Method, device, equipment, storage medium and product for predicting porosity

Abstract

This disclosure relates to a method, apparatus, device, storage medium, and product for predicting porosity, applied in the field of seismic exploration technology. In this disclosure, well logging data from a target well is acquired; based on the measured temperature and geothermal gradient data in the well logging data, a temperature set of the target formation is determined. The porosity of the target formation is determined based on the temperature set of the target formation and a first mapping relationship, whereby the first mapping relationship is a mapping relationship between elastic parameters and porosity at different temperatures of the target formation. The reservoir temperature at different depths of the target formation will vary, thus affecting the prediction results of elastic parameters and porosity. By considering temperature change as an indicator when establishing the relationship between elastic parameters and porosity, more geological factors are taken into account when predicting porosity results, making it adaptable to more complex geological conditions. Therefore, the accuracy of porosity prediction can be improved.

Description

Technical Field

[0001] This disclosure relates to the field of seismic exploration technology, and in particular to a method, apparatus, device, storage medium, and product for predicting porosity. Background Technology

[0002] The cavities in a rock that are not filled with solid material are called pores, and porosity is the ratio of the pore volume to the surface volume of the rock. Porosity is an important indicator for calculating reserves and evaluating reservoir characteristics. By predicting rock porosity, we can understand the oil and gas storage capacity of a rock, providing an important reference for oil and gas exploration and development. Rocks with high porosity generally contain more oil and gas resources, which is beneficial to the development and production of oil and gas reservoirs.

[0003] Generally, the relationship between rock elastic parameters and porosity can be established through rock physical modeling, and porosity can then be predicted using the elastic parameters obtained from pre-stack inversion. However, this prediction method relies on relatively simple judgment conditions and cannot adapt to more complex geological conditions. Therefore, it reduces the accuracy of porosity prediction. Summary of the Invention

[0004] This disclosure provides a method, apparatus, device, storage medium, and product for predicting porosity, which can improve the accuracy of porosity prediction.

[0005] In a first aspect, this disclosure provides a method for predicting porosity, comprising:

[0006] Obtain logging data for the target well;

[0007] Based on the measured temperature and geothermal gradient data in the well logging data, determine the temperature set of the target formation;

[0008] The porosity of the target formation is determined based on the temperature set of the target formation and a first mapping relationship, wherein the first mapping relationship is a mapping relationship between the elastic parameters and porosity of the target formation at different temperatures.

[0009] Optionally, determining the porosity of the target formation based on the temperature set of the target formation and the first mapping relationship includes:

[0010] Obtain the temperature variation range of the target stratum;

[0011] Based on the temperature set of the target formation, pre-stack inversion is performed on the well logging data to determine the elastic parameters of the target formation;

[0012] The temperature coefficient is determined by fitting the temperature set and temperature variation range of the target stratum.

[0013] Based on the elastic parameters and the temperature coefficient, the first mapping relationship is constructed;

[0014] Based on the first mapping relationship, the porosity of the target stratum at the temperature concentration target temperature is determined.

[0015] Optionally, determining the temperature set of the target formation based on the measured temperature and geothermal gradient data in the well logging data includes:

[0016] Based on the geothermal gradient data and the depth domain stratigraphic data of the target well, a second mapping relationship is determined, which is the mapping relationship between the geothermal gradient data and the depth domain stratigraphic data.

[0017] Based on the measured temperature and the second mapping relationship, the temperature set of the target formation is determined.

[0018] Optionally, determining the temperature set of the target formation based on the measured temperature and the second mapping relationship includes:

[0019] Based on the second mapping relationship, the measured temperature is interpolated to obtain the interpolated temperature;

[0020] A temperature set for the target formation is constructed based on the interpolated temperature and the measured temperature.

[0021] Optionally, determining the second mapping relationship based on the geothermal gradient data and the depth domain stratigraphic data of the target well logging includes:

[0022] Obtain seismic data from the target well logging;

[0023] The time-domain stratigraphic data of the target well is determined based on the seismic data;

[0024] The time-domain layer data is transformed to determine the depth-domain layer data;

[0025] Based on the geothermal gradient data, the depth domain layer data is subjected to inverse proportional weighting to determine the second mapping relationship.

[0026] Optionally, after determining the porosity of the target formation based on the temperature set of the target formation and the first mapping relationship, the method further includes:

[0027] Obtain the actual temperature of the target formation and the elastic parameters obtained from the pre-stack inversion;

[0028] Based on the first mapping relationship, query the porosity corresponding to the actual temperature and the elastic parameter;

[0029] The porosity is used as the porosity result of the target formation.

[0030] Secondly, this disclosure provides a porosity prediction device, comprising:

[0031] The acquisition module is used to acquire logging data for the target well.

[0032] The first determining module is used to determine the temperature set of the target formation based on the measured temperature and geothermal gradient data in the well logging data;

[0033] The second determining module is used to determine the porosity of the target formation based on the temperature set of the target formation and a first mapping relationship, wherein the first mapping relationship is a mapping relationship between the elastic parameters of the target formation at different temperatures and the porosity.

[0034] Thirdly, this disclosure provides a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the porosity prediction method described in the above aspects.

[0035] Fourthly, this disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the porosity prediction method described in the above aspects.

[0036] Fifthly, this disclosure provides a computer program product, including a computer program / instructions, which, when executed by a processor, implements the steps of the porosity prediction method described in the preceding aspects.

[0037] In this disclosure, well logging data of the target well is acquired; based on the measured temperature and geothermal gradient data in the well logging data, the temperature set of the target formation is determined. The porosity of the target formation is determined based on the temperature set of the target formation and a first mapping relationship, whereby the first mapping relationship is the mapping relationship between elastic parameters and porosity at different temperatures of the target formation. The reservoir temperature at different depths of the target formation will vary, thus affecting the prediction results of elastic parameters and porosity. By considering temperature change as an indicator when establishing the relationship between elastic parameters and porosity, more geological factors are taken into account when predicting porosity, making it adaptable to more complex geological conditions. Therefore, the accuracy of porosity prediction can be improved. Attached Figure Description

[0038] The present disclosure will be described in more detail below based on embodiments and with reference to the accompanying drawings:

[0039] Figure 1 A flowchart of a porosity prediction method provided in this disclosure.

[0040] Figure 2A flowchart of a method for determining the porosity of a target formation provided in this disclosure.

[0041] Figure 3 A flowchart of the method for determining the second mapping relationship provided in this disclosure.

[0042] Figure 4 A flowchart illustrating the method for determining the porosity of a target formation as provided in this disclosure.

[0043] Figure 5 Another flowchart of a porosity prediction method provided in this disclosure.

[0044] Figure 6 Another flowchart of a porosity prediction method provided in this disclosure.

[0045] Figure 7 This is a schematic diagram of the structure of a porosity prediction device provided in this disclosure. Detailed Implementation

[0046] To enable those skilled in the art to better understand the technical solution of this application, the application scenario of this application will be described first below.

[0047] The cavities in a rock that are not filled with solid material are called pores, and porosity is the ratio of the pore volume to the surface volume of the rock. Porosity is an important indicator for calculating reserves and evaluating reservoir characteristics. By predicting rock porosity, we can understand the rock's oil and gas storage capacity, providing an important reference for oil and gas exploration and development.

[0048] Rock porosity is one of the key parameters for evaluating the quality of oil and gas reservoirs. By predicting rock porosity, we can gain a more accurate understanding of the reservoir's storage capacity and fluid flow characteristics, thereby improving the precision of oil and gas resource exploration. High-porosity reservoirs generally imply better oil and gas storage conditions and higher exploitation potential; therefore, porosity prediction helps identify favorable reservoirs and provides important guidance for oil and gas exploration and development.

[0049] Understanding the distribution and variation of rock porosity is crucial for developing appropriate extraction strategies during oil and gas extraction. Porosity prediction helps engineers assess reservoir permeability and fluid flowability, thereby selecting suitable extraction technologies and equipment. For example, in high-porosity reservoirs, more efficient extraction methods, such as horizontal well drilling and hydraulic fracturing, can be employed to improve oil and gas recovery rates.

[0050] Meanwhile, porosity prediction also helps predict reservoir productivity and exploitation life, providing a scientific basis for long-term oil and gas field development planning. Through rock porosity prediction, geologists can identify potential reservoirs during the exploration phase, thus avoiding ineffective exploration and development work in areas without exploitable value. This not only saves significant exploration and development costs but also improves resource utilization efficiency. Furthermore, porosity prediction can help engineers optimize drilling paths and production parameters, reducing resource waste and environmental pollution during drilling and production.

[0051] In the process of resource exploration and development, comprehensively considering geological factors such as rock porosity helps to achieve comprehensive resource utilization and sustainable development. By predicting rock porosity, we can understand the geological connections and fluid exchange between different reservoirs, providing a scientific basis for the comprehensive utilization of resources. At the same time, porosity prediction also helps to assess the exploitation potential and environmental risks of reservoirs, providing an important reference for formulating sustainable development strategies.

[0052] In summary, rock porosity prediction offers numerous benefits for resource exploration and development, including improved exploration accuracy, optimized extraction strategies, reduced exploration and development costs, and support for comprehensive resource utilization and sustainable development. These benefits not only contribute to the continued healthy development of the oil and gas exploration and development industry but also provide strong support for achieving energy security and sustainable development goals.

[0053] Currently, the relationship between elastic parameters and porosity can be established based on core testing or rock physics modeling results, and porosity can then be calculated using the elastic parameters obtained from pre-stack inversion. However, in this process, only one set of linear or nonlinear relationships exists in the vertical dimension. For increasingly complex geological conditions, using only a simple vertical relationship for porosity prediction is insufficient to obtain high-precision inversion results. Therefore, it reduces the accuracy of porosity prediction.

[0054] To address the aforementioned technical problems, this disclosure provides a method, apparatus, device, storage medium, and product for predicting porosity. In this disclosure, well logging data from a target well is acquired; based on the measured temperature and geothermal gradient data in the well logging data, a temperature set of the target formation is determined. The porosity of the target formation is determined based on the temperature set of the target formation and a first mapping relationship, whereby the first mapping relationship is a mapping relationship between elastic parameters and porosity at different temperatures of the target formation. The reservoir temperature at different depths of the target formation will vary, thus affecting the prediction results of elastic parameters and porosity. By considering temperature change as an indicator when establishing the relationship between elastic parameters and porosity, more geological factors are taken into account when predicting porosity, making it adaptable to more complex geological conditions. Therefore, the accuracy of porosity prediction can be improved.

[0055] To enable those skilled in the art to better understand the technical solutions of this disclosure, and to fully understand and implement the process of how this disclosure applies technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. The embodiments of this disclosure and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort should fall within the protection scope of this disclosure.

[0056] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0057] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0058] Example 1

[0059] Figure 1 This is a flowchart illustrating a porosity prediction method provided in this disclosure. Figure 1 As shown, the method includes:

[0060] S101: Obtain logging data for the target well.

[0061] Well logging, also known as geophysical logging or mine geophysics, is a method of measuring geophysical parameters by utilizing the electrochemical, electrical, acoustic, and radioactive properties of rock formations. Well logging data is geophysical parameter data obtained through well logging technology. The measurement data is transmitted to the surface via cables and drilling fluids. After processing and interpretation, it can be used to evaluate oil and gas reservoirs, providing extremely important data for oil and gas exploration and development.

[0062] The logging data includes various types, including but not limited to the examples below: well number, which identifies the specific well location; depth data, including top and bottom boundary depths, used to determine the formation location; electrical parameters, including resistivity and spontaneous potential, reflecting the conductivity and electrochemical properties of the formation; and acoustic parameters, including acoustic transit time and density, reflecting the acoustic characteristics and physical properties of the formation. In this embodiment, after determining the target logging well, the focus is more on studying the depth data within the logging data.

[0063] S102: Determine the temperature set of the target formation based on the measured temperature and geothermal gradient data in the well logging data.

[0064] Specifically, well logging technology can directly obtain the actual measured temperature at the target well location. Wells are located at a certain depth, and as the depth of the well increases, the ground temperature will also increase. In other words, each well will have its corresponding ground temperature gradient data.

[0065] Furthermore, geothermal gradient data may differ between different well locations. By randomly sampling the measured temperatures at different points in the target well, the geothermal gradient data of the target well can be determined, and the temperature set of the target formation can be determined by interpolation.

[0066] S103: Determine the porosity of the target formation based on the temperature set of the target formation and the first mapping relationship.

[0067] Specifically, a first mapping relationship can be pre-constructed, which is a mapping relationship between elastic parameters and porosity based on different temperatures of the target stratum, that is, the influence of different temperatures on the prediction of elastic parameters and porosity is determined.

[0068] This prediction method takes into account that due to the depth of the same target stratum, there may be a large height difference between the top and bottom of the stratum, or due to fractures, the strata may not be on the same plane and have a certain height difference, which may result in different temperatures in the same stratum. Relying solely on time-domain strata to determine porosity in existing technologies may lead to errors.

[0069] By adding longitudinal indices, the relationship between elastic parameters and porosity is shown to vary with temperature in the longitudinal direction. The porosity prediction results based on this relationship take into account more geological factors, resulting in higher prediction accuracy.

[0070] In this disclosure, well logging data of the target well is acquired; based on the measured temperature and geothermal gradient data in the well logging data, the temperature set of the target formation is determined. The porosity of the target formation is determined based on the temperature set of the target formation and a first mapping relationship, whereby the first mapping relationship is the mapping relationship between elastic parameters and porosity at different temperatures of the target formation. The reservoir temperature at different depths of the target formation will vary, thus affecting the prediction results of elastic parameters and porosity. By considering temperature change as an indicator when establishing the relationship between elastic parameters and porosity, more geological factors are taken into account when predicting porosity, making it adaptable to more complex geological conditions. Therefore, the accuracy of porosity prediction can be improved.

[0071] Example 2

[0072] Based on the above embodiments, such as Figure 2 As shown, Figure 2 A flowchart of a method for determining the porosity of a target formation provided in this disclosure.

[0073] An exemplary method for determining the porosity of a target formation based on its temperature set and a first mapping relationship includes:

[0074] S201: Obtain the temperature variation range of the target formation.

[0075] Specifically, formation data from the target well can be determined using well logging data. Based on this data, the depth and geographical location of the target formation can be determined. Combined with temperature measurements, the formation temperature at the top and bottom boundaries of the target formation can be determined. Based on these top and bottom boundary temperatures, the temperature variation range of the target formation can be determined.

[0076] S202: Based on the temperature set of the target formation, perform pre-stack inversion on the logging data to determine the elastic parameters of the target formation.

[0077] Specifically, using each temperature within the target formation's temperature cluster as a constraint, pre-stack inversion is performed on the elastic parameters in the well logging data to obtain predicted results. These elastic parameters include P-wave velocity, S-wave velocity, and density. Pre-stack inversion can invert P-wave and S-wave impedance and density data volumes, and further determine elastic parameters such as the P-wave / S-wave velocity ratio and Poisson's ratio. Obtaining this information enriches the methods for identifying lithology and fluids, and improves reservoir prediction accuracy. Compared to traditional post-stack seismic inversion techniques, pre-stack inversion fully utilizes the information about amplitude variations with offset.

[0078] S203: Based on the temperature set and temperature variation range of the target stratum, a fitting process is performed to determine the temperature coefficient.

[0079] Specifically, by fitting the temperature set and temperature variation range of the target stratum, the interpolated temperature set can be more closely matched with the temperature variation range inferred from the stratum temperature at random points, thus providing more accurate prediction results. The coefficient of the fitted curve is used as the temperature coefficient for predicting porosity, so that the relationship between elastic parameters and porosity changes with temperature in the vertical direction.

[0080] S204: Based on the elastic parameters and temperature coefficient, construct the first mapping relationship.

[0081] Specifically, a first mapping relationship is constructed based on the relationship between elastic parameters and porosity, as shown in the following formula:

[0082] Porosity = a * P-wave velocity + b * S-wave velocity + c * density

[0083] Where a is the fitting coefficient of the longitudinal wave velocity in the elastic coefficient as a function of temperature, b is the fitting coefficient of the transverse wave velocity in the elastic coefficient as a function of temperature, and c is the fitting coefficient of the density in the elastic coefficient as a function of temperature.

[0084] S205: Based on the first mapping relationship, determine the porosity of the target formation at the temperature concentration target temperature.

[0085] Specifically, based on the first mapping relationship mentioned above, the temperature concentration of the target stratum and the porosity prediction results corresponding to different temperatures are calculated.

[0086] Example 3

[0087] Based on the above embodiments, such as Figure 3 As shown, Figure 3 A flowchart of the method for determining the second mapping relationship provided in this disclosure.

[0088] Exemplary methods for determining the temperature set of a target formation based on measured temperature and geothermal gradient data from well logging data include:

[0089] S301: Determine the second mapping relationship based on geothermal gradient data and depth domain stratigraphic data of the target well.

[0090] The second mapping relationship is the mapping relationship between geothermal gradient data and depth domain strata.

[0091] Further exemplary methods include:

[0092] Obtain seismic data from the target well.

[0093] Specifically, blasting is carried out in the target logging area, and seismic data of the target logging area is collected through the generated seismic waves.

[0094] Determine the time-domain stratigraphic data of the target well based on seismic data.

[0095] Specifically, based on the collected seismic data, the stratigraphic data of the target well is determined in terms of time, that is, the stratigraphic distribution of the target well is determined.

[0096] The temporal layer data is transformed to determine the depth layer data.

[0097] Specifically, the impact of temperature on elastic parameters and porosity in different strata cannot be determined solely through the time dimension. Therefore, it is necessary to convert time-domain stratigraphic data into depth-domain stratigraphic data. In this way, by considering temperature variation as an indicator when establishing the relationship between elastic parameters and porosity, more geological factors are taken into account when predicting porosity results, making it adaptable to more complex geological conditions.

[0098] Based on geothermal gradient data, the depth domain layer data is inversely weighted to determine the second mapping relationship.

[0099] Specifically, the larger the depth domain stratigraphic data, the farther the target formation is from the wellhead of the target logging, and the lower the geothermal temperature. Based on inverse weighting, a second mapping relationship can be obtained: the larger the depth domain stratigraphic data, the less it is affected by temperature.

[0100] S302: Determine the temperature set of the target formation based on the measured temperature and the second mapping relationship.

[0101] Further exemplary methods include:

[0102] Based on the second mapping relationship, the measured temperature is interpolated to obtain the interpolated temperature; based on the interpolated temperature and the measured temperature, the temperature set of the target stratum is constructed.

[0103] Specifically, the temperatures obtained through actual measurements are from random sampling points and do not cover all temperatures in the well logging depth domain. Therefore, based on the second mapping relationship determined above, interpolation is performed on the measured temperatures at different depths of each formation to obtain interpolated temperatures. Combined with the actual measured temperatures, a temperature set for the target formation can be constructed, determining the formation temperature at each depth within the target formation.

[0104] Example 4

[0105] Based on the above embodiments, such as Figure 4 As shown, Figure 4 A flowchart illustrating the method for determining the porosity of a target formation as provided in this disclosure.

[0106] After determining the porosity of the target formation based on its temperature set and the first mapping relationship, the method further includes:

[0107] S401: Obtain the actual temperature of the target formation and the elastic parameters obtained from pre-stack inversion.

[0108] Specifically, in practical applications, for a geological exploration task, it is necessary to clearly define the target stratum for measurement. Based on the collected seismic data and well logging data, the depth domain stratigraphic data and actual temperature of the target stratum can be determined.

[0109] Based on well logging data, the elastic parameters of the target formation can be calculated using pre-stack inversion technology.

[0110] S402: Based on the first mapping relationship, query the porosity corresponding to the actual temperature and elastic parameters.

[0111] Specifically, based on the first mapping relationship between the elastic parameters and porosity at different temperatures of the target stratum, the actual temperature and elastic parameters obtained from the measurements are determined, and the corresponding porosity can be found by searching.

[0112] S403: Use porosity as the porosity result of the target formation.

[0113] This porosity is used as the porosity prediction result for the target formation.

[0114] Example 5

[0115] Based on the above embodiments, this embodiment provides an application example.

[0116] Figure 5 Another flowchart is provided for a porosity prediction method according to this disclosure. Figure 5 As shown, the method includes:

[0117] S501: Collect geothermal gradient data from well logging data.

[0118] Data collected indicates that the temperature increases by 2-3.5℃ for every 100-meter increase in depth of the geothermal gradient in the work area, with some differences between wells. Combining well logging stratification data, the range of temperature variation within the target formation was clarified. Due to tectonic variations, the depth difference within the target formation reaches 600m, meaning a temperature difference of 12-21℃ within the target formation, with a temperature range of 89-111℃.

[0119] S502: Based on geothermal gradient data from a single well and constrained by depth domain strata, a three-dimensional geothermal gradient data volume is obtained using an inverse proportional weighting method.

[0120] Specifically, well logging technology can directly obtain the actual measured temperature at the target well location. Wells are located at a certain depth, and the geothermal temperature increases with depth. This means that each well has its own corresponding geothermal gradient data. Furthermore, geothermal gradient data may differ between different well locations. The geothermal gradient data of the target well can be determined by randomly sampling the measured temperatures at different points within the target well.

[0121] S503: Based on the measured temperature data and the three-dimensional geothermal gradient data volume in the depth domain from the well logging data, a three-dimensional temperature data volume is obtained.

[0122] By interpolating, the temperature data of the target well logging is supplemented, thereby determining the temperature set of the target formation.

[0123] S504: Conduct rock physics experiments to establish the relationship between elastic parameters and porosity at different temperatures.

[0124] Based on the target formation temperature range (89-111℃), elastic parameters and porosity parameters of core data were measured at different temperatures within this range to establish the relationship between elastic parameters and porosity at different temperatures. The relationship is shown in the following formula:

[0125] Porosity = a * P-wave velocity + b * S-wave velocity + c * density

[0126] Where a is the fitting coefficient of the longitudinal wave velocity in the elastic coefficient as a function of temperature, b is the fitting coefficient of the transverse wave velocity in the elastic coefficient as a function of temperature, and c is the fitting coefficient of the density in the elastic coefficient as a function of temperature.

[0127] S505: Collect the pre-stack inversion elastic parameter prediction results, combine them with the three-dimensional temperature data volume, and determine the porosity prediction results based on the relationship between elastic parameters and porosity at different temperatures.

[0128] This prediction method takes into account that due to the depth of the same target stratum, there may be a significant height difference between the top and bottom of the stratum, or due to fractures, the strata may not be on the same plane, resulting in different temperatures within the same stratum. Relying solely on time-domain strata to determine porosity using existing techniques may lead to inaccuracies. By incorporating vertical indices, the relationship between elastic parameters and porosity is shown to change with temperature in the vertical direction. Porosity predictions based on this relationship consider more geological factors, resulting in higher prediction accuracy.

[0129] in, Figure 6 The above is another flowchart of a porosity prediction method provided in this disclosure. Figure 6 Explanation.

[0130] Example 6

[0131] Figure 7 This is a schematic diagram of the structure of a porosity prediction device provided in this disclosure. Figure 7 As shown, the device 700 includes: an acquisition module 710, a first determination module 720, and a second determination module 730.

[0132] The acquisition module 710 is used to acquire logging data of the target well.

[0133] The first determining module 720 is used to determine the temperature set of the target formation based on the measured temperature and geothermal gradient data in the well logging data;

[0134] The second determining module 730 is used to determine the porosity of the target formation based on the temperature set of the target formation and a first mapping relationship, wherein the first mapping relationship is a mapping relationship between the elastic parameters of the target formation at different temperatures and the porosity.

[0135] Optionally, the second determining module is used to:

[0136] Obtain the temperature variation range of the target stratum;

[0137] Based on the temperature set of the target formation, pre-stack inversion is performed on the well logging data to determine the elastic parameters of the target formation;

[0138] The temperature coefficient is determined by fitting the temperature set and temperature variation range of the target stratum.

[0139] Based on the elastic parameters and the temperature coefficient, the first mapping relationship is constructed;

[0140] Based on the first mapping relationship, the porosity of the target stratum at the temperature concentration target temperature is determined.

[0141] Optionally, the first determining module is used to:

[0142] Based on the geothermal gradient data and the depth domain stratigraphic data of the target well, a second mapping relationship is determined, which is the mapping relationship between the geothermal gradient data and the depth domain stratigraphic data.

[0143] Based on the measured temperature and the second mapping relationship, the temperature set of the target formation is determined.

[0144] Optionally, the first determining module is used to:

[0145] Based on the second mapping relationship, the measured temperature is interpolated to obtain the interpolated temperature;

[0146] A temperature set for the target formation is constructed based on the interpolated temperature and the measured temperature.

[0147] Optionally, the first determining module is used to:

[0148] Obtain seismic data from the target well logging;

[0149] The time-domain stratigraphic data of the target well is determined based on the seismic data;

[0150] The time-domain layer data is transformed to determine the depth-domain layer data;

[0151] Based on the geothermal gradient data, the depth domain layer data is subjected to inverse proportional weighting to determine the second mapping relationship.

[0152] Optionally, the device further includes:

[0153] After determining the porosity of the target formation based on the temperature set of the target formation and the first mapping relationship, the actual temperature of the target formation and the elastic parameters obtained from the pre-stack inversion are acquired.

[0154] Based on the first mapping relationship, query the porosity corresponding to the actual temperature and the elastic parameter;

[0155] The porosity is used as the porosity result of the target formation.

[0156] Based on the above embodiments, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the porosity prediction method described in the above embodiments.

[0157] In some embodiments of this example, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the porosity prediction method described in the above embodiments.

[0158] In some embodiments of this example, a computer program product is provided, including a computer program / instructions, which, when executed by a processor, implements the steps of the porosity prediction method described in the above embodiments.

[0159] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic component for performing the methods in the above embodiments. The computer-readable storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof. The computer-readable storage medium may include, but is not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, and computer storage media (e.g., hard disk, floppy disk, solid-state drive, removable disk, CD-ROM, DVD-ROM, Blu-ray disc, etc.).

[0160] Computer-readable storage media may also store at least one computer-executable program / instruction, such as computer-readable instructions. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory.

[0161] Computer-readable storage media may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.

[0162] In addition, the computer device may include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., keyboard, mouse, speakers, etc.).

[0163] The processor can communicate with external devices via the I / O bus through wired or wireless networks.

[0164] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product / computer program product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0165] In the embodiments provided in this disclosure, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this disclosure.

[0166] In this respect, each box in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the boxes may occur in a different order than those shown in the accompanying drawings.

[0167] For example, two consecutive blocks can actually be executed in essentially parallel order, and sometimes they can be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, as well as combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified functions or actions, or using a combination of dedicated hardware and computer instructions.

[0168] It should be noted that, in this disclosure, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0169] While the embodiments disclosed herein are as described above, the foregoing content is merely for the purpose of facilitating understanding of this disclosure and is not intended to limit this disclosure. Any person skilled in the art to which this disclosure pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope of this disclosure; however, the scope of patent protection of this disclosure shall still be determined by the scope defined in the appended claims.

Claims

1. A method of predicting porosity, characterized by, include: Obtain logging data for the target well; Based on the measured temperature and geothermal gradient data in the well logging data, determine the temperature set of the target formation; The porosity of the target formation is determined based on the temperature set of the target formation and a first mapping relationship, wherein the first mapping relationship is a mapping relationship between the elastic parameters and porosity of the target formation at different temperatures.

2. The method of claim 1, wherein, The step of determining the porosity of the target formation based on the temperature set of the target formation and the first mapping relationship includes: Obtain the temperature variation range of the target stratum; Based on the temperature set of the target formation, pre-stack inversion is performed on the well logging data to determine the elastic parameters of the target formation; The temperature coefficient is determined by fitting the temperature set and temperature variation range of the target stratum. Based on the elastic parameters and the temperature coefficient, the first mapping relationship is constructed; Based on the first mapping relationship, the porosity of the target stratum at the temperature concentration target temperature is determined.

3. The method of claim 1, wherein, The step of determining the temperature set of the target formation based on the measured temperature and geothermal gradient data in the well logging data includes: Based on the geothermal gradient data and the depth domain stratigraphic data of the target well, a second mapping relationship is determined, which is the mapping relationship between the geothermal gradient data and the depth domain stratigraphic data. Based on the measured temperature and the second mapping relationship, the temperature set of the target formation is determined.

4. The method of claim 3, wherein, The step of determining the temperature set of the target formation based on the measured temperature and the second mapping relationship includes: Based on the second mapping relationship, the measured temperature is interpolated to obtain the interpolated temperature; A temperature set for the target formation is constructed based on the interpolated temperature and the measured temperature.

5. The method of claim 3, wherein, The step of determining the second mapping relationship based on the geothermal gradient data and the depth domain stratigraphic data of the target well logging includes: Obtain seismic data from the target well logging; The time-domain stratigraphic data of the target well is determined based on the seismic data; The time-domain layer data is transformed to determine the depth-domain layer data; Based on the geothermal gradient data, the depth domain layer data is subjected to inverse proportional weighting to determine the second mapping relationship.

6. The method of claim 1, wherein, After determining the porosity of the target formation based on the temperature set of the target formation and the first mapping relationship, the method further includes: Obtain the actual temperature of the target formation and the elastic parameters obtained from the pre-stack inversion; Based on the first mapping relationship, query the porosity corresponding to the actual temperature and the elastic parameter; The porosity is used as the porosity result of the target formation.

7. A porosity prediction apparatus characterized by comprising: include: The acquisition module is used to acquire logging data for the target well. The first determining module is used to determine the temperature set of the target formation based on the measured temperature and geothermal gradient data in the well logging data; The second determining module is used to determine the porosity of the target formation based on the temperature set of the target formation and a first mapping relationship, wherein the first mapping relationship is a mapping relationship between the elastic parameters of the target formation at different temperatures and the porosity.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, which when executed by the processor, implements the steps of the method of any one of claims 1 to 6.

10. A computer program product comprising computer programs / instructions, characterized in that, The computer program, which when executed by the processor, implements the steps of the method of any one of claims 1 to 6.

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