Reservoir porosity measurement method and device, electronic equipment and storage medium

Abstract

The invention discloses a reservoir porosity measurement method and device, electronic equipment and a storage medium. The method comprises the following steps: determining target parameter information of a target reservoir area, wherein the target parameter information comprises longitudinal wave impedance, transverse wave impedance, density seismic data and rock matrix parameter data; according to the target parameter information, a target hydrocarbon-containing prediction factor data body of the target reservoir area is determined, the target hydrocarbon-containing prediction factor data body correspondingly comprises at least two hydrocarbon-containing prediction factors, and the hydrocarbon-containing prediction factors are used for describing the hydrocarbon content condition in the target reservoir area; determining target fitting curve information adopted by the target reservoir area, wherein the target fitting curve information is used for describing a linear fitting relationship between hydrocarbon-containing predictive factors and porosity in different types of reservoirs; and determining the porosity of the target reservoir area according to the target hydrocarbon-containing predictive factor data body and the target fitting curve information. According to the method, the porosity is determined by constructing the hydrocarbon-containing prediction factor, and the reservoir porosity prediction accuracy is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of petroleum geophysical exploration, and in particular to a reservoir porosity measurement method, device, electronic equipment and storage medium. Background Art

[0002] In the process of oil exploration and development, the physical properties of the reservoir determine the scale of favorable traps and affect the progress of oil exploration and development. The physical properties of the reservoir include pore space, saturation and medium permeability. The pore space determines the size of the reservoir space of the rock, the saturation represents the hydrocarbon content per unit pore space, and the permeability determines the fluidity of the pore fluid.

[0003] In oil exploration and development, pore space represents the scale potential of reservoir rocks and is an important parameter affecting resource evaluation. At present, the reservoir porosity is usually quantitatively calculated using the fitting relationship between porosity and elastic parameters. The conventional method is to use the P-wave impedance obtained by post-stack inversion to fit the reservoir porosity data. In exploration practice, the method of calculating reservoir porosity based on P-wave impedance has been widely used and achieved beneficial results. However, the disadvantage of this method is that due to the lack of shear wave information constraints, the fitting accuracy is not accurate enough in actual application and the porosity of different types of reservoirs cannot be calculated separately, resulting in the prediction accuracy not meeting the exploration requirements. Summary of the invention

[0004] The present invention provides a reservoir porosity measurement method, device, electronic device and storage medium to solve the problem that the conventional porosity calculation method has insufficient fitting accuracy and cannot realize the separate calculation of the porosity of different types of reservoirs, resulting in the prediction accuracy failing to meet the exploration requirements.

[0005] According to one aspect of the present invention, a reservoir porosity measurement method is provided, which is characterized by comprising:

[0006] Determine target parameter information of the target reservoir area, the target parameter information including P-wave impedance, S-wave impedance, density seismic data and rock matrix parameter data;

[0007] Determine a target hydrocarbon content prediction factor data body of the target reservoir region according to the target parameter information, wherein the target hydrocarbon content prediction factor data body correspondingly includes at least two hydrocarbon content prediction factors, and the hydrocarbon content prediction factors are used to describe the hydrocarbon content in the target reservoir region;

[0008] Determine the target fitting curve information used in the target reservoir area, the target fitting curve information is used to describe the linear fitting relationship between the hydrocarbon prediction factor and the porosity in different types of reservoirs;

[0009] The porosity of the target reservoir area is determined based on the target hydrocarbon prediction factor data body and the target fitting curve information.

[0010] According to another aspect of the present invention, there is provided a reservoir porosity measuring device, characterized in that it comprises:

[0011] Data determination module: used to determine target parameter information of the target reservoir area, the target parameter information includes longitudinal wave impedance, shear wave impedance, density seismic data and rock matrix parameter data;

[0012] A data body determination module: used to determine a target hydrocarbon content prediction factor data body of a target reservoir region according to target parameter information, wherein the target hydrocarbon content prediction factor data body includes at least two hydrocarbon content prediction factors, and the hydrocarbon content prediction factors are used to describe the hydrocarbon content in the target reservoir region;

[0013] Fitting curve information determination module: used to determine the target fitting curve information used in the target reservoir area. The target fitting curve information is used to describe the linear fitting relationship between hydrocarbon prediction factors and porosity in different types of reservoirs.

[0014] Porosity determination module: used to determine the porosity of the target reservoir area based on the target hydrocarbon prediction factor data body and the target fitting curve information.

[0015] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the reservoir porosity measurement method described in any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the reservoir porosity measurement method described in any embodiment of the present invention when executed.

[0020] The technical solution of the embodiment of the present invention obtains target parameter information by inverting the original seismic data, constructs hydrocarbon prediction factors according to the joint constraints of the target parameter information, distinguishes the distribution range of water-bearing reservoirs, gas-bearing reservoirs and mudstone formations according to the distribution characteristics of the hydrocarbon prediction factors in the rock physics template, and establishes fitting relationships between hydrocarbon prediction factors and porosity for different types of reservoirs. Finally, based on the fitting relationships between different types of reservoirs and hydrocarbon prediction factors, the porosity data bodies of corresponding types of reservoirs are calculated using the hydrocarbon prediction factor data bodies within the range of the corresponding types of reservoirs. The method is based on the physical analysis of rocks, uses the joint constraints of shear wave impedance and longitudinal wave impedance, constructs hydrocarbon prediction factors, and quantitatively predicts the reservoir porosity by category, thereby ensuring the accuracy of quantitative detection of reservoir physical properties and improving the accuracy of exploration and development.

[0021] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 A flow chart of a reservoir porosity measurement method provided by an embodiment of the present invention;

[0024] Figure 2 A petrophysical interpretation template diagram of porosity-hydrocarbon prediction factor provided by an embodiment of the present invention;

[0025] Figure 3 A reservoir porosity distribution diagram predicted based on hydrocarbon-containing prediction factors provided in an embodiment of the present invention;

[0026] Figure 4 A schematic structural diagram of a reservoir porosity measurement device provided by an embodiment of the present invention;

[0027] Figure 5 A schematic diagram of the structure of an electronic device for implementing the reservoir porosity measurement method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] Figure 1 A flow chart of a reservoir porosity measurement method is provided for an embodiment of the present invention. This embodiment is applicable to reservoir porosity measurement. The method can be performed by a reservoir porosity measurement device. The reservoir porosity measurement device can be implemented in the form of hardware and / or software. The reservoir porosity measurement device can be configured in any electronic device with network communication function. Figure 1 As shown, the method includes:

[0031] S110, determining target parameter information of the target reservoir area, where the target parameter information includes longitudinal wave impedance, shear wave impedance, density seismic data, and rock matrix parameter data.

[0032] The target parameter information may be a parameter that can characterize the physical characteristics of the target reservoir region. Wave impedance may be the stress required to generate a disturbance in the rock so that the particle generates a unit vibration velocity. The greater the wave impedance, the greater the stress required to generate a unit vibration velocity; conversely, the smaller the wave impedance, the smaller the stress required to generate a unit vibration velocity. Longitudinal wave impedance may be the disturbance force in the direction of the vibration velocity that generates a disturbance in the rock so that the particle generates a unit vibration velocity; transverse wave impedance may be the disturbance force in the direction perpendicular to the vibration velocity that generates a disturbance in the rock so that the particle generates a unit vibration velocity.

[0033] Density seismic data can be a parameter that can characterize the reservoir density of the target reservoir area. The matrix can be the rock part in the fractured reservoir, that is, the fractures cut the rocks in the entire reservoir area into many rock blocks, which are called matrix rock blocks. Rock matrix parameter data can be data that can characterize the physical properties of the divided rocks in the reservoir area.

[0034] Seismic data are collected at different sampling points in the target reservoir area, and converted into target parameter data by inversion, wherein the target parameter data includes longitudinal wave impedance, shear wave impedance, density seismic data and rock matrix parameter data.

[0035] Among them, seismic inversion can be the process of converting interface-type seismic data into rock-layer longitudinal wave impedance, shear wave impedance, vibration velocity and other data. Seismic inversion methods based on original seismic data are divided into two categories: pre-stack seismic inversion and post-stack seismic inversion.

[0036] Furthermore, seismic inversion can be achieved through a seismic inversion software system, such as Jason seismic inversion software, STRATA inversion software, etc.; it can also be achieved by using python to establish a neural network model.

[0037] In the above steps, parameter information of the target reservoir area is obtained by means of seismic inversion, which can ensure that more reliable data on reservoir physical properties are obtained.

[0038] S120, determining a target hydrocarbon content prediction factor data body of the target reservoir region according to the target parameter information, wherein the target hydrocarbon content prediction factor data body correspondingly includes at least two hydrocarbon content prediction factors, and the hydrocarbon content prediction factors are used to describe the hydrocarbon content in the target reservoir region.

[0039] Optionally, determining a target hydrocarbon-containing prediction factor data body of a target reservoir region according to target parameter information includes steps A1-A2:

[0040] Step A1: determining a target hydrocarbon prediction factor calculation function used in a target reservoir region, wherein the hydrocarbon prediction factor calculation function pre-constructs a correlation relationship between longitudinal wave impedance, shear wave impedance, density seismic data, and rock matrix parameter data and hydrocarbon prediction factors.

[0041] According to the relationship between the obtained P-wave impedance, S-wave impedance, density seismic data and rock matrix parameter data and the relationship between them and the hydrocarbon-containing prediction factor, the correlation between the P-wave impedance, S-wave impedance, density seismic data and rock matrix parameter data and the hydrocarbon-containing prediction factor is pre-constructed, and deduction is performed based on the obtained correlation relationship to obtain the target hydrocarbon-containing prediction factor calculation function of the target reservoir area.

[0042] Optionally, the target hydrocarbon prediction factor calculation function is obtained by substituting the porosity calculation function into the candidate hydrocarbon prediction factor calculation function, the porosity calculation function is pre-constructed based on the correlation between longitudinal wave impedance and porosity established according to the rock physical characteristics of the target reservoir area, and the candidate hydrocarbon prediction factor calculation function is pre-constructed based on the Hudson theoretical model and the Makvo rock physics approximation relationship.

[0043] Rock physical characteristics can be parameters that can characterize the mechanical, thermal, electrical, acoustic, radiological and other properties of rocks in the reservoir. The Hudson theoretical model can be a method for detecting and analyzing the various properties of cracks in the reservoir area to obtain the propagation law of seismic waves passing through the cracks in the reservoir area, as well as the changes in various properties of seismic waves, and then find the connection between the two.

[0044] Optionally, the target hydrocarbon prediction factor calculation function is constructed as follows:

[0045] According to the Hudson theoretical model and the Makvo rock physics approximation relationship, the candidate hydrocarbon prediction factor calculation function is pre-constructed, which can be expressed by the following formula:

[0046]

[0047] Among them, H 候选 is the candidate hydrocarbon prediction factor, dimensionless; is the porosity, dimensionless; Z p is the longitudinal wave impedance, in g / cm 3 .m / s; Z s is the shear wave impedance, in g / cm 3 .m / s.

[0048] The correlation between P-wave impedance and porosity established based on the rock physical characteristics of the target reservoir area can be expressed by the following formula:

[0049]

[0050] Among them, Z p is the longitudinal wave impedance, in g / cm 3 .m / s; V0 is the longitudinal wave velocity of the rock matrix, in m / s; ρ is the density, in g / cm 3 .

[0051] The porosity calculation function is determined based on the established correlation between longitudinal wave impedance and porosity, and the porosity calculation function can be obtained. The porosity calculation function can be expressed by the following formula:

[0052]

[0053] Substituting the porosity formula into the candidate reservoir hydrocarbon prediction factor formula, the final target hydrocarbon prediction factor formula can be obtained as follows:

[0054]

[0055] Among them, H 目标 is the target hydrocarbon prediction factor, dimensionless; Z p is the longitudinal wave impedance, in g / cm 3 .m / s; Z s is the shear wave impedance, in g / cm 3 .m / s; V0 is the longitudinal wave velocity of the rock matrix, in m / s; ρ is the density, in g / cm 3 .

[0056] Step A2: Substitute the target parameter information into the target hydrocarbon content prediction factor calculation function in sequence to obtain a target hydrocarbon content prediction factor data body corresponding to at least two hydrocarbon content prediction factors.

[0057] Among them, the target hydrocarbon prediction factor calculation function is: H 目标 is the target hydrocarbon prediction factor, Z p is the longitudinal wave impedance, Z s is the shear wave impedance, V0 is the P-wave velocity of the rock matrix parameter data, and ρ is the density.

[0058] S130, determining target fitting curve information used in the target reservoir region, where the target fitting curve information is used to describe the linear fitting relationship between hydrocarbon-containing prediction factors and porosity in different types of reservoirs.

[0059] According to the hydrocarbon-containing prediction factors of different types of reservoirs in the target reservoir area, a linear fitting relationship between the hydrocarbon-containing prediction factor and the porosity is established, thereby obtaining the target fitting curve information of the target reservoir area.

[0060] Optionally, the process of constructing the target fitting curve information used in the target reservoir region includes steps B1-B2:

[0061] Step B1: determining the distribution characteristics of different types of reservoirs in a reference reservoir area located in the same work area as the target reservoir area.

[0062] The reference reservoir region of the same work area may be a reservoir region that is in the same exploration area as the target reservoir region and whose reservoir structure and parameters are known.

[0063] The distribution characteristics of different types of reservoirs in the reference reservoir area located in the same work area as the target reservoir area are determined based on the known reservoir structure and related physical properties.

[0064] Optionally, determining the distribution characteristics of different types of reservoirs in a reference reservoir region located in the same work area as the target reservoir region comprises steps C1-C2:

[0065] Step C1, forward modeling the response characteristics of gas-bearing reservoirs, aquifers and mudstone formations in the reference reservoir area in the rock physics interpretation template, and determining the range of hydrocarbon prediction factors for the corresponding types of reservoirs according to the response characteristics of the different types of reservoirs. The rock physics interpretation template is used to construct a correlation between the porosity of the reference reservoir area and the hydrocarbon prediction factor.

[0066] Forward modeling is a method that uses numerical calculation methods to study the propagation laws of seismic waves in underground media when the structure and parameters of the underground media are known, so as to obtain theoretical seismic records.

[0067] Firstly, a rock physics interpretation template of porosity-hydrocarbon prediction factor is established, and the response characteristics of gas-bearing reservoirs, water-bearing reservoirs and mudstone formations are forward simulated in the rock physics template. Then, the value range of the hydrocarbon prediction factor for the corresponding type of reservoir is determined according to the response characteristics of different types of reservoirs.

[0068] Step C2: establishing a comparison relationship between the hydrocarbon prediction factor and the water-bearing reservoir, gas-bearing reservoir and mudstone formation according to the hydrocarbon prediction factor value range of different types of reservoirs in the reference reservoir area, so as to obtain the distribution characteristics of different types of reservoirs in the reference reservoir area.

[0069] For example, assume that H1 = 0.258 and H2 = 0.23. Figure 3 As shown in the figure, the horizontal axis is porosity, and the vertical axis is the target hydrocarbon prediction factor H 目标 In the figure, the thin solid line is the response characteristic of gas-bearing reservoir, the medium solid line is the response characteristic of water-bearing reservoir, and the thick solid line is the response characteristic of mudstone formation. It can be seen from the distribution characteristics of different types of reservoirs that when the target hydrocarbon prediction factor H 目标 >H1, corresponding to the gas-bearing reservoir distribution area, when the hydrocarbon prediction factor H2≤H 目标 ≤H1, corresponding to the distribution area of ​​water-bearing reservoirs, when H 目标 When <H2, it corresponds to the distribution area of ​​mudstone formation. According to the distribution characteristics of hydrocarbon prediction factors, we can get:

[0070]

[0071] Among them, H 目标 Expressed as the target hydrocarbon prediction factor, dimensionless; H gas is the target hydrocarbon prediction factor corresponding to the gas-bearing reservoir; H water is the target hydrocarbon prediction factor corresponding to the water-bearing reservoir; H shale It is expressed as the target hydrocarbon prediction factor corresponding to the mudstone formation.

[0072] Using the above steps, based on the rock physics interpretation template, it can be seen that no matter how the porosity changes, the hydrocarbon prediction factor attributes can effectively distinguish gas-bearing reservoirs, water-bearing reservoirs and mudstone formations, indicating that the constructed hydrocarbon prediction factor can effectively distinguish different types of reservoirs and is an effective reservoir fluid detection attribute factor.

[0073] Step B2: According to the distribution characteristics of different types of reservoirs in the reference reservoir area, the relationship between the hydrocarbon prediction factor and porosity of the gas-bearing reservoir, the relationship between the hydrocarbon prediction factor and porosity of the water-bearing reservoir, and the relationship between the hydrocarbon prediction factor and porosity of the mudstone formation in the reference reservoir area are established respectively to obtain the target fitting curve information used in the target reservoir area.

[0074] According to the distribution characteristics of different types of reservoirs in the reference reservoir area, namely, the distribution characteristics of gas-bearing reservoirs, water-bearing reservoirs and mudstone formations, the relationship between the hydrocarbon prediction factor and porosity of the gas-bearing reservoirs, the relationship between the hydrocarbon prediction factor and porosity of the water-bearing reservoirs, and the relationship between the hydrocarbon prediction factor and porosity of the mudstone formations are established in the reference reservoir area respectively. The obtained relationship between the hydrocarbon prediction factor and porosity is fitted to the rock interpretation template to obtain the target fitting curve information used in the target reservoir area.

[0075] For example, based on the established porosity-hydrocarbon prediction factor petrophysical interpretation template, the linear relationship between hydrocarbon prediction factor and porosity is fitted for different types of reservoirs, such as Figure 3 As shown in the figure, the gray dotted line is the linear relationship between the hydrocarbon prediction factors and porosity of different types of reservoirs. The fitting relationship of the gas-bearing reservoir is The fitting relationship of water-bearing reservoir is: The fitting relationship of mudstone formation is: in, and They correspond to the porosity values ​​of gas-bearing reservoirs, water-bearing reservoirs and mudstone formations respectively.

[0076] For example, according to the actual reservoir conditions in the work area, the hydrocarbon prediction factors and porosity of different types of reservoirs were linearly fitted, and the correlation coefficients of the fitting lines for different types of reservoirs were statistically analyzed. According to the analysis results, the correlation coefficients of the hydrocarbon prediction factors and porosity were all above 0.95, indicating that the fitting relationship was highly accurate. Therefore, the final fitting relationship and the corresponding correlation coefficients were:

[0077] Gas reservoir Correlation coefficient R = 0.96

[0078] Aquifer Correlation coefficient R = 0.95

[0079] Mudstone formation Correlation coefficient R = 0.98

[0080] Among them, H gas is the target hydrocarbon prediction factor corresponding to the gas-bearing reservoir; H water is the target hydrocarbon prediction factor corresponding to the water-bearing reservoir; H shale is the target hydrocarbon prediction factor corresponding to the mudstone formation; is the porosity value of the gas-bearing reservoir; Porosity value of water-bearing reservoir; Porosity value of mudstone formation.

[0081] S140, determining the porosity of the target reservoir region according to the target hydrocarbon-containing prediction factor data body and the target fitting curve information.

[0082] The porosity of the target reservoir region is determined according to the position of the hydrocarbon-containing prediction factor data in the target hydrocarbon-containing prediction factor data body in the target fitting curve information.

[0083] Optionally, determining the porosity of the target reservoir region according to the target hydrocarbon-containing prediction factor data body and the target fitting curve information includes steps D1-D3:

[0084] Step D1: Determine hydrocarbon content prediction factors of different types of reservoirs in a target reservoir region from a target hydrocarbon content prediction factor data body.

[0085] The reservoir type corresponding to each hydrocarbon-containing prediction factor is determined according to the target hydrocarbon-containing prediction factor data body, and the range of hydrocarbon-containing prediction factors corresponding to different types of reservoirs in the target reservoir area is determined according to the obtained reservoir type.

[0086] Optionally, determining hydrocarbon content prediction factors of different types of reservoirs in the target reservoir area from the target hydrocarbon content prediction factor data body includes steps E1-E2:

[0087] Step E1, determining the target reservoir area distribution comparison information used in the target reservoir area, the target reservoir area distribution comparison information is a comparison relationship between hydrocarbon prediction factors and water-bearing reservoirs, gas-bearing reservoirs and mudstone formations established based on the hydrocarbon prediction factor value ranges of different types of reservoirs in the reference reservoir area, the hydrocarbon prediction factor value ranges of different types of reservoirs in the reference reservoir area are forward modeled in the rock physics interpretation template to simulate the response characteristics of the gas-bearing reservoirs, water-bearing reservoirs and mudstone formations in the reference reservoir area, and the hydrocarbon prediction factor value ranges of the corresponding types of reservoirs are determined based on the response characteristics of the different types of reservoirs.

[0088] The target reservoir area distribution comparison information is determined based on the relationship between the hydrocarbon prediction factor value range of different types of reservoirs in the target reservoir area and different reservoirs.

[0089] Furthermore, the hydrocarbon prediction factor value range of different types of reservoirs in the reference reservoir area is determined based on the response characteristics of the gas-bearing reservoir, aquifer and mudstone formation in the reference reservoir area in the forward simulation in the petrophysical interpretation template, and the hydrocarbon prediction factor value range of the type of reservoir corresponding to the response characteristics of the different types of reservoirs is determined based on the range of the obtained response characteristics. For example, Figure 3 As shown, the hydrocarbon prediction factor value range corresponding to the gas-bearing reservoir distribution area is [H1, 0.34]; the hydrocarbon prediction factor value range corresponding to the water-bearing reservoir distribution area is [H2, H1]; the hydrocarbon prediction factor value range corresponding to the mudstone formation distribution area is [0.18, H2]. The comparison relationship between the hydrocarbon prediction factor and the water-bearing reservoir, gas-bearing reservoir and mudstone formation is established based on the hydrocarbon prediction factor value range of different types of reservoirs in the reference reservoir area, and the obtained comparison relationship is used as the target reservoir area distribution comparison information.

[0090] Step E2: determining the type of reservoir to which each hydrocarbon-containing prediction factor in the target hydrocarbon-containing prediction factor data body belongs based on the target reservoir area distribution comparison information, and obtaining hydrocarbon-containing prediction factors for different types of reservoirs in the target reservoir area.

[0091] According to the reservoir types corresponding to different hydrocarbon-containing prediction factors in the target reservoir area distribution comparison information, the type of reservoir to which each hydrocarbon-containing prediction factor in the target hydrocarbon-containing prediction factor data body belongs is determined; according to the obtained reservoir type, the hydrocarbon-containing prediction factor corresponding to the reservoir type is determined, thereby obtaining the hydrocarbon-containing prediction factors of different types of reservoirs in the target reservoir area.

[0092] Step D2: for hydrocarbon prediction factors of different types of reservoirs, the porosity data of different types of reservoirs in the target reservoir area are calculated by using the linear fitting relationship between hydrocarbon prediction factors of corresponding types of reservoirs and porosity from the target fitting curve information.

[0093] The porosity data of each type of reservoir is calculated according to the hydrocarbon prediction factors of each type of reservoir and the linear fitting relationship between the hydrocarbon prediction factors and the porosity in the reservoir. After the porosity calculation of all types of reservoirs is completed, the porosity data of different types of reservoirs in the target reservoir area are obtained.

[0094] Specifically, the porosity data corresponding to different types of reservoirs are calculated based on the hydrocarbon prediction factors corresponding to different types of reservoirs and the linear fitting relationship between the hydrocarbon prediction factors and the porosity in the reservoirs of this type. The calculation formula is as follows:

[0095]

[0096]

[0097]

[0098] Among them, H gas is the target hydrocarbon prediction factor corresponding to the gas-bearing reservoir, H water is the target hydrocarbon prediction factor corresponding to the water-bearing reservoir, H shale is the target hydrocarbon prediction factor corresponding to the mudstone formation, is the porosity value of the gas-bearing reservoir, The porosity value of the water-bearing reservoir, Porosity value of mudstone formation.

[0099] Step D3: Merge the porosity data of different types of reservoirs in the target reservoir area to obtain the overall porosity in the target reservoir area.

[0100] The porosities of different types of reservoirs in the target reservoir region are merged, and the merged result is used as the overall porosity in the target reservoir region. The merging of the porosities of different types of reservoirs can be performed by taking a union method.

[0101] In the above steps, the porosity data of different types of reservoirs are combined to obtain the overall porosity in the target reservoir area, achieving the purpose of quantitative calculation of the porosity of the work area and providing support for subsequent reservoir evaluation and target optimization.

[0102] For example, Figure 4 As shown in the figure, the dark area corresponds to the porosity development area, and the light area corresponds to the porosity underdevelopment area. It can be seen from the figure that there are two wells drilled in the research area, of which w-1 well is a high-yield gas well, and the porosity interpreted by logging in the target layer reaches 0.24, and w-2 well is a water well, and the porosity in the target layer is 0.16. The porosity values ​​of the well point positions calculated according to the hydrocarbon prediction factor are: the porosity at the w-1 position is about 0.25, and the porosity at the w-2 well position is about 0.15. The prediction errors are controlled within 5%, which can meet the prediction requirements.

[0103] The technical solution of the embodiment of the present invention obtains target parameter information by inverting the original seismic data, constructs hydrocarbon prediction factors according to the joint constraints of the target parameter information, distinguishes the distribution range of water-bearing reservoirs, gas-bearing reservoirs and mudstone formations according to the distribution characteristics of the hydrocarbon prediction factors in the rock physics template, and establishes fitting relationships between hydrocarbon prediction factors and porosity for different types of reservoirs. Finally, based on the fitting relationships between different types of reservoirs and hydrocarbon prediction factors, the porosity data bodies of corresponding types of reservoirs are calculated using the hydrocarbon prediction factor data bodies within the range of the corresponding types of reservoirs. The method is based on the physical analysis of rocks, uses the joint constraints of shear wave impedance and longitudinal wave impedance, constructs hydrocarbon prediction factors, and quantitatively predicts the reservoir porosity by category, thereby ensuring the accuracy of quantitative detection of reservoir physical properties and improving the accuracy of exploration and development.

[0104] Figure 4 The following is a schematic diagram of a reservoir porosity measurement device provided by an embodiment of the present invention. Figure 4 As shown, the device includes: a data determination module 210, a data volume determination module 220, a fitting curve information determination module 230, and a porosity determination module 240, wherein:

[0105] Data determination module 210: used to determine target parameter information of the target reservoir area, the target parameter information includes longitudinal wave impedance, shear wave impedance, density seismic data and rock matrix parameter data;

[0106] Data body determination module 220: used to determine a target hydrocarbon content prediction factor data body of a target reservoir region according to target parameter information, wherein the target hydrocarbon content prediction factor data body correspondingly includes at least two hydrocarbon content prediction factors, and the hydrocarbon content prediction factors are used to describe the hydrocarbon content in the target reservoir region;

[0107] Fitting curve information determination module 230: used to determine target fitting curve information used in the target reservoir area, where the target fitting curve information is used to describe the linear fitting relationship between hydrocarbon prediction factors and porosity in different types of reservoirs;

[0108] Porosity determination module 240: used to determine the porosity of the target reservoir region according to the target hydrocarbon-containing prediction factor data body and the target fitting curve information.

[0109] Optionally, the data body determination module 220 includes:

[0110] Calculation function determination unit: used to determine the target hydrocarbon prediction factor calculation function used in the target reservoir area. The hydrocarbon prediction factor calculation function pre-constructs the correlation between the longitudinal wave impedance, the shear wave impedance, the density seismic data and the rock matrix parameter data and the hydrocarbon prediction factor;

[0111] The data body determination unit is used to sequentially substitute the target parameter information into the target hydrocarbon content prediction factor calculation function to obtain a target hydrocarbon content prediction factor data body corresponding to at least two hydrocarbon content prediction factors; wherein the target hydrocarbon content prediction factor calculation function is: H 目标 is the target hydrocarbon prediction factor, Z p is the longitudinal wave impedance, Z s is the shear wave impedance, V0 is the P-wave velocity of the rock matrix parameter data, and ρ is the density.

[0112] Optionally, the calculation function determination unit is specifically used to:

[0113] The construction process of the target hydrocarbon prediction factor calculation function is as follows:

[0114] The candidate hydrocarbon prediction factor calculation function is pre-constructed based on the Hudson theoretical model and the Makvo rock physics approximate relationship. The candidate hydrocarbon prediction factor calculation function is: H 候选 is a candidate hydrocarbon prediction factor, is the porosity, Z p is the longitudinal wave impedance, Z s is the shear wave impedance;

[0115] The correlation between P-wave impedance and porosity is determined by determining the rock physical characteristics of the target reservoir area. The correlation between P-wave impedance and porosity is expressed as follows: Z p is the P-wave impedance, V0 is the P-wave velocity of the rock matrix, and ρ is the density;

[0116] The porosity calculation function is determined based on the established correlation between longitudinal wave impedance and porosity. The porosity calculation function is:

[0117] Substituting the porosity calculation function into the candidate hydrocarbon prediction factor calculation function, the target hydrocarbon prediction factor calculation function is obtained.

[0118] Optionally, the fitting curve information determination module 230 includes:

[0119] Reservoir distribution characteristics determination unit: used to determine the distribution characteristics of different types of reservoirs in the reference reservoir area located in the same work area as the target reservoir area;

[0120] Fitting curve information determination unit: used to establish the relationship between the hydrocarbon prediction factor and porosity of the gas-bearing reservoir, the relationship between the hydrocarbon prediction factor and porosity of the water-bearing reservoir, and the relationship between the hydrocarbon prediction factor and porosity of the mudstone formation in the reference reservoir area according to the distribution characteristics of different types of reservoirs in the reference reservoir area, and obtain the target fitting curve information used in the target reservoir area.

[0121] Optionally, the reservoir distribution characteristic determination unit includes:

[0122] Hydrocarbon prediction factor value range determination unit: used to forward simulate the response characteristics of gas-bearing reservoirs, aquifers and mudstone formations in the reference reservoir area in the rock physics interpretation template, and determine the hydrocarbon prediction factor value range of the corresponding type of reservoir according to the response characteristics of different types of reservoirs. The rock physics interpretation template is used to construct the correlation between the porosity of the reference reservoir area and the hydrocarbon prediction factor;

[0123] Reservoir distribution characteristic determination unit: used to establish the comparison relationship between hydrocarbon prediction factors and water-bearing reservoirs, gas-bearing reservoirs and mudstone formations according to the hydrocarbon prediction factor value range of different types of reservoirs in the reference reservoir area, so as to obtain the distribution characteristics of different types of reservoirs in the reference reservoir area.

[0124] Optionally, the porosity determination module 240 includes:

[0125] A hydrocarbon-containing prediction factor determination unit: used for determining hydrocarbon-containing prediction factors of different types of reservoirs in a target reservoir area from a target hydrocarbon-containing prediction factor data body;

[0126] Porosity data determination unit: for calculating the porosity data of different types of reservoirs in the target reservoir area by using the linear fitting relationship between the hydrocarbon prediction factor and porosity of the corresponding type of reservoir from the target fitting curve information according to the hydrocarbon prediction factor of the different types of reservoirs;

[0127] Porosity determination unit: used to merge the porosity data of different types of reservoirs in the target reservoir area to obtain the overall porosity in the target reservoir area.

[0128] Optionally, a hydrocarbon prediction factor determination unit includes:

[0129] Reservoir distribution comparison information determination subunit: used to determine the target reservoir area distribution comparison information used in the target reservoir area. The target reservoir area distribution comparison information is a comparison relationship between hydrocarbon prediction factors and water-bearing reservoirs, gas-bearing reservoirs and mudstone formations established based on the hydrocarbon prediction factor value ranges of different types of reservoirs in the reference reservoir area. The hydrocarbon prediction factor value ranges of different types of reservoirs in the reference reservoir area are the response characteristics of the gas-bearing reservoirs, water-bearing reservoirs and mudstone formations in the reference reservoir area forward simulated in the rock physics interpretation template, and the hydrocarbon prediction factor value ranges of the corresponding types of reservoirs are determined based on the response characteristics of the different types of reservoirs.

[0130] The hydrocarbon prediction factor determination subunit is used to determine the type of reservoir to which each hydrocarbon prediction factor in the target hydrocarbon prediction factor data body belongs based on the target reservoir area distribution comparison information, and obtain the hydrocarbon prediction factors of different types of reservoirs in the target reservoir area.

[0131] The reservoir porosity measurement device provided in the embodiment of the present invention can execute the reservoir porosity measurement method provided in any embodiment of the present invention mentioned above, and has the corresponding functions and beneficial effects of executing the reservoir porosity measurement method. For detailed process, please refer to the relevant operations of the reservoir porosity measurement method in the above embodiment.

[0132] Figure 5 A schematic diagram of the structure of an electronic device for implementing the reservoir porosity measurement method of an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0133] like Figure 5 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0134] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0135] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a reservoir porosity measurement method.

[0136] In some embodiments, the reservoir porosity measurement method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the reservoir porosity measurement method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the reservoir porosity measurement method in any other appropriate manner (e.g., by means of firmware).

[0137] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0138] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0139] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0140] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0141] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0142] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0143] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0144] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A reservoir porosity measurement method, characterized in that: include: Determining target parameter information of the target reservoir area, wherein the target parameter information includes longitudinal wave impedance, shear wave impedance, density seismic data, and rock matrix parameter data; Determining a target hydrocarbon content prediction factor data body of a target reservoir region according to the target parameter information, wherein the target hydrocarbon content prediction factor data body correspondingly includes at least two hydrocarbon content prediction factors, and the hydrocarbon content prediction factors are used to describe the hydrocarbon content in the target reservoir region; Determining target fitting curve information used in the target reservoir area, wherein the target fitting curve information is used to describe the linear fitting relationship between hydrocarbon prediction factors and porosity in different types of reservoirs; The porosity of the target reservoir region is determined based on the target hydrocarbon-containing prediction factor data body and the target fitting curve information.

2. The method according to claim 1, characterized in that Determining a target hydrocarbon-containing prediction factor data body of a target reservoir region according to the target parameter information includes: Determining a target hydrocarbon prediction factor calculation function used in the target reservoir area, wherein the hydrocarbon prediction factor calculation function pre-constructs a correlation relationship between longitudinal wave impedance, shear wave impedance, density seismic data, and rock matrix parameter data and hydrocarbon prediction factors; Substituting the target parameter information into the target hydrocarbon content prediction factor calculation function in sequence to obtain a target hydrocarbon content prediction factor data body corresponding to at least two hydrocarbon content prediction factors; Among them, the target hydrocarbon prediction factor calculation function is: H 目标 is the target hydrocarbon prediction factor, Z p is the longitudinal wave impedance, Z s is the shear wave impedance, V0 is the P-wave velocity of the rock matrix parameter data, and ρ is the density.

3. The method according to claim 2, characterized in that The construction process of the target hydrocarbon prediction factor calculation function is as follows: According to the Hudson theoretical model and the Makvo rock physics approximate relationship, a candidate hydrocarbon prediction factor calculation function is pre-constructed. The candidate hydrocarbon prediction factor calculation function is: H 候选 is a candidate hydrocarbon prediction factor, is the porosity, Z p is the longitudinal wave impedance, Z s is the shear wave impedance; The correlation between the P-wave impedance and the porosity established by the rock physical characteristics of the target reservoir area is determined. The correlation between the P-wave impedance and the porosity is expressed as follows: Z p is the P-wave impedance, V0 is the P-wave velocity of the rock matrix, and ρ is the density; The porosity calculation function is determined based on the established correlation between longitudinal wave impedance and porosity. The porosity calculation function is: Substituting the porosity calculation function into the candidate hydrocarbon prediction factor calculation function, the target hydrocarbon prediction factor calculation function is obtained.

4. The method according to claim 1, characterized in that: The process of constructing the target fitting curve information used in the target reservoir area includes: Determine the distribution characteristics of different types of reservoirs in the reference reservoir area located in the same work area as the target reservoir area; According to the distribution characteristics of different types of reservoirs in the reference reservoir area, the relationship between the hydrocarbon prediction factor and porosity of the gas-bearing reservoir, the relationship between the hydrocarbon prediction factor and porosity of the water-bearing reservoir, and the relationship between the hydrocarbon prediction factor and porosity of the mudstone formation in the reference reservoir area are established respectively to obtain the target fitting curve information used in the target reservoir area.

5. The method according to claim 4, characterized in that Determine the distribution characteristics of different types of reservoirs in the reference reservoir area located in the same work area as the target reservoir area, including: Forward modeling the response characteristics of gas-bearing reservoirs, aquifers and mudstone formations in the reference reservoir area in a rock physics interpretation template, and determining the range of hydrocarbon prediction factors for the corresponding types of reservoirs according to the response characteristics of the different types of reservoirs, wherein the rock physics interpretation template is used to construct a correlation between the porosity of the reference reservoir area and the hydrocarbon prediction factor; According to the hydrocarbon prediction factor value range of different types of reservoirs in the reference reservoir area, a comparison relationship between the hydrocarbon prediction factor and water-bearing reservoirs, gas-bearing reservoirs and mudstone formations is established to obtain the distribution characteristics of different types of reservoirs in the reference reservoir area.

6. The method according to any one of claims 1 to 5, characterized in that: Determining the porosity of the target reservoir region according to the target hydrocarbon-containing prediction factor data body and the target fitting curve information includes: Determining hydrocarbon content prediction factors of different types of reservoirs in a target reservoir region from the target hydrocarbon content prediction factor data body; For hydrocarbon prediction factors of different types of reservoirs, the porosity data of different types of reservoirs in the target reservoir area are calculated by using the linear fitting relationship between the hydrocarbon prediction factors of the corresponding types of reservoirs and the porosity from the target fitting curve information; The porosity data of different types of reservoirs in the target reservoir area are merged to obtain the overall porosity in the target reservoir area.

7. The method according to claim 6, characterized in that Determining hydrocarbon-containing prediction factors of different types of reservoirs in a target reservoir area from the target hydrocarbon-containing prediction factor data body includes: Determine the target reservoir area distribution comparison information used in the target reservoir area, wherein the target reservoir area distribution comparison information is a comparison relationship between hydrocarbon prediction factors and water-bearing reservoirs, gas-bearing reservoirs and mudstone formations established according to the hydrocarbon prediction factor value ranges of different types of reservoirs in the reference reservoir area, and the hydrocarbon prediction factor value ranges of different types of reservoirs in the reference reservoir area are forward modeled in the rock physics interpretation template to simulate the response characteristics of the gas-bearing reservoirs, water-bearing reservoirs and mudstone formations in the reference reservoir area, and the hydrocarbon prediction factor value ranges of the corresponding types of reservoirs are determined according to the response characteristics of the different types of reservoirs; The type of reservoir to which each hydrocarbon-containing prediction factor in the target hydrocarbon-containing prediction factor data body belongs is determined based on the target reservoir area distribution comparison information, and hydrocarbon-containing prediction factors of different types of reservoirs in the target reservoir area are obtained.

8. A reservoir porosity measurement device, characterized in that: include: Data determination module: used to determine target parameter information of the target reservoir area, wherein the target parameter information includes longitudinal wave impedance, shear wave impedance, density seismic data and rock matrix parameter data; A data body determination module: used to determine a target hydrocarbon content prediction factor data body of a target reservoir region according to the target parameter information, wherein the target hydrocarbon content prediction factor data body includes at least two hydrocarbon content prediction factors, and the hydrocarbon content prediction factors are used to describe the hydrocarbon content in the target reservoir region; Fitting curve information determination module: used to determine the target fitting curve information used in the target reservoir area, wherein the target fitting curve information is used to describe the linear fitting relationship between the hydrocarbon prediction factor and the porosity in different types of reservoirs; Porosity determination module: used to determine the porosity of the target reservoir area according to the target hydrocarbon-containing prediction factor data body and the target fitting curve information.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the reservoir porosity measurement method according to any one of claims 1 to 7.

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

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