meters (m).

2. The method for predicting the productivity of igneous conglomerate tight sandstone and conglomerate reservoirs according to claim 1, characterized in that, The logging data includes sonic transit time logging curves, neutron logging curves, density logging curves, natural gamma logging curves, and deep resistivity logging curves. The logging parameters include sonic transit time values, neutron porosity values, density values, natural gamma values, resistivity values, porosity values, and permeability values.

3. The method for predicting the productivity of igneous conglomerate tight sandstone and conglomerate reservoirs according to claim 2, characterized in that, In step 1, based on well logging data, K-means clustering and principal component analysis are used to analyze the lithofacies of the igneous conglomerate tight sandstone reservoir. Core samples are obtained by combining the core data of each lithofacies. By analyzing the core samples, the gravel composition and oil occurrence of each lithofacies in the igneous conglomerate tight sandstone reservoir are determined. The oil occurrence of each lithofacies in the igneous conglomerate tight sandstone reservoir is sorted in order of superiority to obtain the order of oil occurrence of all lithofacies in the igneous conglomerate tight sandstone reservoir, and the lithofacies identification index of each lithofacies is determined.

4. The method for predicting the productivity of igneous conglomerate tight sandstone and conglomerate reservoirs according to claim 1, characterized in that, In step 3, core physical property experiments are conducted on core samples of each rock facies, and the porosity and permeability experimental values ​​of each rock facies are measured in the laboratory. For each lithofacies in the igneous conglomerate tight sandstone reservoir, the experimental porosity values ​​were fitted with the measured values ​​of the lithofacies in the well logging data to construct the relationship between porosity and each well logging parameter, and to establish a porosity calculation model for the lithofacies, as shown in formula (2): （2） In the formula, Porosity, expressed as % This is the sound wave time difference value, in µs / ft; Neutron porosity value, in percentages (%) This is the density value, in g / cm³. 3 ; , , and All are porosity calculation coefficients; For each lithofacies in the igneous conglomerate tight sandstone reservoir, the experimental permeability values ​​were fitted with the measured values ​​of the lithofacies in the well logging data to construct the relationship between permeability and each well logging parameter, and to establish a lithofacies porosity calculation model, as shown in formula (3): （3） In the formula, Permeability, in md; , All are permeability calculation coefficients.

5. The method for predicting the productivity of igneous conglomerate tight sandstone and conglomerate reservoirs according to claim 1, characterized in that, In step 5, the comprehensive production capacity evaluation parameters include fracturing engineering parameters and physical property parameters of the perforated and fracturing sections. The fracturing engineering parameters include the injection fluid volume, fracturing fluid usage, proppant addition, half-fracture length, half-fracture height, fracturing section thickness, perforated section thickness, and particle size distribution index. The physical property parameters of the perforated and fracturing sections are depth-weighted sonic transit time, depth-weighted neutron porosity, depth-weighted density, depth-weighted natural gamma value, depth-weighted resistivity, depth-weighted porosity, and depth-weighted permeability. For the fracturing engineering index, perforation thickness, fracturing thickness, injection fluid volume, fracturing fluid volume, proppant volume, half-fracture length, and half-fracture height are selected as analysis parameters for the fracturing engineering index. The relationship between the analysis parameters of the fracturing engineering index and the productivity of the igneous conglomerate tight sandstone reservoir is determined. A fracturing engineering index calculation model is established, and the fracturing engineering index of a single well in the igneous conglomerate tight sandstone reservoir is calculated using the fracturing engineering index calculation model. The calculation model for the fracturing engineering index is shown in formula (10): （10） In the formula, This refers to the fracturing engineering index value; The thickness of the perforation is in meters (m). The amount of sand added is expressed in meters (m). 3 ; This refers to the fracturing fluid usage, in cubic meters (m³). 3 ; The length of half a seam is in meters (m). The height is half the seam height, in meters (m). For the perforation interval index, the lithofacies identification index, grain size identification index, depth-weighted sonic transit time, depth-weighted neutron porosity, depth-weighted density, depth-weighted natural gamma value, depth-weighted resistivity, depth-weighted porosity, and depth-weighted permeability are selected as the perforation interval index analysis parameters. The relationship between the perforation interval index analysis parameters and the productivity of igneous conglomerate tight sandstone reservoirs is determined. A perforation interval index calculation model is established, and the perforation interval index of a single well in an igneous conglomerate tight sandstone reservoir is calculated using the perforation interval index calculation model. The calculation model for the perforation segment index is shown in formula (11): （11） In the formula, The perforation interval index is dimensionless. The acoustic time difference value after deep weighting is dimensionless. The porosity value is a dimensionless value after depth-weighted processing. The permeability value is a dimensionless value after deep weighting. The density value is a dimensionless value after depth weighting. The lithofacies identification index is a dimensionless index obtained after deep weighting. The order recognition index is dimensionless. For the fracturing segment index, the following parameters were selected for analysis: lithofacies identification index, grain size identification index, depth-weighted sonic transit time, depth-weighted neutron porosity, depth-weighted density, depth-weighted natural gamma value, depth-weighted resistivity value, depth-weighted porosity value, and depth-weighted permeability value. The relationship between the fracturing segment index analysis parameters and the productivity of igneous conglomerate tight sandstone reservoirs was determined. A fracturing segment index calculation model was established, and the fracturing segment index of a single well in igneous conglomerate tight sandstone reservoirs was calculated using the fracturing segment index calculation model. The fracturing segment index is shown in formula (12): （12） In the formula, This represents the index value of the fracturing section.

6. The method for predicting the productivity of igneous conglomerate tight sandstone and conglomerate reservoirs according to claim 1, characterized in that, In step 6, the productivity prediction model for igneous conglomerate tight sandstone reservoirs is as follows: （8） In the formula, This is a comprehensive index of production capacity. This is the fracturing engineering index value. This represents the perforation layer index value. This represents the index value of the fracturing section. , , , All of these are capacity forecast coefficients.

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