Method and device for evaluating compressibility coefficient of stress sensitive clastic rock reservoir

Abstract

The invention discloses a compression coefficient evaluation method and device for a stress-sensitive clastic rock reservoir, and relates to the technical field of oil-gas exploration and development, and the method comprises the steps: obtaining logging data and a standard rock core of a target region, carrying out the experimental measurement of the standard rock core, and obtaining the physical parameters of the target region in combination with the logging data; quantifying the pore structure according to the physical parameters to determine a pore structure index; determining a cement influence coefficient of the cement based on the physical parameter; constructing a compression coefficient dynamic model according to the pore structure index and the cement influence coefficient; and determining the compression coefficient of the target area through the compression coefficient dynamic model. The problems that the compression coefficient obtaining process of an existing compression coefficient evaluation method is tedious, and deviation is prone to occurring are solved. The reliability of the compression coefficient can be improved, the experiment cost and the time cost are saved, and reliable data support is provided for oil and gas development.

Description

Technical Field

[0001] The present application relates to the technical field of oil and gas exploration and development, and in particular to a method and device for evaluating the compression coefficient of a stress-sensitive clastic rock reservoir. Background Art

[0002] The rock compression coefficient refers to the change in pore volume per unit volume of rock when the reservoir pressure decreases by 1 MPa. It is an important parameter in the material balance calculation and well test interpretation of oil and gas reservoir engineering. It is mainly used in the following aspects: affecting the establishment and solution of the material balance equation, and then accurately predicting the production dynamics and remaining reserves of oil and gas reservoirs; playing a key role in well test interpretation, it can help interpreters accurately judge the permeability, compressibility and other characteristics of the reservoir, and provide an important basis for the development of oil and gas reservoirs; it can affect the accuracy and reliability of the numerical simulation model, and an accurate compression coefficient can improve the accuracy of the numerical simulation, providing strong support for the optimization of the development plan of the oil and gas reservoir; it can affect the evaluation results of the elastic production capacity and dynamic geological reserves of the oil reservoir, and then evaluate the production potential and economic benefits of the oil reservoir.

[0003] The current research purpose is to develop the compression coefficient of the layer core by simulating different pressure and fluid conditions to detect the change law of the compression coefficient. The main means are core overburden pressure pore permeability test method, theoretical formula method, etc. Although the overburden pressure pore permeability test method has better solved the core pore permeability parameters under the simulated real formation confining pressure condition, the triaxial stress that can be simulated by this method is X=Y=Z, which does not meet the actual stress conditions, and the test fluid medium is gas. In addition, the overall test process of this method is relatively complicated and the test cost is high, especially for low-permeability and ultra-low permeability cores, the test time is long and the accuracy is low. The theoretical formula method needs to determine more parameters, and for the convenience of calculation, many assumptions are made artificially. Therefore, it cannot reflect the actual situation of the actual reservoir rock, is prone to deviation, and the calculation process is cumbersome, which is not conducive to practical production application. Summary of the invention

[0004] The embodiment of the present application provides a method and device for evaluating the compression coefficient of a stress-sensitive clastic reservoir, thereby solving the problem that the process of obtaining the compression coefficient in the existing method for evaluating the compression coefficient is cumbersome and prone to deviation.

[0005] In the first aspect, an embodiment of the present application provides a method for evaluating the compression coefficient of a stress-sensitive clastic reservoir, comprising: obtaining logging data and a standard core of a target area, conducting experimental measurements on the standard core, and obtaining physical parameters of the target area in combination with the logging data; quantifying the pore structure according to the physical parameters to determine a pore structure index; determining a cementation influence coefficient of the cement based on the physical parameters; constructing a compression coefficient dynamic model according to the pore structure index and the cementation influence coefficient; and determining the compression coefficient of the target area through the compression coefficient dynamic model.

[0006] In combination with the first aspect, in a possible implementation, the physical parameters include burial depth and porosity, elastic modulus, porous medium type and cement content corresponding to different burial depths.

[0007] In combination with the first aspect, in a possible implementation, the experimental measurement of the standard core and the acquisition of physical parameters of the target area in combination with the logging data include: constructing a logging curve based on the rock property data in the logging data; wherein the logging data includes rock property data and logging imaging data; determining the elastic modulus at different depths using the acoustic time difference logging curve in the logging curve; measuring the porosity, pore size distribution and pore morphology of standard cores at different burial depths by experimental measurement methods, and determining the type of porous medium therein in combination with the logging imaging data; and determining the cementation content at different burial depths by experimental measurement methods.

[0008] In combination with the first aspect, in a possible implementation method, after the experimental measurement of the standard core and the acquisition of the physical parameters of the target area in combination with the logging data, it also includes: using the physical parameters, the logging curves and the logging imaging data as samples to train a rock property model constructed based on a neural network model; using the trained rock property model to output the porosity, elastic modulus, porous medium type and cement content at different burial depths.

[0009] In combination with the first aspect, in a possible implementation, quantifying the pore structure according to the physical parameter to determine the pore structure index includes: quantifying the pore structure according to the porosity, pore size distribution and pore morphology of the standard core, as follows: ; In the formula, represents the i-th pore structure index, represents the weight coefficient of the i-th porous medium type, represents the porosity of the i-th porous medium type, represents the complexity of throat j, represents the average length of the throat j, represents the average diameter of the throat j, represents the coefficient of variation of the diameter of throat j, n represents the total number of porous media types, m represents the total number of throats, Represents the porosity attenuation coefficient of the i-th porous medium type.

[0010] In combination with the first aspect, in a possible implementation, determining the cement influence coefficient of the cement based on the physical parameter includes: determining the cement influence coefficient of the cement according to the cement content at different burial depths, as follows: , ; In the formula, represents the cementing material influence coefficient of the cementing material d, represents the cement content of cement d, represents the strength factor of the cement d, represents the buried depth of cement d, Indicates the temperature at the burial depth corresponding to the standard core where the cement d is located.

[0011] In combination with the first aspect, in a possible implementation, the compression coefficient dynamic model is constructed according to the pore structure index and the cement influence coefficient as follows: ; In the formula, represents the compression factor of the target area, represents the i-th pore structure index, represents the porosity attenuation coefficient of the i-th porous medium type, represents the pore structure coefficient, represents the cementing material influence coefficient of the cementing material d, represents the cement correction factor, represents the stress sensitivity index, Represents the strength factor of the cement d.

[0012] In combination with the first aspect, in a possible implementation manner, the method for calculating the porosity attenuation coefficient is as follows: ; The calculation method of the stress sensitivity index is as follows: ; The calculation method of the intensity factor is as follows: ;in, ; In the formula, represents the porosity attenuation coefficient of the i-th porous medium type, represents the buried depth of the i-th porous medium type, represents the initial porosity, represents the stress sensitivity index, Indicates the relative content of cement d, represents the elastic modulus of the main mineral component of the cement d, Indicates the temperature at the burial depth of the standard core where the cement d is located. represents the complexity of throat j, represents the strength factor of the cement d, represents the buried depth of cement d, Represents the surface temperature.

[0013] In the second aspect, an embodiment of the present application provides a compression coefficient evaluation device for a stress-sensitive clastic reservoir, comprising: an acquisition module, used to acquire logging data and standard cores of a target area, perform experimental measurements on the standard cores, and acquire physical parameters of the target area in combination with the logging data; a quantification module, used to quantify the pore structure according to the physical parameters to determine a pore structure index; a determination module, used to determine a cementation influence coefficient of a cementation based on the physical parameters; a construction module, used to construct a compression coefficient dynamic model according to the pore structure index and the cementation influence coefficient; and a compression coefficient determination module, used to determine the compression coefficient of the target area through the compression coefficient dynamic model.

[0014] In a third aspect, an embodiment of the present application provides a device, comprising: a processor; a memory for storing processor-executable instructions; when the processor executes the executable instructions, it implements the method described in the first aspect or any possible implementation method of the first aspect.

[0015] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: By quantifying the pore structure, the embodiments of the present application can comprehensively consider the influence of different physical parameters on the pore structure; by determining the cementing influence coefficient, the influence of the burial depth on the cementing content can be considered; by establishing a dynamic model of the compression coefficient, the subsequent process of evaluating the compression coefficient can be simplified, and the compression coefficient can be obtained by only obtaining the physical parameters of the target area and inputting them into the dynamic model of the compression coefficient. This effectively solves the problem that the process of obtaining the compression coefficient by the existing evaluation method of the compression coefficient is cumbersome and prone to deviation. It can improve the reliability of the compression coefficient, save experimental costs and time costs, and provide reliable data support for oil and gas development. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 A flow chart of a method for evaluating the compressibility of a stress-sensitive clastic reservoir provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of a compression coefficient evaluation device for a stress-sensitive clastic reservoir provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] The following describes some of the techniques involved in the embodiments of the present application to facilitate understanding, and they should be considered as merely exemplary. Therefore, it should be appreciated by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, some descriptions of well-known functions and structures are omitted in the following description.

[0020] Figure 1 1 is a flow chart of a method for evaluating the compressibility of a stress-sensitive clastic reservoir provided in an embodiment of the present application, comprising steps 101 to 105. Figure 1 This is only an execution sequence shown in the embodiment of the present application, and does not represent the only execution sequence of the compression coefficient evaluation method for stress-sensitive clastic reservoirs. In the case that the final result can be achieved, Figure 1 The steps shown may be performed in parallel or reversed.

[0021] Step 101: Obtain the logging data and standard core of the target area, conduct experimental measurement on the standard core, and obtain the physical parameters of the target area in combination with the logging data. In the embodiment of the present application, the burial depth range of the target area is determined, and core samples are drilled at different burial depths (sampling can be performed at intervals of 50m, and shallow layers can be densely sampled at intervals of 20m), and the integrity and representativeness of the core samples are ensured to avoid contamination and damage. At the same time, the geological information of the target area, such as lithology, stratigraphy, burial depth, etc., is recorded. The downhole equipment (or electrode system) equipped with sensors is lowered into the well through a cable to measure various rock physical property data. Alternatively, other rock physical property data are obtained by using techniques such as acoustic logging and density logging. Then, the measured rock physical property data is preprocessed. Specifically, the rock physical property data is depth aligned so that the rock physical property data at each depth are aligned at the same sampling point. The rock physical property data is curve smoothed to eliminate small changes caused by non-stratum reasons. Environmental correction is performed on the rock physical property data to eliminate the influence within the instrument detection range, and finally the numerical standardization is performed to eliminate systematic errors.

[0022] In addition, those skilled in the art may also use imaging logging technology to collect formation information along the borehole longitudinally, circumferentially or radially to obtain logging imaging data (a two-dimensional image of the borehole wall or a three-dimensional image within a certain detection depth around the borehole).

[0023] The core samples obtained at different burial depths were cut and polished to prepare standard cores with a diameter of 25 mm and a height of 50 mm, and the standard cores at different burial depths were numbered.

[0024] In the embodiment of the present application, the standard core is experimentally measured, and the physical parameters of the target area are obtained in combination with the logging data, including: constructing a logging curve based on the rock property data in the logging data. The logging data includes rock property data and logging imaging data. The elastic modulus at different depths is determined using the acoustic time difference logging curve in the logging curve. The porosity, pore size distribution and pore morphology of the standard cores at different burial depths are measured by experimental measurement methods, and the type of porous media therein is determined in combination with the logging imaging data. The cement content at different burial depths is determined by experimental measurement methods.

[0025] Specifically, the pre-processed rock property data is plotted as a logging curve. The elastic modulus at different depths is determined using the acoustic time difference logging curve in the logging curve, as follows: .

[0026] In the formula, represents the elastic modulus of the target area at a buried depth of i, represents the rock reservoir density at a burial depth of i in the target area (obtained from the density logging curve in the logging curve), represents the shear wave time difference (obtained from the acoustic wave time difference logging curve in the logging curve), Represents the longitudinal wave time difference (obtained from the acoustic wave time difference logging curve in the logging curve). This method can accurately calculate the elastic modulus of the target area by considering the relationship between the propagation speed of the acoustic wave in the rock and the rock density.

[0027] Porosity is a measure of the proportion of pore space in a rock to the total volume, and pore size distribution refers to the proportion of pores of different pore sizes in a rock. The porosity and pore size distribution of standard cores at different burial depths can be measured by mercury injection, nitrogen adsorption, gas pressure balance and other methods. Pore morphology refers to the shape, size and distribution characteristics of pores in a rock, which can be obtained by analyzing well logging imaging data. In addition, technicians in this field can also obtain porosity, pore size distribution and pore morphology by making rock slices from standard cores or performing scanning electron microscope image analysis on standard cores.

[0028] Clastic reservoirs have various types of pore media, which are mainly divided into two categories according to their genesis: primary pores and secondary pores. Primary pores mainly include intergranular pores and micropores in the matrix. Intergranular pores are the most important type of primary pores in clastic rocks. They are composed of the space between clastic particles. Their porosity decreases with increasing burial depth, but the rate of decrease is relatively slow. Micropores in the matrix refer to the tiny pores contained in the matrix (i.e. fine-grained sediments, such as clay). Secondary pores refer to the pores formed by various geological processes (such as dissolution, compaction, cementation, etc.) during or after diagenesis of clastic rocks, mainly including super-large pores, mold pores, pores in components and cracks. According to the Schmidt standard (Schmidt orthogonalization, a method for finding the orthogonal basis of Euclidean space), super-large pores are pores that exceed 1.2 times the diameter of adjacent particles, and most of them are secondary pores. Mold pores refer to those pores in sandstone that retain their original structural appearance after the shell fragments, carbonate particles, and crystalline minerals (salt, gypsum, siderite) with certain characteristic geometric shapes are dissolved. They belong to the secondary pores of dissolution. Intra-component pores refer to the pores that appear in all components (such as particles, matrix, and cement). Although cracks in sandstone are relatively minor, they can also become important reservoir spaces if strong dissolution occurs along the cracks. Cracks include structural cracks and diagenetic cracks. In addition, there are intragranular pores: pores formed by dissolution inside particles; intergranular pores: pores formed by dissolution between particles; supergranular pores: a special secondary pore whose size exceeds the diameter of adjacent particles; intercrystalline pores: pores formed by the space between crystals, usually appearing in cements or authigenic minerals.

[0029] According to the porosity, pore size distribution and pore morphology, the present application can preliminarily judge the type of porous media, such as intergranular pores, extra-large pores, mold pores, cracks, etc. Specifically, in the formation process of sedimentary rocks, the porosity of primary pores is usually high, because these pores are naturally formed when particles are accumulated, and they maintain a certain continuity in a specific area. The pore size distribution of primary pores is usually narrow, and the pore size is closely related to the particle size distribution of sediments. For example, the size of intergranular pores is usually comparable to the size of sedimentary particles. The pore size distribution of secondary pores is usually wide, and the pore size is related to the intensity of later transformation. For example, pores formed by dissolution may have a larger pore size, while the width of cracks may vary from micrometers to millimeters. The pore morphology of primary pores is usually regular, such as intergranular pores are usually polygonal, and micropores in the matrix are usually circular or elliptical. The pore morphology of secondary pores is usually irregular, such as pores formed by dissolution may be irregular, and cracks may be linear or branched. In addition, those skilled in the art may also use scanning electron microscopy technology, focused ion beam technology, X-ray computed tomography technology, and other technologies to determine the type of porous media.

[0030] In the embodiments of the present application, the core thin section method is mainly used to determine the cement content. Specifically, the standard core is prepared into a core thin section, and then observed under a polarizing microscope to obtain microscopic images of 15-20 fields of view. The cement type, content and other parameters are extracted and statistically analyzed through image analysis software to measure the cement content of the standard cores at different burial depths, and then the cement content at different burial depths in the target area is obtained.

[0031] The method for obtaining the cement content here is only an embodiment of the present application and is not intended to limit the scope of protection of the application. Those skilled in the art may also use other methods to measure the cement content of standard cores at different burial depths to obtain the cement content at different burial depths in the target area.

[0032] In addition, in the embodiment of the present application, physical parameters, logging curves and logging imaging data are used as samples to train the rock property model constructed based on the neural network model. The trained rock property model is used to output the porosity, elastic modulus, porous medium type and cement content at different burial depths.

[0033] Specifically, the above-mentioned measured physical parameters and the corresponding logging curves and logging imaging data are used as samples to train the rock physical property model constructed based on the neural network model, and the porosity, elastic modulus, porous medium type and cement content at different burial depths are output. Neural network models such as multi-layer perceptron (MLP), long short-term memory network (LSTM), recurrent neural network (RNN) and their variants are exemplarily used.

[0034] Step 102: Quantify the pore structure according to the physical parameters to determine the pore structure index. In the embodiment of the present application, the pores and throats together constitute the pore system of the reservoir. The pores serve as storage spaces, and the throats serve as channels for fluid flow. If there is a lack of effective throat connection between the pores, then even if the porosity is very high, the permeability of the reservoir may be very low. The diameter, length and complexity of the throat also have a great influence on the flow of the fluid. If the pore is large and the throat is thin (small diameter), the fluid will be subject to great resistance during the flow. On the contrary, if the pores and throats are of moderate size and match each other, the flow of the fluid will be smoother. The longer the throat length, the greater the pressure loss caused by friction and collision during the flow of the fluid, which may affect the fluid recovery rate and development efficiency of the clastic reservoir. The higher the complexity of the throat, the greater the resistance to flow between the fluids, which will reduce the connectivity between the pores. If the length or complexity of the throat is high, it will hinder the connectivity between the pores, and it is likely to cause some pores to become invalid pores, thereby reducing the effective porosity of the reservoir.

[0035] This application quantifies the pore structure based on the porosity, pore size distribution and pore morphology of the standard core as follows: .

[0036] in, .

[0037] In the formula, represents the i-th pore structure index, represents the weight coefficient of the i-th porous medium type, represents the porosity of the i-th porous medium type, represents the complexity of throat j, represents the average length of the throat j, represents the average diameter of the throat j, represents the coefficient of variation of the diameter of throat j, n represents the total number of porous media types, m represents the total number of throats, represents the porosity attenuation coefficient of the i-th porous medium type, represents the porosity attenuation coefficient of the i-th porous medium type, and characterizes the relationship between the different porous medium types and the burial depth. represents the buried depth of the i-th porous medium type, represents the initial porosity (measured by experiment, in this application the porosity measured data is between 25% and 38%), and e is a mathematical constant, which is approximately equal to 2.71828.

[0038] Among them, the average diameter and average length of the throat can be measured based on thin-section observation experiments, scanning electron microscope experiments or mercury injection tests in the previous laboratory, and the diameter and length of each throat are averaged. The complexity of the throat is based on the thin-section observation experiment. The standard core is made into a thin slice, and an evaluation index system is constructed based on the size, shape, distribution, permeability, porosity and other key parameters of the throat observed under a microscope. The complexity of the throat is obtained by weighting the analytic hierarchy process and fuzzy mathematics. In addition, technical personnel in this field can also conduct physical experiments and use the flow efficiency of the fluid in the throat as the evaluation standard for the complexity of the throat. The coefficient of variation of the throat diameter represents the degree of discreteness of the throat diameter, which is the ratio of the standard deviation of the throat diameter to the average diameter.

[0039] Step 103: Determine the cementing influence coefficient of the cementing material based on the physical parameters. In the embodiment of the present application, as the burial depth of the clastic reservoir increases, the temperature and pressure gradually increase, resulting in changes in diagenesis (diagenesis includes compaction, cementation, and dissolution, etc.). In the early stage of burial of the clastic reservoir, compaction is the dominant factor, which will rapidly reduce the reservoir porosity. As the burial depth increases, the compaction effect gradually weakens, and the cementation effect begins to dominate. The reason for the weakening of the compaction effect is the rearrangement and close contact of the rock particles, as well as the reduction of the pores between the particles, which makes the rock harder and reduces the further compaction space.

[0040] As the burial depth increases, cementation is enhanced and the cement content gradually increases. This is because high temperature and high pressure conditions are conducive to the formation and precipitation of cement. The type of cement may also change with the change of burial depth. For example, in shallow reservoirs, weak cements such as mud and calcium-mud are more common, while in deep reservoirs, hard cements such as siliceous and calcareous are dominant.

[0041] As the cement content increases, the porosity and permeability of the reservoir usually decrease. This is because the cement fills the pores and throats between particles, reducing the seepage space for the fluid. When the cement content is too high, the reservoir may become very dense, making it difficult to migrate and store oil and gas. Under different burial depth conditions, the reservoir type may also change due to different cementation effects. Therefore, it is necessary to comprehensively consider the impact of burial depth on cement content.

[0042] In the embodiment of the present application, the cement influence coefficient of the cement is determined according to the cement content at different burial depths, as follows: , .

[0043] The calculation method of the intensity factor is as follows: ; .

[0044] In the formula, represents the cementing material influence coefficient of the cementing material d, represents the cement content of cement d, represents the strength factor of the cement d, represents the buried depth of cement d, Indicates the temperature at the burial depth of the standard core where the cement d is located. Indicates the relative content of cement d, represents the elastic modulus of the main mineral component of the cement d, represents the strength factor of the cement d, represents the buried depth of cement d, Represents the surface temperature.

[0045] Step 104: construct a dynamic model of compression coefficient according to the pore structure index and the cementing material influence coefficient. In the embodiment of the present application, the dynamic model of compression coefficient is as follows: .

[0046] In the formula, represents the compression factor of the target area, represents the i-th pore structure index, represents the porosity attenuation coefficient of the i-th porous medium type, which is used to characterize the attenuation change of pores of different porous medium types. It represents the pore structure coefficient, which is used to characterize the complexity and irregularity of the pore structure. For example, the pore structure coefficient of the intergranular pore-dissolution pore type is set to 0.8, the pore structure coefficient of the dissolution pore-intergranular pore type is set to 0.6, the pore structure coefficient of the intergranular pore-micropore type is set to 0.4, and the pore structure coefficient of the microcrack-crack type is set to 0.35. represents the cementing material influence coefficient of the cementing material d, represents the cement correction factor. For example, the siliceous cement correction factor is set to 1.0, and the calcareous cement correction factor is set to 1.2. represents the stress sensitivity index, Represents the strength factor of the cement d.

[0047] The calculation method of stress sensitivity index is as follows: .

[0048] In the formula, represents the stress sensitivity index, represents the complexity of throat j.

[0049] Step 105: Determine the compression coefficient of the target area through the compression coefficient dynamic model. In the embodiment of the present application, according to the above compression coefficient dynamic model, the physical parameters (porosity, elastic modulus, porous medium type and cement content) of the target area are input to obtain the compression coefficient of the target area.

[0050] In one embodiment of the present application, in order to verify the accuracy of the method proposed in the present application, cores of different porous media types in some clastic rock reservoirs in the laboratory were selected for comparative experiments, and the results are shown in Table 1 below. Among them, the ground conditions refer to the test conditions at normal temperature and pressure, and the compression coefficient (Cp) measured is the compression coefficient calculated based on the current national standard SY / T5815-2016 rock pore volume compression coefficient determination method. This method uses a high-temperature, high-pressure core holder and a high-precision meter test system to fully simulate the formation conditions for testing. The method used for evaluating the compression coefficient (Cp) is the compression coefficient calculated by the compression coefficient evaluation method described in the present application.

[0051] Table 1 Comparison of compression coefficient (Cp) evaluation and compression coefficient (Cp) measured data

[0052] Although the present application provides method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-creative labor. The order of steps listed in this embodiment is only one way of executing the order of many steps and does not represent the only execution order. When the actual device or client product is executed, it can be executed in sequence or in parallel according to the method shown in this embodiment or the accompanying drawings (for example, in a parallel processor or multi-threaded processing environment).

[0053] like Figure 2 As shown, the embodiment of the present application also provides a compression coefficient evaluation device 200 for stress-sensitive clastic reservoirs. The device includes: an acquisition module 201, a quantification module 202, a determination module 203, a construction module 204 and a compression coefficient determination module 205, as follows.

[0054] The acquisition module 201 is used to acquire well logging data and standard cores of the target area, perform experimental measurements on the standard cores, and acquire physical parameters of the target area in combination with the well logging data.

[0055] The quantification module 202 is used to quantify the pore structure according to the physical parameters to determine the pore structure index.

[0056] The determination module 203 is used to determine the cement influence coefficient of the cement based on the physical parameters.

[0057] The construction module 204 is used to construct a dynamic model of compression coefficient according to the pore structure index and the cementation influence coefficient.

[0058] The compression coefficient determination module 205 is used to determine the compression coefficient of the target area through a compression coefficient dynamic model.

[0059] Some modules in the apparatus described in the present application can be described in the general context of computer executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, classes, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0060] The devices or modules described in the above application embodiments can be implemented by computer chips or entities, or by products with certain functions. For the convenience of description, the above devices are described in various modules according to their functions. When implementing the embodiments of the present application, the functions of each module can be implemented in the same or multiple software and / or hardware. Of course, the module that implements a certain function can also be implemented by combining multiple sub-modules or sub-units.

[0061] The methods, devices or modules described in this application can be implemented in the form of computer-readable program codes. The controller can be implemented in any appropriate manner. For example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program codes (such as software or firmware) that can be executed by the (micro)processor, logic gates, switches, application-specific integrated circuits (English: Application Specific Integrated Circuit; Abbreviation: ASIC), programmable logic controllers and embedded microcontrollers. Examples of controllers include but are not limited to the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20 and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in the form of pure computer-readable program codes, the controller can be implemented in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers and embedded microcontrollers by logically programming the method steps. Therefore, this controller can be considered as a hardware component, and the devices included in it for implementing various functions can also be regarded as structures within the hardware component. Or even, the means for realizing various functions may be regarded as both a software module for realizing the method and a structure within a hardware component.

[0062] An embodiment of the present application further provides a device, comprising: a processor; a memory for storing processor executable instructions; when the processor executes the executable instructions, the method described in the embodiment of the present application is implemented.

[0063] The embodiments of the present application also provide a non-volatile computer-readable storage medium on which a computer program or instruction is stored. When the computer program or instruction is executed, the method described in the embodiments of the present application is implemented.

[0064] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist independently, or two or more modules may be integrated into one module.

[0065] The above storage media include but are not limited to random access memory (RAM), read-only memory (ROM), cache, hard disk drive (HDD) or memory card. The memory can be used to store computer program instructions.

[0066] It can be seen from the description of the above implementation methods that those skilled in the art can clearly understand that the present application can be implemented by means of software plus necessary hardware. Based on this understanding, the technical solution of the present application can be essentially or partly reflected in the prior art in the form of a software product, or it can be reflected in the implementation process of data migration. The computer software product can be stored in a storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a mobile terminal, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present application or certain parts of the embodiments.

[0067] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. All or part of this application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, mobile communication terminals, multi-processor systems, microprocessor-based systems, programmable electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc.

[0068] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit the present application. Although the present application has been described in detail with reference to the aforementioned embodiments, a person of ordinary skill in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some or all of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the present application.

Claims

1. A method for evaluating the compressibility of stress-sensitive clastic reservoirs, characterized in that: include: Acquire well logging data and standard cores of the target area, perform experimental measurements on the standard cores, and acquire physical parameters of the target area in combination with the well logging data; quantifying the pore structure according to the physical parameter to determine a pore structure index; determining a cementing material influence coefficient of the cementing material based on the physical parameters; Constructing a dynamic model of compression coefficient according to the pore structure index and the cementing material influence coefficient; The compression coefficient of the target area is determined by the compression coefficient dynamic model.

2. The method according to claim 1, characterized in that The physical parameters include burial depth and porosity, elastic modulus, porous medium type and cement content corresponding to different burial depths.

3. The method according to claim 2, characterized in that The experimental measurement of the standard core and the acquisition of the physical parameters of the target area in combination with the logging data include: Constructing a logging curve based on the rock property data in the logging data; wherein the logging data includes the rock property data and the logging imaging data; Determining elastic moduli at different depths using the acoustic time difference logging curve in the logging curve; The porosity, pore size distribution and pore morphology of standard cores at different burial depths are measured by experimental determination methods, and the porous medium type therein is determined in combination with the logging imaging data; The cement content at different burial depths is determined by experimental measurement methods.

4. The method according to claim 2, characterized in that: After the standard core is experimentally measured and the physical parameters of the target area are obtained in combination with the logging data, the method further includes: Using the physical parameters, the logging curves and the logging imaging data as samples, training a rock property model constructed based on a neural network model; The trained rock physical property model is used to output the porosity, elastic modulus, porous medium type and cement content at different burial depths.

5. The method according to claim 3, characterized in that: The step of quantifying the pore structure according to the physical parameter to determine a pore structure index comprises: The pore structure is quantified based on the porosity, pore size distribution and pore morphology of the standard core as follows: ； In the formula, represents the i-th pore structure index, represents the weight coefficient of the i-th porous medium type, represents the porosity of the i-th porous medium type, represents the complexity of throat j, represents the average length of the throat j, represents the average diameter of the throat j, represents the coefficient of variation of the diameter of throat j, n represents the total number of porous media types, m represents the total number of throats, Represents the porosity attenuation coefficient of the i-th porous medium type.

6. The method according to claim 1, characterized in that The determining of the cementing material influence coefficient of the cementing material based on the physical parameter comprises: The cementing influence coefficient of the cementing material is determined according to the cementing material content at different burial depths, as follows: ， ； In the formula, represents the cementing material influence coefficient of the cementing material d, represents the cement content of cement d, represents the strength factor of the cement d, represents the buried depth of cement d, Indicates the temperature at the burial depth corresponding to the standard core where the cement d is located.

7. The method according to claim 1, characterized in that The compression coefficient dynamic model is constructed according to the pore structure index and the cementing material influence coefficient as follows: ； Where Cp represents the compression coefficient of the target area, represents the i-th pore structure index, represents the porosity attenuation coefficient of the i-th porous medium type, represents the pore structure coefficient, represents the cementing material influence coefficient of the cementing material d, represents the cement correction factor, represents the stress sensitivity index, Represents the strength factor of the cement d.

8. The method according to claim 7, characterized in that The calculation method of the porosity attenuation coefficient is as follows: ； The calculation method of the stress sensitivity index is as follows: ； The calculation method of the intensity factor is as follows: ;in, ; In the formula, represents the porosity attenuation coefficient of the i-th porous medium type, represents the buried depth of the i-th porous medium type, represents the initial porosity, represents the stress sensitivity index, Indicates the relative content of cement d, represents the elastic modulus of the main mineral component of the cement d, Indicates the temperature at the burial depth of the standard core where the cement d is located. represents the complexity of throat j, represents the strength factor of the cement d, represents the buried depth of cement d, Represents the surface temperature.

9. A compressibility evaluation device for stress-sensitive clastic reservoirs, characterized in that: include: An acquisition module is used to acquire well logging data and standard cores of a target area, perform experimental measurements on the standard cores, and acquire physical parameters of the target area in combination with the well logging data; A quantification module, for quantifying the pore structure according to the physical parameter to determine a pore structure index; A determination module, for determining a cementing material influence coefficient of the cementing material based on the physical parameter; A construction module, used to construct a dynamic model of compression coefficient according to the pore structure index and the cementing material influence coefficient; The compression coefficient determination module is used to determine the compression coefficient of the target area through the compression coefficient dynamic model.

10. A device for performing a method for evaluating the compressibility of a stress-sensitive clastic reservoir, characterized in that: include: processor; a memory for storing processor-executable instructions; When the processor executes the executable instructions, the method according to any one of claims 1 to 8 is implemented.

Images