A method and apparatus for predicting shear wave velocity in a reservoir

Abstract

The application discloses a reservoir shear wave velocity prediction method and device. The method comprises the following steps: constructing a solid matrix model based on mineral content data of a reservoir to be predicted and determining an initial value of the elastic modulus of the solid matrix; determining the longitudinal wave velocity under different porosities based on the initial value of the elastic modulus of the solid matrix and a preset different porosity, comparing the longitudinal wave velocity with the measured longitudinal wave velocity of the reservoir rock sample, adjusting the initial value of the elastic modulus of the solid matrix based on the comparison result, and obtaining the solid matrix model after the adjustment of the elastic modulus; performing lithology correction on the neutron logging data of the reservoir based on the pore correction parameters of various minerals in the reservoir to determine the porosity of the reservoir, adding the porosity into the solid matrix model to establish a dry framework rock physics model; and constructing a rock physics model combined with the pore structure characteristics based on the pore structure of the reservoir and the dry framework rock physics model to determine the elastic modulus of the reservoir and further determine the shear wave velocity. The method can improve the prediction accuracy of the shear wave velocity.

Description

Technical Field

[0001] The present invention relates to the technical field of seismic rock physics, and in particular to a method and device for predicting reservoir shear wave velocity. Background Art

[0002] Dolomite reservoirs have long been a key area of ​​carbonate oil and gas exploration and research. As oil and gas exploration continues to expand deeper, the development mechanism and prediction of deep dolomite reservoirs have become key issues affecting oil and gas exploration. The strong heterogeneity of deep, high-quality dolomite reservoirs, determined by the multi-scale and multi-type reservoir spaces, results in multiple solutions for reservoir prediction, leading to low accuracy in predicting high-quality dolomite reservoirs. To accurately identify deep dolomite reservoirs, many researchers have conducted extensive research using equivalent medium theory in carbonate rock physics experiments, modeling, and the influence of pore structure on carbonate elastic parameters. These studies generally conclude that inverting P- and S-wave velocities in dolomite reservoirs using prestack data can better predict high-quality deep dolomite reservoirs.

[0003] The shear wave velocity of rocks in reservoirs can be measured through indoor rock physics experiments or dipole acoustic logging. However, the former is time-consuming and labor-intensive, while the latter is expensive through well logging. As a result, shear wave data is often lacking in actual exploration. In particular, there is basically no measured shear wave velocity data in old wells, which makes the prediction accuracy of high-quality dolomite reservoirs insufficient.

[0004] Based on extensive research on shear wave velocity prediction, numerous shear wave velocity prediction methods have been developed, primarily categorized into three categories: empirical relationship methods, rock physics model methods, and artificial intelligence methods. The empirical relationship method, based on statistical analysis, is a simple and easy-to-use method for estimating shear wave velocity. However, its application has certain limitations and often fails to meet the requirements for detailed reservoir description. Shear wave velocity prediction based on deep learning has also become a hot topic in academic research in recent years, but it is currently understudied and faces numerous challenges, such as insufficient model generalization. Furthermore, these methods are primarily data-driven and lack clear physical meaning. Rock physics modeling methods primarily investigate various rock physics models in conjunction with actual geological conditions, calculating equivalent elastic parameters of the reservoir and subsequently calculating the P- and S-wave velocities. Based on rock physics analysis, many researchers have developed various shear wave prediction models suitable for diverse geological conditions, achieving more accurate results than empirical formula methods. Rock physics modeling methods, grounded in rigorous mechanical analysis, theoretically offer the ability to accurately predict reservoir shear wave velocities, and therefore have garnered widespread attention. Summary of the Invention

[0005] The inventors of this application have discovered that, in the practice of rock physics modeling, it is necessary to accurately determine the various factors affecting the reservoir's skeletal mineral composition, pore structure, and porosity, and to select appropriate rock physics models for different practical situations in order to accurately predict reservoir shear wave velocities. However, when using rock physics modeling methods to predict shear wave velocities in complex oil and gas reservoir exploration, the solid matrix elastic modulus, porosity, and pore structure of the reservoir cannot be accurately calculated, resulting in poor shear wave prediction accuracy and, consequently, inability to accurately predict high-quality dolomite reservoirs.

[0006] In view of the above problems, the present invention is proposed to provide a method and apparatus for predicting shear wave velocity that overcomes the above problems or at least partially solves the above problems.

[0007] In a first aspect, an embodiment of the present invention provides a reservoir shear wave velocity prediction method, comprising:

[0008] A solid matrix model is constructed based on the mineral content data of the reservoir to be predicted, and an initial value of the elastic modulus of the solid matrix is ​​determined;

[0009] determining, based on an initial value of the elastic modulus of the solid matrix and different preset porosities, the longitudinal wave velocity of the solid matrix at different porosities; comparing the longitudinal wave velocities at the different porosities with the measured longitudinal wave velocities of rock samples in the reservoir; and adjusting the initial value of the elastic modulus of the solid matrix based on the comparison result to obtain a solid matrix model after the elastic modulus is adjusted;

[0010] Based on the pore correction parameters of various minerals in the predicted reservoir, the neutron logging data of the predicted reservoir is corrected for lithology to determine the porosity of the predicted reservoir. The porosity of the predicted reservoir is added to the solid matrix model to establish a dry skeleton rock physics model.

[0011] Based on the pore structure of the reservoir to be predicted and the dry skeleton rock physics model, constructing a rock physics model that combines pore structure characteristics, and determining the elastic modulus of the reservoir to be predicted based on the rock physics model that combines pore structure characteristics;

[0012] The shear wave velocity of the reservoir to be predicted is determined based on the determined elastic modulus of the reservoir to be predicted.

[0013] In some optional embodiments, determining the initial value of the elastic modulus of the solid matrix, wherein the elastic modulus of the solid matrix is ​​characterized by the shear modulus and bulk modulus of the solid matrix, comprises:

[0014] Based on the mineral content data of the reservoir to be predicted, the Voigt-Reuss-Hill model is used to respectively determine the upper limit value, lower limit value, and average value of the solid matrix shear modulus and the upper limit value, lower limit value, and average value of the bulk modulus;

[0015] The average value of the shear modulus and the average value of the bulk modulus of the solid matrix are respectively used as the initial value of the shear modulus and the initial value of the bulk modulus of the solid matrix.

[0016] In some optional embodiments, the upper limit value, lower limit value, and average value of the solid matrix shear modulus and the upper limit value, lower limit value, and average value of the bulk modulus are determined using the Voigt-Reuss-Hill model based on the mineral content data of the reservoir to be predicted, including:

[0017] 1) The upper limit of the solid matrix bulk modulus and the upper limit of the solid matrix shear modulus are determined using the following Voight formula:

[0018]

[0019] Among them, N represents the type of minerals in the reservoir to be predicted, f i represents the volume fraction of type i minerals;

[0020] K i , G i They represent the bulk modulus and shear modulus of the i-th mineral type, respectively. The bulk modulus and shear modulus of a single mineral type are obtained by referring to the data records in the rock physics handbook;

[0021] K V , G V represent the upper limit of the solid matrix bulk modulus and the upper limit of the solid matrix shear modulus, respectively;

[0022] 2) The lower limit of the solid matrix bulk modulus and the lower limit of the solid matrix shear modulus are determined using the following Reuss formula:

[0023]

[0024] Among them, K R , G R represent the lower limits of the solid matrix bulk modulus and the lower limits of the solid matrix shear modulus, respectively;

[0025] 3) The average value of the reservoir bulk modulus and the average value of the shear modulus are determined using the following Hill formula:

[0026] K H =(K V +K R ) / 2, G H =(G V +G R ) / 2

[0027] Among them, K V , G Vrepresent the upper limit of the solid matrix bulk modulus and the upper limit of the solid matrix shear modulus, respectively;

[0028] K R , G R represent the lower limits of the solid matrix bulk modulus and the lower limits of the solid matrix shear modulus, respectively;

[0029] K H , G H represent the average value of the solid matrix bulk modulus and the average value of the solid matrix shear modulus, respectively.

[0030] In some optional embodiments, determining the longitudinal wave velocity of the solid matrix at different porosities based on the initial value of the elastic modulus of the solid matrix and the preset different porosities includes:

[0031] The initial value of the elastic modulus of the solid matrix and the preset porosity are substituted into the DEM model to determine the corresponding longitudinal wave velocity of the solid matrix at different porosities. The preset porosity is the porosity that meets various conditions. The porosity value ranges from 0 to the theoretical maximum porosity. Different longitudinal wave velocities are calculated for different porosities.

[0032] In some optional embodiments, comparing the P-wave velocities at different porosities with the measured P-wave velocities of rock samples in the reservoir, and adjusting the initial value of the solid matrix elastic modulus based on the comparison result to obtain a solid matrix model after the elastic modulus is adjusted, includes:

[0033] Establishing upper and lower limits of the predicted P-wave velocity based on the corresponding P-wave velocities at different preset porosities;

[0034] The measured P-wave velocity of the rock sample in the reservoir is compared with the upper and lower limits of the predicted P-wave velocity. If the measured P-wave velocity of the rock sample in the reservoir is within the upper and lower limits of the predicted P-wave velocity, the shear modulus and bulk modulus of the solid matrix do not need to be adjusted, that is, the initial shear modulus value and the initial bulk modulus value of the solid matrix are retained. Otherwise, the initial shear modulus value and the initial bulk modulus value of the solid matrix are adjusted based on the upper and lower limits, and the average value of the shear modulus and the bulk modulus of the solid matrix.

[0035] In some optional embodiments, adjusting the initial value of the shear modulus and the initial value of the bulk modulus of the solid matrix based on the upper limit, lower limit, and average value of the shear modulus and the bulk modulus of the solid matrix comprises:

[0036] 1) If the measured P-wave velocity of part of the rock samples in the reservoir to be predicted is greater than the upper limit of the predicted P-wave velocity, the optimal bulk modulus is found by least square method between the average value and the upper limit of the bulk modulus of the solid matrix, as the adjusted bulk modulus; the optimal shear modulus is found by least square method between the average value and the upper limit of the shear modulus of the solid matrix, as the adjusted shear modulus;

[0037] 2) If the measured P-wave velocity of part of the rock samples in the reservoir to be predicted is less than the lower limit of the predicted P-wave velocity, the optimal bulk modulus is found by least square method between the average value and the lower limit of the bulk modulus of the solid matrix, as the adjusted bulk modulus; the optimal shear modulus is found by least square method between the average value and the lower limit of the shear modulus of the solid matrix, as the adjusted shear modulus;

[0038] 3) If the measured P-wave velocity of part of the rock samples in the reservoir to be predicted is greater than the upper limit of the predicted P-wave velocity, and the measured P-wave velocity of part of the rock samples is less than the lower limit of the predicted P-wave velocity; based on the rock samples whose measured P-wave velocity is greater than the upper limit of the predicted P-wave velocity, the optimal bulk modulus is found by least square method between the average value and the upper limit of the bulk modulus of the solid matrix, as the upper limit of the bulk modulus of the solid matrix; the optimal shear modulus is found by least square method between the average value and the upper limit of the shear modulus of the solid matrix, as the upper limit of the shear modulus of the solid matrix;

[0039] and based on the rock samples whose measured P-wave velocity is less than the lower limit of the predicted P-wave velocity, the optimal bulk modulus is found by least square method between the average value and the lower limit of the bulk modulus of the solid matrix, as the lower limit of the bulk modulus of the solid matrix; the optimal shear modulus is found by least square method between the average value and the lower limit of the shear modulus of the solid matrix, as the lower limit of the shear modulus of the solid matrix; the average value of the bulk modulus and the shear modulus of the solid matrix is calculated based on the upper limit and the lower limit of the bulk modulus and the shear modulus of the solid matrix respectively, as the adjusted bulk modulus and shear modulus.

[0040] In some optional embodiments, the lithology correction of the neutron logging data of the reservoir to be predicted based on the pore correction parameters of each type of mineral in the reservoir to be predicted, and the determination of the porosity of the reservoir to be predicted, comprises:

[0041] establishing a neutron logging porosity interpretation model based on the measured value of the neutron logging of the reservoir to be predicted and the pore correction parameters of each type of mineral in the reservoir, and determining the porosity of the reservoir to be predicted based on the model.

[0042] In some optional embodiments, establishing a neutron logging porosity interpretation model based on neutron logging measured values ​​of the reservoir to be predicted and porosity correction parameters of various minerals in the reservoir, and determining the porosity of the reservoir to be predicted based on the model includes:

[0043] The porosity of the reservoir is determined using the following formula based on the neutron logging measured value of the reservoir to be predicted, the mineral content of the reservoir, and the neutron logging theoretical value of a single mineral:

[0044]

[0045] Where φ represents the porosity, f i represents the volume content of the i-th type of minerals, CNL represents the neutron logging value of the reservoir, and CNL i It represents the theoretical value of neutron logging for type i minerals. The theoretical value of neutron logging for a single type of mineral is obtained by referring to laboratory data.

[0046] In some optional embodiments, constructing a rock physics model incorporating pore structure characteristics based on the pore structure of the reservoir to be predicted and the dry skeleton rock physics model, and determining the elastic modulus of the reservoir to be predicted based on the rock physics model incorporating pore structure characteristics includes:

[0047] According to the matrix mineral content, porosity and pore structure of the reservoir to be predicted, an equivalent rock physics model combining pore structure characteristics is constructed through the porous media DEM model based on equivalent medium theory;

[0048] The elastic modulus of the reservoir to be predicted is determined based on the equivalent rock physics model combined with the pore structure characteristics.

[0049] In some optional embodiments, determining the shear wave velocity of the reservoir to be predicted based on the determined elastic modulus of the reservoir to be predicted, where the elastic modulus of the reservoir to be predicted is characterized by the bulk modulus and shear modulus of the reservoir, includes:

[0050] Establishing a first relationship expression based on the bulk modulus, density, shear wave velocity, and compressional wave velocity of the reservoir;

[0051] Establishing a second relationship expression based on the shear modulus, shear wave velocity and density of the reservoir;

[0052] The shear wave velocity of the reservoir to be predicted is determined based on the first relationship expression and the second relationship expression.

[0053] In some optional embodiments, the first relationship established based on the bulk modulus, density, shear wave velocity, and compressional wave velocity of the reservoir is expressed as:

[0054]

[0055] Where ρ is the reservoir density, kgem is the bulk modulus of the reservoir, V p is the longitudinal wave velocity of the reservoir, V s is the shear wave velocity of the reservoir.

[0056] In some optional embodiments, a second relationship is established based on the shear modulus, shear wave velocity, and density of the reservoir and is expressed as:

[0057] k dem =V s 2 ρ

[0058] Where ρ is the reservoir density, k dem is the shear modulus of the reservoir, V s is the shear wave velocity of the reservoir.

[0059] An embodiment of the present invention further provides a reservoir shear wave velocity prediction device, comprising:

[0060] An elastic modulus initial value determination module is configured to construct a solid matrix model based on the mineral content data of the reservoir to be predicted and determine the initial value of the elastic modulus of the solid matrix;

[0061] an elastic modulus adjustment module for determining the longitudinal wave velocity of the solid matrix at different porosities based on the initial value of the elastic modulus of the solid matrix and preset different porosities; comparing the longitudinal wave velocities at the different porosities with the measured longitudinal wave velocities of rock samples in the reservoir; and adjusting the initial value of the elastic modulus of the solid matrix based on the comparison result to obtain a solid matrix model after the elastic modulus is adjusted;

[0062] A porosity determination module is used to perform lithologic correction on the neutron logging data of the predicted reservoir based on the pore correction parameters of various minerals in the predicted reservoir, determine the porosity of the predicted reservoir, and add the porosity of the predicted reservoir to the solid matrix model to establish a dry skeleton rock physics model;

[0063] a reservoir elastic modulus determination module, configured to construct a rock physics model incorporating pore structure characteristics based on the pore structure of the reservoir to be predicted and the dry skeleton rock physics model, and determine the elastic modulus of the reservoir to be predicted based on the rock physics model incorporating pore structure characteristics;

[0064] The shear wave velocity prediction module is used to determine the shear wave velocity of the reservoir to be predicted based on the determined elastic modulus of the reservoir to be predicted.

[0065] An embodiment of the present invention further provides a computer storage medium, wherein the computer storage medium stores computer executable instructions, and when the computer executable instructions are executed by a processor, the above-mentioned reservoir shear wave velocity prediction method is implemented.

[0066] An embodiment of the present invention further provides a prediction device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned reservoir shear wave velocity prediction method when executing the program.

[0067] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the above-mentioned reservoir shear wave velocity prediction method.

[0068] The beneficial effects of the above technical solutions provided by the embodiments of the present invention include at least:

[0069] 1) constructing a solid matrix model based on the mineral content data of the reservoir to be predicted and determining an initial value of the elastic modulus of the solid matrix; adjusting the initial value of the elastic modulus of the solid matrix based on a comparison result of the measured P-wave velocity of the rock sample in the reservoir and the P-wave velocity of the solid matrix at different porosities to obtain a solid matrix model after the elastic modulus is adjusted; the adjustment of the initial value of the elastic modulus of the solid matrix takes into account the characteristics of the actual reservoir, lays a data foundation for the subsequent determination of the elastic modulus of the reservoir, and can effectively improve the prediction accuracy of the reservoir shear wave velocity;

[0070] 1) Based on the neutron logging data of the predicted reservoir, the lithologic correction is performed to determine the reservoir porosity and then added to the solid matrix model to establish a dry skeleton rock physics model. This step is low-cost and takes into account the complex mineral composition of the rocks in the actual formation. The lithologic correction of the neutron logging values ​​makes the porosity determination more accurate. The addition of porosity also improves the accuracy of the elastic modulus of the predicted reservoir, further improving the prediction accuracy of the reservoir shear wave velocity.

[0071] 2) Based on the pore structure of the reservoir to be predicted and the dry skeleton rock physics model, a rock physics model is constructed that combines the pore structure characteristics. Based on this model, the elastic modulus of the reservoir to be predicted is determined to achieve the prediction of shear wave velocity. The influence of different pore structures on the elastic modulus of the solid matrix is ​​taken into account, which is more consistent with the rock characteristics of the reservoir and improves the accuracy of the reservoir shear wave velocity prediction. Furthermore, it has important application significance for well logging evaluation, prestack reservoir prediction and hydrocarbon detection.

[0072] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0073] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0075] Figure 1 This is a flow chart of a reservoir shear wave velocity prediction method in Example 1 of the present invention;

[0076] Figure 2 This is a flow chart of rock physics modeling based on well data in Example 1 of the present invention;

[0077] Figure 3 This is a flow chart of a reservoir shear wave velocity prediction method in Example 2 of the present invention;

[0078] Figure 4 This is a schematic diagram of the core mineral content of the reservoir to be predicted in Example 2 of the present invention;

[0079] Figure 5 Schematic diagram of the distribution of the predicted longitudinal wave velocity and the measured longitudinal wave velocity of the sample in Example 2 of the present invention;

[0080] Figure 6 This is a schematic diagram of adjusting the upper limit of the reservoir elastic modulus in Example 2 of the present invention;

[0081] Figure 7 Schematic diagram of adjustment of the lower limit of reservoir elastic modulus in Example 2 of the present invention;

[0082] Figure 8 Schematic diagram for determining the upper and lower limits of the reservoir elastic modulus in Example 2 of the present invention;

[0083] Figure 9 The porosity determined by different methods in Example 2 of the present invention;

[0084] Figure 10 The measured P-wave velocity and density of rocks at different depths in the reservoir to be predicted in the second embodiment of the present invention;

[0085] Figure 11 This is a comparison diagram of the measured shear wave velocity and the predicted shear wave velocity in Example 2 of the present invention;

[0086] Figure 12 This is a cross-plot of the measured shear wave velocity and the predicted shear wave velocity in the second embodiment of the present invention;

[0087] Figure 13 Schematic diagram of the structure of the reservoir shear wave velocity prediction device in an embodiment of the present invention. DETAILED DESCRIPTION

[0088] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0089] To address the existing problem of difficulty in accurately calculating the solid matrix elastic modulus, porosity, and pore structure of a reservoir when predicting reservoir shear wave velocity based on rock physics modeling, an embodiment of the present invention provides a reservoir shear wave velocity prediction method with high predicted shear wave velocity accuracy and low operating cost.

[0090] Example 1

[0091] The first embodiment of the present invention provides a reservoir shear wave velocity prediction method, the process of which is as follows: Figure 1 As shown in Figure 2, the process of rock physics modeling based on well data is as follows: Figure 2 As shown, the following steps are included:

[0092] Step S101: constructing a solid matrix model based on the mineral content data of the reservoir to be predicted, and determining the initial value of the elastic modulus of the solid matrix;

[0093] Step S102: Based on the initial value of the elastic modulus of the solid matrix and the preset different porosities, the longitudinal wave velocity of the solid matrix at different porosities is determined; the longitudinal wave velocity at the different porosities is compared with the measured longitudinal wave velocity of the rock sample in the reservoir, and the initial value of the elastic modulus of the solid matrix is ​​adjusted based on the comparison result to obtain a solid matrix model after the elastic modulus is adjusted;

[0094] Step S103: Based on the pore correction parameters of various minerals in the reservoir to be predicted, the neutron logging data of the reservoir to be predicted is corrected for lithology to determine the porosity of the reservoir to be predicted. The porosity of the reservoir to be predicted is added to the solid matrix model to establish a dry skeleton rock physics model.

[0095] Step S104: constructing a rock physics model incorporating pore structure characteristics based on the pore structure of the reservoir to be predicted and the dry skeleton rock physics model, and determining the elastic modulus of the reservoir to be predicted based on the rock physics model incorporating pore structure characteristics;

[0096] Step S105: determining the shear wave velocity of the reservoir to be predicted based on the determined elastic modulus of the reservoir to be predicted.

[0097] In some optional embodiments, in the above step S101, constructing a solid matrix model based on the mineral content data of the reservoir to be predicted and determining the initial value of the elastic modulus of the solid matrix include:

[0098] obtaining mineral content of the reservoir to be predicted, and constructing a solid matrix model of the reservoir to be predicted based on the mineral content;

[0099] The elastic modulus of the solid matrix is represented by the shear modulus and the bulk modulus of the solid matrix. The upper limit value, the lower limit value, and the average value of the shear modulus and the upper limit value, the lower limit value, and the average value of the bulk modulus of the solid matrix are determined based on the mineral content data of the reservoir to be predicted by using the Voigt-Reuss-Hill model. The average value of the shear modulus and the average value of the bulk modulus of the solid matrix are respectively taken as the initial value of the shear modulus and the initial value of the bulk modulus of the solid matrix.

[0100] In this step, basic mineral analysis is carried out on the reservoir to be predicted to determine the mineral content data of the reservoir to be predicted. Optionally, the mineral content data of the reservoir to be predicted is determined according to the XRD mineral analysis result or the mineral content determined by well logging interpretation of the target area. The bulk modulus and the shear modulus of the matrix minerals in the reservoir are calculated based on the mineral content data of the reservoir to be predicted by using the Voigt-Reuss-Hill model. The upper limit value, the lower limit value, and the average value of the bulk modulus of the solid matrix and the upper limit value, the lower limit value, and the average value of the shear modulus of the solid matrix are determined based on the Voigt-Reuss-Hill model. The determination process is as follows:

[0101] 1) The upper limit value of the bulk modulus and the upper limit value of the shear modulus of the solid matrix are determined by using the following Voight formula:

[0102]

[0103] wherein N represents the type of mineral, f i represents the volume fraction of the i-th mineral;

[0104] K i , G i respectively represent the bulk modulus and the shear modulus of the i-th mineral. The bulk modulus and the shear modulus of a single mineral can be obtained by referring to the data records in the rock physics manual;

[0105] K V , G V respectively represent the upper limit value of the bulk modulus of the solid matrix and the upper limit value of the shear modulus of the solid matrix;

[0106] 2) The lower limit value of the bulk modulus and the lower limit value of the shear modulus of the solid matrix are determined by using the following Reuss formula:

[0107]

[0108] wherein K R , GR represent the lower limits of the solid matrix bulk modulus and the lower limits of the solid matrix shear modulus, respectively;

[0109] 3) Determine the average value of the solid matrix bulk modulus using the following Hill formula based on the upper and lower limits of the solid matrix bulk modulus; and determine the average value of the solid matrix shear modulus based on the upper and lower limits of the solid matrix shear modulus:

[0110] K H =(K V +K R ) / 2, G H =(G V +G R ) / 2

[0111] Among them, K V , G V represent the upper limit of the solid matrix bulk modulus and the upper limit of the solid matrix shear modulus, respectively;

[0112] K R , G R represent the lower limits of the solid matrix bulk modulus and the lower limits of the solid matrix shear modulus, respectively;

[0113] K H , G H represent the average value of the solid matrix bulk modulus and the average value of the solid matrix shear modulus, respectively.

[0114] The average value of the solid matrix bulk modulus and the average value of the solid matrix shear modulus are taken as the initial value of the solid matrix elastic modulus.

[0115] In some optional embodiments, in the above step S102, based on the initial value of the solid matrix elastic modulus and preset different porosities, the longitudinal wave velocity of the solid matrix at different porosities is determined; the longitudinal wave velocities at different porosities are compared with the measured longitudinal wave velocities of rock samples in the reservoir; and based on the comparison result, the initial value of the solid matrix elastic modulus is adjusted to obtain a solid matrix model after the elastic modulus is adjusted, including:

[0116] 1) Substituting the initial value of the solid matrix elastic modulus and the preset porosity into the DEM model to calculate the P-wave velocity of the solid matrix at different porosities. In this step, the average value of the solid matrix bulk modulus, the average value of the solid matrix shear modulus, and the preset porosity are substituted into the DEM model to calculate the P-wave velocity of the solid matrix corresponding to the reservoir to be predicted. The preset porosity is the porosity that meets various conditions and ranges from 0 to the theoretical maximum porosity. Different porosities result in different P-wave velocities.

[0117] 2) establishing upper and lower limits for the predicted P-wave velocity based on the corresponding P-wave velocities at different preset porosities; comparing the measured P-wave velocity of the rock sample in the reservoir with the upper and lower limits of the predicted P-wave velocity; if the measured P-wave velocity of the rock sample in the reservoir is within the upper and lower limits of the predicted P-wave velocity, then the shear modulus and bulk modulus of the solid matrix do not need to be adjusted, that is, the initial shear modulus and bulk modulus of the solid matrix are used; otherwise, based on the upper and lower limits and average values ​​of the shear modulus and bulk modulus of the solid matrix, the initial shear modulus and bulk modulus of the solid matrix are adjusted to better conform to the actual conditions of the reservoir, thereby obtaining a solid matrix model after the shear modulus and bulk modulus are adjusted. The adjustment process is as follows:

[0118] In the following description of the adjustment of shear modulus and bulk modulus, K V , G V represent the upper limit of the solid matrix bulk modulus and the upper limit of the solid matrix shear modulus, respectively; K R , G R represent the lower limit of the solid matrix bulk modulus and the lower limit of the solid matrix shear modulus respectively; K H , G H represent the average value of the solid matrix bulk modulus and the average value of the solid matrix shear modulus respectively; these data are calculated in step S101;

[0119] 1) If the measured P-wave velocity of some rock samples in the reservoir to be predicted is greater than the upper limit of the predicted P-wave velocity, the initial values ​​of the bulk modulus and shear modulus of the solid matrix are adjusted: based on the measured P-wave velocity of the rock samples greater than the upper limit of the predicted P-wave velocity in [K H , K V ], the optimal bulk modulus is found by iterative least square method as the adjusted solid matrix bulk modulus; in [G H , G V ] the optimal shear modulus is iteratively found by the least squares method as the adjusted solid matrix shear modulus.

[0120] 2) If the measured P-wave velocity of some rock samples in the reservoir to be predicted is less than the lower limit of the predicted P-wave velocity, the initial values ​​of the bulk modulus and shear modulus of the solid matrix are adjusted: based on the measured P-wave velocity of the rock samples less than the lower limit of the predicted P-wave velocity in [K R , K H ], the optimal bulk modulus is found by iterative least square method as the adjusted solid matrix bulk modulus; in [G R , G H ] the optimal shear modulus is iteratively found by the least squares method as the adjusted solid matrix shear modulus.

[0121] 3) If the measured P-wave velocity of some rock samples in the reservoir to be predicted is greater than the upper limit of the predicted P-wave velocity, and the measured P-wave velocity of some rock samples is less than the lower limit of the predicted P-wave velocity, then the initial values ​​of the bulk modulus and shear modulus of the solid matrix need to be adjusted, and the upper and lower limits of the bulk modulus and shear modulus of the solid matrix need to be re-determined. The determination process is as follows:

[0122] Based on the measured P-wave velocity of rock samples that is greater than the upper limit of the predicted P-wave velocity, the H , K V ], the optimal bulk modulus is found by iterative least square method as the upper limit of the bulk modulus of the solid matrix; in [G H , G V ], the optimal shear modulus is found by iterative least square method as the upper limit of the shear modulus of the solid matrix; and based on the measured P-wave velocity of rock samples that is less than the lower limit of the predicted P-wave velocity, [K R , K H ], the optimal bulk modulus is found by iterative least square method as the lower limit of the bulk modulus of the solid matrix; in [G R , G H ] The optimal shear modulus is iteratively found by the least squares method as the lower limit of the shear modulus of the solid matrix; the average values ​​of the solid matrix bulk modulus and shear modulus are calculated based on the re-determined upper limits of the solid matrix bulk modulus and shear modulus and the lower limits of the solid matrix bulk modulus and shear modulus as the adjusted bulk modulus and shear modulus of the solid matrix.

[0123] In some optional embodiments, in the above step S103, based on the pore correction parameters of various minerals in the reservoir to be predicted, lithologic correction is performed on the neutron logging data of the reservoir to be predicted to determine the porosity of the reservoir to be predicted, and the porosity of the reservoir to be predicted is added to the solid matrix model to establish a dry skeleton rock physics model, including:

[0124] Neutron logging is calibrated using standard limestone. Neutron logging readings for limestone formations represent the formation's true porosity. However, in actual formations, the mineral composition of the rocks is complex, so lithologic correction is required to determine the reservoir's porosity. A neutron logging porosity interpretation model is developed based on the neutron logging values ​​of the predicted reservoir and the porosity correction parameters for various minerals in the reservoir. The porosity of the predicted reservoir is then determined based on this model.

[0125] Specifically, a neutron logging porosity interpretation model is established based on the neutron logging measured values ​​of the reservoir to be predicted, the mineral content of the reservoir to be predicted, and the neutron logging theoretical values ​​of single minerals. The porosity of the rock to be predicted is determined using the following formula:

[0126]

[0127] Where φ represents the porosity of the reservoir to be predicted, CNL represents the measured value of neutron logging, and f i Represents the volume content of type i minerals, CNL i represents the theoretical neutron logging value of the i-th type of mineral. The theoretical neutron logging value of a single type of mineral is obtained by referring to laboratory data;

[0128] Based on the determined reservoir porosity, it is added to the solid matrix model to form a dry skeleton rock physics model.

[0129] In some optional embodiments, step S104 constructs a rock physics model incorporating pore structure characteristics based on the pore structure and dry skeleton rock physics model of the reservoir to be predicted, and determines the elastic modulus of the reservoir to be predicted based on the rock physics model incorporating pore structure characteristics, including:

[0130] The pore structure of the predicted reservoir is determined based on rock casting thin sections and mineral analysis data. Based on the matrix mineral content, porosity, and pore structure of the predicted reservoir, an equivalent rock physics model incorporating pore structure characteristics is constructed using a porous medium DEM model based on equivalent medium theory. The elastic modulus of the predicted reservoir is determined based on this equivalent rock physics model, which is characterized by the reservoir's bulk modulus and shear modulus. In this step, the shear modulus and bulk modulus of the predicted reservoir are determined using the following formulas.

[0131] 1) The bulk modulus of the reservoir to be predicted is calculated using the following formula:

[0132]

[0133] Where Km and Gm are the bulk modulus and shear modulus of the solid matrix adjusted in step S102, φ represents the porosity corrected in step S103, and K dem * represents the bulk modulus of the reservoir to be predicted;

[0134] 2) Calculate the shear modulus of the reservoir to be predicted using the following formula:

[0135]

[0136] Where Km and Gm are the bulk modulus and shear modulus of the solid matrix adjusted in step S102, φ represents the porosity corrected in step S103, and G dem * represents the first shear modulus of the reservoir to be predicted;

[0137] Among them, the values ​​of other parameters for calculating the bulk modulus and shear modulus of the reservoir to be predicted are as follows:

[0138] S 0i=(2-3f i -3θ i ) / (4θ i -6θ i 2 -4f i ),

[0139] S 1i =(θ i -f i ) / (2-3f i -3θ i ),

[0140] S 2i =4 / 3,

[0141] S 3i =(2θ i -2f i ) / [3(2-3f i -3θ i )],

[0142]

[0143] 式中,

[0144]

[0145] F2'={-0.5A(3fi+5θ i )-4B-0.5A(A+3B)*[7fi+θ i +6θ i 2 -8R(fi-θ i +2θ i 2 )]}R',

[0146] F3'=A(fi+θ i )R',

[0147] F4'=-0.25A(fi-θ i )R',

[0148]

[0149] F7'=[-0.25A(3fi+5θ i )-4Bθ i ]R',

[0150] F8'=[A(-2+0.5fi+2.5θ i )-4B(1-θ i )]R',

[0151] F9'=[A(fi-θ i )-4Bθ i ]R',

[0152] R'=-R 2 ,

[0153] P=F1F2',

[0154] Q=0.2[2F3 -1 +F4 -1 +(F4F5+F6F7-F8F9)*(F2F4) -1 ],

[0155] 式中,

[0156]

[0157] F2=1+A[1+1.5(fi+θ i )-0.5R(3fi+5θ i )]+B(3-4R)+0.5A(A+3B)(3-4R)[fi+θ i -R(fi-θ i +2θ i 2 )],

[0158] F3=1+A[1-(fi+1.5θ i )+R(fi+θ i )],

[0159] F4=1+0.25A[fi+3θ i -R(fi-θ i )],

[0160]

[0161] F6=1+A[1+fi-R(fi+θ i )]+B(1-θ i )(3-4R),

[0162] F7=2+0.25[3fi+9θ i -R(3fi+5θ i )]+Bθ i (3-4R),

[0163] F8=A(1-2R+0.5fi(R-1)+0.5θ i (5R-3)]+B(1-θ i )(3-4R),

[0164] F9=A[(R-1)fi-Rθi ]+Bθ i (3-4R),

[0165] A=G i / G m -1,

[0166]

[0167] f i =α i 2 (3θ i -2) / (1-α i 2 ),

[0168]

[0169] The value of αi is determined by the shape of each pore structure. Different pore shapes result in different pore aspect ratios. For approximately circular pores, the pore aspect ratio is high; for flat pores, the pore aspect ratio is medium; and for linear pores, the pore aspect ratio is very low. Reservoir rock samples with different pore structures, even at the same porosity, can exhibit significant differences in elastic modulus, leading to differences in shear wave velocity.

[0170] In some optional embodiments, the above step S105 of determining the shear wave velocity of the reservoir to be predicted based on the determined elastic modulus of the reservoir to be predicted includes:

[0171] In this step, the elastic modulus of the reservoir to be predicted is characterized by the bulk modulus and shear modulus of the reservoir;

[0172] The first relationship expression is established based on the bulk modulus, density, shear wave velocity and compressional wave velocity of the reservoir, and the formula is as follows:

[0173]

[0174] Where ρ is the reservoir density, kgem is the bulk modulus of the reservoir, V p is the longitudinal wave velocity of the reservoir, V s is the shear wave velocity of the reservoir.

[0175] The second relationship expression is established based on the shear modulus, shear wave velocity and density of the reservoir, and the formula is as follows:

[0176] k dem =V s 2 ρ

[0177] Where ρ is the reservoir density, k dem is the shear modulus of the reservoir, Vs is the shear wave velocity of the reservoir.

[0178] The shear wave velocity of the reservoir to be predicted is determined according to the first relationship expression and the second relationship expression based on the determined bulk modulus, shear modulus and longitudinal wave velocity of the reservoir to be predicted.

[0179] In the above method of this embodiment, the initial value of the solid matrix elastic modulus is adjusted using the least squares method to obtain a solid matrix model with adjusted elastic modulus. This matrix adjustment process takes into account the characteristics of the actual reservoir, resulting in an adjusted elastic modulus that is more realistic and lays a data foundation for the subsequent determination of the reservoir elastic modulus, thereby effectively improving the prediction accuracy of the reservoir shear wave velocity. During porosity calculation, neutron logging data is combined with various pore correction parameters determined in the laboratory to achieve more accurate porosity determination. Incorporating porosity into the solid matrix model further considers its effect on the reservoir elastic modulus, further improving the prediction accuracy of the reservoir shear wave velocity. Based on the equivalent medium theory, an equivalent rock physics model is established using a porous medium DEM model that considers different pore structures, porosities, and matrix mineral compositions. This comprehensively considers the influence of multiple factors on the reservoir elastic modulus, making the equivalent medium theory more consistent with the rock characteristics of the reservoir and effectively improving the prediction accuracy of the reservoir shear wave velocity.

[0180] Example 2

[0181] The second embodiment of the present invention provides a specific implementation process for predicting the shear wave velocity of the reservoir in the fourth section of the Ordos Basin based on the reservoir shear wave velocity prediction method. Further, the error calculation of the predicted shear wave velocity is performed. The process is as follows: Figure 3 As shown;

[0182] The Ordos Basin is a very important oil and gas basin. Based on lithology and sedimentary characteristics, the Majiagou Formation can be subdivided into six sections from bottom to top: the first, third, and fifth sections are mainly evaporative platform deposits, with lithologies mainly consisting of gypsum salt rock, limestone, and dolomite; the second, fourth, and sixth sections are mainly shallow-water carbonate platform deposits, with lithologies mainly consisting of dolomite and limestone. This example mainly uses the fourth section as the research object to predict shear wave velocity, and the steps include:

[0183] Step S201: Based on the core data of the fourth member of the Ma Formation in the Ordos Basin, the study area is determined, and basic mineral analysis is performed on the cores mainly composed of dolomite, limestone and gypsum to construct a solid matrix model, such as Figure 4 The diagram shows the mineral content of cores at different depths. For example, at a depth of 2705 meters, the mineral components are calcite and dolomite, and their content is represented by the length of different colors. At a depth of 3835 meters, the mineral components are dolomite and gypsum, and their content is represented by the length of different colors.

[0184] Step S202: Determine the initial values ​​of the solid matrix bulk modulus and shear modulus based on the mineral analysis results of the rocks in the study area. The specific determination process is referred to step S101;

[0185] Step S203: Based on the initial values ​​of the solid matrix bulk modulus and shear modulus and the preset different porosities, the P-wave velocity of the solid matrix at different porosities is determined; the P-wave velocity at different porosities is compared with the measured P-wave velocity of the rock sample in the reservoir, and the initial values ​​of the solid matrix bulk modulus and shear modulus are adjusted based on the comparison results to obtain a solid matrix model after the bulk modulus and shear modulus are adjusted. The adjustment process is similar to step S102.

[0186] like Figure 5 The two line segments shown are the upper and lower limits of the predicted P-wave velocity. It can be seen that the actual P-wave velocity distribution of some rock samples is outside the upper and lower limits of the predicted P-wave velocity. Therefore, 1) based on the rock samples that are greater than the upper limit of the predicted P-wave velocity, the upper limit of the bulk modulus and shear modulus is re-determined using the least squares method to ensure that the upper limit of the predicted P-wave velocity obtained based on the re-determined upper limit is greater than all the measured P-wave velocities. Figure 6 As shown in the figure, 2) based on the rock samples with a lower limit of the predicted P-wave velocity, the lower limit of the bulk modulus and shear modulus is re-determined by the least square method to ensure that the lower limit of the predicted P-wave velocity obtained based on the re-determined lower limit is less than all the measured P-wave velocities. Figure 7 As shown in the figure, the upper and lower limits of the predicted P-wave velocity are re-determined as follows: Figure 8 3) calculating the average values ​​of the solid matrix bulk modulus and shear modulus based on the re-determined upper limits of the solid matrix bulk modulus and shear modulus and lower limits of the solid matrix bulk modulus and shear modulus as the adjusted bulk modulus and shear modulus.

[0187] Step S204: Calculate the porosity using the neutron logging porosity interpretation model, add the porosity of the reservoir to be predicted into the solid matrix model, and establish a dry skeleton rock physics model. Refer to step S103, as shown in Figure 9In the first column, SP represents natural potential, GR represents natural gamma, and CAL represents wellbore diameter, with different colors used to represent changes at different depths. The second column represents depth. The third column indicates that the research object is the fourth member of the Ma Formation. In the fourth column, Vsh represents mud, Dolo represents dolomite, lime represents calcite, and Anhy represents gypsum, and different colors are used to represent the mineral content at different depths. In the fifth column, RLLD and RLLS are represented by different color curves for deep and shallow lateral resistivity logging at different depths, respectively. The sixth column shows the porosity obtained by the three methods. It can be seen that the porosity calculated based on the neutron logging porosity interpretation model has a small error compared with the porosity measured in the laboratory, while the original interpreted porosity, that is, the porosity determined without this method, has a large error compared with the porosity measured in the laboratory.

[0188] Step S205: Based on the pore structure of the reservoir to be predicted and the dry skeleton rock physics model, a rock physics model incorporating pore structure characteristics is constructed using the porous medium DEM model. The bulk modulus and shear modulus of the reservoir to be predicted are determined based on the rock physics model incorporating pore structure characteristics, as described in step S104.

[0189] Influenced by diagenesis, the pore structure of the Ma 4 Member reservoir in the Ordos Basin is complex. Dolomitization produces pores with approximately circular morphology, i.e., large aspect ratios. Dissolution produces flat pores with medium aspect ratios. Fractures, in turn, produce linear pores with very low aspect ratios. Based on these different pore structure characteristics, a rock physics model was constructed using a porous media DEM model to determine the bulk modulus and shear modulus of the predicted reservoir.

[0190] Step S206: determining the shear wave velocity of the reservoir based on the determined bulk modulus and shear modulus of the reservoir, refer to step S105;

[0191] Step S207: quantitatively analyzing the error in predicting the shear wave velocity based on the measured shear wave velocity;

[0192] The following error formula is used to determine the predicted error based on the measured shear wave velocities of rock samples at different depths in the reservoir and the predicted shear wave velocities of the reservoir at different depths determined in this embodiment:

[0193] Δ=|Vs_predicted - Vs_measured| / Vs_measured*100%

[0194] Where Δ represents the prediction error, Vs_predicted represents the shear wave velocity predicted by this method, and Vs_measured represents the shear wave velocity actually measured in the reservoir. A smaller Δ indicates a smaller difference between the predicted and measured values, indicating a higher prediction accuracy.

[0195] Figure 10 , Figure 11 Left coordinates and Figure 4 The left coordinate of corresponds to the core depth. Figure 10 are the measured P-wave velocity and density of rock samples at different depths, where Vp represents the measured P-wave velocity. Figure 11 The figure shows a comparison between the predicted shear wave velocity and the measured shear wave velocity determined based on the method of this embodiment. The black line is the measured shear wave velocity and the red line is the predicted shear wave velocity. It can be seen from the figure that the predicted shear wave velocity is basically consistent with the measured shear wave velocity. The above error formula is used to calculate the predicted shear wave velocity error of rocks at different depths, as shown in the figure below. Figure 12 The figure shows a crossplot of the measured and predicted shear wave velocities. The horizontal axis represents the predicted shear wave velocity, the vertical axis represents the measured shear wave velocity, and the color scale represents the prediction error. The figure shows that the data for all cores in the crossplot are generally near the 1:1 line. Experimental data confirms that the predicted shear wave velocity error is generally within 5%, indicating that the method used in this embodiment has a good application effect in the study area and a high degree of shear wave velocity prediction accuracy.

[0196] In the methods of the above-mentioned embodiments, by adjusting the elastic modulus of the solid matrix using the least squares method, performing lithologic correction based on neutron logging to more accurately interpret rock porosity, and using the porous medium DEM model to establish a rock physics model that takes into account the pore structure, the accuracy of the elastic modulus of the reservoir can be effectively improved, and the prediction accuracy of the shear wave velocity can be further improved. The predicted shear wave velocity has important application significance for logging evaluation, prestack reservoir prediction, and hydrocarbon detection.

[0197] Based on the same inventive concept, the embodiment of the present invention further provides a reservoir shear wave velocity prediction device, which can be set in a computer. The structure of the device is as follows: Figure 13 As shown, including:

[0198] The elastic modulus initial value determination module 10 constructs a solid matrix model based on the mineral content data of the reservoir to be predicted and determines the elastic modulus initial value of the solid matrix;

[0199] The elastic modulus adjustment module 20 is configured to determine the P-wave velocity of the solid matrix at different porosities based on the initial value of the solid matrix elastic modulus and preset different porosities; compare the P-wave velocity at the different porosities with the measured P-wave velocity of the rock sample in the reservoir; and adjust the initial value of the solid matrix elastic modulus based on the comparison result to obtain a solid matrix model after the elastic modulus is adjusted.

[0200] A porosity determination module 30 is configured to perform lithologic correction on the neutron logging data of the reservoir to be predicted based on the porosity correction parameters of various minerals in the reservoir to be predicted, determine the porosity of the reservoir to be predicted, and add the porosity of the reservoir to be predicted to the solid matrix model to establish a dry skeleton rock physics model;

[0201] a reservoir elastic modulus determination module 40 for constructing a rock physics model incorporating pore structure characteristics based on the pore structure of the reservoir to be predicted and the dry skeleton rock physics model, and determining the elastic modulus of the reservoir to be predicted based on the rock physics model incorporating pore structure characteristics;

[0202] The shear wave velocity prediction module 50 is configured to determine the shear wave velocity of the reservoir to be predicted based on the determined elastic modulus of the reservoir to be predicted.

[0203] Regarding the reservoir shear wave velocity prediction device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method and will not be elaborated on here.

[0204] The above-mentioned method and device of the embodiment of the present invention can effectively improve the prediction accuracy of shear wave velocity with very low operating costs, thereby obtaining more accurate and reliable pre-stack reservoir prediction basic data, providing support for high-precision reservoir prediction and hydrocarbon detection in the study area.

[0205] An embodiment of the present invention further provides a computer storage medium, wherein the computer storage medium stores computer executable instructions, and when the computer executable instructions are executed by a processor, the above-mentioned reservoir shear wave velocity prediction method is implemented.

[0206] An embodiment of the present invention further provides a prediction device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for predicting reservoir shear wave velocity as described above is implemented.

[0207] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the reservoir shear wave velocity prediction method as described above.

[0208] Unless otherwise specifically stated, terms such as process, calculate, compute, determine, display, and the like may refer to the actions and / or processes of one or more processing or computing systems, or similar devices, that manipulate and convert data represented as physical (e.g., electronic) quantities within registers or memories of a processing system into other data similarly represented as physical quantities within the memories, registers, or other such information storage, transmission, or display devices of the processing system. Information and signals may be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.

[0209] It should be understood that the specific order or hierarchy of steps in the disclosed processes is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process can be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in an exemplary order and are not intended to be limited to the specific order or hierarchy described.

[0210] In the foregoing detailed description, various features are grouped together in a single embodiment to simplify the disclosure. This method of disclosure should not be interpreted as reflecting an intention that embodiments of the claimed subject matter require more features than are expressly recited in each claim. On the contrary, as reflected in the appended claims, the invention comprises less than all the features of any individual disclosed embodiment. The appended claims are therefore hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

[0211] Those skilled in the art will also appreciate that the various illustrative logic blocks, modules, circuits, and algorithmic steps described in conjunction with the embodiments herein may be implemented as electronic hardware, computer software, or a combination thereof. In order to clearly illustrate the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps described above are generally described around their functions. Whether such functions are implemented as hardware or software depends on the specific application and the design constraints imposed on the entire system. A skilled person may implement the described functions in an adaptable manner for each specific application, but such implementation decisions should not be interpreted as departing from the scope of protection of this disclosure.

[0212] The steps of the methods or algorithms described in conjunction with the embodiments herein may be directly embodied as hardware, software modules executed by a processor, or a combination thereof. The software module may be located in a RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, register, hard disk, removable disk, CD-ROM, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium may also be an integral part of the processor. The processor and storage medium may be located in an ASIC. The ASIC may be located in a user terminal. Of course, the processor and storage medium may also be present in a user terminal as discrete components.

[0213] For software implementation, the techniques described in this application can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. These software codes can be stored in a memory unit and executed by a processor. The memory unit can be implemented within the processor or external to the processor. In the latter case, it is communicatively coupled to the processor via various means, which are well known in the art.

[0214] The foregoing description includes examples of one or more embodiments. Of course, it is not possible to describe all possible combinations of components or methods for the purposes of describing the above embodiments, but one of ordinary skill in the art will recognize that the various embodiments may be further combined and arranged. Therefore, the embodiments described herein are intended to encompass all such changes, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent the term "comprising" is used in the specification or claims, the term is intended to be encompassed in a manner similar to the term "including," as explained in terms of "including," used as a transitional word in the claims. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or."

Claims

1. A reservoir shear wave velocity prediction method, characterized in that: include: A solid matrix model is constructed based on the mineral content data of the reservoir to be predicted, and an initial value of the elastic modulus of the solid matrix is ​​determined; determining, based on an initial value of the elastic modulus of the solid matrix and different preset porosities, the longitudinal wave velocity of the solid matrix at different porosities; comparing the longitudinal wave velocities at the different porosities with the measured longitudinal wave velocities of rock samples in the reservoir; and adjusting the initial value of the elastic modulus of the solid matrix based on the comparison result to obtain a solid matrix model after the elastic modulus is adjusted; Based on the pore correction parameters of various minerals in the predicted reservoir, the neutron logging data of the predicted reservoir is corrected for lithology to determine the porosity of the predicted reservoir. The porosity of the predicted reservoir is added to the solid matrix model to establish a dry skeleton rock physics model. Based on the pore structure of the reservoir to be predicted and the dry skeleton rock physics model, constructing a rock physics model that combines pore structure characteristics, and determining the elastic modulus of the reservoir to be predicted based on the rock physics model that combines pore structure characteristics; The shear wave velocity of the reservoir to be predicted is determined based on the determined elastic modulus of the reservoir to be predicted.

2. The method according to claim 1, wherein The determining of the initial value of the elastic modulus of the solid matrix, wherein the elastic modulus of the solid matrix is ​​characterized by the shear modulus and bulk modulus of the solid matrix, comprises: Based on the mineral content data of the reservoir to be predicted, the Voigt-Reuss-Hill model is used to respectively determine the upper limit value, lower limit value, and average value of the solid matrix shear modulus and the upper limit value, lower limit value, and average value of the bulk modulus; The average value of the shear modulus and the average value of the bulk modulus of the solid matrix are respectively used as the initial value of the shear modulus and the initial value of the bulk modulus of the solid matrix.

3. The method according to claim 2, wherein The method of using the Voigt-Reuss-Hill model based on the mineral content data of the reservoir to be predicted to respectively determine the upper limit value, lower limit value, and average value of the solid matrix shear modulus and the upper limit value, lower limit value, and average value of the bulk modulus includes: The upper limit of the solid matrix bulk modulus and the upper limit of the solid matrix shear modulus are determined using the following Voight formula: Among them, N represents the type of minerals in the reservoir to be predicted, f i represents the volume fraction of type i minerals; K i , G i They represent the bulk modulus and shear modulus of the i-th mineral type, respectively. The bulk modulus and shear modulus of a single mineral type are obtained by referring to the data records in the rock physics handbook; K V , G V represent the upper limit of the solid matrix bulk modulus and the upper limit of the solid matrix shear modulus, respectively; The lower limit of the solid matrix bulk modulus and the lower limit of the solid matrix shear modulus are determined using the following Reuss formula: Among them, K R , G R represent the lower limits of the solid matrix bulk modulus and the lower limits of the solid matrix shear modulus, respectively; The average value of the reservoir bulk modulus and the average value of the shear modulus are determined using the following Hill formula: K H =(K V +K R ) / 2,G H =(G V +G R ) / 2 Among them, K V , G V represent the upper limit of the solid matrix bulk modulus and the upper limit of the solid matrix shear modulus, respectively; K R , G R represent the lower limits of the solid matrix bulk modulus and the lower limits of the solid matrix shear modulus, respectively; K H , G H represent the average value of the solid matrix bulk modulus and the average value of the solid matrix shear modulus, respectively.

4. The method according to claim 1, wherein The method of determining the longitudinal wave velocity of the solid matrix at different porosities based on the initial value of the elastic modulus of the solid matrix and the preset different porosities includes: The initial value of the elastic modulus of the solid matrix and the preset porosity are substituted into the DEM model to determine the corresponding longitudinal wave velocity of the solid matrix at different porosities. The preset porosity is the porosity that meets various conditions. The porosity value ranges from 0 to the theoretical maximum porosity. Different longitudinal wave velocities are calculated for different porosities.

5. The method according to claim 2, wherein The method comprises comparing the P-wave velocities at different porosities with the measured P-wave velocities of rock samples in the reservoir, and adjusting the initial value of the elastic modulus of the solid matrix based on the comparison result to obtain a solid matrix model after the elastic modulus is adjusted, including: Establishing upper and lower limits of the predicted P-wave velocity based on the corresponding P-wave velocities at different preset porosities; The measured P-wave velocity of the rock sample in the reservoir is compared with the upper and lower limits of the predicted P-wave velocity. If the measured P-wave velocity of the rock sample in the reservoir is within the upper and lower limits of the predicted P-wave velocity, the shear modulus and bulk modulus of the solid matrix do not need to be adjusted, that is, the initial shear modulus value and the initial bulk modulus value of the solid matrix are retained. Otherwise, the initial shear modulus value and the initial bulk modulus value of the solid matrix are adjusted based on the upper and lower limits, and the average value of the shear modulus and the bulk modulus of the solid matrix.

6. The method according to claim 5, wherein The adjusting of the initial value of the shear modulus and the initial value of the bulk modulus of the solid matrix based on the upper limit, lower limit and average value of the shear modulus and the bulk modulus of the solid matrix comprises: If the measured P-wave velocity of some rock samples in the reservoir to be predicted is greater than the upper limit of the predicted P-wave velocity, the optimal bulk modulus is iteratively found by the least squares method between the average value and the upper limit of the bulk modulus of the solid matrix, and the adjusted bulk modulus is used; the optimal shear modulus is iteratively found by the least squares method between the average value and the upper limit of the shear modulus of the solid matrix, and the adjusted shear modulus is used; If the measured P-wave velocity of some rock samples in the reservoir to be predicted is less than the lower limit of the predicted P-wave velocity, the optimal bulk modulus is iteratively found by the least squares method between the average value and the lower limit of the bulk modulus of the solid matrix, and the adjusted bulk modulus is used; the optimal shear modulus is iteratively found by the least squares method between the average value and the lower limit of the shear modulus of the solid matrix, and the adjusted shear modulus is used; If the measured P-wave velocity of some rock samples in the reservoir to be predicted is greater than the upper limit of the predicted P-wave velocity, and at the same time, the measured P-wave velocity of some rock samples is less than the lower limit of the predicted P-wave velocity; then, based on the rock samples whose measured P-wave velocity is greater than the upper limit of the predicted P-wave velocity, the optimal bulk modulus is iteratively found by the least squares method between the average value and the upper limit of the bulk modulus of the solid matrix, and the optimal shear modulus is iteratively found by the least squares method between the average value and the upper limit of the shear modulus of the solid matrix, and the optimal shear modulus is used as the upper limit of the shear modulus of the solid matrix; Based on rock samples whose measured P-wave velocity is less than the lower limit of the predicted P-wave velocity, the optimal bulk modulus is iteratively found by the least squares method between the average value and the lower limit of the bulk modulus of the solid matrix, which is used as the lower limit of the solid matrix bulk modulus; the optimal shear modulus is iteratively found by the least squares method between the average value and the lower limit of the shear modulus of the solid matrix, which is used as the lower limit of the shear modulus of the solid matrix; based on the re-determined upper limits of the solid matrix bulk modulus and shear modulus and lower limits of the solid matrix bulk modulus and shear modulus, the average values ​​of the solid matrix bulk modulus and shear modulus are respectively calculated as the adjusted bulk modulus and shear modulus.

7. The method according to claim 1, wherein The lithologic correction of the neutron logging data of the reservoir to be predicted is performed based on the pore correction parameters of various minerals in the reservoir to be predicted to determine the porosity of the reservoir to be predicted, including: A neutron logging porosity interpretation model is established based on the neutron logging measured values ​​of the reservoir to be predicted and the porosity correction parameters of various minerals in the reservoir, and the porosity of the reservoir to be predicted is determined based on the model.

8. The method according to claim 7, wherein The method includes establishing a neutron logging porosity interpretation model based on the neutron logging measured values ​​of the reservoir to be predicted and the porosity correction parameters of various minerals in the reservoir, and determining the porosity of the reservoir to be predicted based on the model, including: The porosity of the reservoir is determined using the following formula based on the neutron logging measured value of the reservoir to be predicted, the mineral content of the reservoir, and the neutron logging theoretical value of a single mineral: Where φ represents the porosity, f i represents the volume content of the i-th type of minerals, CNL represents the neutron logging value of the reservoir, and CNL i It represents the theoretical value of neutron logging for type i minerals. The theoretical value of neutron logging for a single type of mineral is obtained by referring to laboratory data.

9. The method according to claim 1, wherein The step of constructing a rock physics model incorporating pore structure characteristics based on the pore structure of the reservoir to be predicted and the dry skeleton rock physics model, and determining the elastic modulus of the reservoir to be predicted based on the rock physics model incorporating pore structure characteristics comprises: According to the matrix mineral content, porosity and pore structure of the reservoir to be predicted, an equivalent rock physics model combining pore structure characteristics is constructed through the porous media DEM model based on equivalent medium theory; The elastic modulus of the reservoir to be predicted is determined based on the equivalent rock physics model combined with the pore structure characteristics.

10. The method according to claim 1, wherein The method of determining the shear wave velocity of the reservoir to be predicted based on the determined elastic modulus of the reservoir to be predicted, wherein the elastic modulus of the reservoir to be predicted is characterized by the bulk modulus and shear modulus of the reservoir, comprises: Establishing a first relationship expression based on the bulk modulus, density, shear wave velocity, and compressional wave velocity of the reservoir; A second relationship expression is established based on the shear modulus, shear wave velocity and density of the reservoir; The shear wave velocity of the reservoir to be predicted is determined based on the first relationship expression and the second relationship expression.

11. The method according to claim 10, wherein The first relationship based on the bulk modulus, density, shear wave velocity and compressional wave velocity of the reservoir is expressed as: Where ρ is the reservoir density, kgem is the bulk modulus of the reservoir, V p is the longitudinal wave velocity of the reservoir, V s is the shear wave velocity of the reservoir.

12. The method according to claim 10, wherein The second relationship is established based on the shear modulus, shear wave velocity and density of the reservoir as follows: k dem =V s 2 r Where ρ is the reservoir density, k dem is the shear modulus of the reservoir, V s is the shear wave velocity of the reservoir.

13. A reservoir shear wave velocity prediction device, characterized in that: include: An elastic modulus initial value determination module is configured to construct a solid matrix model based on the mineral content data of the reservoir to be predicted and determine the initial value of the elastic modulus of the solid matrix; an elastic modulus adjustment module for determining the longitudinal wave velocity of the solid matrix at different porosities based on the initial value of the elastic modulus of the solid matrix and preset different porosities; comparing the longitudinal wave velocities at the different porosities with the measured longitudinal wave velocities of rock samples in the reservoir; and adjusting the initial value of the elastic modulus of the solid matrix based on the comparison result to obtain a solid matrix model after the elastic modulus is adjusted; A porosity determination module is used to perform lithologic correction on the neutron logging data of the predicted reservoir based on the pore correction parameters of various minerals in the predicted reservoir, determine the porosity of the predicted reservoir, and add the porosity of the predicted reservoir to the solid matrix model to establish a dry skeleton rock physics model; a reservoir elastic modulus determination module, configured to construct a rock physics model incorporating pore structure characteristics based on the pore structure of the reservoir to be predicted and the dry skeleton rock physics model, and determine the elastic modulus of the reservoir to be predicted based on the rock physics model incorporating pore structure characteristics; The shear wave velocity prediction module is used to determine the shear wave velocity of the reservoir to be predicted based on the determined elastic modulus of the reservoir to be predicted.

14. A computer storage medium, characterized in that The computer storage medium stores computer executable instructions, which, when executed by a processor, implement a reservoir shear wave velocity prediction method according to any one of claims 1 to 12.

15. A prediction device, characterized in that include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a reservoir shear wave velocity prediction method according to any one of claims 1 to 12 is implemented.

16. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed by a processor, the method for predicting reservoir shear wave velocity according to any one of claims 1 to 12 is implemented.

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