Reservoir shear wave velocity prediction method and device

Abstract

The invention 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 an elastic modulus of a solid matrix; determining longitudinal wave velocities under different porosities based on the initial value of the elasticity modulus of the solid matrix and preset different porosities, comparing the longitudinal wave velocities with the actually measured longitudinal wave velocities of the reservoir rock sample, and adjusting the initial value of the elasticity modulus of the solid matrix based on a comparison result to obtain a solid matrix model after the elasticity modulus is adjusted; lithology correction is carried out on neutron logging data of the reservoir based on pore correction parameters of various minerals in the reservoir to determine the porosity of the reservoir, and the porosity of the reservoir is added into a solid matrix model to establish a dry skeleton rock physical model; and based on the pore structure of the reservoir and the dry skeleton rock physical model, constructing a rock physical model combined with the pore structure characteristics so as to determine the elastic modulus of the reservoir and further determine the shear wave velocity. The prediction precision of the shear wave velocity can be improved.

Description

Technical Field

[0001] The 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 always been the focus of carbonate oil and gas exploration and research. As the exploration of oil and gas gradually expands to deeper layers, the development mechanism and prediction of deep dolomite reservoirs have become key issues affecting oil and gas exploration. The strong heterogeneity determined by the multi-scale and multi-type reservoir space of deep high-quality dolomite reservoirs causes the reservoir prediction results to be multi-solution, resulting in low prediction accuracy of high-quality dolomite reservoirs. In order to accurately identify deep dolomite reservoirs, many scholars have used equivalent medium theory to carry out a lot of research on carbonate rock physics experiments, modeling, and the influence of pore structure on carbonate elastic parameters. These studies generally believe that the use of pre-stack data to invert the P- and S-wave velocities of dolomite reservoirs can better predict deep dolomite high-quality reservoirs.

[0003] The shear wave velocity of rocks in the reservoir can be obtained through indoor rock physics experimental measurements or dipole acoustic logging measurements, but the former experimental measurement is time-consuming and laborious, and the latter actual measurement through logging is costly, resulting in a lack of shear wave data in actual exploration, especially in old wells where there is basically no measured shear wave velocity data, which makes the prediction accuracy of high-quality dolomite reservoirs insufficient.

[0004] Based on a large number of studies on shear wave velocity prediction, many shear wave velocity prediction methods have been developed, which are mainly divided into three categories: empirical relationship method, rock physics model method and artificial intelligence method. The empirical relationship method is based on statistical analysis and is a simple and easy shear wave velocity estimation method, but its application has certain limitations and it is often difficult to meet the needs of fine reservoir description; shear wave velocity prediction based on deep learning has also become a hot topic in academic research in recent years, but it has not been fully studied yet. There are many difficult problems such as insufficient model generalization ability that need to be solved urgently, and this type of method is mainly data-driven and lacks clear physical meaning; the rock physics modeling method mainly studies various rock physics models in combination with actual geological conditions, obtains the equivalent elastic parameters of the reservoir by calculation, and then calculates the longitudinal and shear wave velocities of the reservoir. Based on rock physics analysis, many scholars have developed a variety of shear wave prediction models suitable for different geological conditions, and obtained more accurate results than the empirical formula method. The rock physics modeling method is based on rigorous mechanical analysis and can accurately predict the shear wave velocity of the reservoir in theory, so it has received widespread attention. Summary of the invention

[0005] The inventors of this application have found that in the practice of rock physics modeling, it is necessary to accurately determine the skeleton mineral composition of the reservoir, the pore structure of the reservoir, and various factors affecting the porosity of the reservoir, and select appropriate rock physics models for different actual situations in order to achieve accurate prediction of the shear wave velocity of the reservoir. However, when using rock physics modeling methods to predict shear wave velocity 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 accuracy of shear wave prediction, and thus cannot accurately predict high-quality dolomite reservoirs.

[0006] In view of the above problems, the present invention is proposed to provide a method and device 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] 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;

[0010] 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, the porosity of the reservoir to be predicted is determined, and the porosity of the reservoir to be predicted 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, a rock physics model combined with pore structure characteristics is constructed, and the elastic modulus of the reservoir to be predicted is determined based on the rock physics model combined with 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, 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:

[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 shear modulus of the solid matrix 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, average value of the solid matrix shear modulus and the upper limit value, lower limit value, average value of the bulk modulus are determined based on the mineral content data of the reservoir to be predicted using the Voigt-Reuss-Hill model, 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] Where N represents the type of minerals in the reservoir to be predicted, f i represents the volume fraction of the i-th type of minerals;

[0020] K i , G i They represent the bulk modulus and shear modulus of the i-th type of minerals, respectively. The bulk modulus and shear modulus of a single type of mineral 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 limit of the solid matrix bulk modulus and the lower limit 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 limit of the solid matrix bulk modulus and the lower limit 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 longitudinal wave velocity corresponding to the solid matrix at different porosities; the preset porosity is the porosity that meets various conditions, and the porosity value ranges from 0 to the theoretical maximum value of the porosity. Different longitudinal wave velocities are calculated to obtain different longitudinal wave velocities.

[0032] In some optional embodiments, the P-wave velocities at different porosities are compared with the measured P-wave velocities of rock samples in the reservoir, and an 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, including:

[0033] The upper and lower limits of the predicted P-wave velocity are established 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 value of the shear modulus and the initial value of the bulk modulus of the solid matrix; otherwise, based on the upper and lower limits and average values ​​of the shear modulus and the bulk modulus of the solid matrix, the initial value of the shear modulus and the initial value of the bulk modulus of the solid matrix are adjusted respectively.

[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 value, the lower limit value and the average value of the shear modulus and the bulk modulus of the solid matrix comprises:

[0036] 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 optimal bulk modulus is iteratively found by the 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 iteratively found by the 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 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 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 iteratively found by the 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 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 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, as the upper limit of the bulk modulus of the solid matrix; 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, as the upper limit of the shear modulus of the solid matrix;

[0039] 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 iteratively found by the least squares 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 iteratively found by the least squares 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; 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, the average values ​​of the solid matrix bulk modulus and shear modulus are respectively calculated 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 porosity correction parameters of various minerals in the reservoir to be predicted to determine the porosity of the reservoir to be predicted includes:

[0041] 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.

[0042] In some optional embodiments, the method of 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 includes:

[0043] 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, the following formula is used to determine the reservoir porosity:

[0044]

[0045] Where φ represents the porosity, f i represents the volume content of the i-th type of mineral, CNL represents the neutron logging value of the reservoir, and CNL i It represents the theoretical value of neutron logging for the i-th type of 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, 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 includes:

[0047] According to the matrix mineral content, porosity and pore structure of the reservoir to be predicted, an equivalent rock physics model combined with pore structure characteristics is constructed through the porous medium 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, the determining of 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:

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

[0051] A second relationship expression is established 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 P-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] The embodiment of the present invention further provides a reservoir shear wave velocity prediction device, comprising:

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

[0061] An elastic modulus adjustment module is used to determine 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; compare the longitudinal wave velocity at the different porosities with the measured longitudinal wave velocity of the rock sample in the reservoir, and adjust 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 lithology 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, add the porosity of the reservoir to be predicted to the solid matrix model, and establish a dry skeleton rock physics model;

[0063] A reservoir elastic modulus determination module is used to construct a rock physics model combined with 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 combined with the 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, the computer program implements the above-mentioned reservoir shear wave velocity prediction method.

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

[0069] 1) Based on the mineral content data of the reservoir to be predicted, a solid matrix model is constructed and an initial value of the elastic modulus of the solid matrix is ​​determined. Based on the comparison result of the measured longitudinal wave velocity of the rock sample in the reservoir and the longitudinal wave velocity of the solid matrix at different porosities, the initial value of the elastic modulus of the solid matrix is ​​adjusted 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 shear wave velocity of the reservoir;

[0070] 1) Based on the neutron logging data of the reservoir to be predicted, the lithology is corrected to determine the porosity of the reservoir and added to the solid matrix model to establish a dry skeleton rock physics model. This step has low operating costs. Considering that the mineral composition of the rock in the actual formation is relatively complex, the neutron logging value is corrected for lithology, making the porosity determination more accurate. The addition of porosity improves the accuracy of the elastic modulus of the reservoir to be predicted, and further improves 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 combined with the pore structure characteristics is constructed. Based on this model, the elastic modulus of the reservoir to be predicted is determined to realize the prediction of the shear wave velocity. The influence of different pore structures on the elastic modulus of the solid matrix is ​​taken into account, which is more in line 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 partly become apparent from the description, or understood by practicing the present invention. The purpose 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 Embodiment 1 of the present invention;

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

[0077] Figure 3 This is a flow chart of a reservoir shear wave velocity prediction method in Embodiment 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 It is a schematic diagram of the distribution of the predicted longitudinal wave velocity and the measured longitudinal wave velocity of the sample in the second embodiment of the present invention;

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

[0081] Figure 7 This is a schematic diagram of adjusting the lower limit of reservoir elastic modulus in the second embodiment of the present invention;

[0082] Figure 8 This is a schematic diagram for determining the upper and lower limits of the reservoir elastic modulus in the second embodiment of the present invention;

[0083] Fig. 9 is the porosity determined by different methods in Example 2 of the present invention;

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

[0085] Fig.11 This is a comparison diagram between the measured shear wave velocity and the predicted shear wave velocity in the second embodiment of the present invention;

[0086] Fig.12 It 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] Fig.13 Schematic diagram of the structure of a reservoir shear wave velocity prediction device in an embodiment of the present invention. DETAILED DESCRIPTION

[0088] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the 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. On the contrary, 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] In order to solve the problem in the prior art that it is difficult to accurately calculate the solid matrix elastic modulus, porosity and pore structure of the reservoir when predicting the reservoir shear wave velocity based on rock physics modeling, an embodiment of the present invention provides a reservoir shear wave velocity prediction method, which has high predicted shear wave velocity accuracy and low operating cost.

[0090] Embodiment 1

[0091] Embodiment 1 of the present invention provides a reservoir shear wave velocity prediction method, the process of which is as follows: Figure 1 The process of rock physics modeling based on well data is shown in 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, determining the longitudinal wave velocity of the solid matrix at different porosities; comparing the longitudinal wave velocity at the different porosities with the measured longitudinal wave velocity of the rock sample 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;

[0094] Step S103: Based on the pore correction parameters of various minerals in the reservoir to be predicted, lithology correction is performed on the neutron logging data of the reservoir to be predicted, the porosity of the reservoir to be predicted is determined, the porosity of the reservoir to be predicted is added to the solid matrix model, and a dry skeleton rock physics model is established;

[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 includes:

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

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

[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 results of the reservoir to be predicted in the target area or the mineral content interpreted by well logging; based on the mineral content data of the reservoir to be predicted, the Voigt-Reuss-Hill model is used to calculate the bulk modulus and shear modulus of various matrix minerals in the reservoir, and the upper limit value, lower limit value and average value of the solid matrix bulk modulus, and the upper limit value, lower limit value and average value of the solid matrix shear modulus after the mineral is determined are obtained based on the Voigt-Reuss-Hill model. The determination process is as follows:

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

[0102]

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

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

[0105] 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;

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

[0107]

[0108] Among them, K R , GR represent the lower limit of the solid matrix bulk modulus and the lower limit of the solid matrix shear modulus, respectively;

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

[0116] 1) Substitute the initial value of the elastic modulus of the solid matrix and the preset porosity into the DEM model to calculate the longitudinal 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 longitudinal wave velocity of the solid matrix corresponding to the reservoir to be predicted; the preset porosity is the porosity that meets various conditions, ranging from 0 to the theoretical maximum porosity, and different longitudinal wave velocities are calculated to obtain different longitudinal wave velocities.

[0117] 2) Establishing the upper and lower limits of the predicted P-wave velocity based on the corresponding P-wave velocity under 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, the shear modulus and bulk modulus of the solid matrix do not need to be adjusted, that is, the initial value of the shear modulus and the initial value of the bulk modulus of the solid matrix; otherwise, based on the upper and lower limits of the shear modulus and the bulk modulus of the solid matrix, the initial value of the shear modulus and the initial value of the bulk modulus of the solid matrix are adjusted to be more in line with the actual situation of the reservoir, and a solid matrix model after the shear modulus and the bulk modulus are adjusted is obtained, and 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 They represent the lower limits of the solid matrix bulk modulus and 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 predicted reservoir 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 iteratively found by the least squares 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 predicted reservoir 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 iteratively found by the least squares 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, the initial values ​​of the bulk modulus and shear modulus of the solid matrix should 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 iteratively found by the least squares method as the upper limit of the bulk modulus of the solid matrix; in [G H , G V ], the optimal shear modulus is iteratively found by the least square method as the upper limit of the shear modulus of the solid matrix; and based on the measured P-wave velocity of the rock sample that is less than the lower limit of the predicted P-wave velocity, [K R , K H ], the optimal bulk modulus is iteratively found by the least squares method as the lower limit of the bulk modulus of the solid matrix; in [G R , G H ] in which 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 porosity correction parameters of various minerals in the reservoir to be predicted, the neutron logging data of the reservoir to be predicted is subjected to lithology correction, the porosity of the reservoir to be predicted is determined, 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 with standard limestone, and the reading of neutron logging for limestone formations is the true porosity of the formation. However, in actual formations, the mineral composition of the rock is relatively complex, so lithology correction is required to determine the porosity of the reservoir. 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;

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

[0126]

[0127] Among them, 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 the i-th type of minerals, CNL i represents the theoretical value of neutron logging for the i-th type of minerals. The theoretical value of neutron logging for 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, the above step S104 constructs a rock physics model combined with 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 combined with the pore structure characteristics, including:

[0130] Based on the rock casting thin section and mineral analysis data, the pore structure of the reservoir to be predicted is determined. According to the matrix mineral content, porosity and pore structure of the reservoir to be predicted, an equivalent rock physics model combined with the pore structure characteristics is constructed through the porous medium DEM model based on the equivalent medium theory; based on the equivalent rock physics model combined with the pore structure characteristics, the elastic modulus of the reservoir to be predicted is determined, and the elastic modulus of the reservoir to be predicted is characterized by the bulk modulus and shear modulus of the reservoir. In this step, the shear modulus and bulk modulus of the reservoir to be predicted are determined based on the following formula.

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

[0132]

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

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

[0135]

[0136] Wherein, Km and Gm are the solid matrix bulk modulus and shear modulus 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] wherein,

[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] wherein,

[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 based on the shape of each pore structure. The pore aspect ratio αi varies with the shape of the pore structure. If the pore shape is approximately circular, the pore aspect ratio αi is large; if the pore shape is flat, the pore aspect ratio αi is medium; if the pore shape is linear, the pore aspect ratio αi is very small. Reservoir rock samples with different pore structures will have very different elastic moduli at the same porosity, which will cause differences in shear wave velocity.

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

[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 P-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 the present embodiment, the initial value of the elastic modulus of the solid matrix is ​​adjusted by the least square method to obtain a solid matrix model after the elastic modulus is adjusted. The process of changing the matrix takes into account the characteristics of the actual reservoir, and the adjusted elastic modulus is more in line with reality, which lays a data foundation for the subsequent determination of the elastic modulus of the reservoir, thereby effectively improving the prediction accuracy of the reservoir shear wave velocity. When calculating the porosity, neutron logging data is used in combination with various pore correction parameters determined in the laboratory to make the determination of the porosity more accurate. Adding the porosity to the solid matrix model further considers the influence of the porosity on the elastic modulus of the reservoir, and further improves the prediction accuracy of the reservoir shear wave velocity. Based on the equivalent medium theory, an equivalent rock physics model considering different pore structures, porosities and mineral components of the matrix is ​​established using the porous medium DEM model, and the influence of various factors on the reservoir elastic modulus is comprehensively considered, so that the equivalent medium theory is more in line with the rock characteristics of the reservoir, and the prediction accuracy of the reservoir shear wave velocity is effectively improved.

[0180] Embodiment 2

[0181] Embodiment 2 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. According to the lithology and sedimentary characteristics, the Majiagou Formation can be subdivided into six sections from bottom to top: the first section, the third section and the fifth section are mainly evaporative platform deposits, and the lithology is mainly gypsum salt rock, limestone and dolomite; the second section, the fourth section and the sixth section are mainly shallow-water carbonate platform deposits, and the lithology is mainly dolomite and limestone. This embodiment mainly takes the fourth section as the research object to predict the shear wave velocity, and the steps include:

[0183] Step S201: Based on the core data of the fourth member of 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 the core at different depths; for example, at a depth of 2705 meters, the mineral components are calcite and dolomite, and their contents are represented by the lengths of different colors. At a depth of 3835 meters, the mineral components are dolomite and gypsum, and their contents are represented by the lengths 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 refers to step S101;

[0185] Step S203: based on the initial values ​​of the bulk modulus and shear modulus of the solid matrix and the preset different porosities, determine the P-wave velocity of the solid matrix at different porosities; compare the P-wave velocity at different porosities with the measured P-wave velocity of the rock sample in the reservoir, and adjust the initial values ​​of the bulk modulus and shear modulus of the solid matrix based on the comparison result to obtain a solid matrix model after the bulk modulus and shear modulus are adjusted. The adjustment process refers 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 with a velocity greater than the upper limit of the predicted P-wave velocity, the upper limit values ​​of the bulk modulus and shear modulus are 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 value is greater than all the measured P-wave velocities. Figure 6 As shown in FIG2 , 2) based on the rock samples with a velocity lower than the predicted P-wave velocity lower limit, the lower limit values ​​of the bulk modulus and the shear modulus are re-determined using the least squares method to ensure that the predicted P-wave velocity lower limit obtained based on the re-determined lower limit value is less than all the measured P-wave velocities, such as 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 As shown, 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 Fig. 9In the first column, SP represents natural potential, GR represents natural gamma, and CAL represents well diameter, and different colors are used to represent changes at different depths; the second column is 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, Anhy represents gypsum and other minerals, 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 change curves, which are deep lateral resistivity logging and shallow lateral resistivity logging corresponding to different depths; the sixth column is 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 with the porosity measured in the laboratory, while the original interpreted porosity, that is, the porosity determined without this method, has a large error 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 combined with pore structure characteristics is constructed using the porous medium DEM model, and the bulk modulus and shear modulus of the reservoir to be predicted are determined based on the rock physics model combined with the pore structure characteristics, referring to step S104;

[0189] Affected by diagenesis, the pore structure of the Ma 4th Member reservoir in the Ordos Basin is complex. Dolomitization produces pores with a circular shape, that is, pores with a large pore aspect ratio, while dissolution produces flat pores with a medium pore aspect ratio. The pore shape of the fracture will produce linear pores, that is, pores with a very small pore aspect ratio. Based on the consideration of different pore structure characteristics, the rock physics model is constructed using the porous medium DEM model to determine the bulk modulus and shear modulus of the reservoir to be predicted.

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

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

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

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

[0194] In the formula, Δ represents the prediction error, Vs_predicted represents the shear wave velocity predicted by this method, and Vs_actually measured represents the shear wave velocity actually measured in the reservoir. The smaller Δ is, the smaller the difference between the predicted value and the measured value is, indicating that the prediction accuracy is higher.

[0195] Fig.10 , Fig.11 The left coordinates and Figure 4 The left coordinate of corresponds to the core depth. Fig.10 are the measured P-wave velocity and density of rock samples at different depths, where Vp represents the measured P-wave velocity, Fig.11 The figure is a comparison chart of the predicted shear wave velocity and the measured shear wave velocity determined based on the method of this embodiment, where 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 error of the shear wave velocity of rocks at different depths, such as Fig.12 The figure shows the intersection diagram of the actual measured shear wave velocity and the predicted shear wave velocity. The horizontal axis in the figure is the predicted shear wave velocity, the vertical axis is the measured shear wave velocity, and the color scale represents the prediction error. It can be observed from the figure that the data of all cores in the intersection diagram are basically near the 1:1 line. The experimental data proves that the prediction error of the shear wave velocity is basically within 5%, indicating that the application effect of the method of this embodiment in the study area is good, and the prediction accuracy of the shear wave velocity is high.

[0196] In the methods of the above-mentioned embodiments, by adjusting the elastic modulus of the solid matrix using the least squares method, performing lithology correction based on neutron logging to more accurately interpret the 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: Fig.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 used to determine 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; compare the longitudinal wave velocity at the different porosities with the measured longitudinal wave velocity of the rock sample in the reservoir, and adjust 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;

[0200] A porosity determination module 30 is used to perform lithology 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, add the porosity of the reservoir to be predicted to the solid matrix model, and establish a dry skeleton rock physics model;

[0201] A reservoir elastic modulus determination module 40 is used to construct a rock physics model combined with 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 combined with the pore structure characteristics;

[0202] The shear wave velocity prediction module 50 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.

[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 here.

[0204] The above method and device of the embodiment of the present invention can effectively improve the prediction accuracy of shear wave velocity with very low operating cost, 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, a reservoir shear wave velocity prediction method 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, the method for predicting reservoir shear wave velocity as described above is implemented.

[0208] Unless otherwise specifically stated, terms such as processing, computing, calculating, determining, displaying, etc. may refer to the actions and / or processes of one or more processing or computing systems, or similar devices, which operate 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 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 mentioned 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 process 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 protection of the present disclosure. The attached method claims present the 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 above 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 the embodiments of the claimed subject matter require more features than are clearly stated in each claim. On the contrary, as reflected in the appended claims, the invention is in a state of having less than all the features of the disclosed individual embodiments. Therefore, the appended claims are 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 can all be implemented as electronic hardware, computer software, or a combination thereof. In order to clearly illustrate the interchangeability between hardware and software, various illustrative components, blocks, modules, circuits, and steps are generally described above around their functions. Whether such functions are implemented as hardware or software depends on specific applications and the design constraints imposed on the entire system. A skilled person can implement the described functions in an alternative manner for each specific application, but such implementation decisions should not be interpreted as departing from the scope of protection of the present disclosure.

[0212] The steps of the method or algorithm described in conjunction with the embodiments herein may be directly embodied as hardware, a software module executed by a processor, or a combination thereof. The software module may be located in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a mobile disk, a 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 can write information to the storage medium. Of course, the storage medium may also be an integral part of the processor. The processor and the storage medium may be located in an ASIC. The ASIC may be located in a user terminal. Of course, the processor and the 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 with 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 outside the processor. In the latter case, it is coupled to the processor in a communication manner via various means, which are well known in the art.

[0214] The above description includes examples of one or more embodiments. Of course, it is impossible to describe all possible combinations of components or methods for the purpose of describing the above embodiments, but it should be recognized by those skilled in the art that the various embodiments may be further combined and arranged. Therefore, the embodiments described herein are intended to cover all such changes, modifications and variations that fall within the scope of protection of the appended claims. In addition, with respect to the term "comprising" used in the specification or claims, the word is covered in a manner similar to the term "including", just as "including," is explained as a transitional word in the claims. In addition, any term "or" used in the specification of the claims is intended to mean "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; 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; 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, the porosity of the reservoir to be predicted is determined, and the porosity of the reservoir to be predicted 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, a rock physics model combined with pore structure characteristics is constructed, and the elastic modulus of the reservoir to be predicted is determined based on the rock physics model combined with 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, characterized in that 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 shear modulus of the solid matrix 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, characterized in that 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, average value of the solid matrix shear modulus and the upper limit value, lower limit value, 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: Where N represents the type of minerals in the reservoir to be predicted, f i represents the volume fraction of the i-th type of minerals; K i , G i They represent the bulk modulus and shear modulus of the i-th type of minerals, respectively. The bulk modulus and shear modulus of a single type of mineral 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 limit of the solid matrix bulk modulus and the lower limit 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 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.

4. The method according to claim 1, characterized in that 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 longitudinal wave velocity corresponding to the solid matrix at different porosities; the preset porosity is the porosity that meets various conditions, and the porosity value ranges from 0 to the theoretical maximum value of the porosity. Different longitudinal wave velocities are calculated to obtain different longitudinal wave velocities.

5. The method according to claim 2, characterized in that 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: The upper and lower limits of the predicted P-wave velocity are established 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 value of the shear modulus and the initial value of the bulk modulus of the solid matrix; otherwise, based on the upper and lower limits and average values ​​of the shear modulus and the bulk modulus of the solid matrix, the initial value of the shear modulus and the initial value of the bulk modulus of the solid matrix are adjusted respectively.

6. The method according to claim 5, characterized in that 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 value, the lower limit value and the 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 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 iteratively found by the least square method between the average value and the upper limit of the shear modulus of the solid matrix, as the adjusted shear modulus; 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 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 iteratively found by the least square method between the average value and the lower limit of the shear modulus of the solid matrix, as the adjusted shear modulus; 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 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 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 iteratively found by the 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; 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 iteratively found by the least squares 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 iteratively found by the least squares 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; 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, 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, characterized in that The method of performing lithology correction on the neutron logging data of the reservoir to be predicted 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 includes: 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, characterized in that The method of 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 includes: The porosity of the reservoir is determined by 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 mineral, CNL represents the neutron logging value of the reservoir, and CNL i It represents the theoretical value of neutron logging for the i-th type of 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, characterized in that The step of constructing a rock physics model combining 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 combining 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 combined with pore structure characteristics is constructed through the porous medium 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, characterized in that 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: A first relationship expression is established 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, characterized in that 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 P-wave velocity of the reservoir, V s is the shear wave velocity of the reservoir.

12. The method according to claim 10, characterized in that The second relationship based on the shear modulus, shear wave velocity and density of the reservoir is expressed as: 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 used to construct a solid matrix model based on the mineral content data of the reservoir to be predicted, and to determine the elastic modulus initial value of the solid matrix; An elastic modulus adjustment module is used to determine 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; compare the longitudinal wave velocity at the different porosities with the measured longitudinal wave velocity of the rock sample in the reservoir, and adjust 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 lithology 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, add the porosity of the reservoir to be predicted to the solid matrix model, and establish a dry skeleton rock physics model; A reservoir elastic modulus determination module is used to construct a rock physics model combined with 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 combined with the 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, and when the computer executable instructions are executed by a processor, a reservoir shear wave velocity prediction method according to any one of claims 1 to 12 is implemented.

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 as described in any one of claims 1 to 12 is implemented.

16. A computer program product, characterized in that The computer program product comprises 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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