Method, device and equipment for determining continuous thickness of class i reservoir and storage medium

Abstract

The present disclosure provides a method and device for determining the continuous thickness of a Class I reservoir, an equipment and a storage medium, belonging to the technical field of geophysical exploration. The method comprises: performing rock physics analysis on the Class I reservoir of a test well in a study area to obtain the rock physics relationship between the physical parameters and elastic parameters of the Class I reservoir; calibrating the test well with seismic data to determine the time-depth relationship and extract the seismic wavelet; establishing a low-frequency model; performing seismic inversion based on the low-frequency model, the time-depth relationship and the seismic wavelet to obtain the objective function and the reflection coefficient in geostatistics; performing pre-stack geostatistical inversion on the study area based on the low-frequency model, the objective function and the reflection coefficient to obtain the target elastic parameter; and determining the continuous thickness of the Class I reservoir in the study area according to the target elastic parameter and the rock physics relationship. The present disclosure can accurately determine the continuous thickness of the Class I reservoir.

Description

Technical Field

[0001] This disclosure belongs to the field of geophysical exploration technology, and specifically relates to a method, apparatus, equipment and storage medium for determining the continuous thickness of Class I reservoirs. Background Technology

[0002] A reservoir is the storage space for oil and gas; oil and gas can only be found where there are reservoirs, and the spatial distribution of a reservoir directly determines the scale of an oil and gas field's reserves. Based on the different physical properties of reservoirs at different depths, reservoirs can be classified into several major categories, generally including Class I, Class II, and Class III reservoirs, with Class I reservoirs having the highest gas content. Therefore, Class I reservoirs are a crucial foundation for oil and gas field reserve evaluation and development design. The continuous thickness of Class I reservoirs is an important quantitative parameter characterizing their spatial distribution, making the determination of the continuous thickness of Class I reservoirs essential.

[0003] In related technologies, deterministic seismic inversion techniques are primarily used to determine the continuous thickness of Class I reservoirs. Specifically, the rock physical relationships between the physical and elastic parameters of the Class I reservoir are first obtained. Then, a low-frequency model is built using well logging data from known test wells, and well-seismic calibration is performed. Seismic inversion is conducted based on the results of the well-seismic calibration and the low-frequency model. Reservoir physical parameters are then obtained based on the inversion results and the rock physical relationships. Finally, the continuous thickness of the Class I reservoir is determined based on these reservoir physical parameters.

[0004] However, since the frequency of the wavelet extracted from the seismic data in the above method is limited, and the inversion is performed using a defined wavelet and low-frequency model, the resulting acoustic impedance information has low resolution and cannot identify thinner reservoirs. Summary of the Invention

[0005] This disclosure provides a method for determining the continuous thickness of Class I reservoirs, which can accurately determine the continuous thickness of relatively thin Class I reservoirs. The technical solution is as follows:

[0006] This disclosure provides a method for determining the continuous thickness of a Class I reservoir, the method comprising:

[0007] Rock physical analysis was performed on the Class I reservoirs of the test wells in the study area to obtain the rock physical relationship between the physical property parameters and elastic parameters of the Class I reservoirs.

[0008] The test wells were subjected to well-seismic calibration to determine the time-depth relationship and extract seismic wavelets;

[0009] Establish a low-frequency model;

[0010] Based on the low-frequency model, the time-depth relationship, and the seismic wavelet, seismic inversion is performed to obtain the objective function and reflection coefficient in geostatistics. The objective function includes the probability density function and the spatial variation function.

[0011] Based on the low-frequency model, the objective function, and the reflection coefficient, pre-stack geostatistical inversion is performed on the area under study to obtain the target elastic parameters;

[0012] Based on the target elastic parameters and the rock physical relationship, the continuous thickness of the Class I reservoir in the area under study is determined.

[0013] In another implementation of this disclosure, the step of performing pre-stack geostatistical inversion on the area under study based on the low-frequency model, the objective function, and the reflection coefficient to obtain the target elastic parameters includes:

[0014] Using the Markov chain Monte Carlo algorithm, the reflection coefficient is randomly inverted at least 10 times in the low-frequency model through the probability density function and the variogram function to obtain at least 10 implementations;

[0015] Statistical analysis is performed on no fewer than 10 implementations, and the average value is calculated to obtain the target elasticity parameter.

[0016] In another implementation of this disclosure, determining the continuous thickness of the Class I reservoir in the area under study based on the target elastic parameter and the rock physical relationship includes:

[0017] Based on the target elastic parameters and the rock physical relationship, the key physical property parameters of the Class I reservoir in the area under study are determined;

[0018] Based on the key physical property parameters of Class I reservoirs in the area under study and the reservoir classification criteria, the continuous thickness of Class I reservoirs in the area under study is determined.

[0019] In another implementation of this disclosure, before determining the continuous thickness of the Class I reservoir in the area under study based on the key physical property parameters and reservoir classification criteria of the Class I reservoir in the area under study, the determination method further includes:

[0020] The reliability of key physical property parameters of Class I reservoirs in the area under study is assessed.

[0021] If the reliability of the key physical property parameter does not meet the requirements, the target elastic parameter and the key physical property parameter are obtained again until the key physical property parameter with acceptable reliability is obtained.

[0022] In another implementation of this disclosure, the determination of the reliability of key physical property parameters of Class I reservoirs in the area under study includes:

[0023] The key physical property parameters are compared and analyzed with the logging data and drilling data of the test well;

[0024] When the key physical property parameters are consistent with the well logging data and drilling data of the test well, the reliability of the key physical property parameters meets the requirements;

[0025] If the key physical property parameters are inconsistent with the well logging data and drilling data of the test well, then the reliability of the key physical property parameters does not meet the requirements.

[0026] In another implementation of this disclosure, a device for determining the continuous thickness of a Class I reservoir is also provided. The device includes: a rock physics relationship determination module, used to perform rock physics analysis on Class I reservoirs in test wells in the study area to obtain the rock physics relationship between the physical properties and elastic parameters of the Class I reservoir; a well-seismic calibration module, used to perform well-seismic calibration on the test wells to determine the time-depth relationship and extract seismic wavelets; a low-frequency model establishment module, used to establish a low-frequency model; an objective function determination module, used to perform seismic inversion based on the low-frequency model, the time-depth relationship, and the seismic wavelets to obtain an objective function and reflection coefficient in geostatistics, wherein the objective function includes a probability density function and a spatial variation function; a target elastic parameter determination module, used to perform pre-stack geostatistical inversion on the study area based on the low-frequency model, the objective function, and the reflection coefficient to obtain target elastic parameters; and a continuous thickness determination module, used to determine the continuous thickness of the Class I reservoir in the study area according to the target elastic parameters and the rock physics relationship.

[0027] In another implementation of this disclosure, the target elasticity parameter determination module is further configured to:

[0028] Using the Markov chain Monte Carlo algorithm, the reflection coefficient is randomly inverted at least 10 times in the low-frequency model through the probability density function and the variogram function to obtain at least 10 implementations;

[0029] Statistical analysis is performed on no fewer than 10 implementations, and the average value is calculated to obtain the target elasticity parameter.

[0030] In another implementation of this disclosure, the continuous thickness determination module is further configured to:

[0031] Based on the target elastic parameters and the rock physical relationship, the key physical property parameters of the Class I reservoir in the area under study are determined;

[0032] Based on the key physical property parameters of Class I reservoirs in the area under study and the reservoir classification criteria, the continuous thickness of Class I reservoirs in the area under study is determined.

[0033] In another implementation of this disclosure, a computer device is also provided, the computer device including a processor and a memory configured to store processor-executable instructions; the processor is configured to execute the method for determining the continuous thickness of a Class I reservoir as described above.

[0034] In another implementation of this disclosure, a computer storage medium is also provided, on which computer instructions are stored, which, when executed by a processor, implement the method for determining the continuous thickness of the Class I reservoir described above.

[0035] The beneficial effects of the technical solutions provided in this disclosure are:

[0036] When determining the continuous thickness of Class I reservoirs in a work area using the method provided in this disclosure, the method first classifies the reservoirs using basic data, performs rock physical analysis on Class I reservoirs to obtain the rock physical relationship between physical property parameters and elastic parameters, and then performs seismic inversion based on the rock physical relationship combined with pre-stack seismic data. This greatly improves the seismic resolution and enables the high-precision determination of the continuous thickness of Class I reservoirs. It has achieved good application results in deep shale gas areas. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a flowchart of a method for determining the continuous thickness of a Class I reservoir provided in an embodiment of this disclosure;

[0039] Figure 2 This is a flowchart of another method for determining the continuous thickness of a Class I reservoir provided in this disclosure embodiment;

[0040] Figure 3 A schematic diagram of the logging curves provided in the embodiments of this disclosure;

[0041] Figure 4 This is a schematic diagram of the structure of the device for determining the continuous thickness of a Class I reservoir provided in an embodiment of this disclosure;

[0042] Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of this disclosure. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this disclosure clearer, the embodiments of this disclosure will be described in further detail below with reference to the accompanying drawings.

[0044] To facilitate understanding, some relevant content in this application will be explained first.

[0045] Seismic inversion is the process of using surface-observed seismic data, constrained by known geological laws and drilling and logging data, to image (solve) the spatial structure and physical properties of underground rock strata.

[0046] The purpose of seismic inversion is to obtain a subsurface medium model from known observation data through calculation and analysis, thereby making effective predictions about reservoirs and improving production efficiency. In well location detection, drilling and logging data are characterized by detailed vertical information, while seismic data is characterized by strong horizontal information continuity. Seismic inversion technology organically combines these two, converting interface-type seismic data into lithological data such as wave impedance and velocity (elastic parameters), allowing for direct comparison with drilling and logging data.

[0047] For example, suppose the earthquake records in the seismic data are as follows:

[0048] S(t)=R(t)*W(t)+N(t) (1)

[0049] Where S(t) is the seismic record in the seismic data; R(t) is the reflection coefficient of the subsurface interface; W(t) is the seismic wavelet; and N(t) is the noise.

[0050] Seismic inversion involves removing wavelets and noise from seismic records to obtain a sequence of reflection coefficients that only reflect changes in subsurface interfaces. The velocity of seismic waves at each subsurface boundary and the density of the subsurface layer can then be determined from the sequence of reflection coefficients, thereby inferring the distribution of the subsurface medium.

[0051] The condition for seismic reflection is that the media on both sides of the interface have different wave impedances Z (wave impedance = velocity / density product, Z = ρυ). If the density and velocity are known, then the reflection coefficient R at the i-th interface is... i It can be obtained using the following formula:

[0052]

[0053] Z i Let Z be the wave impedance of the i-th interface. i+1 Let be the wave impedance of the (i+1)th interface.

[0054] If the reflection coefficients are obtained, the inversion process can be realized. Different algorithms can be used to obtain wave impedance data from the seismic reflection coefficient sequence, thus transforming the seismic data into a rock-type wave impedance profile, which allows the seismic data to be directly compared with drilling, well logging, and other data.

[0055] This disclosure provides a method for determining the continuous thickness of a Class I reservoir, such as... Figure 1 As shown, the determination method includes:

[0056] S101: Perform rock physical analysis on Class I reservoirs in test wells in the study area to obtain the rock physical relationship between the physical properties and elastic parameters of Class I reservoirs.

[0057] The concept of continuous thickness of Class I reservoirs applies to shale gas, so when we write "reservoir" or "gas field" below, we are referring to shale reservoirs and shale gas fields.

[0058] S102: Perform well-seismic calibration on the test well to determine the time-depth relationship and extract the seismic wavelet.

[0059] S103: Establish a low-frequency model.

[0060] Based on pre-stack seismic data, and constrained by well logging and drilling data, an initial geological framework that best reflects the geological characteristics of the subsurface sedimentary body is established using seismic interpretation horizons and faults, resulting in a low-frequency model.

[0061] Due to limitations of the seismic acquisition system, the obtained seismic data does not contain low-frequency components below 10Hz, so these must be extracted from other data to compensate for them.

[0062] Starting from seismic data and based on well logging and drilling data, a low-frequency model that basically reflects the geological characteristics of sedimentary bodies can be established.

[0063] S104: Seismic inversion is performed based on low-frequency models, time-depth relationships, and seismic wavelets to obtain the objective function and reflection coefficient in geostatistics. The objective function includes the probability density function and the spatial variation function.

[0064] S105: Based on the low-frequency model, objective function, and reflection coefficient, pre-stack geostatistical inversion is performed on the study area to obtain the target elastic parameters.

[0065] S106: Determine the continuous thickness of Class I reservoirs in the area under study based on the target elastic parameters and rock physics relationships.

[0066] When determining the continuous thickness of Class I reservoirs within a work area using the method provided in this disclosure, the method first performs rock physical analysis on the Class I reservoirs of the test wells in the study area to obtain the rock physical relationship between the physical and elastic parameters of the Class I reservoirs. Then, it performs well-seismic calibration on the test wells to obtain the probability density function and spatial variation function. This allows for stochastic inversion based on the probability density function and spatial variation function, ensuring that the inversion path follows the mathematical laws of the spatial variation function. Under the constraint of the probability density function, the target elastic parameters are obtained, improving seismic resolution and enabling high-precision determination of the continuous thickness of Class I reservoirs. This method has achieved good application results in deep shale gas areas.

[0067] Figure 2 This is a flowchart illustrating another method for determining the continuous thickness of a Class I reservoir provided in this disclosure. See also... Figure 2 The methods for determining this include:

[0068] S201: Acquire basic data, which includes core data from test wells, logging data, drilling data from exploration wells, and seismic records from the area under study, including seismic velocity fields and pre-stack seismic data.

[0069] The core data includes mineral composition data, reservoir physical property data, and relevant test data based on the core.

[0070] Well logging data includes various well logging curves. Figure 3 This is a schematic diagram of the logging curves provided in the embodiments of this disclosure. Figure 3 In the well logging section, there are four logging curves, which will be introduced in order from left to right below.

[0071] The first logging channel reflects lithology, including radioactive uranium logging, uranium-free gamma logging, natural gamma logging, and caliber logging. The second channel reflects porosity, including density logging, neutron logging, core density logging, and sonic logging. The third channel reflects oil-bearing properties, including shallow resistivity logging and deep resistivity logging. The fourth channel reflects clay mineral types, including uranium-thorium ratio logging.

[0072] Figure 3 The right side of the diagram shows the data from the four logging curves on the left, which were used to calculate the reservoir's physical properties and lithological profiles at different depths in the tested well. These physical properties include porosity, water saturation, brittleness index, organic carbon content, and gas content.

[0073] Drilling data from exploratory wells includes the depth of the target layer, well depth, structural location, etc., of each exploratory well during the drilling process.

[0074] The seismic velocity field is the velocity field of seismic waves obtained by interpreting seismic data.

[0075] Prestack seismic data is the data that returns the reflected waves of the common shot gather recorded by the detector to the reflection interface that produced them, and the diffracted waves converge to the diffraction point that produced them.

[0076] S202: Based on the basic data, the reservoirs in the test wells are classified to obtain different types of reservoirs, including Class I, Class II and Class III reservoirs.

[0077] Step S202 includes:

[0078] S2021: Based on the logging curves and core data in the basic data, calculate the reservoir's organic carbon content, porosity, gas saturation, and brittleness index. (These parameters are key physical properties.)

[0079] (1) Core positioning is carried out using logging curves and core data.

[0080] When drilling cores, the depth of the core obtained may not be the actual depth of the stratum where the core is located due to the influence of core drilling tools. Therefore, the first task before conducting analysis and research on the core data is to reposition the core.

[0081] The so-called core repositioning refers to repositioning the core to the true depth of the strata where the core is located using well logging curves. In practice, the mineral composition data of the core and the data from rock electrical tests are compared and analyzed with the well logging curves to obtain the true depth of the core.

[0082] (2) Based on the core data, the logging curves are calibrated to determine the calculation models for organic carbon content, porosity, and gas saturation.

[0083] For example, based on core data, the results of well logging interpretation can be compared to obtain calculation models for organic carbon content, porosity, and gas saturation.

[0084] For example, by conducting relevant tests on core samples at different depths, the porosity of the corresponding core samples can be detected. Then, the porosity corresponding to the different depths can be linearly fitted with the parameters (sonic waves, density) on the logging curves in the second logging curve channel to obtain a calculation model of porosity with acoustic waves or density.

[0085] The calculation models for other physical property parameters are similar.

[0086] In general, not every test well in the area under study will undergo core testing; core testing is only conducted on a portion of the test wells.

[0087] Core testing refers to a series of tests conducted on core samples from test wells located in specific target layers. Core testing includes tests for core permeability, porosity, oil content, and water saturation.

[0088] (3) Determine the calculation method for the mineral brittleness content.

[0089] In this embodiment, based on the sonic logging curve, the mineral brittleness index is calculated one by one using various existing mineral brittleness index calculation expressions, and compared with core data to select the optimal mineral brittleness index expression.

[0090] For example, when calculating the mineral brittleness index, the Poisson's ratio and Young's modulus proposed by Rickman (2008) are used. All expressions related to elastic parameters (there are more than 80 mineral brittleness index calculation expressions internationally) are selected and calculated, and then compared with the mineral brittleness index in the core data. The best calculation expression is selected as the formula for calculating the mineral brittleness content.

[0091] (4) Based on the calculation model and calculation method, the organic carbon content, porosity, gas saturation and brittleness index of the reservoir are obtained.

[0092] The organic carbon content, porosity, and gas saturation of the reservoir were calculated based on the established calculation model. The reservoir brittleness index was calculated using the same method.

[0093] 2022: Based on the organic carbon content, porosity, gas saturation, and brittleness index of the reservoir, the reservoirs in the test wells are classified to obtain different types of reservoirs.

[0094] In this embodiment, the reservoirs in the test well are classified according to the following conditions:

[0095] Among them, the organic carbon content of Class I reservoirs is greater than 3%, porosity is greater than 5%, gas saturation is greater than 4%, and brittleness index is greater than 3%.

[0096] The organic carbon content of Class II reservoirs is 2-3%, porosity is 3-5%, gas saturation is 3-4%, and brittleness index is 2-3%.

[0097] The organic carbon content of Class III reservoirs is less than 2%, porosity is less than 3%, gas saturation is less than 3%, and brittleness index is less than 2%.

[0098] S203: Perform rock physics analysis on Class I reservoirs in the test well to obtain the rock physics relationship between the physical properties and elastic parameters of Class I reservoirs.

[0099] The physical properties are the aforementioned organic carbon content, porosity, gas saturation, and brittleness index. Since physical properties include more than just the parameters mentioned above, these are referred to as key physical properties. Elastic parameters include longitudinal wave impedance, transverse wave impedance, and density.

[0100] Rock physics analysis can bridge the gap between key physical parameters and elastic parameters, thus establishing a dynamic connection between geology and earthquakes.

[0101] Rock physical analysis is a prerequisite for geostatistical inversion, mainly to check the ability of elastic parameters to identify lithology.

[0102] S204: Well-seismic calibration is performed based on well logging curves and seismic records in the basic data.

[0103] Well-seismic calibration serves as a bridge between well logging data and seismic records. The accuracy of the calibration results directly affects the accuracy of seismic inversion. Well-seismic calibration involves calibrating sonic and density logging curves within the work area to establish a correspondence between the depth and time domains, conducting detailed reservoir calibration, and clarifying the reservoir's response characteristics on seismic profiles.

[0104] In this embodiment, S204 includes:

[0105] S2041: Based on the acoustic logging curve and density logging curve, obtain the longitudinal wave impedance of each test well.

[0106] When a seismic wave propagates in a medium, the ratio of the pressure acting on a unit area to the flow rate of particles passing perpendicularly through that area per unit time (i.e., area multiplied by the vibration velocity of the particles) has the meaning of resistance and is called wave impedance.

[0107] S2042: Adjust different wavelets for convolution to match the synthetic seismic record with the seismic record in the area under study, and obtain the correspondence between the time and depth of the test well.

[0108] S2042: Implemented in the following way:

[0109] (1) Extract wavelets from seismic records in the area to be studied.

[0110] Wavelet is a term commonly used in seismic exploration. It refers to the seismic waves generated by artificial shot points, which propagate in the underground medium and undergo reflection and refraction. The resulting pulse signal is then received by a geophone deployed on the ground. It has finite energy and a definite start time, and has 1-2 non-periodic phases.

[0111] In this embodiment of the disclosure, a wavelet can be extracted based on experience, according to the wavelet waveform in the seismic record.

[0112] (2) The longitudinal wave impedance of each test well is convolved with the extracted wavelet to obtain the synthetic seismic record of each test well.

[0113] In geophysics, convolution mainly refers to the process of seismic waves propagating in the subsurface medium. For example, a seismic signal A (P-wave impedance) is mixed with another signal (preset wavelet) and received by another station. The signal C received by the station is the convolution of A and B.

[0114] (3) Compare the synthetic seismic record with the seismic record in the area to be studied. If the synthetic seismic record is inconsistent with the seismic record in the area to be studied, then revise the preset wavelet until the synthetic seismic record is consistent with the seismic record in the area to be studied, and obtain the correspondence between time and depth.

[0115] In this embodiment, the seismic records include pre-processed pre-stack seismic data.

[0116] When preprocessing prestack seismic data, techniques such as prestack denoising, amplitude processing, surface consistency correction, and multiple suppression can be used to obtain prestack seismic data that meets the requirements of prestack inversion, with relatively consistent amplitude, high signal-to-noise ratio and resolution, and the best phase characteristics.

[0117] Prestack seismic data are prestack CRP (Common reflection point) gather data.

[0118] In seismic data acquisition, when the reflection interface is horizontal, different traces can always be found in different common shot point gathers on the survey line. They all come from a common point on the subsurface interface. This point is called the common depth point or common reflection point. The corresponding record traces with the common reflection point form the common reflection point gather, which is called the CRP gather.

[0119] (4) Using the correspondence between time and depth, the geological strata of each test well are converted from the depth domain to the time domain to complete the well-seismic calibration.

[0120] S205: Based on the results of well-seismic calibration, determine the time-depth relationship and reflection system, and extract the seismic wavelet.

[0121] When the synthetic seismic record matches the seismic record in the area under study, the corresponding preset wavelet is the seismic mean wavelet to be extracted.

[0122] Once the average wavelet of the earthquake is obtained, the reflection coefficient can be obtained according to formula (1) mentioned above, where the noise in formula (1) can be obtained empirically.

[0123] S206: Establish a low-frequency model.

[0124] Based on pre-stack seismic data, and constrained by well logging and drilling data, an initial geological framework that best reflects the geological characteristics of the subsurface sedimentary body is established using seismic interpretation horizons and faults, resulting in a low-frequency model.

[0125] Due to limitations of the seismic acquisition system, the obtained seismic data does not contain low-frequency components below 10Hz, so these must be extracted from other data to compensate for them.

[0126] Starting from seismic data and based on well logging and drilling data, a low-frequency model that basically reflects the geological characteristics of sedimentary bodies can be established.

[0127] S207: Seismic inversion is performed based on low-frequency models, time-depth relationships, and seismic wavelets to obtain the objective function and reflection coefficient in geostatistics. The objective function includes the probability density function and the spatial variation function.

[0128] The probability density function describes the probability distribution of a certain attribute in space, representing the probability distribution of elastic parameters corresponding to a specific rock.

[0129] The variogram is a measure of the spatial variability of regionalized variables. It reflects the variation of reservoirs in three-dimensional space. Using all the structural information provided by the variogram, it can be used to analyze and understand the geological problems under study.

[0130] In this embodiment, based on the well-seismic calibration results, the lithology type and number of each reservoir are first divided. Then, histogram statistics are performed on various data (P-wave impedance, S-wave impedance, etc.) for each lithology in each reservoir to determine the probability density function of each data for each lithology. Finally, the variation function is fitted to each lithology and its properties in each layer to obtain the variation function.

[0131] Taking a specific reservoir as an example, this reservoir may contain different types of lithology, such as granite and marble. Histograms are plotted on various data points related to the granite in this reservoir. The horizontal axis of the histogram can represent P-wave impedance or S-wave impedance, while the vertical axis represents the probability distribution. This allows the probability density function to be determined from the histogram.

[0132] S208: Based on the low-frequency model, objective function, and reflection coefficient, pre-stack geostatistical inversion is performed on the study area to obtain the target elastic parameters.

[0133] S208 includes:

[0134] S2081: Using the Markov chain Monte Carlo algorithm, the reflection coefficients are randomly inverted at least 10 times in the low-frequency model through the probability density function and the variogram function to obtain at least 10 implementations.

[0135] In this embodiment, when performing random inversion, several test wells are selected as well points. Under the condition of well point constraints, random inversion is performed to extract multiple possible implementations, no less than 10 times, corresponding to no less than 10 implementations, that is, the inversion generates 10 implementations of random paths.

[0136] The above-mentioned implementation refers to the impedance data obtained through inversion.

[0137] S2082: Perform statistical analysis on no fewer than 10 implementations, calculate the average value, and obtain the target elasticity parameter.

[0138] Since the 10 realizations obtained in the stochastic inversion are all different, it is necessary to calculate the average value of the data from multiple realizations in order to improve the accuracy of the target elasticity parameters.

[0139] By conducting statistical analysis on 10 implementations and calculating the average value, the final target elasticity parameter is obtained.

[0140] S209: Determine the continuous thickness of Class I reservoirs in the area under study based on the target elastic parameters and rock physical relationships.

[0141] Step S209 includes:

[0142] S2091: Based on the target elastic parameters and rock physical relationships, determine the key physical property parameters of Class I reservoirs in the area under study.

[0143] The target elastic parameter is the wave impedance information. By analyzing the wave impedance information and the rock physics relationship obtained from seismic rock physics analysis, the key physical property parameters of Class I reservoirs can be obtained.

[0144] S2092: Based on the key physical property parameters of Class I reservoirs in the area under study and the reservoir classification criteria, the seismic prediction data volume of Class I reservoirs is obtained.

[0145] Since the physical property parameters of each Class I reservoir obtained through step S2091 may not necessarily belong to Class I reservoirs according to the reservoir classification criteria in step S2022, it is necessary to reclassify the obtained physical property parameters of Class I reservoirs.

[0146] During the classification process, the Class I reservoirs in the study area are reclassified according to the calculated key physical property parameters of the Class I reservoirs and the reservoir classification criteria in step S2022. This yields the Class I reservoirs across the entire spatial area of ​​the study area. Correspondingly, the physical property parameters of the Class I reservoirs constitute the seismic prediction data volume.

[0147] In this embodiment, since the earthquake prediction data volume is obtained by random inversion, the reliability of the earthquake prediction data volume needs to be verified.

[0148] S2093: Verify the reliability of the seismic prediction data body based on well logging data and drilling data to determine the reliability of the seismic prediction data body.

[0149] The reliability of the seismic prediction data volume for Class I reservoirs in the study area is assessed. If the reliability of the seismic prediction data volume does not meet the requirements, the target elastic parameters and the seismic prediction data volume are obtained again until the seismic prediction data volume with the required reliability is obtained.

[0150] If the reliability of the seismic prediction data volume meets the requirements, the qualified seismic prediction data volume will be used as a parameter to determine the continuous thickness of Class I reservoirs in the area under study.

[0151] During the evaluation, the seismic prediction data volume (reflected on the graph as a lithological profile) is compared and analyzed with well logging data and drilling data (corresponding well logging curves). If the seismic prediction data volume obtained from the inversion around each test well is consistent with the well logging data of the test well, and the sand body connectivity between wells is good, it indicates that the target elastic parameters are accurate and the seismic prediction data volume meets the requirements. When the seismic prediction data is inconsistent with the well logging data and drilling data of the test well, the seismic prediction data volume does not meet the requirements, that is, the reliability of the obtained seismic prediction data volume is not high. In this case, it is necessary to perform random inversion again to obtain the target elastic parameters and seismic prediction data volume again.

[0152] S2094: Determine the continuous thickness of Class I reservoirs in the area under study based on the seismic prediction data of Class I reservoirs.

[0153] Since the earthquake prediction data obtained from the above inversion process is for each reservoir, once the earthquake prediction data is obtained, the formation depth of multiple Class I reservoirs in the reservoir corresponding to the earthquake prediction data can be obtained. Thus, the thickness of each Class I reservoir in the formation is the continuous thickness of each Class I reservoir.

[0154] This disclosure also provides an apparatus for determining the continuous thickness of Class I reservoirs, combined with... Figure 4The determination device includes: a rock physics relationship determination module 401, used to perform rock physics analysis on Class I reservoirs in the test wells in the study area to obtain the rock physics relationship between the physical properties and elastic parameters of Class I reservoirs; a well-seismic calibration module 402, used to perform well-seismic calibration on the test wells to determine the time-depth relationship and extract seismic wavelets; a low-frequency model establishment module 403, used to establish a low-frequency model; an objective function determination module 404, used to perform seismic inversion based on the low-frequency model, time-depth relationship and seismic wavelets to obtain the objective function and reflection coefficient in geostatistics, the objective function including the probability density function and spatial variation function; an objective elastic parameter determination module 405, used to perform pre-stack geostatistical inversion on the study area based on the low-frequency model, objective function and reflection coefficient to obtain the target elastic parameters; and a continuous thickness determination module 406, used to determine the continuous thickness of Class I reservoirs in the study area according to the target elastic parameters and rock physics relationship.

[0155] Optionally, the target elasticity parameter determination module 404 is further configured to: use the Markov chain Monte Carlo algorithm to perform no less than 10 random inversions on the low-frequency model through the probability density function and the variogram function to obtain no less than 10 realizations; perform statistical analysis on the no less than 10 realizations, calculate the average value, and obtain the target elasticity parameter.

[0156] Optionally, the continuous thickness determination module 405 is also used to: determine the key physical property parameters of Class I reservoirs in the area under study based on the target elastic parameters and rock physical relationships; and determine the continuous thickness of Class I reservoirs in the area under study based on the key physical property parameters of Class I reservoirs in the area under study and the reservoir classification criteria.

[0157] Optionally, the determining device further includes a reliability judgment module, which is used to judge the reliability of key physical property parameters of Class I reservoirs in the study area; if the reliability of the key physical property parameters does not meet the requirements, the target elastic parameters and key physical property parameters are obtained again until the key physical property parameters that meet the reliability requirements are obtained.

[0158] Optionally, the reliability judgment module is used to compare and analyze the key physical property parameters with the logging data and drilling data of the test well; when the key physical property parameters are consistent with the logging data and drilling data of the test well, the reliability of the key physical property parameters meets the requirements; when the key physical property parameters are inconsistent with the logging data and drilling data of the test well, the reliability of the key physical property parameters does not meet the requirements.

[0159] Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of this disclosure, combined with... Figure 5 The computer device 500 may include one or more of the following components: processor 501, memory 502, communication interface 503, and bus 504.

[0160] Processor 501 includes one or more processing cores. Processor 501 executes various functional applications and information processing by running software programs and modules. Memory 502 and communication interface 503 are connected to processor 501 via bus 504. Memory 502 can be used to store at least one instruction, which processor 501 uses to execute to implement the various steps in the above method.

[0161] Furthermore, memory 502 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, including but not limited to: magnetic disks or optical disks, electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), read-only memory (ROM), magnetic storage, flash memory, and programmable read-only memory (PROM).

[0162] This disclosure also provides a non-transitory computer-readable storage medium, which, when the instructions in the storage medium are executed by the processor of a computer device, enables the computer device to perform the method for determining the continuous thickness of Class I reservoirs provided in the embodiments of this application.

[0163] A computer program product containing instructions, when run on a computer, causes the computer to execute the method for determining the continuous thickness of Class I reservoirs provided in the embodiments of this application.

[0164] The above description is merely an optional embodiment of this disclosure and is not intended to limit this disclosure. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the protection scope of this disclosure.

Claims

1. A method for determining the continuous thickness of a Class I reservoir, characterized in that, The determination method includes: Rock physical analysis was performed on the Class I reservoirs of the test wells in the study area to obtain the rock physical relationship between the physical property parameters and elastic parameters of the Class I reservoirs. Well-seismic calibration is performed on the test wells to determine the time-depth relationship and extract seismic wavelets. The well-seismic calibration includes: obtaining the P-wave impedance of each test well based on sonic logging curves and density logging curves; extracting wavelets from seismic records in the area under study; performing convolution calculations on the P-wave impedance of each test well and the extracted wavelets to obtain synthetic seismic records for each test well; comparing the synthetic seismic records with the seismic records in the area under study; if the synthetic seismic records do not match the seismic records in the area under study, revising the preset wavelets until the synthetic seismic records match the seismic records in the area under study, thus obtaining the time-depth correspondence of the test wells; using the time-depth correspondence of the test wells, converting the geological strata of each test well from the depth domain to the time domain, thus completing the well-seismic calibration. A low-frequency model was established based on pre-stack seismic data. Under the constraints of well logging and drilling data, an initial geological framework reflecting the geological characteristics of the subsurface sedimentary body was established using seismic interpretation horizons and faults, thus obtaining the low-frequency model. Based on the low-frequency model, the time-depth relationship, and the seismic wavelet, seismic inversion is performed to obtain the objective function and reflection coefficient in geostatistics. The objective function includes the probability density function and the spatial variation function. Based on the low-frequency model, the objective function, and the reflection coefficient, pre-stack geostatistical inversion is performed on the area under study to obtain the target elastic parameters; Based on the target elastic parameters and the rock physical relationship, the continuous thickness of Class I reservoirs in the area under study is determined.

2. The determination method according to claim 1, characterized in that, Based on the low-frequency model, the objective function, and the reflection coefficient, pre-stack geostatistical inversion is performed on the area under study to obtain the target elastic parameters, including: Using the Markov chain Monte Carlo algorithm, the reflection coefficient is randomly inverted at least 10 times in the low-frequency model through the probability density function and the variogram function to obtain at least 10 implementations; Statistical analysis is performed on no fewer than 10 implementations, and the average value is calculated to obtain the target elasticity parameter.

3. The determination method according to claim 2, characterized in that, The step of determining the continuous thickness of Class I reservoirs in the area under study based on the target elastic parameters and the rock physical relationship includes: Based on the target elastic parameters and the rock physical relationship, the key physical property parameters of the Class I reservoir in the area under study are determined; Based on the key physical property parameters of Class I reservoirs in the area under study and the reservoir classification criteria, the continuous thickness of Class I reservoirs in the area under study is determined.

4. The determination method according to claim 3, characterized in that, Before determining the continuous thickness of Class I reservoirs in the area under study based on the key physical property parameters and reservoir classification criteria of Class I reservoirs in the area under study, the determination method further includes: The reliability of key physical property parameters of Class I reservoirs in the area under study is assessed. If the reliability of the key physical property parameter does not meet the requirements, the target elastic parameter and the key physical property parameter are obtained again until the key physical property parameter with acceptable reliability is obtained.

5. The determination method according to claim 4, characterized in that, The determination of the reliability of key physical property parameters of Class I reservoirs in the area under study includes: The key physical property parameters are compared and analyzed with the logging data and drilling data of the test well; When the key physical property parameters are consistent with the well logging data and drilling data of the test well, the reliability of the key physical property parameters meets the requirements; If the key physical property parameters are inconsistent with the well logging data and drilling data of the test well, then the reliability of the key physical property parameters does not meet the requirements.

6. A device for determining the continuous thickness of a Class I reservoir, characterized in that, The determining device includes: The rock physics relationship determination module is used to perform rock physics analysis on the Class I reservoirs of the test wells in the study area to obtain the rock physics relationship between the physical property parameters and elastic parameters of the Class I reservoirs. The well-seismic calibration module is used to perform well-seismic calibration on the test wells to determine the time-depth relationship and extract seismic wavelets. The well-seismic calibration includes: obtaining the P-wave impedance of each test well based on the sonic logging curve and density logging curve; extracting wavelets from the seismic records in the area under study; performing convolution calculation on the P-wave impedance of each test well and the extracted wavelets to obtain the synthetic seismic records of each test well; comparing the synthetic seismic records with the seismic records in the area under study; if the synthetic seismic records are inconsistent with the seismic records in the area under study, revising the preset wavelets until the synthetic seismic records are consistent with the seismic records in the area under study, thus obtaining the time-depth correspondence of the test wells; using the time-depth correspondence of the test wells, converting the geological strata of each test well from the depth domain to the time domain, thus completing the well-seismic calibration. The low-frequency model building module is used to build a low-frequency model. Based on pre-stack seismic data, and under the constraints of well logging and drilling data, it uses seismic interpretation horizons and faults to establish an initial geological framework reflecting the geological characteristics of the subsurface sedimentary body, thereby obtaining the low-frequency model. The objective function determination module is used to perform seismic inversion based on the low-frequency model, the time-depth relationship, and the seismic wavelet to obtain the objective function and reflection coefficient in geostatistics. The objective function includes a probability density function and a spatial variation function. The target elasticity parameter determination module is used to perform pre-stack geostatistical inversion on the area under study based on the low-frequency model, the objective function, and the reflection coefficient to obtain the target elasticity parameters; The continuous thickness determination module is used to determine the continuous thickness of the Class I reservoir in the area under study based on the target elastic parameters and the rock physical relationship.

7. The determining device according to claim 6, characterized in that, The target elasticity parameter determination module is also used for: Using the Markov chain Monte Carlo algorithm, the reflection coefficient is randomly inverted at least 10 times in the low-frequency model through the probability density function and the variogram function to obtain at least 10 implementations; Statistical analysis is performed on no fewer than 10 implementations, and the average value is calculated to obtain the target elasticity parameter.

8. The determining device according to claim 6, characterized in that, The continuous thickness determination module is also used for: Based on the target elastic parameters and the rock physical relationship, the key physical property parameters of the Class I reservoir in the area under study are determined; Based on the key physical property parameters of Class I reservoirs in the area under study and the reservoir classification criteria, the continuous thickness of Class I reservoirs in the area under study is determined.

9. A computer device, characterized in that, The computer device includes a processor and a memory configured to store processor-executable instructions; the processor is configured to perform the method for determining the continuous thickness of a Class I reservoir as described in any one of claims 1 to 5.

10. A computer storage medium storing computer instructions thereon, characterized in that, When the computer instructions are executed by the processor, they implement the method for determining the continuous thickness of Class I reservoirs as described in any one of claims 1 to 5.

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