Method and system for determining density of reservoir in less-well area

Abstract

The invention provides a method and system for determining the density of a reservoir in a less-well area, a storage medium and a processor, and belongs to the technical field of geophysical exploration. The method comprises the steps that a rock physical model is constructed based on logging, the incidence relation between a density curve and an elastic parameter of logging is determined through the rock physical model, the incidence relation between the density curve and the elastic parameter is a relational expression of density and a first parameter under the constraint of the longitudinal and transverse wave velocity ratio, and the first parameter is the ratio of the shear modulus to the longitudinal wave velocity; acquiring a low-frequency data volume and an intermediate-frequency data volume of the reservoir in the less-well area, and combining the low-frequency data volume and the intermediate-frequency data volume to obtain an absolute data volume which comprises longitudinal wave impedance, transverse wave impedance and a longitudinal-transverse wave velocity ratio; and according to the longitudinal wave impedance, the transverse wave impedance and the longitudinal and transverse wave velocity ratio, calculating a first parameter of the reservoir in the less-well area, and substituting the absolute data volume and the first parameter into an association relationship between the density curve and the elastic parameter to obtain the density of the reservoir in the less-well area. According to the embodiment of the invention, stable and reliable reservoir density data of the less-well area can be obtained.

Description

Technical Field

[0001] The present invention relates to the field of geophysical exploration technology, and in particular to a method, system, storage medium and processor for determining reservoir density in a well-poor area. Background Art

[0002] In geophysical reservoir prediction and characterization, density parameter is an important elastic parameter, which is directly related to seismic reflection characteristics, as well as the prediction of reservoir mineral type, mineral content, porosity and permeability, fluid type and content.

[0003] In the prior art, density parameters are generally predicted in areas with few wells by conventional pre-stack simultaneous inversion technology, which uses the AVO information contained in pre-stack seismic data to obtain density, P-wave velocity ratio and P-wave impedance through simultaneous inversion of multiple partially stacked data volumes, providing elastic parameters or parameter combinations for lithology and fluid identification. However, pre-stack simultaneous inversion has poor stability, and the density results are unstable and have low accuracy. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide a method, system, storage medium and processor for determining reservoir density in a well-poor area. The embodiments of the present invention can solve or partially solve the problems existing in the prior art.

[0005] In order to achieve the above object, an embodiment of the present invention provides a method for determining reservoir density in a well-poor region, the method comprising:

[0006] A rock physics model is constructed based on well logging, and a correlation relationship between a density curve of well logging and an elastic parameter is determined through the rock physics model, wherein the correlation relationship between the density curve and the elastic parameter is a relationship between density and a first parameter under the constraint of the longitudinal and transverse wave velocity ratio, wherein the first parameter is a ratio of the shear modulus to the longitudinal wave velocity;

[0007] Acquire a low-frequency data volume and a medium-frequency data volume of a reservoir in a well-poor region, and merge the low-frequency data volume and the medium-frequency data volume to obtain an absolute data volume, wherein the absolute data volume includes longitudinal wave impedance, shear wave impedance, and longitudinal and shear wave velocity ratios;

[0008] The first parameter of the reservoir in the well-sparse area is calculated based on the longitudinal wave impedance, transverse wave impedance and the longitudinal and transverse wave velocity ratio, and the absolute data body and the first parameter are substituted into the correlation relationship between the density curve and the elastic parameter, and the reservoir density in the well-sparse area is obtained using well control simulation technology.

[0009] Optionally, substituting the absolute data volume and the first parameter into the correlation relationship between the density curve and the elastic parameter, and obtaining the reservoir density of the well-poor area by using the well control simulation technology, comprises:

[0010] Determine the relationship between the density and the first parameter applicable to the reservoir in the well-poor area according to the longitudinal and transverse wave velocity ratios in the absolute data volume;

[0011] The first parameter is substituted into the applicable relationship, and the reservoir density of the well-sparsely-welled area is obtained by using well control simulation technology.

[0012] Optionally, the relationship between density, shear modulus, and longitudinal wave velocity under the constraint of the longitudinal and transverse wave velocity ratio includes at least one of the following relationship formulas:

[0013] When Vp / Vs>1.9, ρ=0.20×μ / Vp+2.07;

[0014] When Vp / Vs is 1.8-1.9, ρ=0.20×μ / Vp+1.96;

[0015] When Vp / Vs is 1.7-1.8, ρ=0.21×μ / Vp+1.82;

[0016] When Vp / Vs is 1.64-1.7, ρ=0.21×μ / Vp+1.69;

[0017] When Vp / Vs is 1.58-1.64, ρ=0.17×μ / Vp+1.74;

[0018] When Vp / Vs<1.58, ρ=0.15×μ / Vp+1.76;

[0019] Where Vp / Vs is the ratio of longitudinal and transverse wave velocities, Vp is the longitudinal wave velocity, and μ is the shear modulus.

[0020] Optionally, the low-frequency data volume is obtained by:

[0021] Obtain the gathers of pre-stack seismic data and perform partial stacking of the gathers to obtain a seismic stack volume;

[0022] Performing joint well-seismic calibration based on the seismic stack to determine the position of the target layer and the stratigraphic framework of the target layer;

[0023] A low-frequency data volume is obtained by using the target layer position, the target layer stratigraphic framework and the work area well data, wherein the work area well data is obtained by forward modeling of the rock physics model and / or actual measurement.

[0024] Optionally, before partially stacking the gathers to obtain the partial stacked body, the method further includes: performing quality analysis on the gathers of the pre-stack seismic data, and performing gather optimization processing based on the analysis results;

[0025] The gather optimization process includes at least one of removing interlayer multiple waves, suppressing noise and performing dynamic correction.

[0026] Optionally, the intermediate frequency data volume is obtained by seismic pre-stack simultaneous inversion.

[0027] Optionally, the step of constructing a rock physics model based on well logging and determining the correlation between the density curve of the well logging and the elastic parameters through the rock physics model includes:

[0028] Obtaining the rock mineral component content, pore-related information in the rock and fluid saturation from the well logging, and inputting the rock mineral component content, pore-related information in the rock and fluid saturation into the initial rock physics model to perform transverse and longitudinal wave velocity modeling;

[0029] Adjusting the skeleton point parameters of the initial rock physics model so that the correlation between the model data and the measured data is greater than a set threshold value, so as to obtain a relationship between density, shear modulus and P-wave velocity under the constraint of P-wave velocity ratio;

[0030] The initial rock physics model includes: a KT rock physics model and / or a differential effective medium model.

[0031] Accordingly, an embodiment of the present invention further provides a system for determining reservoir density in a region with few wells, the system comprising: a rock physics modeling module, a data acquisition module and a density calculation module;

[0032] The rock physics modeling module is used to build a rock physics model based on well logging, and determine the correlation between the density curve of the well logging and the elastic parameters through the rock physics model, wherein the correlation between the density curve and the elastic parameters is a relationship between the density and the first parameter under the constraint of the longitudinal and transverse wave velocity ratio, wherein the first parameter is the ratio of the shear modulus to the longitudinal wave velocity;

[0033] The data acquisition module is used to acquire a low-frequency data volume and an intermediate-frequency data volume of a reservoir in a well-poor area, and merge the low-frequency data volume and the intermediate-frequency data volume to obtain an absolute data volume, wherein the absolute data volume includes longitudinal wave impedance, transverse wave impedance and longitudinal and transverse wave velocity ratio;

[0034] The density calculation module is used to calculate the first parameter of the reservoir in the well-sparse area based on the longitudinal wave impedance, transverse wave impedance and longitudinal and transverse wave velocity ratio, and substitute the absolute data body and the first parameter into the correlation relationship between the density curve and the elastic parameter to obtain the reservoir density in the well-sparse area using well control simulation technology.

[0035] Optionally, substituting the absolute data volume and the first parameter into the correlation relationship between the density curve and the elastic parameter, and obtaining the reservoir density of the well-poor area by using the well control simulation technology, comprises:

[0036] Determine the relationship between the density and the first parameter applicable to the reservoir in the well-poor area according to the longitudinal and transverse wave velocity ratios in the absolute data volume;

[0037] The first parameter is substituted into the applicable relationship, and the reservoir density of the well-sparsely-welled area is obtained by using well control simulation technology.

[0038] Accordingly, an embodiment of the present invention further provides a machine-readable storage medium, on which instructions are stored, and the instructions are used to enable a machine to execute the method for determining the reservoir density of a sparsely-welled area described in the present application.

[0039] Correspondingly, an embodiment of the present invention further provides a processor for running a program, wherein the program, when being run, is used to execute the method for determining the reservoir density in a region with few wells.

[0040] In an embodiment of the present invention, a rock physics model is constructed based on well logging. Through the rock physics model, the correlation between the density curve of the well logging and the elastic parameters is determined. Then, the absolute data body of the reservoir in the sparsely-welled area is substituted into the correlation between the density curve and the elastic parameters, so as to obtain the density data of the reservoir in the sparsely-welled area with strong stability and high reliability, thereby providing a basis for the next step of reservoir physical property prediction and sweet spot prediction.

[0041] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0043] Figure 1 is a flow chart of a method for determining reservoir density in a well-poor region provided by an embodiment of the present invention;

[0044] Figure 2 It is a curve environment correction diagram of the NB17-1-4 well in the study area provided by an embodiment of the present invention;

[0045] Figure 3 It is the forward diagram of the well logging interpretation and rock physics model of the study area NB17-1-1 provided in the embodiment of the present invention;

[0046] Figure 4 It is a density-shear modulus and longitudinal wave velocity intersection diagram of the study area provided by the embodiment of the present invention;

[0047] Figure 5 It is a schematic diagram of creating a low-frequency body in a research area provided by an embodiment of the present invention;

[0048] Figure 6 It is a schematic diagram of conventional prestack inversion results of the study area provided by an embodiment of the present invention;

[0049] Figure 7It is a well-connected cross-section diagram of density inversion in the study area provided by an embodiment of the present invention;

[0050] Figure 8 It is a quality control effect diagram of density inversion of the study area provided by an embodiment of the present invention;

[0051] Fig. 9 It is a block diagram of the system structure for determining reservoir density in a well-poor area provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0053] Figure 1 is a flow chart of a method for determining reservoir density in a well-poor area provided by an embodiment of the present invention;

[0054] like Figure 1 As shown, the method for determining the reservoir density in a well-polluted area comprises:

[0055] S110: constructing a rock physics model based on well logging, and determining the correlation between the density curve of the well logging and the elastic parameters through the rock physics model;

[0056] In step S110, a rock physics model is constructed based on well logging. When determining the correlation between the density curve of well logging and the elastic parameters through the rock physics model, the rock mineral component content, pore-related information in the rock and fluid saturation of the well logging can be first obtained, and the rock mineral component content, pore-related information in the rock and fluid saturation are input into the initial rock physics model to perform transverse and longitudinal wave velocity modeling; then, the skeleton point parameters of the initial rock physics model are adjusted so that the correlation between the model data and the measured data is greater than a set threshold value, so as to obtain the relationship between the density and the shear modulus and the longitudinal wave velocity under the constraint of the longitudinal and transverse wave velocity ratio;

[0057] Wherein the initial rock physics model may include a KT rock physics model and / or a differential effective medium model;

[0058] Further, the correlation between the density curve and the elastic parameter is a relationship between the density and the first parameter under the constraint of the longitudinal and transverse wave velocity ratio, the first parameter is the ratio of the shear modulus to the longitudinal wave velocity, and the relationship between the density and the first parameter under the constraint of the longitudinal and transverse wave velocity ratio includes at least one of the following:

[0059] When Vp / Vs>1.9, ρ=0.20×μ / Vp+2.07;

[0060] When Vp / Vs is 1.8-1.9, ρ=0.20×μ / Vp+1.96;

[0061] When Vp / Vs is 1.7-1.8, ρ=0.21×μ / Vp+1.82;

[0062] When Vp / Vs is 1.64-1.7, ρ=0.21×μ / Vp+1.69;

[0063] When Vp / Vs is 1.58-1.64, ρ=0.17×μ / Vp+1.74;

[0064] When Vp / Vs<1.58, ρ=0.15×μ / Vp+1.76;

[0065] Where Vp / Vs is the ratio of longitudinal and transverse wave velocities, Vp is the longitudinal wave velocity, and μ is the shear modulus.

[0066] S120: acquiring a low-frequency data volume and a medium-frequency data volume of a reservoir in a well-pollinated area, and merging the low-frequency data volume and the medium-frequency data volume to obtain an absolute data volume, wherein the absolute data volume includes longitudinal wave impedance, transverse wave impedance, and longitudinal and transverse wave velocity ratios;

[0067] In step S120, the intermediate frequency data volume is obtained by seismic pre-stack simultaneous inversion;

[0068] The low-frequency data volume is obtained by:

[0069] Obtain the gathers of pre-stack seismic data and perform partial stacking of the gathers to obtain a seismic stack volume;

[0070] Performing joint well-seismic calibration based on the seismic stack to determine the position of the target layer and the stratigraphic framework of the target layer;

[0071] A low-frequency data volume is obtained by using the target layer position, the target layer stratigraphic framework and the work area well data, wherein the work area well data is obtained by forward modeling of the rock physics model and / or actual measurement.

[0072] Preferably, before partially stacking the gathers to obtain a partial stacking body, the method further includes: performing quality analysis on the gathers of the pre-stack seismic data, and performing gather optimization processing based on the analysis results; the gather optimization processing includes removing at least one of interlayer multiple waves, suppressing noise, and dynamic correction.

[0073] S130: Calculate the first parameter of the reservoir in the well-sparse area according to the longitudinal wave impedance, transverse wave impedance and longitudinal and transverse wave velocity ratio, substitute the absolute data body and the first parameter into the correlation relationship between the density curve and the elastic parameter, and obtain the reservoir density in the well-sparse area using well control simulation technology.

[0074] In step S130, when the absolute data body and the first parameter are substituted into the correlation relationship between the density curve and the elastic parameter to obtain the reservoir density in the well-sparse area, the relationship between the density and the first parameter applicable to the reservoir in the well-sparse area can be determined based on the longitudinal and transverse wave velocity ratios in the absolute data body, and then the first parameter is substituted into the applicable relationship to obtain the reservoir density in the well-sparse area using well control simulation technology.

[0075] The implementation mode of the present invention is further described in detail below with reference to a specific example (density inversion of braided river delta reservoir in Hua 3-Hua 4 segment of offshore X oil field).

[0076] Five wells were drilled in the study area. All logging, mud logging, analysis and testing data were collected before the study. First, the quality control of the logging curve was carried out. Through histograms, intersection diagrams, and well comparison diagrams, the integrity of the curve, the collapse of the wellbore curve, the accuracy of the density curve, and the distribution of the longitudinal and transverse wave curves were checked to determine the reliability and accuracy of the original curve, find outliers and correction directions. The analysis found that some mudstone sections in the area had serious wellbore expansion, resulting in abnormally low density curves (such as Figure 2 shown).

[0077] Before building a rock physics model based on well logging, the logging curve environment is first corrected, then multi-well consistency processing is performed, and finally reservoir parameter logging evaluation is performed. The data obtained from the logging reservoir parameter evaluation is used for rock physics modeling to ensure the accuracy of data acquisition and model building.

[0078] Specifically, when correcting the logging curve environment, first select sample points with relatively good well conditions, and use multivariate fitting to establish the correction relationship between the density curve and gamma, neutron porosity, deep resistivity, and acoustic wave time difference. Then, apply this relationship to other abnormal well sections to replace and correct the curves that cause the anomalies. In the comparison chart of the curves before and after correction, the last three tracks from left to right are the acoustic wave time difference, density, and neutron curves, respectively. The red one is after correction, and the black one is the actual measurement curve. Corrections were made in the problematic well sections, and the other well sections remained consistent with the original measurement curves (such as Figure 2 shown).

[0079] Furthermore, when performing multi-well consistency processing, due to the differences in logging time, measuring instruments, measuring environment, and operating personnel, there will be systematic errors between the same logging series of different wells, which need to be eliminated. Specifically, first select the marker layer, and eliminate the systematic errors between different wells horizontally; vertically, eliminate the systematic errors caused by different periods of measurement in the same target layer of the same well. In actual research, the thin interlayer of sandstone and mudstone above is used as the standard layer, which has stable lithology, good lateral continuity, and stable distribution. According to the selected standard layer, the well data in the study area are statistically corrected by histogram standardization to eliminate the systematic errors of the measurement environment. After correction, the probability statistical trends and value ranges of the elastic parameters in the target layer between each well are basically consistent. Establish a logging curve series with lateral multi-well consistency that meets the needs of reservoir prediction, provide regional lateral consistency input data for the subsequent rock volume model and rock physics modeling, and provide a guarantee for obtaining coordinated and consistent rock physics modeling forward modeling results.

[0080] Furthermore, reservoir parameter logging evaluation includes determination of mineral skeleton point parameters, establishment of rock mineral volume model, and logging fluid interpretation. The intersection of acoustic time difference and density curve, as well as the intersection of neutron and density curve of the target layer in the study area were analyzed to determine the clay skeleton point (CNL=0.33, DEN=2.75g / cm3, DT=81us / ft) and quartz skeleton point (CNL=-0.06, DEN=2.65g / cm3, DT=52us / ft). Using the optimal logging interpretation method, various physical response linear equations of rocks are established through the response characteristics of different mineral components and porosity, and logging curves representing the mineral and pore characteristics of formation rocks are defined as input (gamma calculation of clay content, acoustic time difference, neutron, density, etc.), given the corresponding curve formation representation skeleton parameters, multiple linear response equations are established to obtain the volume model of rock. The volume model evaluation results are basically consistent with the previous evaluation results, but the extreme phenomenon of evaluating non-reservoir layers as 100% pure mudstone with a porosity of 0% due to the conventional logging interpretation of prominent reservoirs has been eliminated. The results of this logging evaluation are compared with the results of the whole rock analysis, and the analysis results are highly consistent, indicating that this interpretation is more reasonable (such as Figure 3 shown).

[0081] Furthermore, in the rock physics modeling stage, the elasticity of the rock is manifested as the equivalent elasticity of a multiphase body, which can be summarized into four components: matrix modulus, dry rock skeleton modulus, pore fluid modulus and environmental factors (pressure, temperature, sound wave frequency, etc.). The rock physics theoretical model aims to establish the theoretical relationship between these moduli. It idealizes the actual rock through certain assumptions and establishes a universal relationship through the inherent physical principles. In this study, the differential effective medium model Xu&White model was used. First, the parameters such as the content of each mineral component, total porosity, and fluid saturation obtained by logging formation evaluation were input into the rock physics modeling module, and then a suitable rock physics model was selected for modeling of longitudinal and transverse wave velocities. By adjusting the skeleton parameters, the model data and the measured data reached a suitable correlation, and then the optimized rock physics model and skeleton parameter points that can be used in the work area were determined. Figure 3 The last four curves are the comparison between the rock physics forward modeling results and the measured results, from left to right, they are P-wave velocity, S-wave velocity, density and P-S wave velocity ratio. The red curve is the rock physics forward modeling curve, the black curve is the original measured curve, and the trend and shape of the forward modeling curve are basically consistent with the measured curve.

[0082] Furthermore, based on the accurate logging curves and rock physics models, the relationship between elastic parameters is analyzed to determine the nonlinear relationship between the density curve and μ / Vp, Vp / Vs.

[0083] Furthermore, through the gather optimization processing, a partial stacking volume is obtained. Specifically, the seismic CRP gather quality analysis is first performed to investigate the seismic acquisition bin, coverage times, offset distance, incident angle, frequency, signal-to-noise ratio, multiple waves, residual dynamic correction, AVO characteristics, fidelity, etc.; then, gather optimization processing is performed, including removing interlayer multiple waves, suppressing noise, and dynamic correction; after that, the gather is partially stacked, and a partial seismic stacking volume is generated according to the incident angle and coverage times.

[0084] Furthermore, a joint well-seismic calibration is performed based on the seismic stack to determine the target layer position and the stratigraphic framework of the target layer, and a low-frequency data body is obtained by using the target layer position, the stratigraphic framework of the target layer and the well data in the work area. Specifically, a joint well-seismic calibration is performed based on the full stack seismic body. First, a longitudinal wave impedance curve is generated using the longitudinal wave curve and the density curve (transverse wave curve); then, the dominant sub-waves beside the well are extracted, and multiple rounds of well-seismic calibration are performed to determine the target layer position; then, a fine structural interpretation of the target layer is performed, and the horizon and fault interpretation are performed using human-computer interaction to establish the stratigraphic framework of the target layer; all the well data in the work area (longitudinal wave impedance, Vp / Vs, density) are used, and the global frequency division Kriging technology is used to generate a low-frequency data body (such as Figure 5 shown).

[0085] Furthermore, the low-frequency data volume and the medium-frequency data volume are merged to obtain an absolute data volume. Specifically, pre-stack wavelet extraction is first performed, and the well logging longitudinal wave, shear wave, and density curve are used to extract the wellbore wavelet of the pre-stack seismic volume, and the main frequency, bandwidth, phase, length and other parameters of the pre-stack wavelet are optimized; then, based on the traditional Aki-Richard formula, pre-stack simultaneous inversion is performed to obtain the medium-frequency data volume, and the medium-frequency data volume and the low-frequency data volume are merged to obtain the absolute data volume.

[0086] Finally, under the constraint of the P-wave velocity ratio, the density data volume is fitted using the density-shear modulus and P-wave velocity relationship in the partition interval to obtain the density data of the reservoir in the well-poor area (such as Figure 7 The density curve of the completed well is used to quality control the density body of the inversion, and the density inversion body is slightly modified to obtain the final density body (as shown in Figure 8 shown).

[0087] In an embodiment of the present invention, a rock physics model is constructed based on well logging. Through the rock physics model, the correlation between the density curve of the well logging and the elastic parameters is determined. Then, the absolute data body of the reservoir in the sparsely-welled area is substituted into the correlation between the density curve and the elastic parameters, so as to obtain the density data of the reservoir in the sparsely-welled area with strong stability and high reliability, thereby providing a basis for the next step of reservoir physical property prediction and sweet spot prediction.

[0088] Accordingly, an embodiment of the present invention also provides a system for determining reservoir density in a well-poor region, such as Fig. 9 As shown, the system includes: a rock physics modeling module, a data acquisition module and a density calculation module;

[0089] The rock physics modeling module is used to build a rock physics model based on well logging, and determine the correlation between the density curve of the well logging and the elastic parameters through the rock physics model, wherein the correlation between the density curve and the elastic parameters is a relationship between the density and the first parameter under the constraint of the longitudinal and transverse wave velocity ratio, wherein the first parameter is the ratio of the shear modulus to the longitudinal wave velocity;

[0090] The data acquisition module is used to acquire a low-frequency data volume and an intermediate-frequency data volume of a reservoir in a well-poor area, and merge the low-frequency data volume and the intermediate-frequency data volume to obtain an absolute data volume, wherein the absolute data volume includes longitudinal wave impedance, transverse wave impedance and longitudinal and transverse wave velocity ratio;

[0091] The density calculation module is used to calculate the first parameter of the reservoir in the well-sparse area based on the longitudinal wave impedance, transverse wave impedance and longitudinal and transverse wave velocity ratio, and substitute the absolute data body and the first parameter into the correlation relationship between the density curve and the elastic parameter to obtain the reservoir density in the well-sparse area using well control simulation technology.

[0092] Optionally, substituting the absolute data volume and the first parameter into the correlation relationship between the density curve and the elastic parameter, and obtaining the reservoir density of the well-poor area by using the well control simulation technology, comprises:

[0093] Determine the relationship between the density and the first parameter applicable to the reservoir in the well-poor area according to the longitudinal and transverse wave velocity ratios in the absolute data volume;

[0094] The first parameter is substituted into the applicable relationship, and the reservoir density of the well-sparsely-welled area is obtained by using well control simulation technology.

[0095] Accordingly, an embodiment of the present invention further provides a machine-readable storage medium, on which instructions are stored, and the instructions are used to enable a machine to execute the method for determining the reservoir density of a sparsely-welled area described in the present application.

[0096] Accordingly, an embodiment of the present invention further provides a processor, which is used to run a program, wherein the program is used to execute the method for determining the reservoir density of a well-poor region when it is run.

[0097] In an embodiment of the present invention, a rock physics model is constructed based on well logging. Through the rock physics model, the correlation between the density curve of the well logging and the elastic parameters is determined. Then, the absolute data body of the reservoir in the sparsely-welled area is substituted into the correlation between the density curve and the elastic parameters, so as to obtain the density data of the reservoir in the sparsely-welled area with strong stability and high reliability, thereby providing a basis for the next step of reservoir physical property prediction and sweet spot prediction.

[0098] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0099] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0100] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0101] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0102] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0103] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0104] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0105] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0106] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for determining reservoir density in a well-poor area, characterized in that: The method includes: A rock physics model is constructed based on well logging, and a correlation relationship between a density curve of well logging and an elastic parameter is determined through the rock physics model, wherein the correlation relationship between the density curve and the elastic parameter is a relationship between density and a first parameter under the constraint of the longitudinal and transverse wave velocity ratio, wherein the first parameter is a ratio of the shear modulus to the longitudinal wave velocity; Acquire a low-frequency data volume and a medium-frequency data volume of a reservoir in a well-poor region, and merge the low-frequency data volume and the medium-frequency data volume to obtain an absolute data volume, wherein the absolute data volume includes longitudinal wave impedance, shear wave impedance, and longitudinal and shear wave velocity ratios; The first parameter of the reservoir in the well-sparse area is calculated based on the longitudinal wave impedance, transverse wave impedance and the longitudinal and transverse wave velocity ratio, and the absolute data body and the first parameter are substituted into the correlation relationship between the density curve and the elastic parameter, and the reservoir density in the well-sparse area is obtained using well control simulation technology.

2. The method for determining reservoir density in a well-poor region according to claim 1, characterized in that: Substituting the absolute data volume and the first parameter into the correlation relationship between the density curve and the elastic parameter, and obtaining the reservoir density of the well-poor area by using the well control simulation technology, comprises: Determine the relationship between the density and the first parameter applicable to the reservoir in the well-poor area according to the longitudinal and transverse wave velocity ratios in the absolute data volume; The first parameter is substituted into the applicable relationship, and the reservoir density of the well-sparsely-welled area is obtained by using well control simulation technology.

3. The method for determining reservoir density in a well-poor area according to claim 1 or 2, characterized in that: The relationship between density, shear modulus and longitudinal wave velocity under the constraint of the longitudinal and transverse wave velocity ratio includes at least one of the following relationship: When Vp / Vs>1.9, ρ=0.20×μ / Vp+2.07; When Vp / Vs is 1.8-1.9, ρ=0.20×μ / Vp+1.96; When Vp / Vs is 1.7-1.8, ρ=0.21×μ / Vp+1.82; When Vp / Vs is 1.64-1.7, ρ=0.21×μ / Vp+1.69; When Vp / Vs is 1.58-1.64, ρ=0.17×μ / Vp+1.74; When Vp / Vs<1.58, ρ=0.15×μ / Vp+1.76; Where Vp / Vs is the ratio of longitudinal and transverse wave velocities, Vp is the longitudinal wave velocity, and μ is the shear modulus.

4. The method for determining reservoir density in a well-poor region according to claim 1, characterized in that: The low-frequency data volume is obtained by: Obtain the gathers of pre-stack seismic data and perform partial stacking of the gathers to obtain a seismic stack volume; Performing joint well-seismic calibration based on the seismic stack to determine the position of the target layer and the stratigraphic framework of the target layer; A low-frequency data volume is obtained by using the target layer position, the target layer stratigraphic framework and the work area well data, wherein the work area well data is obtained by forward modeling of the rock physics model and / or actual measurement.

5. The method for determining reservoir density in a well-poor region according to claim 4, characterized in that: Before partially stacking the gathers to obtain a partial stacked body, the method further includes: performing quality analysis on the gathers of the pre-stack seismic data, and performing gather optimization processing based on the analysis results; The gather optimization process includes at least one of removing interlayer multiple waves, suppressing noise and performing dynamic correction.

6. The method for determining reservoir density in a well-poor region according to claim 1, characterized in that: The intermediate frequency data volume is obtained through seismic pre-stack simultaneous inversion.

7. The method for determining reservoir density in a well-polluted area according to claim 1, characterized in that: The step of building a rock physics model based on well logging and determining the correlation between the density curve of the well logging and the elastic parameters through the rock physics model includes: Obtaining the rock mineral component content, pore-related information in the rock and fluid saturation from the well logging, and inputting the rock mineral component content, pore-related information in the rock and fluid saturation into the initial rock physics model to perform transverse and longitudinal wave velocity modeling; Adjusting the skeleton point parameters of the initial rock physics model so that the correlation between the model data and the measured data is greater than a set threshold value, so as to obtain a relationship between density, shear modulus and P-wave velocity under the constraint of P-wave velocity ratio; The initial rock physics model includes: a KT rock physics model and / or a differential effective medium model.

8. A system for determining reservoir density in a well-poor region, characterized in that: The system includes: a rock physics modeling module, a data acquisition module and a density calculation module; The rock physics modeling module is used to build a rock physics model based on well logging, and determine the correlation between the density curve of the well logging and the elastic parameters through the rock physics model, wherein the correlation between the density curve and the elastic parameters is a relationship between the density and the first parameter under the constraint of the longitudinal and transverse wave velocity ratio, wherein the first parameter is the ratio of the shear modulus to the longitudinal wave velocity; The data acquisition module is used to acquire a low-frequency data volume and an intermediate-frequency data volume of a reservoir in a well-poor area, and merge the low-frequency data volume and the intermediate-frequency data volume to obtain an absolute data volume, wherein the absolute data volume includes longitudinal wave impedance, transverse wave impedance and longitudinal and transverse wave velocity ratio; The density calculation module is used to calculate the first parameter of the reservoir in the well-sparse area based on the longitudinal wave impedance, transverse wave impedance and longitudinal and transverse wave velocity ratio, and substitute the absolute data body and the first parameter into the correlation relationship between the density curve and the elastic parameter to obtain the reservoir density in the well-sparse area using well control simulation technology.

9. The system for determining reservoir density in a region with few wells according to claim 8, characterized in that: Substituting the absolute data volume and the first parameter into the correlation relationship between the density curve and the elastic parameter, and obtaining the reservoir density of the well-poor area by using the well control simulation technology, comprises: Determine the relationship between the density and the first parameter applicable to the reservoir in the well-poor area according to the longitudinal and transverse wave velocity ratios in the absolute data volume; The first parameter is substituted into the applicable relationship, and the reservoir density of the well-sparsely-welled area is obtained by using well control simulation technology.

10. A machine-readable storage medium having instructions stored thereon, the instructions being used to enable a machine to execute the method for determining reservoir density in a sparsely-welled area as described in any one of claims 1 to 7 of the present application.

11. A processor, characterized in that: Used to run a program, wherein the program, when run, is used to execute the method for determining the reservoir density of a sparsely-welled area as described in any one of claims 1-7.

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