A method of classifying conglomerate reservoirs

Abstract

The application provides a conglomerate reservoir reservoir classification method, and belongs to the technical field of oil and gas development exploration, and solves the problem that the existing oil layer classification method cannot accurately predict the oil production and development potential of different wells. A conglomerate reservoir reservoir classification method comprises the following steps: calculating the static Young's modulus of all wells according to the measured longitudinal wave data of all wells, the measured transverse wave data of part of the wells and the measured static Young's modulus of part of the wells; obtaining the fitting relationship between the static Young's modulus and the rock brittleness index according to the measured mineral content data of part of the wells and the measured static Young's modulus of part of the wells; calculating the rock brittleness index of all wells based on the fitting relationship between the static Young's modulus and the rock brittleness index according to the static Young's modulus of all wells; and establishing the four-quadrant classification standard of the reservoir reservoir according to the rating parameters and the rock brittleness index of all wells, so as to determine the reservoir reservoir category.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil and gas exploration and development, and particularly relates to a conglomerate reservoir classification method. BACKGROUND

[0002] Due to the influence of particle size, gravel composition, gravel distribution density and strength, the porosity and permeability characteristics of conglomerate reservoirs are generally poor, the heterogeneity is strong, and the natural productivity of conglomerate oil and gas reservoirs is relatively low. In order to carry out industrial production, reservoir reconstruction is usually required. Before reservoir reconstruction, the reservoir needs to be classified. Different types of reservoirs use different reconstruction methods, so reasonable oil layer classification is crucial for the optimization of fracturing design and production control of horizontal wells, and also provides a favorable basis for fine evaluation of the productivity of horizontal wells.

[0003] At present, the common oil layer classification methods can be roughly summarized into three categories: ①Using cross-plot technology to optimize parameters such as physical properties and pore structure which have a greater impact on reservoir quality, establishing different reservoir type charts, comprehensive evaluation indexes or given parameter distribution range; ②Inductive classification through the distribution characteristics of sedimentary facies, lithofacies and reservoir space; ③Comprehensive analysis of core test data, optimization of key parameters, and establishment of classification model by using mathematical algorithms such as clustering analysis, neural network and support vector machine. However, due to the characteristics of complex lithology change, high gravel content and strong heterogeneity of dense conglomerate, the fracturing operation is difficult, the reservoir characteristics of different layer systems are different, the lithology is complex, the physical and mechanical properties are greatly different, and the geological characteristics and production performance show certain contradictions. Therefore, the existing oil layer classification model based on various geological parameters cannot well judge the production capacity, seriously restricts the potential evaluation of further production of subsequent wells, and cannot meet the requirements of oil and gas exploration and development.

[0004] Therefore, the existing oil layer classification method cannot accurately predict the oil production and development potential of different wells. SUMMARY

[0005] The present application aims to provide a conglomerate reservoir classification method to alleviate the technical problem that the existing oil layer classification method cannot accurately predict the oil production and development potential of different wells.

[0006] The conglomerate reservoir classification method provided by the present application comprises:

[0007] According to the measured P-wave data of all wells, the measured S-wave data of part of the wells and the measured static Young's modulus of part of the wells, the static Young's modulus of all wells is calculated;

[0008] According to the measured mineral content data of part of the wells and the measured static Young's modulus of part of the wells, the fitting relationship between the static Young's modulus and the rock brittleness index is obtained;

[0009] According to the static Young's modulus of all wells, the rock brittleness indexes of all wells are calculated based on the fitting relationship between the static Young's modulus and the rock brittleness index;

[0010] According to the rating parameters and the rock brittleness indexes of all wells, the reservoir reservoir four-quadrant classification standard is established, and the reservoir reservoir category is determined.

[0011] Further, before the step of calculating the static Young's modulus of all wells according to the measured P-wave data of all wells, the measured S-wave data of part of the wells and the measured static Young's modulus data of part of the wells, it further includes:

[0012] According to the measured mineral content and the compensated neutron, a statistical scatter plot is established;

[0013] Based on the statistical scatter plot, the compensated neutron limit value of the high-plasticity mineral conglomerate and the low-plasticity mineral conglomerate is obtained with the mineral content of 50% as the boundary;

[0014] According to the compensated neutron limit value, the division standard of the high-plasticity mineral conglomerate and the low-plasticity mineral conglomerate is established.

[0015] Further, the step of calculating the static Young's modulus of all wells according to the measured P-wave data of all wells, the measured S-wave data of part of the wells and the measured static Young's modulus, specifically includes:

[0016] According to the measured S-wave data and the measured P-wave data of part of the wells, a fitting function is obtained;

[0017] According to the fitting function and the measured P-wave data of all wells, the predicted S-wave data of all wells is obtained;

[0018] According to the predicted S-wave data and the measured P-wave data of all wells, the dynamic Young's modulus of all wells is calculated by using an empirical formula;

[0019] According to the measured static Young's modulus of part of the wells and the dynamic Young's modulus of all wells, the dynamic-static Young's modulus conversion formula of the high-plasticity mineral conglomerate and the low-plasticity mineral conglomerate is established based on the division standard of the high-plasticity mineral conglomerate and the low-plasticity mineral conglomerate;

[0020] According to the dynamic Young's modulus of all wells, the static Young's modulus of all wells is obtained based on the dynamic-static Young's modulus conversion formula of the high-plasticity mineral conglomerate and the low-plasticity mineral conglomerate.

[0021] Further, the S-wave data includes S-wave time difference and S-wave velocity;

[0022] The P-wave data includes P-wave time difference and P-wave velocity;

[0023] Further, the step of obtaining the fitting function according to the measured shear wave data and the measured longitudinal wave data of the partial well specifically comprises:

[0024] Obtaining the fitting function according to the shear wave interval travel time and the longitudinal wave interval travel time of the partial well;

[0025] The fitting function is a quadratic function of the shear wave interval travel time with respect to the longitudinal wave interval travel time.

[0026] Further, the empirical formula is

[0027] wherein E d is the dynamic Young's modulus, Rho is the density, V s is the shear wave velocity, and V p is the longitudinal wave velocity.

[0028] Further, the measured mineral content data includes quartz mineral content, carbonate mineral content and clay mineral content.

[0029] Further, the step of obtaining the fitting relationship between the static Young's modulus and the rock brittleness index according to the measured mineral content data of the partial well and the measured static Young's modulus of the partial well specifically comprises:

[0030] Calculating the brittleness index of the partial well by using the mineral combination method formula according to the quartz mineral content, the carbonate mineral content and the clay mineral content of the partial well;

[0031] Obtaining the fitting relationship between the static Young's modulus and the rock brittleness index according to the brittleness index of the partial well and the measured static Young's modulus.

[0032] Further, the rating parameters include porosity, saturation and daily oil production.

[0033] Further, the step of establishing the oil reservoir classification standard and determining the oil reservoir category according to the rating parameters and the rock brittleness index of all wells specifically comprises:

[0034] Establishing an oil reservoir classification coordinate system with the product of porosity and saturation as the X-axis and the brittleness index as the Y-axis according to the porosity, the saturation, the daily oil production and the rock brittleness index;

[0035] Dividing the daily oil production into four intervals from more to less, marking the daily oil production in each interval on the oil reservoir classification coordinate system by using different colors to obtain a final coordinate system;

[0036] Establishing an oil reservoir four-quadrant classification standard including Class I, Class II, Class III and Class IV according to the final coordinate system;

[0037] Determining the oil reservoir category according to the oil reservoir four-quadrant classification standard.

[0038] The conglomerate reservoir classification method provided by the application comprises the following steps: calculating the static Young's modulus of all wells according to the measured P-wave data of all wells, the measured S-wave data of part of the wells and the measured static Young's modulus of part of the wells; obtaining the fitting relationship between the static Young's modulus and the rock brittleness index according to the measured mineral content data of part of the wells and the measured static Young's modulus of part of the wells; calculating the rock brittleness index of all wells based on the fitting relationship between the static Young's modulus and the rock brittleness index according to the static Young's modulus of all wells; and establishing a four-quadrant classification standard of reservoirs and determining the reservoir classification according to the rating parameters and the rock brittleness index of all wells.

[0039] The conglomerate reservoir classification method provided by the application comprises the following steps: calculating the static Young's modulus of all wells according to the measured P-wave data of all wells, the measured S-wave data of part of the wells and the measured static Young's modulus of part of the wells; obtaining the fitting relationship between the static Young's modulus and the rock brittleness index according to the measured mineral content data of part of the wells and the measured static Young's modulus of part of the wells; calculating the rock brittleness index of all wells based on the fitting relationship between the static Young's modulus and the rock brittleness index according to the static Young's modulus of all wells; and establishing a four-quadrant classification standard of reservoirs and determining the reservoir classification according to the rating parameters and the rock brittleness index of all wells. BRIEF DESCRIPTION OF DRAWINGS

[0040] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed in the specific embodiments or the prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0041] Figure 1 The conglomerate reservoir classification method flow chart provided for the embodiment 1 of the present application;

[0042] Figure 2 The detailed flow chart of the conglomerate reservoir classification method provided for the embodiment 2 of the present application;

[0043] Figure 3Figure 2 is a compensated neutron scatter plot of plastic mineral content for Example 2 of the present application;

[0044] Figure 4 Figure 3 is a relationship plot of shear travel time and compressional travel time for Example 2 of the present application;

[0045] Figure 5 Figure 4 is a Young's modulus conversion plot for high plasticity mineral conglomerate for Example 2 of the present application;

[0046] Figure 6 Figure 5 is a Young's modulus conversion plot for low plasticity mineral conglomerate for Example 2 of the present application;

[0047] Figure 7 Figure 6 is a static Young's modulus and brittleness scatter plot for Example 2 of the present application;

[0048] Figure 8 Figure 7 is a comparison of the comprehensive interpretation results and measured values for the brittleness interpretation model for Example 2 of the present application;

[0049] Figure 9 Figure 8 is a conglomerate reservoir classification four-quadrant plot for Example 2 of the present application.

[0050] Figure 10 Figure 9 is a plot of sanding intensity and whether industrial oil flow is reached for wells with poor brittleness for Example 2 of the present application;

[0051] Figure 11 Figure 10 is a plot of sanding intensity x brittleness and daily oil production for Example 2 of the present application. DETAILED DESCRIPTION

[0052] In order to make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions of the present application will be described below in connection with the drawings. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of the present application.

[0053] The terms "comprising" and "having" and any variations thereof in the embodiments of the present application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but can optionally include other steps or units that are not listed, or can optionally include other steps or units that are inherent to the process, method, product, or device.

[0054] The current common oil reservoir classification method is roughly summarized into three types: ① using crossplot technique to optimize parameters such as physical property and pore structure which have greater influence on reservoir quality, establishing different reservoir type chart, comprehensive evaluation index or given parameter distribution range; ② classifying by distribution characteristics of sedimentary facies, lithofacies and reservoir space; ③ comprehensively analyzing core test data, optimizing key parameters and using mathematical algorithms such as clustering analysis, neural network and support vector machine to establish classification model. However, due to the characteristics of complex lithology change, high gravel content and strong heterogeneity of the dense conglomerate, the fracturing operation is difficult, the reservoir characteristics of different layer systems are different, the lithology is complex, the physical property and mechanical property are greatly different, and the geological characteristics and production performance show certain contradiction, so the production of the region with good physical property condition is not necessarily high. Therefore, the existing oil reservoir classification model based on various geological parameters cannot well judge the production size, seriously restricts the further production potential evaluation of subsequent wells, and cannot meet the requirements of oil and gas exploration and development.

[0055] Therefore, the existing oil reservoir classification method cannot accurately predict the oil production and development potential of different wells.

[0056] To solve the above problems, the embodiment of the present application provides a conglomerate reservoir reservoir classification method.

[0057] Embodiment 1:

[0058] As Figure 1 shown, the conglomerate reservoir reservoir classification method provided by the embodiment of the present application comprises:

[0059] S1: calculating the static Young's modulus of all wells according to the measured longitudinal wave data of all wells, the measured transverse wave data of part of the wells and the measured static Young's modulus of part of the wells;

[0060] S2: obtaining the fitting relationship between the static Young's modulus and the rock brittleness index according to the measured mineral content data of part of the wells and the measured static Young's modulus of part of the wells;

[0061] S3: calculating the rock brittleness index of all wells based on the fitting relationship between the static Young's modulus and the rock brittleness index according to the static Young's modulus of all wells;

[0062] S4: establishing the reservoir reservoir four-quadrant classification standard and determining the reservoir reservoir type according to the rating parameter and the rock brittleness index of all wells.

[0063] The conglomerate reservoir classification method provided by the embodiment of the present application is used to calculate the static Young's modulus of all wells through the shear wave data, the longitudinal wave data, the dynamic and static Young's modulus and the mineral content data, to establish the fitting relationship between the static Young's modulus and the rock brittleness index, and to further obtain the rock brittleness index of all wells. The four-quadrant classification standard of the reservoir is established based on the combination of the rating parameters and the rock brittleness index, so as to determine the reservoir classification. The four-quadrant conglomerate reservoir classification standard is formulated by using the geological engineering integration idea. The conglomerate reservoir rock mechanics model is established by calculating the key mechanical parameters such as the shear wave, the longitudinal wave and the dynamic and static Young's modulus. The rock brittleness index is introduced as an important index for the oil layer classification based on the rock mechanics model and in combination with the actual production index, the oil layer classification method is updated, and the four-quadrant conglomerate reservoir classification standard is formed, which mainly uses the resource property (porosity and saturation) and secondarily uses the transformation property (brittleness index). The method avoids the limitation of using a single physical property to determine the oil production, and the four-quadrant conglomerate reservoir classification standard can accurately predict the oil production of different wells and the development potential in the later period, thereby providing key guiding data for oil and gas exploration and development.

[0064] In a possible implementation, before step S1, the method further comprises:

[0065] S01: establishing a statistical scatter plot according to the measured mineral content and the compensated neutron;

[0066] S02: obtaining the compensated neutron limit value of the high-plasticity mineral conglomerate and the low-plasticity mineral conglomerate based on the statistical scatter plot and taking the mineral content of 50% as the limit;

[0067] S03: establishing the division standard of the high-plasticity mineral conglomerate and the low-plasticity mineral conglomerate according to the compensated neutron limit value.

[0068] The division standard of the high-plasticity mineral conglomerate and the low-plasticity mineral conglomerate is completed through the above steps, which provides a basis for calculating the dynamic and static Young's modulus conversion formula of different plasticity in step S1.

[0069] In a possible implementation, step S1 specifically comprises:

[0070] S11: obtaining a fitting function according to the measured shear wave data and the measured longitudinal wave data of part of the wells;

[0071] S12: obtaining the predicted shear wave data of all the wells according to the fitting function and the measured longitudinal wave data of all the wells;

[0072] S13: calculating the dynamic Young's modulus of all the wells by using an empirical formula according to the predicted shear wave data and the measured longitudinal wave data of all the wells;

[0073] S14: According to the measured static Young's modulus of the partial wells and the dynamic Young's modulus of all the wells, a dynamic-static Young's modulus conversion formula of the high plasticity mineral conglomerate and the low plasticity mineral conglomerate is respectively established based on the classification standard of the high plasticity mineral conglomerate and the low plasticity mineral conglomerate.

[0074] S15: According to the dynamic Young's modulus of all the wells, the static Young's modulus of all the wells is obtained based on the dynamic-static Young's modulus conversion formula of the high plasticity mineral conglomerate and the low plasticity mineral conglomerate.

[0075] The calculation of the static Young's modulus of all the wells is completed through the above steps, and data is provided for the calculation of the rock brittleness index of all the wells through the static Young's modulus and the rock brittleness index fitting relationship in step S2.

[0076] In a possible implementation, the shear wave data includes shear wave time difference and shear wave velocity; the compressional wave data includes compressional wave time difference and compressional wave velocity.

[0077] In a possible implementation, step S11 specifically includes:

[0078] S111: The fitting function is obtained according to the shear wave time difference and the compressional wave time difference of the partial wells.

[0079] S112: The fitting function is a quadratic function of the shear wave time difference with respect to the compressional wave time difference.

[0080] In a possible implementation, the empirical formula is

[0081] wherein, E d is the dynamic Young's modulus, Rho is the density, V s is the shear wave velocity, and V p is the compressional wave velocity.

[0082] In a possible implementation, the measured mineral content data includes quartz mineral content, carbonate mineral content and clay mineral content.

[0083] In a possible implementation, step S2 specifically includes:

[0084] S21: The brittleness index of the partial wells is calculated by using the mineral combination method formula according to the quartz mineral content, the carbonate mineral content and the clay mineral content of the partial wells.

[0085] S22: The fitting relationship between the static Young's modulus and the rock brittleness index is obtained according to the brittleness index and the measured static Young's modulus of the partial wells.

[0086] In a possible implementation, the rating parameters include porosity, saturation and daily oil production.

[0087] In a possible implementation, according to the rating parameter and the rock brittleness index of all wells,

[0088] The step of establishing the four-quadrant classification standard of the reservoir reservoir and determining the reservoir reservoir category includes the following steps:

[0089] According to the porosity, saturation, daily oil production and rock brittleness index, a reservoir reservoir classification coordinate system is established, in which the product of porosity and saturation is taken as the X axis, and the brittleness index is taken as the Y axis.

[0090] The daily oil production is divided into four intervals from more to less, and the daily oil production in each interval is marked on the reservoir reservoir classification coordinate system using different colors to obtain a final coordinate system.

[0091] According to the final coordinate system, a four-quadrant classification standard of the reservoir reservoir is established, including type I, type II, type III and type IV.

[0092] For example, according to the data point distribution area and data point color type on the final coordinate system, the wells are classified according to the classification standards of type I, type II, type III and type IV.

[0093] According to the four-quadrant classification standard of the reservoir reservoir, the reservoir reservoir category is determined.

[0094] Among them, the type I reservoir reservoir has better resource property, better transformation type and larger development potential;

[0095] The type II reservoir reservoir has better resource property, general transformation type and general development potential.

[0096] The type III reservoir reservoir has general resource property, better transformation type and general development potential.

[0097] The type IV reservoir reservoir has general resource property, general transformation type and basically no development potential.

[0098] Example 2:

[0099] The embodiment of the present application is aimed at the reservoir reservoir classification of Mahu 1 well area, and the specific implementation manner is as follows:

[0100] I. Reservoir reservoir object: Mahu 1 well area.

[0101] II. Regional geological overview: Mahu 1 well area is located in Mahunannan reservoir group, one of the six three-dimensional reservoir groups in Mahu hydrocarbon sag. In 2017-2018, the upper Wuerhe group reservoir controlled reserves were declared, the oil-bearing area was 131.8 km2, and the oil geological reserves were 157,000 tons. It is the most realistic replacement area and main battlefield for future continuous stable and high production. There are three sets of development layer series in the upper Wuerhe group of Permian system, P3w2 1 and P3w2 2The rock debris is mainly composed of lithic conglomerate and feldspathic lithic conglomerate, with high content of rock debris and low compositional maturity, and the average Young's modulus is 9.2 GPa; P3w1 2 The rock debris is mainly composed of feldspathic conglomerate, with high content of quartz and feldspar, high compositional maturity, and the average Young's modulus is 22.1 GPa, with large difference in mechanical properties. The mechanical properties of the rock determine the difficulty of reservoir reconstruction, and the difficulty of reservoir reconstruction in the study area is quite different. The resources (sand thickness, oil layer thickness, porosity and measured oil saturation) of the three sets of layers are basically the same, and the difference in reworkability (static Young's modulus, static Poisson's ratio) is large.

[0102] III. The reservoir classification process is as shown in Figure 2

[0103] Step 1 Through the corresponding relationship between the measured mineral content of the KXX1 well in the study area and the compensated neutron CNL, as shown in Figure 3 The compensated neutron CNL limit value of high plasticity mineral conglomerate and low plasticity mineral conglomerate is divided according to the mineral content of 50% through the statistical scatter diagram, that is, when CNL≥0.21, it is high plasticity mineral conglomerate, and when CNL<0.21, it is low plasticity mineral conglomerate.

[0104] Step 2 Through the shear wave time difference in the measured shear wave data and the compressional wave time difference in the compressional wave data of a few wells, the fitting function is as follows:

[0105] Shear wave time difference = 0.00790151 x compressional wave time difference 2 -1.27936 x compressional wave time difference + 300.398

[0106] The fitting relationship diagram of shear wave time difference and compressional wave time difference is as shown in Figure 4 The results show that the fitting function has strong applicability in this area.

[0107] Step 3 The comparison results of the fitting shear wave and the measured shear wave show that the fitting shear wave has high correlation with the measured shear wave. Through the measured compressional wave data of all wells and the fitting function, the shear wave data of all wells are obtained.

[0108] Step 4 According to the shear wave prediction results and the compressional wave data of all wells, the dynamic Young's modulus E d of all wells is calculated by the empirical formula.

[0109] The empirical formula is as follows:

[0110]

[0111] In the formula: E d is the dynamic Young's modulus; Rho is the density; V s is the shear wave velocity in the shear wave data; V​p For the longitudinal wave velocity in the longitudinal wave data.

[0112] Step 5 According to the calculated dynamic Young's modulus E d and the measured static Young's modulus E s of a few wells, the dynamic-static Young's modulus conversion formula of high plasticity mineral conglomerate and low plasticity mineral conglomerate is established respectively, as shown in Figure 5 、 Figure 6 .

[0113] The scatter plot shown in Figure 5 、 Figure 6 can obtain the relationship between the dynamic Young's modulus and the static Young's modulus as follows:

[0114] The relationship of high plasticity is: E s = 0.5463 × E d - 4.6979 × CNL ≥ 0.21

[0115] The relationship of low plasticity is: E s = 0.4756 × E d + 9.969 × CNL < 0.21

[0116] No matter high plasticity mineral or low plasticity mineral, the dynamic Young's modulus and the static Young's modulus have good correlation. According to the above relationship, the mutual conversion of dynamic Young's modulus and static Young's modulus can be realized.

[0117] Step 6 According to the dynamic Young's modulus E d of all wells, the static Young's modulus E s of all wells is calculated through the conversion formula of Step 5.

[0118] Step 7 According to the measured quartz mineral content Vq, carbonate rock mineral content V cb and clay mineral content V cl of a few wells, the brittle index BRIT is calculated by using the mineral combination method formula.

[0119] The mineral combination method formula is as follows:

[0120]

[0121] In the formula: BRIT is the rock brittle index; V q is the quartz mineral content; V cb is the carbonate rock mineral content; V cl is the clay mineral content.

[0122] Step 8 The measured static Young's modulus Es and the brittle index BRIT of a few wells are fitted, as shown in Figure 7The static Young's modulus E s The fitting relationship between the static Young's modulus E s and the rock brittleness index BRIT is a positive linear relationship, so the static Young's modulus can be used to represent the rock brittleness.

[0123] The fitting relationship is BRIT = 0.9687 x E s + 33.142

[0124] As Figure 8 shown, the coincidence rate of the comprehensive interpretation result of the brittleness interpretation model and the measured value is high, so the model is considered to be suitable for the research in the area.

[0125] Step 9, the fitting relationship between the static Young's modulus E s and the rock brittleness index BRIT and the static Young's modulus E s of all the wells are used to calculate the rock brittleness index BRIT of all the wells.

[0126] Step 10, according to the rating parameters porosity Por, saturation So, daily oil production and the rock brittleness index BRIT of all the wells, an oil reservoir classification standard is established, which is mainly based on resource (porosity and saturation) and supplemented by reconstruction (brittleness), and is divided into four types. As Figure 9 shown, the "four-quadrant" diagram of the conglomerate reservoir reservoir classification is shown, in which the brittleness is the vertical axis, the oil-bearing coefficient (i.e. porosity x saturation) is the horizontal axis, different colors are used to mark the daily oil production in different intervals, and according to Figure 9 the data point distribution area and the data point color type, the oil reservoir classification standard is established as shown in Table 1.

[0127] Table 1

[0128]

[0129] Class I: porosity Por x saturation So > 5.5%, brittleness index ≥ 50%, daily oil production ≥ 20 t / d;

[0130] Class II: porosity Por x saturation So > 5.5%, brittleness index < 50%, daily oil production range 5-15 t / d (including the limit value);

[0131] Class III: porosity Por x saturation So range 3.0%-5.5% (including the limit value), brittleness index ≥ 50%, daily oil production range 5-15 t / d (including the limit value);

[0132] Class IV: porosity Por x saturation So range 3.0%-5.5% (including the limit value), brittleness index < 50%, daily oil production < 5%.

[0133] Porosity (Por) × saturation (So) is less than 3.0%, indicating poor resource value, and therefore it is not included in reservoir classification.

[0134] IV. Verification of Oil Reservoir Classification Results:

[0135] Compared to its neighboring well, Mahu XX3, Mahu XX2 has inferior geological and engineering parameters, but it is more brittle. According to the new reservoir classification standard, Mahu XX2 is classified as a Class I reservoir, while Mahu XX3 is classified as a Class II reservoir. After fracturing, Mahu XX2 achieved a daily oil production of 20.05 t / d, while Mahu XX3 only produced 2.34 t / d. Therefore, the new reservoir classification standard shows a good match with actual production and is suitable for research in this block.

[0136] Table 2 shows a comparison between Mahu XX2 and Mahu XX3.

[0137] Table 2

[0138]

[0139] V. Fracturing Design Recommendations:

[0140] like Figure 10 As shown, wells with poor brittleness (brittleness index < 50%) have a sand strength of less than 5m. 3 / m, which is difficult to achieve industrial oil flow, but when the sand addition intensity is high, even with poor brittleness, industrial oil flow can still be achieved, indicating that when the resource is good but the modification is poor, the modification intensity needs to be increased.

[0141] pass Figure 11 The cross-plot of sand strength × brittleness and daily oil production shows that the greater the sand strength × brittleness, the higher the oil production.

[0142] For Class II and Class IV oil reservoirs, it is recommended to increase the fracturing intensity during fracturing, and the sand addition intensity should be >5m. 3 / m.

[0143] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0144] Corresponding to the above method, this embodiment of the invention also provides a computer-readable storage medium storing machine-executable instructions. When the machine-executable instructions are invoked and executed by a processor, the machine-executable instructions cause the processor to perform the steps of the above method.

[0145] The apparatus provided by the embodiments of the present application can be specific hardware on a device or software or firmware installed on the device, etc. The apparatus provided by the embodiments of the present application has the same implementation principle and technical effects as the foregoing method embodiments, and for brevity of description, the part not mentioned in the apparatus embodiment part can be referred to the corresponding content in the foregoing method embodiments. It can be clearly understood by those skilled in the art that, for the convenience and brevity of description, the specific working process of the system, apparatus and unit described above can be referred to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0146] In several embodiments of the present application, it should be understood that the disclosed apparatus and method can also be implemented by other manners. The apparatus embodiments described above are only schematic, for example, the flowcharts and block diagrams in the drawings show the possible implementation architectures, functions and operations of the apparatus, method and computer program product according to the embodiments of the present application. In this regard, each block in the flowcharts or block diagrams can represent a module, a program segment or a part of code, which contains one or more executable instructions for implementing the specified logic function. It should also be noted that, in some alternative implementation manners, the functions noted in the blocks can also occur in different order from that noted in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and sometimes they can also be executed in reverse order, depending on the functions involved. It should also be noted that, each block in the block diagrams and / or flowcharts, and the combination of blocks in the block diagrams and / or flowcharts, can be implemented by a dedicated hardware-based system for implementing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0147] For another example, the division of the units is only a logical function division, and another division manner can be used in actual implementation. For yet another example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some communication interfaces, apparatuses or units, which can be electrical, mechanical or other forms.

[0148] In addition, each functional unit in the embodiments provided by the present application can be integrated into one processing unit, or each unit can exist physically, or two or more units can be integrated into one unit.

[0149] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the parts that contribute to the prior art or parts of the technical solutions can be embodied in the form of software products. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0150] It should be noted that similar reference numbers and letters represent similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings, in addition, the terms "first", "second", "third" and the like are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0151] Finally, it should be noted that the above-described embodiments are only specific implementations of the present application, which are used to illustrate the technical solutions of the present application, but not to limit them, the protection scope of the present application is not limited thereto, although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily think of changes to the technical solutions recorded in the foregoing embodiments within the technical range disclosed by the present application, or make equivalent replacement to some technical features thereof, and these modifications, changes or replacements do not make the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application. They should be covered within the protection scope of the present application.

Claims

1. A method for classifying conglomerate oil reservoirs, characterized in that, include: Based on the measured P-wave data of all wells, the measured S-wave data of some wells, and the measured static Young's modulus of some wells, the static Young's modulus of all wells was calculated. Based on the measured mineral content data of some wells and the measured static Young's modulus of some wells, the fitting relationship between the static Young's modulus and the rock brittleness index was obtained. Based on the static Young's modulus of all wells, and the fitting relationship between the static Young's modulus and the rock brittleness index, the rock brittleness index of all wells is calculated. Based on the rating parameters and the rock brittleness index of all wells, a four-quadrant classification standard for oil reservoirs is established to determine the reservoir category; the rating parameters include porosity, saturation, and daily oil production. This step specifically includes: Based on porosity, saturation, daily oil production, and rock brittleness index, a reservoir classification coordinate system is established with the porosity-saturation multiplier as the X-axis and the brittleness index as the Y-axis. Daily oil production is divided into four intervals from highest to lowest, and the daily oil production within each interval is marked with a different color on the reservoir classification coordinate system to obtain the final coordinate system. Based on the final coordinate system, a four-quadrant classification standard for reservoirs, including Class I, Class II, Class III, and Class IV, is established. The reservoir category is determined according to the four-quadrant classification standard.

2. The method for classifying conglomerate reservoirs according to claim 1, characterized in that, Before the step of calculating the static Young's modulus of all wells based on the measured P-wave data of all wells, the measured S-wave data of some wells, and the measured static Young's modulus data of some wells, the method further includes: A statistical scatter plot was constructed based on the measured mineral content and compensated neutrons. Based on the statistical scatter plot, the compensated neutron boundary value between high-plasticity mineral conglomerate and low-plasticity mineral conglomerate was obtained with 50% mineral content as the boundary. Based on the compensated neutron threshold, a classification standard for high-plasticity mineral conglomerate and low-plasticity mineral conglomerate is established.

3. The method for classifying conglomerate reservoirs according to claim 2, characterized in that, The step of calculating the static Young's modulus of all wells based on the measured P-wave data of all wells, the measured S-wave data of some wells, and the measured static Young's modulus of some wells specifically includes: Based on the measured shear wave and longitudinal wave data from some wells, a fitting function was obtained; Based on the fitting function and the measured P-wave data of all wells, the predicted S-wave data of all wells are obtained. Based on the predicted shear wave data and measured longitudinal wave data of all wells, the dynamic Young's modulus of all wells was calculated using empirical formulas. Based on the measured static Young's modulus of some wells and the dynamic Young's modulus of all wells, and based on the classification criteria of high-plasticity mineral conglomerate and low-plasticity mineral conglomerate, conversion formulas for the dynamic and static Young's modulus of high-plasticity mineral conglomerate and low-plasticity mineral conglomerate are established respectively. Based on the dynamic Young's modulus of all wells, and using the conversion formula between the dynamic and static Young's modulus of high-plasticity mineral conglomerate and low-plasticity mineral conglomerate, the static Young's modulus of all wells is obtained.

4. The method for classifying conglomerate reservoirs according to claim 3, characterized in that, The shear wave data includes shear wave time difference and shear wave velocity; The P-wave data includes P-wave time difference and P-wave velocity.

5. The method for classifying conglomerate oil reservoirs according to claim 4, characterized in that, The step of obtaining the fitting function based on the measured shear wave data and measured p-wave data from a portion of the wells specifically includes: The fitting function is obtained based on the shear wave and p-wave transit times of some wells; The fitting function is a quadratic function of the change in transverse wave time difference with respect to longitudinal wave time difference.

6. The method for classifying conglomerate reservoirs according to claim 4, characterized in that, The empirical formula is: Where E d Rho is the dynamic Young's modulus, and V is the density. s V is the transverse wave velocity. p The velocity is the longitudinal wave velocity.

7. The method for classifying conglomerate reservoirs according to claim 1, characterized in that, The measured mineral content data includes the content of quartz minerals, carbonate rock minerals, and clay minerals.

8. The method for classifying conglomerate reservoirs according to claim 7, characterized in that, The step of obtaining the fitting relationship between the static Young's modulus and the rock brittleness index based on the measured mineral content data and the measured static Young's modulus of some wells specifically includes: Based on the quartz mineral content, carbonate rock mineral content, and clay mineral content of some wells, the brittleness index of some wells was calculated using the mineral combination method. Based on the brittleness index and measured static Young's modulus of some wells, the fitting relationship between static Young's modulus and rock brittleness index was obtained.

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