A method and system for determining reservoir density in a few-well area

Abstract

The application provides a method, system, storage medium and processor for determining reservoir density of a few-well area, and belongs to the technical field of geophysical exploration. The method comprises the following steps: constructing a rock physics model based on well logging; determining the correlation between the density curve and the elastic parameters of the well logging through the rock physics model; 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 ratio of the P-wave velocity to the S-wave velocity, and the first parameter is the ratio of the shear modulus to the P-wave velocity; obtaining a low-frequency data body and a medium-frequency data body of the reservoir of the few-well area, and merging the two data bodies to obtain an absolute data body, wherein the absolute data body contains the P-wave impedance, the S-wave impedance and the ratio of the P-wave velocity to the S-wave velocity; calculating the first parameter of the reservoir of the few-well area according to the P-wave impedance, the S-wave impedance and the ratio of the P-wave velocity to the S-wave velocity; and substituting the absolute data body and the first parameter into the correlation between the density curve and the elastic parameters to obtain the reservoir density of the few-well area. The embodiment of the application can obtain stable and reliable reservoir density data of the few-well area.

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] Conventional prestack simultaneous inversion is commonly used to predict density parameters in areas with few wells. This technique utilizes the AVO information contained in prestack seismic data. By simultaneously inverting multiple partially stacked data volumes, density, P-wave velocity ratios, and P-wave impedances are obtained, providing elastic parameters or parameter combinations for identifying lithology and fluids. However, prestack simultaneous inversion suffers from poor stability, resulting in unstable and inaccurate density results. 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] To achieve the above objectives, 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 the well logging, and a correlation relationship between a density curve of the 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 the 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 wellbore area is calculated based on the longitudinal wave impedance, shear wave impedance and longitudinal and shear 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 to obtain the reservoir density in the wellbore area 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 using a well control simulation technology, includes:

[0010] Determining a relationship between density and a first parameter applicable to a reservoir in a well-poor region 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-poor area is obtained 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 pre-stack seismic data gathers 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 target layer position and the target layer stratigraphic framework;

[0023] A low-frequency data volume is obtained by utilizing 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 performing partial stacking of the gathers to obtain the partial stacked volume, the method further comprises: 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 well logging, and inputting the rock mineral component content, pore-related information in the rock, and fluid saturation into an 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, thereby obtaining a relationship between density, shear modulus, and P-wave velocity under the constraint of the 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 construct 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 a 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 a medium-frequency data volume of a reservoir in a well-poor area, 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 ratio;

[0034] The density calculation module is used to calculate the first parameter of the reservoir in the well-poor area based on the longitudinal wave impedance, shear wave impedance and longitudinal and shear 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 of the well-poor 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 using a well control simulation technology, includes:

[0036] Determining a relationship between density and a first parameter applicable to a reservoir in a well-poor region 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-poor area is obtained using well control simulation technology.

[0038] Accordingly, an embodiment of the present invention further provides a machine-readable storage medium having stored thereon instructions for enabling a machine to execute the method for determining reservoir density in a sparsely-welled area as described in the present application.

[0039] Accordingly, an embodiment of the present invention further provides a processor for running a program, wherein the program, when run, is used to execute the method for determining the reservoir density of 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 to obtain density data of the reservoir in the sparsely-welled area with strong stability and high reliability, 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 detailed description, they are used to explain the embodiments of the present invention, but do not constitute a limitation of 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 region with few wells provided by an embodiment of the present invention;

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

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

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

[0048] Figure 6 Schematic diagram of conventional prestack inversion results for the study area provided by an embodiment of the present invention;

[0049] Figure 7This is a cross-sectional diagram of density inversion of a study area provided by an embodiment of the present invention;

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

[0051] Figure 9 This is a block diagram of the system structure for determining reservoir density in a region with few wells provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The following describes the specific implementation of the embodiment of the present invention in detail with reference to the accompanying drawings. It should be understood that the specific implementation described herein 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 This is a flow chart of a method for determining reservoir density in a region with few wells provided by an embodiment of the present invention;

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

[0055] S110: constructing a rock physics model based on the 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 the well logging data. When determining the correlation between the density curve of the well logging data and the elastic parameters through the rock physics model, the rock mineral component content, pore-related information in the rock, and fluid saturation from the well logging data 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, so as to obtain the relationship between density, shear modulus, and longitudinal wave velocity under the constraint of the longitudinal and transverse wave velocity ratio.

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

[0058] Furthermore, the correlation between the density curve and the elastic parameter is a relationship between the density and a first parameter under the constraint of the longitudinal and transverse wave velocity ratio, the first parameter being 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, p=0.20xμ / Vp+1.96;

[0061] when Vp / Vs is 1.7-1.8, p=0.21xμ / Vp+1.82;

[0062] when Vp / Vs is 1.64-1.7, p=0.21xμ / Vp+1.69;

[0063] when Vp / Vs is 1.58-1.64, p=0.17xμ / Vp+1.74;

[0064] when Vp / Vs<1.58, p=0.15xμ / Vp+1.76;

[0065] wherein Vp / Vs is the ratio of P-wave velocity to S-wave velocity, Vp is P-wave velocity, and μ is shear modulus.

[0066] S120: obtaining a low-frequency data volume and a medium-frequency data volume of the few-well zone reservoir, and merging the low-frequency data volume and the medium-frequency data volume to obtain an absolute data volume, the absolute data volume comprising P-wave impedance, S-wave impedance, and P-S wave velocity ratio;

[0067] In step S120, the medium-frequency data volume is obtained through simultaneous pre-stack inversion.

[0068] The low-frequency data volume is obtained through the following way:

[0069] A gather of pre-stack seismic data is obtained, and a seismic stack volume is obtained through partial stack of the gather.

[0070] Joint well-seismic calibration is performed based on the seismic stack volume to determine the target layer position and the target layer stratigraphic framework.

[0071] The 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 through the rock physical model forward and / or actual measurement.

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

[0073] S130: calculating a first parameter of the few-well zone reservoir according to the P-wave impedance, the S-wave impedance, and the P-S wave velocity ratio, and substituting the absolute data volume and the first parameter into the correlation between the density curve and the elastic parameter to obtain the density of the few-well zone reservoir by using well-controlled simulation technology.

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

[0075] The embodiments of the present invention are further described in detail below with reference to a specific example (density inversion of a braided river delta reservoir in the Hua 3-Hua 4 interval of the 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 pattern of the longitudinal and transverse wave curves were checked to determine the reliability and accuracy of the original curve, find outliers and correct the direction. The analysis found that the borehole diameter of some mudstone sections in the area was severely expanded, resulting in an abnormally low density curve (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 establishment.

[0078] Specifically, when correcting the logging curve environment, first select sample points with relatively good well conditions, and use a multivariate fitting method to establish a correction relationship between the density curve and gamma, neutron porosity, deep resistivity, and acoustic time difference. Then, apply this relationship to other abnormal well sections to perform alternative corrections on the curves that cause the anomaly. In the comparison chart of the curves before and after correction, the last three curves from left to right are the acoustic time difference, density, and neutron curves, of which 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, systematic errors may exist between the same logging series from different wells due to differences in logging time, measurement instruments, measurement environment, and operator performance, which need to be eliminated. Specifically, a marker layer is first selected to eliminate systematic errors between different wells horizontally. Vertically, systematic errors caused by different measurement periods within the same target interval in the same well are eliminated. In actual research, the thin interbedded sandstone and mudstone layers above are used as the standard layer, which has stable lithology, good lateral continuity, and stable distribution. Based on the selected standard interval, the well data within the study area are statistically corrected for histogram normalization to eliminate systematic errors in the measurement environment. After correction, the probability statistical trends and value ranges of elastic parameters within the target intervals between wells are essentially consistent. A series of well logging curves with horizontal multi-well consistency that meets the needs of reservoir prediction is established. This provides regionally consistent input data for the subsequent development of rock volume models and rock physics modeling, ensuring consistent rock physics modeling forward results.

[0080] Furthermore, reservoir parameter logging evaluation includes determining mineral skeleton point parameters, establishing a rock mineral volume model, and interpreting logging fluids. Intersection analysis of acoustic transit time and density curves, as well as neutron and density curves, was performed on the target interval within the study area. The clay skeleton point (CNL = 0.33, DEN = 2.75 g / cm³, DT = 81 us / ft) and the quartz skeleton point (CNL = -0.06, DEN = 2.65 g / cm³, DT = 52 us / ft) were determined. Using optimized logging interpretation methods, linear equations for various rock physical responses were established based on the response characteristics of different mineral components and porosity. Logs representing the mineralogy and pore characteristics of the formation were defined as input (gamma-ray calculated clay content, acoustic transit time, neutron, density, etc.). Given the corresponding log skeleton parameters representing the formation, multiple linear response equations were established to obtain a rock volume model. The results of this volume model evaluation are basically consistent with the previous evaluation results, but it eliminates the extreme phenomenon that the conventional logging interpretation is a prominent reservoir and the non-reservoir layer is evaluated as 100% pure mudstone with a porosity of 0%. 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, during the rock physics modeling phase, rock elasticity manifests 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, acoustic frequency, etc.). The rock physics theoretical model aims to establish theoretical relationships between these moduli. It idealizes actual rock through certain assumptions and establishes universal relationships through inherent physical principles. In this study, the differential effective medium model (Xu & White) was used. Parameters such as the mineral content, total porosity, and fluid saturation obtained from well logging formation evaluation were first input into the rock physics modeling module. An appropriate rock physics model was then selected for modeling P- and S-wave velocities. By adjusting the skeleton parameters, the model data and measured data were appropriately correlated. Finally, the optimized rock physics model and skeleton parameter points for use in the work area were determined. Figure 3 The last four plots compare rock physics forward modeling results with measured results. From left to right, they represent P-wave velocity, S-wave velocity, density, and P-S wave velocity ratio. The red curve represents the rock physics forward model, while the black curve represents the original measured curve. The forward modeled curves generally match the measured curves in terms of trend and morphology.

[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 gather optimization processing, a partial stack volume is obtained. Specifically, the seismic CRP gather quality analysis is first performed to investigate the seismic acquisition bin, number of coverages, offset, incidence angle, frequency, signal-to-noise ratio, multiples, residual dynamic correction, AVO characteristics, and fidelity. Then, gather optimization processing is performed, including removing interlayer multiples, suppressing noise, and performing dynamic correction. Finally, a partial stack of gathers is performed to generate a partial seismic stack volume based on the incidence angle and number of coverages.

[0084] Furthermore, a joint well-seismic calibration is performed based on the seismic superposition body to determine the target layer position and the target layer stratigraphic framework, and the target layer position, target layer stratigraphic framework and the well data in the work area are used to obtain a low-frequency data body. 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 wavelet beside the well is 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 is performed using human-computer interaction to establish the target layer stratigraphic framework; all the well data in the work area (longitudinal wave impedance, Vp / Vs, density) are used, and the global frequency division Kriging technology is adopted to generate a low-frequency data body (such as Figure 5 shown).

[0085] Furthermore, the low-frequency and intermediate-frequency data volumes are merged to obtain an absolute data volume. Specifically, pre-stack wavelet extraction is first performed. By using well logging compressional waves, shear waves, and density curves, the wellbore wavelets of the pre-stack seismic volume are extracted, and the parameters of the pre-stack wavelet, such as the dominant frequency, bandwidth, phase, and length, are optimized. Then, based on the traditional Aki-Richard formula, pre-stack simultaneous inversion is performed to obtain an intermediate-frequency data volume. This is then combined with the low-frequency data volume to obtain the absolute data volume.

[0086] Finally, under the constraint of the P-wave and S-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 Using the density curve of the completed well to quality control the density body of the inversion, the density inversion body is slightly modified and improved 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 to obtain density data of the reservoir in the sparsely-welled area with strong stability and high reliability, providing a basis for the next step of reservoir physical property prediction and sweet spot prediction.

[0088] Accordingly, the embodiment of the present invention also provides a system for determining the reservoir density in a well-poor area, such as Figure 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 construct 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 a 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 a medium-frequency data volume of a reservoir in a well-poor area, 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 ratio;

[0091] The density calculation module is used to calculate the first parameter of the reservoir in the well-poor area based on the longitudinal wave impedance, shear wave impedance and longitudinal and shear 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 of the well-poor 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 using a well control simulation technology, includes:

[0093] Determining a relationship between density and a first parameter applicable to a reservoir in a well-poor region 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-poor area is obtained using well control simulation technology.

[0095] Accordingly, an embodiment of the present invention further provides a machine-readable storage medium having stored thereon instructions for enabling a machine to execute the method for determining reservoir density in a sparsely-welled area as described in the present application.

[0096] Accordingly, an embodiment of the present invention further provides a processor for running a program, wherein the program is used to execute the method for determining the reservoir density of a well-poor region when the program 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 to obtain density data of the reservoir in the sparsely-welled area with strong stability and high reliability, 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 can be provided as methods, systems, or computer program products. Therefore, the present application can 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 can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain 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 produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. 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 can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0101] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

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

[0103] The memory can include non-persistent memory and / or volatile memory, such as random access memory (RAM) and / or cache memory, non-volatile memory, such as read-only memory (ROM), EPROM, and / or flash memory. The memory is an example of computer-readable media.

[0104] Computer-readable media includes permanent and non-permanent, removable and non-removable media implemented in any method or technology for storage of information such as 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 disc read-only memory (CD-ROM), digital versatile disc (DVD), or other optical storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer-readable media does not include transitory media, such as modulated data signals and carrier waves.

[0105] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0106] The above are merely 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 modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all 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 the well logging, and a correlation relationship between a density curve of the 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 linear relationship between density and a first parameter corresponding to different ranges of longitudinal and transverse wave velocity ratios, wherein the first parameter is a ratio of shear modulus to 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 wellbore area is calculated based on the longitudinal wave impedance, shear wave impedance and longitudinal and shear 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 to obtain the reservoir density in the wellbore area using well control simulation technology.

2. The method for determining reservoir density in a well-poor area according to claim 1, wherein: 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 using well control simulation technology, includes: Determining a relationship between density and a first parameter applicable to a reservoir in a well-poor region 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-poor area is obtained 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 correlation relationship between the density curve and the elastic parameter includes at least one of the following relationship formulas: 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 area according to claim 1, wherein: The low-frequency data volume is obtained by: Obtain pre-stack seismic data gathers 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 target layer position and the target layer stratigraphic framework; A low-frequency data volume is obtained by utilizing 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 area according to claim 4, wherein: Before partially stacking the gathers to obtain the seismic stack volume, 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 area according to claim 1, wherein: The intermediate frequency data volume is obtained through seismic pre-stack simultaneous inversion.

7. The method for determining reservoir density in a region with few wells according to claim 1, wherein: The method 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: 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 an 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 longitudinal wave velocity under the constraint of the longitudinal and transverse wave velocity ratio; wherein 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 configured to construct a rock physics model based on well logging, and determine, through the rock physics model, a correlation between a density curve of the well logging and an elastic parameter, wherein the correlation between the density curve and the elastic parameter is a linear relationship between density and a first parameter corresponding to different ranges of longitudinal and transverse wave velocity ratios, wherein the first parameter is a ratio of shear modulus to longitudinal wave velocity; The data acquisition module is used to acquire a low-frequency data volume and a medium-frequency data volume of a reservoir in a well-poor area, 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 ratio; The density calculation module is used to calculate the first parameter of the reservoir in the well-poor area based on the longitudinal wave impedance, shear wave impedance and longitudinal and shear 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 of the well-poor area using well control simulation technology.

9. The system for determining reservoir density in a region with few wells according to claim 8, wherein: 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 using well control simulation technology, includes: Determining a relationship between density and a first parameter applicable to a reservoir in a well-poor region 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-poor area is obtained 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 according to 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 is used to execute the method for determining the reservoir density of a sparsely welled area according to any one of claims 1 to 7 when run.

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