Coal reservoir gas content prediction method and device, electronic equipment and medium

Abstract

The embodiment of the invention discloses a coal reservoir gas content prediction method and device, electronic equipment and a medium. The method comprises the following steps: determining the adsorbed gas content of a coal rock sample according to data obtained by performing experimental treatment on a first coal rock sample; according to data obtained by testing the second coal rock sample, predicting the free gas content of the coal rock sample; wherein the first coal rock sample and the second coal rock sample are obtained by dividing the coal rock sample into at least two parts; and determining the total gas content of the coal rock sample according to the adsorbed gas content and the free gas content, and determining a coal reservoir gas content prediction model according to the total gas content and the logging data so as to predict the gas content of the coal reservoir based on the coal reservoir gas content prediction model. According to the scheme, the total gas content in the coal rock sample is calculated, the prediction model is determined according to the total gas content and the logging data, prediction of the gas content of the coal reservoir based on the prediction model is achieved, and the scheme is simple, convenient, short in time consumption and low in cost.

Description

Technical Field

[0001] The present application relates to the field of oil and gas exploration, and in particular to a method, device, electronic equipment and medium for predicting the gas content of coal reservoirs. Background Art

[0002] The gas content of deep coal seams is a key geological parameter that characterizes the gas-bearing characteristics of deep coal reservoirs. It is the main basis for estimating deep coalbed methane resources and formulating development plans. Accurately predicting the gas content of deep coal reservoirs is of great significance to the development of deep coalbed methane.

[0003] At present, the methods for testing the gas content of deep coal reservoirs include conventional coring, closed coring, and pressure-maintaining coring, but these methods are time-consuming and costly. Summary of the invention

[0004] The present application provides a method, device, electronic equipment and medium for predicting the gas content of coal reservoirs. The method calculates the total gas content in coal rock samples and determines a coal reservoir gas content prediction model based on the total gas content and logging data. The method predicts the gas content of coal reservoirs based on the coal reservoir gas content prediction model. The method is simple, time-saving and cost-effective.

[0005] According to one aspect of the present application, a method for predicting gas content in a coal reservoir is provided, the method comprising:

[0006] Determining the adsorbed gas content of the coal rock sample based on data obtained by experimentally processing the first coal rock sample;

[0007] Predicting the free gas content of the coal rock sample based on data obtained by testing the second coal rock sample; wherein the first coal rock sample and the second coal rock sample are obtained by dividing the coal rock sample into at least two parts;

[0008] The total gas content of the coal rock sample is determined based on the adsorbed gas content and the free gas content, and a coal reservoir gas content prediction model is determined based on the total gas content and logging data, so as to predict the gas content of the coal reservoir based on the coal reservoir gas content prediction model.

[0009] According to another aspect of the present application, a coal reservoir gas content prediction device is provided, the device comprising:

[0010] An adsorbed gas content determination module, used to determine the adsorbed gas content of the coal rock sample based on data obtained by experimentally processing the first coal rock sample;

[0011] A free gas content prediction module, used to predict the free gas content of the coal rock sample according to data obtained by testing the second coal rock sample; wherein the first coal rock sample and the second coal rock sample are obtained by dividing the coal rock sample into at least two parts;

[0012] The coal reservoir gas content prediction module is used to determine the total gas content of the coal rock sample according to the adsorbed gas content and the free gas content, and to determine the coal reservoir gas content prediction model according to the total gas content and logging data, so as to predict the gas content of the coal reservoir based on the coal reservoir gas content prediction model.

[0013] According to another aspect of the present application, an electronic device is provided, the device comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by at least one processor, and the computer program is executed by at least one processor so that the at least one processor can execute the coal reservoir gas content prediction method of any embodiment of the present application.

[0017] According to another aspect of the present application, a computer-readable storage medium is provided, which stores computer instructions, and the computer instructions are used to enable a processor to implement the coal reservoir gas content prediction method of any embodiment of the present application when executed.

[0018] The technical solution of the embodiment of the present application determines the adsorbed gas content of the coal rock sample based on the data obtained by experimentally processing the first coal rock sample; predicts the free gas content of the coal rock sample based on the data obtained by testing the second coal rock sample; wherein the first coal rock sample and the second coal rock sample are obtained by dividing the coal rock sample into at least two parts; the total gas content of the coal rock sample is determined based on the adsorbed gas content and the free gas content, and the coal reservoir gas content prediction model is determined based on the total gas content and logging data, so as to predict the gas content of the coal reservoir based on the coal reservoir gas content prediction model. The technical solution of the embodiment of the present application realizes the prediction of the gas content of the coal reservoir based on the coal reservoir gas content prediction model by calculating the total gas content in the coal rock sample and determining the coal reservoir gas content prediction model based on the total gas content and logging data. The solution is simple, time-saving and low-cost.

[0019] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 This is a flow chart of a method for predicting gas content in a coal reservoir provided according to Example 1 of the present application;

[0022] Figure 2 This is a flow chart of a method for predicting gas content in a coal reservoir provided in Example 2 of the present application;

[0023] Figure 3a This is a correlation analysis diagram between gamma and total gas content provided in Example 2 of the present application;

[0024] Figure 3b This is a correlation analysis diagram between natural potential and total gas content provided in Example 2 of the present application;

[0025] Figure 3c This is a correlation analysis diagram between well diameter and total gas content provided in Example 2 of the present application;

[0026] Figure 3d This is a correlation analysis diagram between the total hydrocarbon value and the total gas content provided in Example 2 of the present application;

[0027] Figure 3e This is a correlation analysis diagram between neutrons and total gas content provided in Example 2 of the present application;

[0028] Figure 3f This is a correlation analysis diagram between density and total gas content provided in Example 2 of the present application;

[0029] Figure 4 It is a structural schematic diagram of a coal reservoir gas content prediction device provided according to Example 3 of the present application;

[0030] Figure 5 It is a structural schematic diagram of an electronic device for implementing a method for predicting gas content in a coal reservoir provided in Example 4 of the present application. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0032] It should be noted that the terms "first", "second", "third", "fourth", "actual", "preset", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0033] Embodiment 1

[0034] Figure 1 This is a flow chart of a method for predicting the gas content of a coal reservoir provided in Example 1 of the present application. The present application example can be applied to predicting the gas content of a deep coal reservoir. The method can be executed by a coal reservoir gas content prediction device, which can be implemented in the form of hardware and / or software, and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0035] S110. Determine the adsorbed gas content of the coal rock sample based on data obtained from experimental processing of the first coal rock sample.

[0036] Among them, the first coal rock sample is a coal rock sample with a particle size of 0.25mm to 0.18mm (60-80 mesh) prepared according to the standard. The adsorbed gas content of the coal rock sample refers to the amount of gas adsorbed on its surface by the coal rock sample through adsorption under certain conditions. Adsorption refers to the interaction between gas molecules and solid or liquid molecules, causing the gas molecules to stay or adhere to the surface of the solid or liquid. The amount of adsorbed gas content depends on many factors, including the properties of the adsorbent, environmental conditions (such as temperature, pressure, etc.), the characteristics of the gas molecules, and the surface area and pore structure during the adsorption process. It can be understood that different substances have different adsorption characteristics and adsorption capacities.

[0037] The adsorbed gas content can usually be obtained through experimental measurement or calculation. In the embodiment of the present application, the adsorbed gas content of the coal rock sample can be determined based on the data obtained by experimentally processing the first coal rock sample.

[0038] Specifically, based on the data obtained from experimental processing of the first coal rock sample, the adsorbed gas content of the coal rock sample is determined, including: determining the formation pressure at the depth of the coal rock sample based on the depth and pressure gradient of the sampling coal rock sample; determining the adsorbed gas content of the coal rock sample based on the formation pressure, Langmuir volume and Langmuir pressure.

[0039] The pressure gradient indicates the increase in formation pressure for every 100 meters of depth increase, and the unit is MPa / 100m. The pressure gradient of the sampled coal and rock samples can be obtained by querying the formation pressure gradient of the area where the sampled coal and rock samples are located according to the literature. After obtaining the depth and pressure gradient of the sampled coal and rock samples, the formation pressure at the depth of the coal and rock samples can be calculated. The formula is P = gH*pressure gradient. Among them, P is the formation pressure, the unit is MPa, g is the gravity constant, the value is 9.81N / kg, and H is the depth of the sampled coal and rock samples, the unit is m.

[0040] After calculating the formation pressure, the adsorbed gas content of the coal rock sample is determined according to the formation pressure, the Langmuir volume and the Langmuir pressure, that is, the adsorbed gas content of the coal rock sample is determined by the Langmuir equation. Among them, the Langmuir equation is obtained by placing a coal rock sample of a certain particle size in a closed container for degassing according to the experimental requirements, and then measuring the volume of methane gas adsorbed when it reaches adsorption equilibrium under the same temperature and different pressure conditions, and then fitting the isothermal adsorption curve of methane in the coal rock. Specifically, the Langmuir equation is: Among them, V S is the adsorption gas content, in m 3 / t, P is the formation pressure, in MPa, V L is the Langmuir volume in cm 3 / g,P L is the Langmuir pressure, in MPa. After the formation pressure is calculated, the Langmuir volume and the Langmuir pressure are determined. Substituting the formation pressure, the Langmuir volume and the Langmuir pressure into the Langmuir equation, the adsorbed gas content of the coal rock sample can be obtained.

[0041] S120. Predict the free gas content of the coal rock sample based on the data obtained from testing the second coal rock sample; wherein the first coal rock sample and the second coal rock sample are obtained by dividing the coal rock sample into at least two parts.

[0042] Among them, the second coal rock sample is a coal rock sample processed into a standard cylindrical plug shape with a length*diameter of 50mm*25mm. It should be noted that the first coal rock sample and the second coal rock sample are obtained by dividing the coal rock sample into at least two parts, that is, a coal rock sample is divided into at least two parts, and two of them are taken as the first coal rock sample and the second coal rock sample. The free gas content of the coal rock sample refers to the percentage or absolute amount of free gas present in the coal rock sample. Free gas generally refers to gas that is insoluble in liquid or solid and exists in the form of free molecules. In the embodiment of the present application, the free gas content of the coal rock sample can be predicted based on the data obtained by testing the second coal rock sample.

[0043] Specifically, based on the data obtained from testing the second coal rock sample, the free gas content of the coal rock sample is predicted, including: determining the first coefficient based on the adsorbed gas content, the natural gas volume coefficient and the adsorbed phase density; determining the second coefficient based on the porosity, gas saturation, the natural gas volume coefficient and the apparent relative density of the coal rock sample; and taking the difference between the first coefficient and the second coefficient as the free gas content of the coal rock sample.

[0044] When calculating the first coefficient, it is necessary to first calculate the natural gas volume coefficient, which is the ratio of the density of methane under standard conditions to the density of methane under formation conditions, that is, Among them, B g is the natural gas volume coefficient, ρ 标况 is the density of methane under standard conditions, which is 0.00071 g / cm 3 , ρ 地层 It is the density of methane under formation conditions, which can be obtained by querying the temperature and pressure data under formation conditions.

[0045] After the natural gas volume coefficient is calculated, the first coefficient can be determined according to the adsorbed gas content, natural gas volume coefficient and adsorbed phase density calculated in step S110. The calculation formula of the first coefficient is: Among them, B g is the natural gas volume coefficient, ρ s is the adsorption phase density, which is 0.34 g / cm 3 , M is the molar mass of methane, V s is the adsorption gas content.

[0046] When calculating the second coefficient, it is necessary to first calculate the porosity, gas saturation and apparent relative density of the coal rock sample.

[0047] Generally speaking, when processing the second coal rock sample, it is usually necessary to ensure that the two cross sections of the cylinder of the second coal rock sample are parallel and perpendicular to its axis. Then, the pore volume of the second coal rock sample under different overburden pressures is determined according to Boyle's law. Specifically, according to Boyle's law, the following equation can be obtained: P1V1=P2(V1+V P ), where P1 is the initial pressure of the pressure vessel filled with gas, in MPa, P2 is the pressure after the pressure vessel gas is connected to the pores of the rock sample, in MPa, and V1 is the volume of the pressure vessel, in cm 3 , V P is the pore volume of the second coal rock sample, in cm 3 . In addition to V P Except for the above, the other values ​​are known or can be obtained by measurement. Therefore, the pore volume V of the second coal rock sample can be calculated. P .

[0048] In calculating the pore volume V of the second coal rock sample P After that, the porosity of the second coal rock sample can be calculated. It should be noted that the calculation of the porosity of the second coal rock sample under overburden pressure must take into account the decrease in the total volume of the second coal rock sample caused by the increase in overburden pressure. The calculation formula for the porosity of the second coal rock sample is: in, is the porosity of the coal rock sample, V P is the pore volume of the second coal rock sample, V b is the total volume of the second coal rock sample, ΔV P is the difference between the initial pore volume of the second coal rock sample and the pore volume at any pressure. From this, the porosity of the coal rock sample can be calculated.

[0049] The pores of coal rock samples are filled with gas and water, and the total pore volume occupied by gas and water is 100%. Therefore, the gas saturation of coal rock samples can be indirectly calculated by calculating the water saturation of coal rock samples. According to Archie's formula, the water saturation of coal rock samples is Among them, a is the lithology coefficient, ranging from 0.5 to 1.5; b is the lithology index, close to 1; m is the cementation coefficient, ranging from 1.5 to 3; n is the saturation index; is the porosity of the coal rock sample; R w is the formation water resistivity; R t is the formation resistivity. In the above parameters, the formation resistivity R t It can be obtained through the deep lateral resistivity of the borehole. Therefore, the only parameter that needs to be solved is the formation water resistivity R w .

[0050] In the prior art, the formation water resistivity Rw Generally, it is obtained by using the calculation chart of the equivalent NaCl coefficient of various ions of Schlumberger Company. The specific process is: take the NaCl solution as the standard, determine the equivalent coefficient K of the conductivity of other solutions relative to the NaCl solution at the standard temperature according to the calculation chart of the equivalent NaCl coefficient, multiply it by the mineralization of the corresponding ion, calculate the equivalent NaCl solution mineralization, and then determine the formation water resistivity by the equivalent NaCl solution mineralization and the formation temperature. However, this method is extremely inconvenient. In the embodiment of the present application, an approximate calculation method is used to calculate the resistivity of the NaCl solution and use it as the formation water resistivity. Specifically, first use equation C Nacl =y 1.05 Calculate the value of y and substitute y into the equation Calculate R Nacl The value of R w The value of R w =R Nacl Among them, C Nacl is the mass concentration of equivalent NaCl solution, in ppm, T is the reservoir temperature, in °C, R Nacl is the resistivity of NaCl solution, in Ω·m, R w It is the formation water resistivity, unit is Ω·m.

[0051] The formation water resistivity R w Substitution The water saturation S of the coal rock sample can be obtained W , then the gas saturation S of the coal sample g =1-S w . From this, the gas saturation of the coal rock sample can be obtained.

[0052] According to the standard requirements, a coal rock sample of a certain particle size is weighed, the surface is sealed with wax, and then placed in a density bottle with sodium dodecyl sulfate solution as an infiltrant. The volume of sodium dodecyl sulfate solution displaced by the particles of the waxed coal rock sample is measured, and after subtracting the volume of the wax, the volume of the particles of the coal rock sample is obtained, and then the apparent relative density of the coal rock sample is calculated. Specifically, the calculation formula for the apparent relative density of the coal rock sample is: Among them, ρ b is the apparent relative density of the coal rock sample, in g / cm 3 ; m1 is the mass of the coal rock sample, in g; m2 is the mass of the waxed coal rock sample particles, in g; m3 is the mass of the density bottle, the waxed coal rock sample particles and the aqueous solution, in g; m4 is the mass of the density bottle and the aqueous solution, in g; d s is the density of sodium dodecyl sulfate solution, in g / cm 3 ; is the density of paraffin wax, in g / cm 3 ρ 水 is the density of water in g / cm 3 . From this, the apparent relative density of the coal rock sample can be calculated.

[0053] After calculating the porosity, gas saturation and apparent relative density of the coal rock sample, the second coefficient can be determined according to the porosity, gas saturation, natural gas volume coefficient and apparent relative density of the coal rock sample. Specifically, the calculation formula of the second coefficient is: Among them, B g is the natural gas volume coefficient, ρ b is the apparent relative density of the coal rock sample, is the porosity of the coal rock sample, S g is the gas saturation of coal rock samples.

[0054] The free gas content of the coal rock sample is Therefore, the free gas content of coal rock samples can be predicted.

[0055] S130. Determine the total gas content of the coal rock sample based on the adsorbed gas content and the free gas content, and determine a coal reservoir gas content prediction model based on the total gas content and logging data, so as to predict the gas content of the coal reservoir based on the coal reservoir gas content prediction model.

[0056] In the embodiment of the present application, after determining the adsorbed gas content and predicting the free gas content, the total gas content of the coal rock sample can be determined according to the adsorbed gas content and the free gas content, and the coal reservoir gas content prediction model can be determined according to the correlation between the total gas content and the well logging data, so as to predict the gas content of the coal reservoir based on the coal reservoir gas content prediction model. Compared with the current coal reservoir gas content testing method, the technical solution provided by the embodiment of the present application is simpler, less time-consuming and less costly.

[0057] The technical solution of the embodiment of the present application determines the adsorbed gas content of the coal rock sample based on the data obtained by experimentally processing the first coal rock sample; predicts the free gas content of the coal rock sample based on the data obtained by testing the second coal rock sample; wherein the first coal rock sample and the second coal rock sample are obtained by dividing the coal rock sample into at least two parts; the total gas content of the coal rock sample is determined based on the adsorbed gas content and the free gas content, and the coal reservoir gas content prediction model is determined based on the total gas content and logging data, so as to predict the gas content of the coal reservoir based on the coal reservoir gas content prediction model. The technical solution of the embodiment of the present application realizes the prediction of the gas content of the coal reservoir based on the coal reservoir gas content prediction model by calculating the total gas content in the coal rock sample and determining the coal reservoir gas content prediction model based on the total gas content and logging data. The solution is simple, time-saving and low-cost.

[0058] Embodiment 2

[0059] Figure 2 This is a flow chart of a coal reservoir gas content prediction method provided in Example 2 of this application. This application example is optimized based on the above example. For solutions not described in detail in this application example, see the above example. Figure 2 As shown, the method of the embodiment of the present application specifically includes the following steps:

[0060] S210. Determine the adsorbed gas content of the coal rock sample based on the data obtained from the experimental processing of the first coal rock sample.

[0061] S220. Predict the free gas content of the coal rock sample based on the data obtained from testing the second coal rock sample; wherein the first coal rock sample and the second coal rock sample are obtained by dividing the coal rock sample into at least two parts.

[0062] S230. The sum of the adsorbed gas content and the free gas content is taken as the total gas content of the coal rock sample.

[0063] In the embodiment of the present application, after the adsorbed gas content is determined and the free gas content is predicted, the sum of the adsorbed gas content and the free gas content can be used as the total gas content of the coal rock sample, that is, V gas =V s +V f , where V gas is the total gas content, V s is the adsorbed gas content, V f is the free gas content.

[0064] S240. Determine a coal reservoir gas content prediction model based on the total gas content and logging data.

[0065] In an embodiment of the present application, after the total gas content of the coal rock sample is calculated, a coal reservoir gas content prediction model can be determined based on the correlation between the total gas content and the logging data, so as to predict the gas content of the coal reservoir based on the coal reservoir gas content prediction model.

[0066] Specifically, a coal reservoir gas content prediction model is determined based on the total gas content and well logging data, including: for different well logging data, respectively determining the correlation data between each well logging data and the total gas content; based on the correlation data, selecting target well logging data from different well logging data; and determining a coal reservoir gas content prediction model based on the target well logging data and the total gas content.

[0067] Coal reservoirs have three highs and three lows in logging response characteristics, namely, high resistivity, high acoustic time difference, high neutrons, low density, low gamma, and low natural potential. For different logging data, the logging curves corresponding to different logging data can be corrected according to the above logging response characteristics, and the correlation analysis between each logging curve and the total gas content can be carried out to determine the correlation data between each logging data and the total gas content.

[0068] For example, Figure 3a A correlation analysis diagram between gamma and total gas content is shown, in which the X-axis is the total gas content Q and the Y-axis is the gamma GR. Figure 3b A correlation analysis diagram between natural potential and total gas content is shown, in which the X-axis is the total gas content Q and the Y-axis is the natural potential SP. Figure 3c A correlation analysis diagram between well diameter and total gas content is shown, in which the X-axis is the total gas content Q and the Y-axis is the well diameter CAL. Figure 3d A correlation analysis diagram between the total hydrocarbon value and the total gas content is shown, in which the X-axis is the total gas content Q and the Y-axis is the total hydrocarbon value. Figure 3e A correlation analysis diagram between neutrons and total gas content is shown, in which the X-axis is the total gas content Q and the Y-axis is the neutron CN. Figure 3f A correlation analysis diagram between density and total gas content is shown, in which the X-axis is the total gas content Q and the Y-axis is the density DEN.

[0069] After determining the correlation data between each logging data and the total gas content, the logging data with high correlation with the total gas content can be selected as the target logging data according to the correlation data, and the coal reservoir gas content prediction model can be determined according to the target logging data and the total gas content.

[0070] Specifically, a coal reservoir gas content prediction model is determined based on target logging data and total gas content, including: based on fitting software, performing multivariate linear regression fitting on the target logging data and the total gas content to obtain a coal reservoir gas content prediction model; wherein the target logging data are at least two items.

[0071] Among them, multiple linear regression is a statistical model method that fits multiple independent variables with a dependent variable by establishing a linear relationship. It can be used to predict and explain the relationship between the dependent variable and multiple independent variables. The process is to estimate the value of the fitting coefficient by minimizing the error term, and by finding the best fitting coefficient, a well-fitting multiple linear regression model is obtained.

[0072] In the embodiment of the present application, the target logging data can be used as an independent variable and the total gas content can be used as a dependent variable to perform multiple linear regression fitting based on the fitting software, and the fitting coefficients of each target logging data can be obtained to obtain a coal reservoir gas content prediction model. It should be noted that when performing multiple linear regression fitting, the independent variable target logging data is at least two items.

[0073] For example, using Origin software, multiple linear regression is performed on the target logging data and the total gas content to establish a coal reservoir gas content prediction model. The specific formula is Q = a*A+b*B+c*C+d*D+…, where Q is the predicted gas content, a, b, c, d are the fitting coefficients of each target logging data, A, B, C, D are each target logging data, such as A can be gamma GR, B can be density DEN, C can be acoustic time difference AC, and D can be natural potential SP. It can be understood that different coal reservoirs correspond to different target logging data in the prediction model, and should be selected according to actual conditions.

[0074] S250. Determine the detected gas content of the coal reservoir based on the logging data.

[0075] In the embodiments of the present application, the detected gas content of the coal rock reservoir can be determined based on some parameters and relationships in the logging data. The following are some commonly used methods: 1. Gamma ray logging. The gamma ray intensity can be correlated with the content of radioactive elements in the formation, and then related to the saturation of the rock. By analyzing the gamma logging curve, combined with the empirical formula or model for a specific geological area, the gas content of the underground reservoir can be estimated. 2. Acoustic logging. Acoustic logging can provide information related to the porosity and saturation of the formation. Since the presence of gas in the formation will affect the propagation speed of the sound wave, the gas content in the reservoir can be inferred by analyzing the changes in the acoustic logging curve. 3. Density logging. Density logging can measure the actual density or effective density of the formation, and by comparing it with the relationship between the rock type and saturation, the gas content of the reservoir can be obtained. In this way, the detected gas content of the coal rock reservoir can be determined.

[0076] S260. Determine the predicted gas content of the coal reservoir based on the coal reservoir gas content prediction model.

[0077] S270. If the difference between the detected gas content and the predicted gas content is greater than a preset difference, the coal reservoir gas content prediction model is optimized until the difference between the predicted gas content calculated based on the optimized coal reservoir gas content prediction model and the detected gas content is less than or equal to the preset difference.

[0078] If the difference between the detected gas content and the predicted gas content is greater than the preset difference, it means that the coal reservoir gas content prediction model, that is, the multivariate linear regression fitting result between the target logging data and the total gas content, is not accurate. At this time, the coal reservoir gas content prediction model needs to be optimized until the difference between the predicted gas content and the detected gas content calculated based on the optimized coal reservoir gas content prediction model is less than or equal to the preset difference. It can be understood that the smaller the preset difference, the higher the prediction accuracy of the coal reservoir gas content prediction model.

[0079] The embodiment of the present application provides a method for predicting the gas content of a coal reservoir, which determines the adsorbed gas content of the coal rock sample based on data obtained by experimentally processing a first coal rock sample; predicts the free gas content of the coal rock sample based on data obtained by testing a second coal rock sample; wherein the first coal rock sample and the second coal rock sample are obtained by dividing the coal rock sample into at least two parts; the sum of the adsorbed gas content and the free gas content is taken as the total gas content of the coal rock sample; a coal reservoir gas content prediction model is determined based on the total gas content and logging data; the detected gas content of the coal rock reservoir is determined based on the logging data; the predicted gas content of the coal rock reservoir is determined based on the coal reservoir gas content prediction model; if the difference between the detected gas content and the predicted gas content is greater than a preset difference, the coal reservoir gas content prediction model is optimized until the difference between the predicted gas content and the detected gas content calculated based on the optimized coal reservoir gas content prediction model is less than or equal to the preset difference. The technical solution of the embodiment of the present application calculates the total gas content in the coal rock sample and determines the coal reservoir gas content prediction model based on the total gas content and logging data, thereby realizing the prediction of the gas content of the coal reservoir based on the coal reservoir gas content prediction model. The solution is simple, time-saving and low-cost. At the same time, the predicted gas content is verified by detecting the gas content to optimize the coal reservoir gas content prediction model, which can improve the accuracy of the prediction results.

[0080] Embodiment 3

[0081] Figure 4 This is a schematic diagram of the structure of a coal reservoir gas content prediction device provided in Example 3 of the present application. The device can execute the coal reservoir gas content prediction method provided in any embodiment of the present application, and has the corresponding functional modules and beneficial effects of the execution method. Figure 5 As shown, the device comprises:

[0082] The adsorbed gas content determination module 310 is used to determine the adsorbed gas content of the coal rock sample based on the data obtained by performing experimental processing on the first coal rock sample;

[0083] The free gas content prediction module 320 is used to predict the free gas content of the coal rock sample according to the data obtained by testing the second coal rock sample; wherein the first coal rock sample and the second coal rock sample are obtained by dividing the coal rock sample into at least two parts;

[0084] The coal reservoir gas content prediction module 330 is used to determine the total gas content of the coal rock sample according to the adsorbed gas content and the free gas content, and to determine the coal reservoir gas content prediction model according to the total gas content and the logging data, so as to predict the gas content of the coal reservoir based on the coal reservoir gas content prediction model.

[0085] Optionally, the adsorbed gas content determination module 310 includes:

[0086] A formation pressure determination unit, used to determine the formation pressure at the depth where the coal rock sample is located according to the depth and pressure gradient of the coal rock sample;

[0087] The adsorbed gas content determination unit is used to determine the adsorbed gas content of the coal rock sample according to the formation pressure, the Langmuir volume and the Langmuir pressure.

[0088] Optionally, the free gas content prediction module 320 includes:

[0089] A first coefficient determination unit, used to determine a first coefficient according to the adsorbed gas content, the natural gas volume coefficient and the adsorbed phase density;

[0090] A second coefficient determination unit, used to determine the second coefficient according to the porosity, gas saturation, natural gas volume coefficient and apparent relative density of the coal rock sample;

[0091] The free gas content prediction unit is used to use the difference between the first coefficient and the second coefficient as the free gas content of the coal rock sample.

[0092] Optionally, the coal reservoir gas content prediction module 330 includes:

[0093] The total gas content determination unit is used to take the sum of the adsorbed gas content and the free gas content as the total gas content of the coal rock sample.

[0094] Optionally, the coal reservoir gas content prediction module 330 includes:

[0095] A correlation data determination unit, used for respectively determining the correlation data of each well logging data and the total gas content for different well logging data;

[0096] A target well logging data selection unit, used for selecting target well logging data from different well logging data according to the correlation data;

[0097] The coal reservoir gas content prediction unit is used to determine a coal reservoir gas content prediction model based on the target logging data and the total gas content.

[0098] Optionally, the coal reservoir gas content prediction unit comprises:

[0099] The coal reservoir gas content prediction subunit is used to perform multivariate linear regression fitting on the target logging data and the total gas content based on fitting software to obtain a coal reservoir gas content prediction model; wherein the target logging data is at least two items.

[0100] Optionally, the device further comprises:

[0101] A detection gas content determination module is used to determine the detection gas content of the coal reservoir according to the well logging data;

[0102] A predicted gas content determination module is used to determine the predicted gas content of the coal reservoir based on the coal reservoir gas content prediction model;

[0103] The prediction model optimization module is used to optimize the coal reservoir gas content prediction model if the difference between the detected gas content and the predicted gas content is greater than a preset difference, until the difference between the predicted gas content calculated based on the optimized coal reservoir gas content prediction model and the detected gas content is less than or equal to the preset difference.

[0104] A coal reservoir gas content prediction device provided in an embodiment of the present application can execute a coal reservoir gas content prediction method provided in any embodiment of the present application, and has corresponding functional modules and beneficial effects of the execution method.

[0105] Embodiment 4

[0106] Figure 5 A schematic diagram of an electronic device 10 that can be used to implement an embodiment of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0107] like Figure 5As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0108] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0109] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a coal reservoir gas content prediction method.

[0110] In some embodiments, the coal reservoir gas content prediction method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the coal reservoir gas content prediction method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the coal reservoir gas content prediction method in any other appropriate manner (for example, by means of firmware).

[0111] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0112] The computer program for implementing the method of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable coal reservoir gas content prediction device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0113] In the context of the present application, a computer readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device or equipment. A computer readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer readable storage medium may be a machine readable signal medium. A more specific example of a machine readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0114] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0115] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0116] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0117] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this application can be executed in parallel, sequentially or in different orders, as long as the information expected by the technical solution of this application can be achieved, and this document is not limited here.

[0118] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A method for predicting gas content in coal reservoirs, characterized in that: The method comprises: Determining the adsorbed gas content of the coal rock sample based on data obtained by experimentally processing the first coal rock sample; Predicting the free gas content of the coal rock sample based on data obtained by testing the second coal rock sample; wherein the first coal rock sample and the second coal rock sample are obtained by dividing the coal rock sample into at least two parts; The total gas content of the coal rock sample is determined based on the adsorbed gas content and the free gas content, and a coal reservoir gas content prediction model is determined based on the total gas content and logging data, so as to predict the gas content of the coal reservoir based on the coal reservoir gas content prediction model.

2. The method according to claim 1, characterized in that Based on the data obtained by experimentally processing the first coal rock sample, the adsorbed gas content of the coal rock sample is determined, including: Determine the formation pressure at the depth where the coal rock sample is located according to the sampling depth and pressure gradient of the coal rock sample; The adsorbed gas content of the coal rock sample is determined based on the formation pressure, Langmuir volume and Langmuir pressure.

3. The method according to claim 1, characterized in that: Based on the data obtained from testing the second coal rock sample, the free gas content of the coal rock sample is predicted, including: Determining a first coefficient according to the adsorbed gas content, the natural gas volume coefficient, and the adsorbed phase density; Determine the second coefficient according to the porosity, gas saturation, natural gas volume coefficient and apparent relative density of the coal rock sample; The difference between the first coefficient and the second coefficient is used as the free gas content of the coal rock sample.

4. The method according to claim 1, characterized in that: Determining the total gas content of the coal rock sample according to the adsorbed gas content and the free gas content includes: The sum of the adsorbed gas content and the free gas content is taken as the total gas content of the coal rock sample.

5. The method according to claim 1, characterized in that According to the total gas content and the logging data, a coal reservoir gas content prediction model is determined, including: For different well logging data, respectively determine the correlation data between each well logging data and the total gas content; Selecting target well logging data from different well logging data according to the correlation data; A coal reservoir gas content prediction model is determined based on the target logging data and the total gas content.

6. The method according to claim 1, characterized in that According to the target well logging data and the total gas content, a coal reservoir gas content prediction model is determined, including: Based on the fitting software, the target logging data and the total gas content are subjected to multivariate linear regression fitting to obtain a coal reservoir gas content prediction model; wherein the target logging data are at least two items.

7. The method according to claim 1, characterized in that The method further comprises: Determine the detected gas content of coal reservoirs based on well logging data; Based on the coal reservoir gas content prediction model, determine the predicted gas content of the coal reservoir; If the difference between the detected gas content and the predicted gas content is greater than a preset difference, the coal reservoir gas content prediction model is optimized until the difference between the predicted gas content calculated based on the optimized coal reservoir gas content prediction model and the detected gas content is less than or equal to the preset difference.

8. A coal reservoir gas content prediction device, characterized in that: The device comprises: An adsorbed gas content determination module, used to determine the adsorbed gas content of the coal rock sample based on data obtained by experimentally processing the first coal rock sample; A free gas content prediction module, used to predict the free gas content of the coal rock sample according to data obtained by testing the second coal rock sample; wherein the first coal rock sample and the second coal rock sample are obtained by dividing the coal rock sample into at least two parts; The coal reservoir gas content prediction module is used to determine the total gas content of the coal rock sample according to the adsorbed gas content and the free gas content, and to determine the coal reservoir gas content prediction model according to the total gas content and logging data, so as to predict the gas content of the coal reservoir based on the coal reservoir gas content prediction model.

9. An electronic device, characterized in that: The device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the coal reservoir gas content prediction method described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the coal reservoir gas content prediction method described in any one of claims 1-7 when executed.

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