Saturation calculation method and device based on nuclear magnetic data inversion, equipment and medium
By using a method based on nuclear magnetic resonance data inversion, complex reservoirs are grouped and model parameters are adjusted, which solves the problem of low accuracy in saturation calculation in existing technologies and achieves higher accuracy in reservoir saturation calculation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- PETROCHINA CO LTD
- Filing Date
- 2022-05-30
- Publication Date
- 2026-07-21
Smart Images
Figure CN117191847B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of geological exploration technology, specifically to a method, apparatus, equipment, and medium for calculating saturation based on nuclear magnetic resonance data inversion. Background Technology
[0002] Reservoir pore structure classification is the prerequisite and foundation for oil and gas field geological evaluation and reservoir logging evaluation, and it is also an important basis for oil and gas field development. For reservoirs with a certain amount of clay content and complex pore structure, the pore structure and pore fluid distribution pattern both affect the rock resistivity. Resistivity is a major measurement value in logging, and the oil and gas saturation of the reservoir can be estimated based on the resistivity. This unsaturation effect varies point by point in different parts of the reservoir. Currently used models such as the Archie saturation model and the Waxman-Smits model use various parameters to try to eliminate the influence of unexpected factors such as fluid saturation on resistivity.
[0003] As complex reservoirs account for an increasingly larger proportion of exploration and development, the accuracy of saturation calculation in complex reservoirs is relatively low due to the influence of pore structure and pore fluid in different parts of the reservoir during actual well logging. Summary of the Invention
[0004] This application addresses the problem of low accuracy in calculating saturation in complex reservoirs by proposing a saturation calculation method based on NMR data inversion. The specific technical solution is as follows:
[0005] In a first aspect of this application, a method for calculating saturation based on NMR data inversion is proposed, the method comprising:
[0006] Based on the X-ray tube model, the standard NMR data of multiple standard rock core samples are inverted to obtain the pore structure parameters corresponding to each standard rock core sample;
[0007] Based on the pore structure parameters, the multiple standard rock core samples are grouped, and the saturation model parameters corresponding to each group are obtained.
[0008] Write the saturation model parameters corresponding to each group into the well logging numerical processing program;
[0009] The saturation of the core sample is obtained by picking up the saturation model parameters corresponding to the pore structure parameters to be measured using the logging numerical processing program; wherein the pore structure parameters to be measured are obtained by inverting the nuclear magnetic resonance data of the core sample.
[0010] Optionally, obtain the saturation model parameters corresponding to each group, including:
[0011] Obtain rock conductivity data corresponding to multiple standard rock core samples;
[0012] Based on the rock conductivity data corresponding to each group, the damped least squares method is used to process the rock conductivity data of that group to obtain the saturation model parameters corresponding to that group.
[0013] Optionally, rock electrical conductivity data is obtained through the following steps:
[0014] Each of the standard rock core samples was subjected to nuclear magnetic resonance experiments to obtain multiple standard nuclear magnetic data.
[0015] Rock conductivity tests were conducted on multiple standard rock core samples to obtain rock conductivity data.
[0016] Optionally, the pore structure parameters include at least spherical pore pattern, tubular pore pattern and equivalent pore radius ratio, wherein the equivalent pore radius ratio is the ratio of the tube radius to the sphere radius in the spherical tube model.
[0017] Optionally, based on the pore structure parameters, the plurality of standard core samples are grouped, including:
[0018] Based on the pore structure parameters corresponding to each of the multiple standard core samples, a first correlation coefficient is obtained. The first correlation coefficient represents the degree of correlation between the pore structure parameters corresponding to any one of the standard core samples and the pore structure parameters corresponding to another standard core sample.
[0019] The pore structure parameters are classified according to the first correlation coefficient;
[0020] Based on the rock conductivity data, standard core samples under each category are grouped.
[0021] Optionally, the saturation of the core sample is obtained by using the logging numerical processing program to pick up the saturation model parameters corresponding to the pore structure parameters to be measured, including:
[0022] Using the well logging numerical processing program, a second correlation coefficient is calculated based on the pore structure parameters to be measured; wherein, the second correlation coefficient represents the degree of correlation between the pore structure parameters in the well logging numerical processing program and the pore structure parameters to be measured;
[0023] Based on the second correlation coefficient, the saturation model parameters corresponding to the pore structure parameters to be measured are picked, and the saturation of the rock core to be measured is calculated.
[0024] Optionally, based on the second correlation coefficient, the target saturation model parameters are invoked, including:
[0025] Obtain the target pore structure parameter, which is the pore structure parameter with the largest second correlation coefficient with the pore structure parameter to be measured in the well logging numerical processing program;
[0026] Based on the target pore structure parameters, pick the saturation model parameters corresponding to the pore structure parameters to be measured.
[0027] In a second aspect of this application, a saturation calculation apparatus based on NMR data inversion is proposed, the apparatus comprising:
[0028] The first acquisition module is used to invert the standard NMR data of multiple standard core samples based on the X-ray tube model to obtain the pore structure parameters corresponding to each standard core sample.
[0029] Grouping module: used to group the multiple standard core samples based on the pore structure parameters, and obtain the saturation model parameters corresponding to each group;
[0030] The second acquisition module is used to write the saturation model parameters corresponding to each group into the well logging numerical processing program;
[0031] Picking module: used to pick up the saturation model parameters corresponding to the pore structure parameters to be measured using the logging numerical processing program, and obtain the saturation of the core sample to be measured; wherein, the pore structure parameters to be measured are obtained by inverting the NMR data of the core sample to be measured.
[0032] In a third aspect of this application, an electronic device is provided, the electronic device comprising:
[0033] At least one processor; and,
[0034] A memory communicatively connected to the at least one processor; wherein,
[0035] The memory stores instructions that can be executed by the at least one processor, which enables the at least one processor to perform the saturation calculation method based on NMR data inversion.
[0036] In a fourth aspect of this application, a computer-readable storage medium is provided, storing a computer program that, when executed by a processor, implements the saturation calculation method based on NMR data inversion.
[0037] This application has the following beneficial effects:
[0038] This application provides a method for calculating saturation based on NMR data inversion. Multiple standard core samples are grouped according to pore structure parameters, and the saturation model parameters corresponding to each group are written into a well logging numerical processing program. When calculating saturation, the well logging numerical processing program can be used to pick up the saturation model parameters corresponding to the pore structure parameters to be measured, thereby obtaining the saturation of the core sample based on these saturation model parameters. Therefore, during well logging data processing, the pore parameters to be measured of the core sample are input into the well logging numerical processing program, which can automatically pick up the saturation model parameters corresponding to the pore structure parameters. Since the saturation model parameters in the well logging numerical processing program are obtained based on the pore structure parameters of different groups, when calculating saturation based on the saturation models corresponding to different saturation parameter models, the saturation model parameters can be changed in real time to address the differences in pore structure and pore fluid influence in different parts of complex reservoirs. This eliminates the influence of pore structure and pore fluid distribution on rock conductivity, effectively improving the calculation accuracy of saturation in complex reservoirs. Attached Figure Description
[0039] To more clearly illustrate the technical solution of this application, the drawings used in the description of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0040] Figure 1 This is a flowchart illustrating the saturation calculation method based on NMR data inversion in this application embodiment;
[0041] Figure 2 This is a system flowchart of the saturation calculation method based on NMR data inversion in the embodiments of this application;
[0042] Figure 3 The present application provides NMR data for a standard rock core sample from a specific region.
[0043] Figure 4 This is a schematic diagram of the pore structure parameters obtained by inverting the NMR data of a standard rock core sample from a certain region based on a X-ray tube model in an embodiment of this application.
[0044] Figure 5 This is a schematic diagram showing the grouping results of Cd path parameters of multiple standard core samples from a certain region according to the first correlation coefficient in an embodiment of this application.
[0045] Figure 6 for Figure 5 A schematic diagram of the brine model parameters for the first group of standard core samples;
[0046] Figure 7 for Figure 5 A schematic diagram of the oil-water saturation model parameters of the first group of standard core samples;
[0047] Figure 8 This is a schematic diagram of the logging numerical processing program in this application picking up saturation model parameters based on the pore structure parameters to be measured;
[0048] Figure 9 This is a schematic diagram of the saturation calculation device based on NMR data inversion in the embodiments of this application. Detailed Implementation
[0049] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.
[0050] For reservoirs with a certain amount of clay content and complex pore structure, the pore structure and pore fluid distribution pattern both affect the resistivity of the rock, thus affecting the accuracy of saturation calculation. Moreover, the influence of this unsaturation varies point by point in different parts of the reservoir. Usually, models such as the Archie model and the Waxman-Smits model are used to use various parameters to eliminate the influence of unexpected factors of fluid saturation on resistivity. However, since it is impossible to change the saturation parameters in real time according to the differences in the influence of different parts of complex reservoirs, the accuracy of saturation calculation in complex reservoirs is reduced.
[0051] In existing technologies, during actual well logging data processing, the parameters of the saturation model are generally determined layer by layer to account for reservoir heterogeneity. This approach eliminates the impact of reservoir heterogeneity on the accuracy of saturation calculation to some extent. However, determining the saturation parameters layer by layer is very labor-intensive, and it is also difficult to eliminate the impact of severely heterogeneous reservoirs on the accuracy of saturation calculation.
[0052] Based on this, embodiments of this application provide a saturation calculation method based on NMR data inversion. The specific concept is as follows: multiple standard core samples are grouped according to pore structure parameters, and the saturation model parameters corresponding to each group are written into the well logging numerical processing program. When calculating the saturation, the well logging numerical processing program can be used to pick up the saturation model parameters corresponding to the pore structure parameters to be measured, so as to obtain the saturation of the core sample to be measured based on the saturation model parameters.
[0053] Reference Figure 1 , Figure 1 A flowchart illustrating the saturation calculation method based on NMR data inversion according to the first embodiment of this application is shown. The specific steps are as follows:
[0054] S101. Based on the X-ray tube model, the standard NMR data of multiple standard core samples are inverted to obtain the pore structure parameters corresponding to each standard core sample.
[0055] In the specific implementation process, the sphere-tube model is a model used to describe the internal pore structure of rocks. It is a model formed by accumulating pores as approximate spheres and throats as approximate tubes. The standard core sample is a core plunger sample after preprocessing. The standard NMR data is the data obtained from the standard core sample through NMR experiments. The pore structure parameters are the parameters obtained by inverting the standard NMR data based on the sphere-tube model, specifically including the T2 total spectrum, spherical pore spectrum, tubular pore spectrum, and equivalent pore radius ratio.
[0056] S102. Based on the pore structure parameters, the multiple standard rock core samples are grouped, and the saturation model parameters corresponding to each group are obtained.
[0057] In the specific implementation process, the saturation model parameters are used to construct the saturation model. Different saturation models are required for different reservoirs to improve the accuracy of saturation calculation.
[0058] In complex reservoirs, the pore structure and pore fluid distribution patterns differ in different parts, and both pore structure and pore fluid distribution patterns affect the rock resistivity. For different types of rocks, the pore structure parameters are grouped, and the saturation model parameters of each group are obtained. Therefore, the saturation model obtained based on the corresponding saturation model parameters can eliminate the influence of pore structure and pore fluid distribution patterns on rock conductivity, thus making the saturation results calculated based on rock resistivity more accurate.
[0059] S103. Write the saturation model parameters corresponding to each group into the well logging numerical processing program.
[0060] In the specific implementation process, the logging numerical processing program is used to store the saturation model parameters corresponding to different groups. When the input pore structure parameter to be measured matches the pore structure parameter of any group, the saturation model parameter of that group can be called to construct a saturation model to calculate the saturation corresponding to the pore structure parameter to be measured.
[0061] S104. Using the well logging numerical processing program, pick up the saturation model parameters corresponding to the pore structure parameters to be measured, and obtain the saturation of the core sample to be measured; wherein, the pore structure parameters to be measured are obtained by inverting the nuclear magnetic resonance data of the core sample to be measured.
[0062] In the specific implementation process, the rock pore structure parameters of the part to be tested are obtained by inversion based on the nuclear magnetic resonance data of the core to be tested and the tube model. The core to be tested refers to the reservoir that needs to be measured in the actual logging process.
[0063] This application provides a saturation calculation method based on NMR data inversion. Multiple standard core samples are grouped according to pore structure parameters, and the saturation model parameters corresponding to each group are written into a well logging numerical processing program. When calculating saturation, the well logging numerical processing program can be used to pick up the saturation model parameters corresponding to the pore structure parameters to be measured, thereby obtaining the saturation of the core sample based on these saturation model parameters. Therefore, during well logging data processing, the pore parameters to be measured of the core sample are input into the well logging numerical processing program, which can automatically pick up the saturation model parameters corresponding to the pore structure parameters. Since the saturation model parameters in the well logging numerical processing program are obtained based on the pore structure parameters of different groups, when calculating saturation based on the saturation models corresponding to different saturation parameter models, the saturation model parameters are changed in real time to address the differences in pore structure and pore fluid influence in different parts of complex reservoirs. This can eliminate the influence of pore structure and pore fluid distribution on rock conductivity, effectively improving the calculation accuracy of saturation in complex reservoirs.
[0064] In some embodiments, obtaining the saturation model parameters corresponding to each group includes:
[0065] Obtain rock conductivity data corresponding to multiple standard rock core samples;
[0066] Based on the rock conductivity data corresponding to each group, the damped least squares method is used to process the rock conductivity data of that group to obtain the saturation model parameters corresponding to that group.
[0067] In the specific implementation process, the rock conductivity data is obtained by conducting rock conductivity experiments on standard rock core samples. The damped least squares method is a method based on the evaluation function to construct the relationship between pore structure parameters and saturation model parameters. When the saturation model parameters are changed, multiple pore structure parameters are made to approach the target value, and then the evaluation function tends to the minimum value.
[0068] This scheme employs damped least squares to establish the relationship between pore structure parameters and saturation model parameters for standard core samples within each group. This effectively links the pore structure parameters and saturation model parameters corresponding to each group, allowing for direct retrieval of the corresponding saturation model parameters when inputting pore structure parameters for any group during actual well logging. Using damped least squares, the calculated saturation is more accurate based on the derived saturation model parameters. Furthermore, it effectively and correctly establishes the Agyn relationship between pore structure parameters and saturation model parameters, accelerating the convergence speed of the evaluation function.
[0069] In some embodiments, rock electrical conductivity data is obtained through the following steps:
[0070] Each of the standard rock core samples was subjected to nuclear magnetic resonance experiments to obtain multiple standard nuclear magnetic data.
[0071] Rock conductivity tests were conducted on multiple standard rock core samples to obtain rock conductivity data.
[0072] In the specific implementation process, before conducting experiments on standard rock core samples, pretreatment of the samples is required, including leveling and grinding, oil washing, and salt washing. Specifically, the rock conductivity data obtained from the rock conductor test, brine saturation conductivity test, and oil-gas displacement conductivity test include parameters for both brine saturation and oil-water saturation.
[0073] In some embodiments, the pore structure parameters include at least spherical pore pattern, tubular pore pattern and equivalent pore radius ratio, wherein the equivalent pore radius ratio is the ratio of the tube radius to the sphere radius in the spherical tube model.
[0074] In the specific implementation process, the pore structure parameters also include the T2 spectrum, which is a time constant describing the recovery process of the transverse component of nuclear magnetization intensity, and is therefore called the transverse relaxation time. The transverse relaxation process is caused by the exchange of energy within the nuclear spin system, so it is also called the spin-spin relaxation time. Spherical and tubular pore spectra can be obtained from the T2 spectrum; based on these spectra, the equivalent pore radius ratio at each inversion point can be obtained, and the Cd path parameters of the pneumatic-tube model can be obtained from multiple equivalent pore radius ratios. Finally, multiple Cd path parameters and saturation model parameters are stored together in the logging numerical processing program. During actual logging, the saturation model parameter corresponding to the Cd path parameter with the highest correlation to the input Cd path parameter can be retrieved directly based on the correlation between the input Cd path parameter and the multiple Cd path parameters stored in the logging numerical processing program.
[0075] In some embodiments, the plurality of standard core samples are grouped based on the pore structure parameters, including:
[0076] Based on the pore structure parameters corresponding to each of the multiple standard core samples, a first correlation coefficient is obtained. The first correlation coefficient represents the degree of correlation between the pore structure parameters corresponding to any one of the standard core samples and the pore structure parameters corresponding to another standard core sample.
[0077] The pore structure parameters are classified according to the first correlation coefficient;
[0078] Based on the rock conductivity data, standard core samples under each category are grouped.
[0079] In the specific implementation process, when obtaining the first correlation coefficient, the pore structure parameters of one standard core sample are used as the standard pore structure parameters. The correlation coefficients between the pore structure parameters of the other standard core samples are calculated, and the first correlation coefficients with the standard core sample are sorted. The standard core samples with the largest first correlation coefficients are grouped into one category with the standard core sample. When further classifying the other standard core samples, the same method is used for classification until the pore structure parameters of all standard core samples are classified.
[0080] After classifying the pore structure parameters, it is necessary to group them accordingly based on the rock conductivity data. Since the rock conductivity data includes saturated brine parameters and saturated oil-water parameters, multiple standard core samples under each classification are grouped based on the saturated brine parameters and saturated oil-water parameters. Generally, the pore structure parameters are divided into two groups according to the saturated brine parameters and saturated oil-water parameters. Thus, the damped least squares method can be used to process the rock conductivity experimental data to obtain the saturation model parameters corresponding to the pore structure parameters of each group.
[0081] In some embodiments, the saturation of the core sample is obtained by using the well logging numerical processing program to pick up the saturation model parameters corresponding to the pore structure parameters to be measured, including:
[0082] Using the well logging numerical processing program, a second correlation coefficient is calculated based on the pore structure parameters to be measured; wherein, the second correlation coefficient represents the degree of correlation between the pore structure parameters in the well logging numerical processing program and the pore structure parameters to be measured;
[0083] Based on the second correlation coefficient, the saturation model parameters corresponding to the pore structure parameters to be measured are picked, and the saturation of the rock core to be measured is calculated.
[0084] Specifically, based on the second correlation coefficient, the saturation model parameters corresponding to the pore structure parameters to be measured are picked, including:
[0085] Obtain the target pore structure parameter, which is the pore structure parameter with the largest second correlation coefficient with the pore structure parameter to be measured in the well logging numerical processing program;
[0086] Based on the target pore structure parameters, pick the saturation model parameters corresponding to the pore structure parameters to be measured.
[0087] In the specific implementation process, during the logging process, the pore structure parameters of the core sample to be tested are input into the logging numerical processing program. The second correlation coefficient of each pore structure parameter in the logging numerical processing program can be calculated based on the pore structure parameters to be tested. The saturation model parameter corresponding to the pore structure parameter with the largest second correlation coefficient can be obtained. That is, the saturation of the core sample to be tested can be calculated based on the saturation model parameter.
[0088] This scheme calculates a second correlation coefficient based on the pore structure parameters in the well logging numerical processing program and the pore structure parameters to be measured. It can pick the saturation model parameter corresponding to the pore structure parameter with the largest second correlation coefficient, and thus obtain the saturation of the core to be measured based on the saturation model parameter. It can pick the corresponding saturation model parameter for different parts of complex reservoirs, effectively improving the calculation accuracy of saturation of complex reservoirs.
[0089] Reference Figure 2 , Figure 2 This paper illustrates the logical structure diagram of the saturation calculation method based on NMR data inversion in the second embodiment of this application, combined with... Figure 2 The embodiments of this application are illustrated by way of example:
[0090] S1. Perform nuclear magnetic resonance experiments and rock conductivity experiments on multiple standard rock core samples to obtain the nuclear magnetic resonance data and rock conductivity data of the standard rock core samples.
[0091] Multiple standard core samples are drilled. The number of standard core samples depends on the number of groups to be formed, with each group containing at least 3 core samples.
[0092] Standard core samples undergo routine pretreatment, including leveling and grinding, oil washing, and salt washing.
[0093] For each standard core sample, nuclear magnetic resonance (NMR) experiments, brine saturation conductivity experiments, and hydrocarbon displacement conductivity experiments were conducted to obtain the NMR data of the standard core sample (refer to...). Figure 3 ), saturated salt water parameters and saturated oil-water parameters.
[0094] Figure 3In this context, Am represents the original NMR signal in the NMR experiment, mv represents the signal amplitude, and t represents the time of the NMR experiment, with the unit being ms.
[0095] S2. Based on the inversion of NMR data using the X-ray tube model, the pore structure parameters corresponding to each of the standard core samples are obtained.
[0096] The pore structure parameters include the T2 total spectrum, spherical pore spectrum, tubular pore spectrum, and equivalent pore radius ratio. The T2 total spectrum is the time constant describing the recovery process of the transverse component of nuclear magnetization, and is therefore called the transverse relaxation time. Based on the equivalent pore radius ratio, the Cd path parameters are obtained. The Cd path parameters reflect the configuration relationship between the tubular and spherical radii in each inversion point relaxation component, as follows:
[0097]
[0098] Among them, C di R represents the path parameter Cd at the i-th placement point. ci R represents the radius of the tubular hole at point i. si This represents the radius of the spherical hole at point i.
[0099] Reference Figure 4 , Figure 4 This illustration shows a schematic diagram of pore structure parameters obtained from the inversion of NMR data of a standard core sample from a certain region using a X-ray tube model, as shown in this application embodiment. Here, ACI tubular pore refers to the tubular pore spectrum, ASI spherical pore refers to the spherical pore spectrum, TC time refers to the transverse relaxation time, T2 optimization refers to the T2 total spectrum obtained by inverting the NMR data based on the X-ray tube model, and T2 original refers to the T2 total spectrum obtained using other methods in the prior art. Figure 4 It can be seen that the original T2 image and the optimized T2 image are not significantly different.
[0100] S3. Based on the pore structure parameters and rock conductivity data, the multiple standard rock core samples are grouped.
[0101] like Figure 5 As shown, the grouping process is generally as follows:
[0102] Based on the pore structure parameters of multiple standard core samples, the first correlation coefficient between one pore structure parameter and the pore structure parameters of the other standard core samples is calculated. The standard core samples corresponding to the pore structure parameter with the largest first correlation coefficient are grouped into the same category as the standard core samples corresponding to the standard pore structure parameters. Each category is then grouped according to the rock conductivity data to obtain the final grouping results.
[0103] Figure 5 In the pore structure CD classification table, series 1 to series 6 represent 6 different standard core samples, CD represents the Cd path parameters of the standard core sample, and the first group to the fourth group are obtained based on the first correlation coefficient classification.
[0104] S31. Select one pore structure parameter from multiple standard core samples as the standard pore structure parameter. Calculate the first correlation coefficient between the pore structure parameters of the remaining standard core samples and the standard pore structure parameter. The pore structure parameter includes the Cd path parameter. For example, if the samples are grouped into 4 groups, each containing 3 standard core samples, then there should be 12 standard core samples with Cd path parameters. Calculate the first correlation coefficient using the following formula:
[0105]
[0106] Where R is the first correlation coefficient, and C d1i C d2i Let Cd be the Cd path parameter of the standard pore structure parameter and the Cd path parameter of any other pore structure parameter at point i. These are the average values of the equivalent pore radius ratios at each point of the Cd path parameters for the standard pore structure parameters and the Cd path parameters for any other pore structure parameter.
[0107] S32. Classify the standard core samples corresponding to the pore structure parameter with the largest first correlation coefficient with the standard pore structure parameter into the same category as the standard core samples corresponding to the standard pore structure parameter.
[0108] S33. For the remaining standard core samples, select a new standard core sample with the same pore structure parameters as the standard pore structure parameters, and repeat steps S31 and S32 until all classifications are completed.
[0109] S34. Group each category according to the rock conductivity data to obtain the final grouping results.
[0110] Reference Figure 6 and Figure 7 , Figure 6 and Figure 7 They are shown respectively Figure 5 A schematic diagram of the brine-saturated model parameters and oil-water-saturated model parameters of the first group of standard core samples. Figure 6 In this context, F represents formation factors, POR represents porosity, and Coredata represents standard core samples. Figure 7 In this context, I represents the resistance increase factor, and S... W This indicates the water saturation of the formation; Core data represents standard rock core samples.
[0111] S4. The damped least squares method is used to process the rock conductivity data of each group to obtain the saturation model parameters corresponding to each group, as shown in the following formula:
[0112] log(R F = c log(φ) + d[log(φ)] 2
[0113]
[0114]
[0115]
[0116] log(R I ) = e log(S w )+f[log(S w )] 2
[0117]
[0118]
[0119] Among them, R F denoted as porosity scaling factor, characterizing the influence of pore structure and distribution on the electrical conductivity of rocks; c and d represent saturation model parameters related to pore structure; e and f represent saturation model parameters related to pore fluid distribution morphology, where pore fluid distribution morphology can specifically refer to hydrocarbon saturation; C0 is the initial pore structure efficiency, characterizing the influence of pore structure on the electrical conductivity of rocks, obtained based on the distribution curve of the spherical tube model, and in this scheme refers to the ratio of the median values of the cumulative distribution curves of fracture pores and spherical pores; C F , where C0 is the initial pore structure efficiency calculated based on C0; V C and V S These are the pore volumes of tubes and spheres, respectively, in μm. 3 ;R I The saturation scaling factor characterizes the influence of hydrocarbon quantity and distribution on the electrical conductivity of rocks; C IThe saturation structure efficiency characterizes the influence of hydrocarbon distribution and pore structure morphology on the electrical conductivity of rocks; F and I represent formation factors and resistivity amplification coefficients, respectively; R0 and R... t Resistivity of the brine core and resistivity of the oil-water core are respectively, in ΩM; Rw is the resistivity of the brine prepared in the experiment, in ΩM; and S W These are formation porosity and water saturation, respectively. Porosity refers to the cavities in a rock that are not filled by solid matter. Porosity is the ratio of the volume of pores in a rock to the volume of the rock's surface. Water saturation is the ratio of the volume of pores occupied by water in an oil reservoir to the volume of pores in the rock.
[0120] According to the above formula, during the calculation process, R F R W V C and V S All are known parameters, and F, I, and S W The parameters were obtained through nuclear magnetic resonance experiments and rock conductivity tests. Based on the above formulas, the saturation model parameters c, d, e, and f corresponding to each group can be derived.
[0121] In the process of calculating using the damped least squares method, an initial value needs to be assigned to F and I, and iterative calculations are performed according to the above formula to eventually make the evaluation function approach the minimum value, thereby obtaining the final saturation model parameters.
[0122] S5. Write the pore structure parameters of each group and the saturation model parameters corresponding to each group into the well logging data processing program.
[0123] S6. During well logging, the NMR data of the core sample is inverted based on the pneumatic tube model to obtain the pore structure parameters to be measured. The well logging data processing program is then called to pick the saturation model parameters corresponding to the pore structure parameters to be measured. Based on these saturation model parameters and the resistivity and porosity data obtained during the well logging process, the saturation S is calculated. W .
[0124] See Figure 8 ,exist Figure 8In this model, EREM stands for saturation model, the logging Cd path is the Cd path parameter of the pore structure parameter to be measured, c1, d1, e1 and f1 are the saturation model parameters in the first group, c2, d2, e2 and f2 are the saturation model parameters in the second group, c3, d3, e3 and f3 are the saturation model parameters in the third group, and c4, d4, e4 and f4 are the saturation model parameters in the fourth group.
[0125] This scheme obtains saturation model parameters based on pore structure parameters of different groups. Therefore, when calculating saturation based on saturation models corresponding to different saturation parameter models, the saturation model parameters are changed in real time to address the differences in pore structure and pore fluid influence in different parts of complex reservoirs. This eliminates the influence of pore structure and pore fluid distribution on rock conductivity, effectively improving the calculation accuracy of saturation in complex reservoirs.
[0126] Furthermore, to achieve the above objectives, embodiments of this application also provide a saturation calculation apparatus based on NMR data inversion, referring to... Figure 9 , Figure 9 A schematic diagram of a saturation calculation device based on NMR data inversion according to a third embodiment of this application is shown. The device includes:
[0127] First acquisition module 1001: used to invert the standard NMR data of multiple standard core samples based on the X-ray tube model to obtain the pore structure parameters corresponding to each standard core sample;
[0128] Grouping module 1002: used to group the multiple standard core samples based on the pore structure parameters, and obtain the saturation model parameters corresponding to each group;
[0129] The second acquisition module 1003 is used to write the saturation model parameters corresponding to each group into the well logging numerical processing program;
[0130] Picking module 1004: used to pick up the saturation model parameters corresponding to the pore structure parameters to be measured using the well logging numerical processing program, and obtain the saturation of the core to be measured; wherein, the pore structure parameters to be measured are obtained by inverting the nuclear magnetic resonance data of the core to be measured.
[0131] It should be noted that each module in the saturation calculation device based on NMR data inversion in this embodiment corresponds one-to-one with each step in the saturation calculation method based on NMR data inversion in the aforementioned embodiment. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned saturation calculation method based on NMR data inversion, and will not be repeated here.
[0132] In some embodiments, the grouping module 1002 is specifically used for:
[0133] Obtain rock conductivity data corresponding to multiple standard rock core samples;
[0134] Based on the rock conductivity data corresponding to each group, the damped least squares method is used to process the rock conductivity data of that group to obtain the saturation model parameters corresponding to that group.
[0135] In some embodiments, the grouping module 1002 is further configured to:
[0136] Based on the pore structure parameters corresponding to each of the multiple standard core samples, a first correlation coefficient is obtained. The first correlation coefficient represents the degree of correlation between the pore structure parameters corresponding to any one of the standard core samples and the pore structure parameters corresponding to another standard core sample.
[0137] The pore structure parameters are classified according to the first correlation coefficient;
[0138] Based on the rock conductivity data, standard core samples under each category are grouped.
[0139] In some embodiments, the picking module 1004 includes:
[0140] The calculation submodule is used to calculate a second correlation coefficient based on the pore structure parameters to be measured using the well logging numerical processing program; wherein the second correlation coefficient represents the degree of correlation between the pore structure parameters in the well logging numerical processing program and the pore structure parameters to be measured.
[0141] Picking submodule: used to pick the saturation model parameters corresponding to the pore structure parameters to be tested according to the second correlation coefficient, and calculate the saturation of the rock core to be tested.
[0142] In some embodiments, the picking submodule is specifically used for:
[0143] Obtain the target pore structure parameter, which is the pore structure parameter with the largest second correlation coefficient with the pore structure parameter to be measured in the well logging numerical processing program;
[0144] Based on the target pore structure parameters, pick the saturation model parameters corresponding to the pore structure parameters to be measured.
[0145] Furthermore, to achieve the above objectives, a fourth embodiment of this application also proposes an electronic device, the electronic device comprising:
[0146] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the saturation calculation method based on NMR data inversion.
[0147] Furthermore, to achieve the above objectives, the fifth embodiment of this application also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the saturation calculation method based on NMR data inversion.
[0148] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present application.
[0149] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.
[0150] The above provides a detailed description of the saturation calculation method, apparatus, equipment, and medium based on NMR data inversion. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A saturation calculation method based on NMR data inversion, characterized in that, The method includes: Based on the X-ray tube model, the standard NMR data of multiple standard rock core samples are inverted to obtain the pore structure parameters corresponding to each standard rock core sample; Based on the pore structure parameters, the multiple standard rock core samples are grouped, and the saturation model parameters corresponding to each group are obtained. Write the saturation model parameters corresponding to each group into the well logging numerical processing program; The saturation of the core sample is obtained by picking up the saturation model parameters corresponding to the pore structure parameters to be measured using the logging numerical processing program; wherein the pore structure parameters to be measured are obtained by inverting the nuclear magnetic resonance data of the core sample. The saturation of the core sample is obtained by using the logging numerical processing program to pick up the saturation model parameters corresponding to the pore structure parameters to be measured, including: Using the well logging numerical processing program, a second correlation coefficient is calculated based on the pore structure parameters to be measured; wherein, the second correlation coefficient represents the degree of correlation between the pore structure parameters in the well logging numerical processing program and the pore structure parameters to be measured; Based on the second correlation coefficient, the saturation model parameters corresponding to the pore structure parameters to be measured are picked, and the saturation of the rock core to be measured is calculated. Based on the second correlation coefficient, the saturation model parameters corresponding to the pore structure parameters to be measured are picked, including: Obtain the target pore structure parameter, which is the pore structure parameter with the largest second correlation coefficient with the pore structure parameter to be measured in the well logging numerical processing program; Based on the target pore structure parameters, pick the saturation model parameters corresponding to the pore structure parameters to be measured.
2. The method according to claim 1, characterized in that, Obtain the saturation model parameters for each group, including: Obtain rock conductivity data corresponding to multiple standard rock core samples; Based on the rock conductivity data corresponding to each group, the damped least squares method is used to process the rock conductivity data of that group to obtain the saturation model parameters corresponding to that group.
3. The method according to claim 2, characterized in that, Rock electrical conductivity data were obtained through the following steps: Each of the standard rock core samples was subjected to nuclear magnetic resonance experiments to obtain multiple standard nuclear magnetic data. Rock conductivity tests were conducted on multiple standard rock core samples to obtain rock conductivity data.
4. The method according to claim 1, characterized in that, The pore structure parameters include at least spherical pore pattern, tubular pore pattern and equivalent pore radius ratio, wherein the equivalent pore radius ratio is the ratio of the tube radius to the sphere radius in the spherical tube model.
5. The method according to claim 2, characterized in that, Based on the pore structure parameters, the multiple standard core samples are grouped, including: Based on the pore structure parameters corresponding to each of the multiple standard core samples, a first correlation coefficient is obtained. The first correlation coefficient represents the degree of correlation between the pore structure parameters corresponding to any one of the standard core samples and the pore structure parameters corresponding to another standard core sample. The pore structure parameters are classified according to the first correlation coefficient; Based on the rock conductivity data, standard core samples under each category are grouped.
6. A saturation calculation device based on NMR data inversion, characterized in that, The device includes: The first acquisition module is used to invert the standard NMR data of multiple standard core samples based on the X-ray tube model to obtain the pore structure parameters corresponding to each standard core sample. Grouping module: used to group the multiple standard core samples based on the pore structure parameters, and obtain the saturation model parameters corresponding to each group; The second acquisition module is used to write the saturation model parameters corresponding to each group into the well logging numerical processing program; Picking module: used to pick up the saturation model parameters corresponding to the pore structure parameters to be measured using the well logging numerical processing program, and obtain the saturation of the core sample to be measured; wherein, the pore structure parameters to be measured are obtained by inverting the nuclear magnetic resonance data of the core sample to be measured; The picking module includes: The calculation submodule is used to calculate a second correlation coefficient based on the pore structure parameters to be measured using the well logging numerical processing program; wherein the second correlation coefficient represents the degree of correlation between the pore structure parameters in the well logging numerical processing program and the pore structure parameters to be measured. Picking submodule: used to pick the saturation model parameters corresponding to the pore structure parameters to be tested according to the second correlation coefficient, and calculate the saturation of the rock core to be tested; The picking submodule is specifically used for: Obtain the target pore structure parameter, which is the pore structure parameter with the largest second correlation coefficient with the pore structure parameter to be measured in the well logging numerical processing program; Based on the target pore structure parameters, pick the saturation model parameters corresponding to the pore structure parameters to be measured.
7. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the saturation calculation method based on NMR data inversion as described in any one of claims 1 to 5.
8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the saturation calculation method based on NMR data inversion as described in any one of claims 1 to 5.