Reservoir dynamic reserve prediction method, reserve production degree prediction method and device

Abstract

This application provides a method and apparatus for predicting the dynamic reserve utilization degree of an oil reservoir. The method for predicting the dynamic reserve utilization degree includes: acquiring formation pressure, surface crude oil density, natural gas relative density, and production gas-oil ratio measured during oil production; determining the crude oil volume factor under the original formation pressure based on the formation pressure, surface crude oil density, natural gas relative density, and production gas-oil ratio; determining the real-time crude oil volume factor during oil production based on the crude oil volume factor under the original formation pressure; and determining the dynamic reserves of the oil reservoir based on the crude oil volume factor under the original formation pressure, the real-time crude oil volume factor, and the corresponding comprehensive compressibility coefficient of the reservoir. The method for predicting the reserve utilization degree includes: acquiring the dynamic reserves of the oil reservoir; acquiring the static reserves of the oil reservoir; and predicting the reservoir reserve utilization degree based on the dynamic reserves and the static reserves. This method aims to achieve the effect of predicting the dynamic reserves and utilization degree of an oil reservoir.

Description

Technical Field

[0001] This application relates to the field of oil and gas extraction, and in particular to a method and apparatus for predicting the dynamic reserves of oil reservoirs and the degree of reserve utilization. Background Technology

[0002] In the process of oil and gas extraction, the prediction of dynamic reserves provides an important basis for extraction. Dynamic reserves, also known as dynamic capacity, movable capacity, or dynamic method reserves, are reserves calculated using dynamic methods. Dynamic reserves have the following characteristics: ① They can refer to both oil and gas reservoirs and individual wells; ② They are theoretically movable and are usually smaller than volumetric reserves; ③ They are obtained based on dynamic data; ④ They include both recoverable and unrecoverable reserves, representing a value between proven geological reserves and technically recoverable reserves, and this parameter is related to current technological levels and well patterns; ⑤ They are time-sensitive.

[0003] For fractured and broken reservoirs in ultra-deep formations, due to the depth of the formation and the complex structure of the reservoir, it is difficult to assess the dynamic reserves using relevant experimental techniques, such as PVT analysis, well test analysis, and dynamic reserve assessment after water injection. Consequently, it is difficult to accurately predict the extent of reserve utilization. Summary of the Invention

[0004] This application provides a method and apparatus for predicting the dynamic reserves of an oil reservoir, as well as a method and apparatus for predicting the degree of reserve utilization, in order to accurately predict the degree of reserve utilization in fractured and broken oil reservoirs.

[0005] In a first aspect, embodiments of this application provide a method for predicting dynamic oil reservoir reserves, including:

[0006] To obtain formation pressure, surface crude oil density, natural gas relative density, and production gas-oil ratio measured during the oil production process;

[0007] The crude oil volume factor under the original formation pressure is determined based on the formation pressure, surface crude oil density, natural gas relative density, and production gas-oil ratio.

[0008] The real-time crude oil volume factor during the oil extraction process is determined based on the crude oil volume factor under the original formation pressure.

[0009] The dynamic reserves of the reservoir are determined based on the crude oil volume factor under the original formation pressure, the real-time crude oil volume factor, and the reservoir's comprehensive compressibility factor.

[0010] In one possible implementation, the crude oil volume factor under the original formation pressure is determined based on formation pressure, surface crude oil density, natural gas relative density, and production gas-oil ratio, including:

[0011] By substituting formation pressure, surface crude oil density, natural gas relative density, and production gas-oil ratio into the prediction model, the crude oil volume factor under the original formation pressure is obtained. The prediction model is obtained by regression analysis of the crude oil volume factor under the original formation pressure based on experimental data.

[0012] In one possible implementation, determining the real-time crude oil volume factor during oil production based on the crude oil volume factor under the original formation pressure includes:

[0013] Based on the pressure-volume-temperature (PVT) experimental data of reservoirs with similar fluid properties to the described reservoir, a curve showing the relationship between the crude oil volume factor and formation pressure for reservoirs with similar fluid properties to the described reservoir was plotted.

[0014] Based on the crude oil volume factor under the original formation pressure, the original formation pressure, and the relationship curve between the crude oil volume factor and formation pressure of reservoirs with similar fluid properties to the reservoir, the relationship curve between the crude oil volume factor and formation pressure of the reservoir is reconstructed.

[0015] Secondly, this application provides a method for predicting the degree of oil reservoir utilization, including:

[0016] The dynamic reserves of the oil reservoir are obtained according to the first aspect above and / or various possible implementations of the first aspect;

[0017] Obtain the static reserves of the reservoir;

[0018] Based on the dynamic reserves and the static reserves of the reservoir, the degree of reservoir reserve utilization is predicted.

[0019] In one possible implementation, the degree of reservoir reserve utilization is predicted based on dynamic reserves and static reserves of the reservoir, including: predicting the degree of reservoir reserve utilization based on the ratio of dynamic reserves to static reserves of the reservoir.

[0020] In one possible implementation, the static reserves of the reservoir are obtained by means of:

[0021] The porosity distribution in the reservoir was obtained using geostatistical inversion.

[0022] The volume of the reservoir is obtained by volume integration of the pores in the reservoir.

[0023] A calibration template is created using three-dimensional forward modeling. The volume of the reservoir is then corrected using the calibration template to obtain the static reserves of the reservoir.

[0024] In one possible implementation, the elastic production capacity index of the reservoir is predicted based on dynamic reserves, crude oil volume factor, real-time crude oil volume factor, and reservoir comprehensive compressibility factor. The elastic production capacity index reflects the changes in the internal liquid supply units of the reservoir during the utilization of reservoir reserves.

[0025] In one possible implementation, the reservoir reserve utilization depth is determined based on the reservoir's static temperature-depth relationship curve and the corresponding flow temperature-depth relationship curve. The static temperature-depth relationship curve reflects the relationship between static temperature and depth, while the flow temperature-depth relationship curve reflects the relationship between flow temperature and depth.

[0026] Thirdly, embodiments of this application provide a reservoir dynamic reserve prediction device, comprising:

[0027] The acquisition module is used to acquire formation pressure, surface crude oil density, natural gas relative density, and production gas-oil ratio measured during the oil production process.

[0028] The processing module is used to determine the crude oil volume factor under the original formation pressure based on formation pressure, surface crude oil density, natural gas relative density, and production gas-oil ratio; and to determine the real-time crude oil volume factor during the oil extraction process based on the crude oil volume factor.

[0029] The processing module is also used to determine the dynamic reserves of the reservoir based on the crude oil volume factor, the real-time crude oil volume factor, and the reservoir's comprehensive compressibility factor.

[0030] Fourthly, embodiments of this application provide an oil reservoir utilization prediction device, comprising:

[0031] The dynamic reserves prediction module is used to predict the dynamic reserves of oil reservoirs.

[0032] The static reserves acquisition module is used to acquire the static reserves of the reservoir based on the geostatistical inversion method and the volumetric integral method.

[0033] The utilization degree prediction module is used to predict the utilization degree of the oil reservoir reserves based on the dynamic reserves and the static reserves of the oil reservoir.

[0034] Fifthly, embodiments of this application provide a computing device, including: a memory and a processor;

[0035] The memory stores computer-executed instructions;

[0036] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or the first aspect and / or the second aspect and / or various possible implementations of the second aspect as described above.

[0037] In a sixth aspect, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or the second aspect and / or various possible implementations thereof.

[0038] In a seventh aspect, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect and the second aspect and / or the second aspect.

[0039] The oil reservoir reserve utilization prediction method, device, storage medium, and program product provided in this application first determine the crude oil volume factor under the original formation based on formation pressure, surface crude oil density, natural gas relative density, and production oil-gas ratio. Then, based on the crude oil volume factor under the original formation, the real-time crude oil volume factor during oil production is determined. Based on the crude oil volume factor under the original formation, the real-time crude oil volume factor, and the comprehensive compressibility coefficient of the oil reservoir, the dynamic reserves of the oil reservoir are determined. Finally, based on the dynamic and static reserves of the oil reservoir, the oil reservoir reserve utilization degree is predicted, achieving the effect of accurately predicting the reserve utilization degree of fractured and broken oil reservoirs. Attached Figure Description

[0040] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0041] Figure 1 Flowchart of the method for predicting the extent of reservoir utilization provided in this application Figure 1 ;

[0042] Figure 2 This is a schematic diagram of the porosity inversion profile provided in this application;

[0043] Figure 3 This is a schematic diagram of the three-dimensional karst reservoir model provided in this application;

[0044] Figure 4 A schematic diagram of the forward modeling results based on the wave equation algorithm provided in this application;

[0045] Figure 5 A schematic diagram of the calibration template provided for this application;

[0046] Figure 6 This is a schematic diagram of the reservoir structure provided in this application;

[0047] Figure 7 A schematic diagram illustrating the changes in the flexible capacity index provided for this application;

[0048] Figure 8 This is a schematic diagram of an oil well production scenario provided in this application;

[0049] Figure 9 A schematic diagram of the reservoir dynamic reserve prediction device provided in this application;

[0050] Figure 10 A schematic diagram of the reservoir reserve utilization prediction device provided in this application;

[0051] Figure 11 A schematic diagram of the computing device provided in this application.

[0052] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0053] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0054] In the process of oil and gas extraction, the prediction of dynamic reserves provides an important basis for extraction. Dynamic reserves, also known as dynamic capacity, movable reserves, or dynamic method reserves, are reserves calculated using dynamic methods. Dynamic reserves have the following characteristics: ① They can refer to both oil and gas reservoirs and individual wells; ② They are theoretically movable and are usually smaller than volumetric reserves; ③ They are obtained based on dynamic data; ④ They include both recoverable and unrecoverable reserves, representing a value between proven geological reserves and technically recoverable reserves, and this parameter is related to current technological levels and well patterns; ⑤ They are time-sensitive. Generally, dynamic reserves can characterize the degree of reservoir exploitation, and the exploitability of an oil reservoir can be predicted by predicting the value of dynamic reserves.

[0055] Currently, commonly used methods for predicting dynamic reserves include pressure-volume-temperature (PVT) analysis, well test analysis, and post-water injection dynamic reserve assessment. For karst or constant-volume reservoirs, the dynamic reserves obtained by these methods are relatively reliable, and the degree of reservoir reserve utilization can be accurately predicted based on the dynamic reserves.

[0056] Fracture-breach reservoirs, also known as fracture-controlled reservoirs, are generally located in ultra-deep formations (below 6000 meters), primarily developing within tight limestone strata in ultra-deep carbonate platforms. The reservoir space types are mainly inter-clast pores, fracture cavities, and tectonic fractures formed by fractures. Due to different controlling factors, fracture-breach reservoirs differ significantly from karst or constant-volume reservoirs in terms of reservoir development location, morphology, internal structure, reservoir filling characteristics, and production characteristics. Therefore, commonly used dynamic reserve prediction methods, such as PVT analysis, well test analysis, and post-injection dynamic reserve assessment, can accurately predict dynamic reserves for karst or constant-volume reservoirs. However, due to the deep formations of fracture-breach reservoirs in ultra-deep formations, these techniques cannot predict dynamic reserves in such conditions. Furthermore, the complex reservoir space structure of fracture-breach reservoirs makes it difficult to experimentally determine their dynamic reserves and exploitability.

[0057] To address the aforementioned problems, this application provides a method for predicting dynamic oil reservoir reserves. First, the crude oil volume factor beneath the original formation is determined based on formation pressure, surface crude oil density, natural gas relative density, and the production oil-gas ratio. Then, the real-time crude oil volume factor during oil production is determined based on the crude oil volume factor beneath the original formation. Finally, the dynamic reserves of the oil reservoir are determined based on the crude oil volume factor beneath the original formation, the real-time crude oil volume factor, and the overall compressibility coefficient of the reservoir. This application also provides a method for predicting the degree of oil reservoir reserve utilization. After obtaining the dynamic reserves, the degree of oil reservoir reserve utilization is comprehensively predicted based on the dynamic and static reserves.

[0058] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0059] The method for predicting reservoir dynamic reserves and reservoir reserve utilization provided in this application can be applied to fractured reservoirs that meet the following characteristics: the reservoir spatial structure is basically clear, and the reservoir prediction and porosity prediction meet the development requirements; it is an elastically driven reservoir with no external water energy supply; it only considers the reserve utilization of large fractured-vuggy reservoirs within the well-controlled area, and the uncontrolled areas are basically unutilized and not considered for the time being; and the reservoir has not been degassed during the oil extraction process.

[0060] Figure 1 Flowchart of the reservoir dynamic reserve prediction method provided in this application Figure 1 ,like Figure 1 As shown, the method includes:

[0061] S101. Obtain formation pressure, surface crude oil density, natural gas relative density, and production gas-oil ratio measured during the oil production process.

[0062] Formation pressure refers to the pressure of oil, gas, and water within the pores of a formation. Formation pressure is related to the depth of the formation and is generally expressed as the pressure of the hydrostatic column connecting the formation to the surface. Furthermore, the formation pressure of an oil reservoir is also related to the sealing conditions of the structure. Surface crude oil density refers to the mass of one cubic meter of crude oil under standard conditions (usually 20℃, 0.1 MPa). The relative density of natural gas is the ratio of the density of natural gas under the same pressure and temperature conditions to the density of dry air; the specific value may vary depending on the composition and conditions of the natural gas. The production gas ratio refers to the volume of natural gas carried out per ton of crude oil produced during well production, when oil and gas are simultaneously discharged from the well.

[0063] In one embodiment, a formation tester is lowered into the oil well to measure formation pressure and obtain fluid samples. Analysis of the fluid samples yields the surface crude oil density, natural gas relative density, and production gas-oil ratio. Alternatively, formation pressure, surface crude oil density, natural gas relative density, and production gas-oil ratio can also be obtained through other experimental methods; this application does not limit the methods used to obtain these parameters.

[0064] S102. Determine the crude oil volume factor under the original formation pressure based on the formation pressure, surface crude oil density, natural gas relative density, and production gas-oil ratio.

[0065] The crude oil volume factor under original formation pressure refers to the ratio of the volume of crude oil in the formation before human extraction to its volume after degassing at the surface. As oil wells are extracted, the crude oil volume factor changes with variations in formation pressure.

[0066] In one implementation, the crude oil volume factor under the original formation is determined by establishing a predictive model of the crude oil volume factor under the original formation.

[0067] S103. Determine the real-time crude oil volume coefficient during the oil extraction process based on the crude oil volume coefficient under the original formation pressure.

[0068] During the oil extraction process, the formation pressure changes with activities such as water injection and oil and gas extraction. The real-time crude oil volume coefficient changes with the formation pressure. By obtaining the real-time formation pressure during the extraction process, the real-time crude oil volume coefficient can be determined based on the relationship between the crude oil volume coefficient and the formation pressure under different formation pressures.

[0069] S104. Determine the dynamic reserves of the reservoir based on the crude oil volume factor under the original formation pressure, the real-time crude oil volume factor, and the reservoir's comprehensive compressibility factor.

[0070] The reservoir compressibility coefficient refers to the change in volume of reservoir fluid per unit volume under a unit pressure change.

[0071] In one implementation, the crude oil compressibility coefficient is first calculated, and then the crude oil compressibility coefficient is added to the rock compressibility coefficient to obtain the reservoir comprehensive compressibility coefficient.

[0072] The formula for calculating the compressibility coefficient of crude oil is:

[0073]

[0074] Among them, C oil P is the compressibility coefficient of crude oil, and P is the formation pressure at a certain moment. i denoted as the original formation pressure, b is an index characterizing the trend of crude oil volume coefficient with formation pressure, and ΔP is the oil pressure drop.

[0075] The crude oil compressibility coefficient C was calculated. oil Afterwards, the reservoir's overall compressibility coefficient C eff =C oil +C rock C rock is the rock's compressibility coefficient, obtained from geological experimental studies.

[0076] After obtaining the crude oil volume factor under initial formation pressure, the real-time crude oil volume factor, and the corresponding comprehensive compressibility factor of the reservoir, the dynamic reserves of the reservoir can be determined.

[0077]

[0078] Where N is the dynamic reserve, N p It is the cumulative output of a stage, B o B represents the real-time crude oil volume factor. oi C is the crude oil volume factor under the original formation pressure. eff ΔP is the reservoir's overall compressibility coefficient, and ΔP is the oil pressure drop.

[0079] In one implementation, a production indicator curve is plotted based on the relationship between dynamic reserves N and oil pressure drop ΔP. The production indicator curve can visually display the dynamic reserve changes of the reservoir. By combining dynamic reserves and static reserves, the availability of reservoir reserves can be predicted.

[0080] In the above embodiments, the crude oil volume factor under the original formation pressure is first determined based on the formation pressure, surface crude oil density, natural gas relative density, and production gas-oil ratio. Then, the real-time crude oil volume factor during the oil production process is predicted based on the crude oil volume factor under the original formation pressure. Combining the crude oil volume factor under the original formation pressure, the real-time crude oil volume factor, and the reservoir's overall compressibility coefficient, dynamic reserves are predicted. The method for predicting the exploitability of reservoir reserves in this application emphasizes the use of data prediction methods, reducing reliance on experimental data. It can accurately predict the dynamic reserves of fractured reservoirs in ultra-deep formations where complex experimental environments are difficult to establish. Furthermore, the method for predicting dynamic reservoir reserves provided in this application also gives a specific method for obtaining the crude oil volume factor under the original formation pressure, which will be described in detail below.

[0081] In one implementation, formation pressure, surface crude oil density, natural gas relative density, and production gas-oil ratio are substituted into the prediction model to obtain the crude oil volume factor under the original formation pressure. The prediction model is obtained by regression analysis of the crude oil volume factor under the original formation pressure based on experimental data.

[0082] In one implementation, based on statistical principles, regression analysis methods such as least squares, stepwise regression, partial least squares regression, Lasso method, and ridge regression are used. Using experimental data, linear regression analysis is conducted on the crude oil volume factor under formation pressure, surface crude oil density, natural gas relative density, production gas-oil ratio, and original formation pressure. The linear relationship model between the crude oil volume factor under original formation pressure and formation pressure, surface crude oil density, natural gas relative density, and production gas-oil ratio is obtained as a prediction model. The measured formation pressure, surface crude oil density, natural gas relative density, and production gas-oil ratio during the extraction process are substituted into the prediction model to determine the crude oil volume factor under original formation pressure.

[0083] For example, build a model

[0084] B oi =f(P,GOR,ρ0,ρ g )

[0085] Among them, B oi ρ is the crude oil volume factor under the original formation pressure; P is the formation pressure; GOR is the production oil-gas ratio; ρ0 is the surface crude oil density; ρ g This represents the relative density of natural gas.

[0086] f(·) represents the linear relationship between the above parameters. For example, the model can be specifically written as:

[0087] B oi =a1P+a2GOR+a3ρ0+a4ρ g

[0088] Where a1, a2, a3, and a4 are constants representing the proportionality coefficients of each variable. Using the least squares method and experimental data for each parameter, linear regression analysis was performed on each parameter to obtain the optimal values ​​of a1, a2, a3, and a4, determine the model expression, and incorporate the measured values ​​of P, GOR, ρ0, and ρ during the mining process. g By substituting the value into the defined model expression, the crude oil volume factor B under the original formation pressure can be obtained. oi .

[0089] The oil reservoir dynamic reserve prediction method provided in this application uses regression analysis to establish a crude oil volume factor prediction model under the original formation pressure. Based on the formation pressure, surface crude oil density, natural gas relative density, and production gas-oil ratio, the crude oil volume factor under the original formation pressure is predicted. For fractured and broken reservoirs, compared with experimental measurement methods, this method is less difficult and more accurate, and can improve the efficiency and accuracy of predicting the exploitability of fractured and broken reservoirs.

[0090] In one embodiment, determining the real-time crude oil volume factor during oil extraction based on the crude oil volume factor under the original formation pressure includes the following steps:

[0091] S201. Based on the pressure-volume-temperature (PVT) experimental data of reservoirs with similar fluid properties, plot the relationship curve between the crude oil volume factor and formation pressure of reservoirs with similar fluid properties.

[0092] In one implementation, PVT experiments are performed on reservoir samples to obtain the relationship between the crude oil volume factor and the formation pressure under different formation pressures. Based on multiple sets of experimental data, the relationship between the crude oil volume factor and the formation pressure under different formation pressures can be fitted into a power-law curve.

[0093] For example, the relationship between the crude oil volume factor and formation pressure under different formation pressures in reservoirs with similar fluid properties to those to be predicted is expressed as follows:

[0094] B = k1P b

[0095] Where B is the crude oil volume coefficient, k1 is a constant, P is the formation pressure, and b is a constant.

[0096] S202. Based on the relationship curves between the crude oil volume factor and formation pressure under the original formation pressure, the original formation pressure, and the crude oil volume factor and formation pressure of reservoirs with similar fluid properties, reconstruct the relationship curve between the crude oil volume factor and formation pressure of the reservoir.

[0097] Assuming that in regions with similar fluid properties, the trend of crude oil volume factor variation with formation pressure is consistent, the index b can be determined based on the relationship curve between crude oil volume factor and formation pressure under different formation pressures in reservoirs with similar fluid properties to the reservoir to be predicted. The expression for the relationship curve between crude oil volume factor and formation pressure under different formation pressures in the reservoir to be predicted is assumed to be...

[0098] B o =kP b

[0099] Among them, B o is the crude oil volume coefficient, k is a constant, P is the formation pressure, and b is a constant.

[0100] The crude oil volume factor B beneath the original strata of the reservoir to be predicted is... oi Substituting the original formation pressure into the above formula, we can determine the coefficient k, thereby obtaining the relationship curve expression between the crude oil volume factor and the formation pressure under different formation pressures in reservoirs with similar fluid properties to the reservoir to be predicted.

[0101] The oil reservoir dynamic reserve prediction method provided in this application provides a method for predicting oil reserves in ultra-deep fractured reservoirs where it is difficult to establish experimental data collection and calculation of crude oil volume coefficients under different formation pressures. First, the crude oil volume coefficient under the original formation pressure is obtained. Then, by analogy with the relationship curve between the crude oil volume coefficient and formation pressure of reservoirs with similar fluid properties, the relationship curve between the crude oil volume coefficient and formation pressure of the reservoir is reconstructed. Finally, the crude oil volume coefficient under different formation pressures is predicted through theoretical calculations, reducing the difficulty of obtaining the crude oil volume coefficient and improving the efficiency of oil and gas exploration and production.

[0102] Based on the oil reservoir dynamic reserve prediction method in the above embodiments, this application also provides a method for predicting the degree of oil reservoir reserve utilization, including the following steps:

[0103] S301. Obtain the dynamic reserves of the oil reservoir, wherein the dynamic reserves are obtained according to various possible implementation methods described in the above embodiments.

[0104] S302. Obtain the static reserves of the oil reservoir.

[0105] Static reserves refer to reserves calculated using static geological parameters. Static reserves are obtained through geological and geophysical exploration, well logging, geochemistry, rock and fluid experimental analysis, and mainly include parameters such as oil-bearing area, effective thickness, effective porosity, and oil saturation of the reservoir.

[0106] In one implementation, the static reserves of an oil reservoir can be obtained using geostatistical inversion and volumetric integration methods, the specific methods of which will be described in the following embodiments.

[0107] S303. Based on the dynamic reserves and the static reserves of the reservoir, predict the utilization level of the reservoir reserves.

[0108] In one embodiment, the above-described method for predicting the utilization level of oil reservoir reserves can be used to predict the utilization level of oil reservoirs containing multiple reservoirs, or a certain reservoir within an oil reservoir can be regarded as a sub-reservoir and its utilization level can be predicted separately.

[0109] The oil reservoir utilization prediction method provided in this application uses theoretical prediction to predict the dynamic reserves of the oil reservoir, and then predicts the utilization degree of the oil reservoir reserves. This reduces the difficulty of predicting the utilization degree of oil reservoir reserves in fractured bodies under ultra-deep strata. At the same time, it comprehensively considers the dynamic and static reserves of the oil reservoir, and improves the reliability of the oil reservoir utilization degree prediction results.

[0110] In one implementation, the degree of reservoir reserve utilization is predicted based on the ratio of dynamic reserves to static reserves.

[0111] Furthermore, the degree of reservoir utilization can be classified based on the ratio of dynamic reserves to static reserves by setting a threshold. For example, when the ratio of dynamic reserves to static reserves is greater than the threshold, the reservoir is predicted to be of a strong utilization type; when the ratio is less than the threshold, the reservoir is predicted to be of a weak utilization type.

[0112] For example, a threshold of 0.6 is set. When the ratio of dynamic reserves to static reserves is greater than 0.6, the predicted utilization level of reservoir reserves is strong; when the ratio of dynamic reserves to static reserves is less than 0.6, the predicted utilization level of reservoir reserves is weak.

[0113] In one implementation, multiple thresholds can be set, and the utilization level of oil reservoir reserves can be divided into multiple levels based on the relationship between the ratio of dynamic reserves to static reserves and the threshold size.

[0114] In one implementation, a production indicator curve is plotted based on the relationship between dynamic reserves and oil pressure drop. The production indicator curve can intuitively display the dynamic reserve changes of the reservoir. By combining the relationship between the ratio of dynamic reserves to static reserves and a threshold, the availability of reservoir reserves can be intuitively predicted.

[0115] The method for predicting the degree of oil reservoir utilization provided in this application predicts the strength of oil reservoir utilization based on the ratio of dynamic reserves to static reserves. By setting thresholds, the degree of oil reservoir utilization is classified to obtain the prediction results of the degree of oil reservoir utilization, providing a basis for subsequent oil reservoir exploitation.

[0116] In one possible implementation, the static reserves of the reservoir are obtained using geostatistical inversion and volumetric integration methods, including the following steps:

[0117] S401. Obtain the porosity distribution in the reservoir through geostatistical inversion.

[0118] Geostatistical inversion, based on well logging and seismic geological information, combines stochastic function theory, geostatistical methods, and traditional inversion theory. Because well logging data has high vertical resolution, the vertical variability function is calculated from well logging data, while the horizontal variability function is calculated from horizontally continuous seismic data. This allows for the accurate determination of variability functions in any direction and reflects the spatial variation characteristics of reservoirs. Therefore, geostatistical inversion combines the advantages of high vertical resolution of well logging data and high horizontal resolution of seismic data, providing parameter basis for volumetric reservoir calculation.

[0119] First, the target formations are classified based on total porosity. For example, in the study area, the lithology is mainly sparry sandstone and sparry bioclastic limestone, with a clay content generally below 5%. Core porosity varies from 1.5% to 4.2%, with an average of 3.2%. Well logging porosity varies from 0.117% to 72.395% due to lost circulation and venting in some wells, with an average of 2.42%. Pore types include large cavities, solution cavities, and fractures. Reservoir types in geostatistical inversion are classified based on total porosity calculated from well logging according to the following criteria: Reservoir: Filled fractured-cavitary reservoirs with a total porosity greater than 1.5%; Matrix: Total porosity less than 1.5%.

[0120] Secondly, the probability density distribution functions of rock elastic parameters corresponding to specific lithologies of different strata were determined. Based on the understanding gained from rock physical analysis and combined with the geological characteristics of the study area, the numerical distribution characteristics of the inverted elastic parameters were constrained using the probability density functions of reservoir types. Analysis of well logging data can help determine the probability density functions of each reservoir type in the study area. However, since current research suggests that the reservoirs in the study area are located at the top of the formation, drilling too deep often leads to rapid water flooding, and wells often experience lost circulation and venting when encountering high-quality limestone reservoirs. Therefore, well completion is generally chosen at this point. Most wells in the study area lack real well logging data for the main reservoir portion, and data directly from well logging statistics is not applicable to the study of the area. Through optimized analysis of wells with relatively deep drilling and reliable data in the study area, and after completing basic well logging data processing and rock physical analysis, it was found that the total porosity and P-wave impedance of the limestone reservoirs in this region have a good correlation. For example, the probability density distribution function of the rock elastic parameters of the matrix is ​​defined as a Gaussian probability density function with a mean of 1.635e7 kg / m³*m / s and a standard deviation of 3.2e5 kg / m³*m / s; the probability density distribution function of the rock elastic parameters of the reservoir is defined as a log-Gaussian probability density function with a mean of 1.4e7 kg / m³*m / s, a standard deviation of 1e6 kg / m³*m / s, and a maximum value of 1.55e7 kg / m³*m / s.

[0121] In addition, it is necessary to estimate the lithological proportions of the strata. Lithological proportions are based on the geological understanding of the region and act as a soft constraint on geostatistical inversion, allowing the results to incorporate more geological information. These include surface karst zones, vertically seeping karst zones, and runoff karst zones. Surface karst zones, affected by vertical seepage of surface water and shallow runoff, form numerous but relatively small caves. In vertically seeping karst zones, groundwater leaches and dissolves downwards along faults or fissures, easily forming a series of vertically or high-angled caves. In runoff karst zones, the accumulation of leached water from above creates strong underground runoff, easily forming large-scale karst conduits (such as underground rivers). Traditional methods can estimate the lithological proportions of each lithology in the work area by statistically analyzing the lithology defined above ground.

[0122] Finally, based on the stratigraphic classification, the probability density distribution function of rock elastic parameters corresponding to specific lithologies of different stratigraphic types, and the lithological proportions of the stratigraphy, the time-domain impedance volume distribution in the stratigraphy can be obtained. Using time-depth transformation, the depth-domain impedance volume distribution can be obtained. Then, porosity co-simulation technology is used to perform porosity volume inversion to obtain the pore distribution in the stratigraphy. The porosity inversion profile is shown below. Figure 2 As shown.

[0123] Among them, the porosity co-simulation adopts the "cloud transformation" method. The "cloud transformation" is based on the statistics of actual data and describes the relationship between reservoir P-wave impedance and porosity using the probability density function method. It uses nonlinear table transformation to describe and simulate data with non-Gaussian distribution probability, rather than the linear transformation in traditional methods. It fully considers the fact that there is a certain standard deviation in the porosity range corresponding to a single P-wave impedance value.

[0124] S402. The volume of the reservoir is obtained by volume integration of the pores in the reservoir.

[0125] A porosity threshold value is set for the reservoir, and the porosity volume in the depth domain is sculpted to obtain fracture-vuggy bodies with high porosity values ​​only at the locations of the effective reservoir. Volume integration is performed on each fracture-vuggy body to obtain its seismic apparent volume. Volume integration is a special solution proposed in this invention to address the heterogeneity of the reservoir. It employs a point-by-point accumulation method, calculating the volume of each point in the reservoir individually and then summing them. Only in this way can the calculation accuracy meet the requirements for calculating the volume of small-scale heterogeneous geological bodies. For example, for a depth domain data volume with a surface area of ​​20m × 20m and a sampling rate of 5m, assuming the porosity of the j-th element in the i-th fracture-vuggy body is φ... ij Then the volume of the slit is:

[0126]

[0127] Where V i This represents the volume of the i-th fissure, which is obtained using seismic data and can be considered as the "apparent volume" in the seismic sense.

[0128] S403. A calibration template is created through three-dimensional forward modeling. The volume of the reservoir is then corrected using the calibration template to obtain the static reserves of the reservoir.

[0129] To obtain the "true volume" in a geological sense, three-dimensional forward modeling is needed to determine the amplification effect of earthquakes on the geological model in terms of width, and then create a calibration template. This template can be used to correct the "apparent volume" to the "true volume," with an error that meets the standards for reserve calculation.

[0130] Using 3D forward modeling software, a 3D karst reservoir model is established, such as... Figure 3 As shown, multiple fractured-vuggy reservoir models were established based on different distances between the fractured-vuggy body and the buried hill surface, different oil-water density velocities during filling of the fractured-vuggy reservoir, the number of longitudinally developed fractured-vuggy reservoirs, and different volumes, among other factors. Forward modeling based on the wave equation algorithm was then performed. Figure 4As shown. The impedance values ​​of the fractured-vuggy reservoirs were obtained through geostatistical inversion. The impedance range of the fractured-vuggy reservoirs was set for fracture sculpting. The calculated volume was compared with the volume designed in the original model, and a correction template was created between the two. Figure 5 As shown, this provides a basis for calculating the true volume of actual storage tanks.

[0131] When converting the seismic "apparent volume" into the geological "true volume", the corresponding correction amount is found in the gauge based on the width of the inversion feature corresponding to the fracture-cavity body. Then, the integral result of the actual calculated volume is divided by this correction amount to obtain the true volume. Then, according to the volumetric method reserve calculation formula, the static reserves of the oil reservoir can be obtained.

[0132] The method for predicting the utilization of oil reservoir reserves provided in this application uses geostatistical inversion, volumetric integration, and calibration using three-dimensional forward modeling. It focuses on using statistical methods to predict the static reserves of oil reservoirs, reducing reliance on field tests and lowering measurement costs and difficulty.

[0133] In addition to predicting the utilization degree of reservoir reserves based on dynamic and static reserves, the reservoir reserve utilization degree prediction method provided in this application also enhances the accuracy of the prediction results from aspects such as the elastic production capacity index. This aspect will be described in detail below.

[0134] In one implementation, the elastic production capacity index of the reservoir is predicted based on dynamic reserves, crude oil volume factor, real-time crude oil volume factor, and reservoir comprehensive compressibility factor.

[0135] The elastic productivity index reflects the changes in the internal fluid supply units of an oil reservoir during the utilization of its reserves. It is defined as the fluid production per unit pressure drop. For reservoirs with stable fluid supply, the elastic productivity index remains constant. However, it changes as new reservoirs are introduced or the reservoir's energy weakens during extraction.

[0136] like Figure 6 As shown, Figure 6 There are three oil reservoirs: reservoir A, reservoir B, and reservoir C. Each reservoir is divided into several reservoir groups. For example, reservoir A has four reservoir groups: reservoir group 1, reservoir group 2, reservoir group 3, and reservoir group 4; reservoir B has two reservoir groups: reservoir group 5 and reservoir group 6; and reservoir C has three reservoir groups: reservoir group 7, reservoir group 8, and reservoir group 9.

[0137] As oil wells are continuously explored and extracted, the reservoir within the oil field may be continuously interconnected. The cumulative production of the aforementioned oil field will change with pressure variations, such as... Figure 7 As shown. E pia E pib and E picThese represent the relationship between cumulative output and pressure reduction for storage groups A, B, and C, respectively. The elastic capacity index is for each... Figure 4 The slope of each line segment, combined with Figure 6 The distribution and exploitation status of reservoirs in each reservoir can be understood. In reservoir A, during exploitation, fluid was initially supplied by reservoirs 1 and 2, followed by the connection of reservoirs 3 and 4. Therefore, the line segment shows two turning points, and the slope changes, representing the change in the elastic productivity index brought about by the connection of reservoirs. Reservoirs B and C have relatively dispersed reservoir spatial distributions, and no new reservoirs were connected during exploitation, so the slope did not change, meaning the elastic productivity index remained unchanged.

[0138] As the above analysis shows, the elasticity productivity index can clearly indicate changes in the reservoir's internal fluid supply capacity. Based on dynamic reserves, crude oil volume factor, real-time crude oil volume factor, and reservoir comprehensive compressibility factor, the reservoir's elasticity productivity index can be predicted. Specifically, it can be calculated using the following formula.

[0139]

[0140] Among them, E pi This represents the elastic capacity index, where N represents dynamic reserves, and B represents the dynamic reserves. oi B represents the crude oil volume coefficient. o C represents the real-time crude oil volume coefficient. eff This represents the overall compressibility coefficient of the reservoir.

[0141] By calculating the elasticity production capacity index and observing its changes, we can predict the communication and production capacity of the reservoir within the oil reservoir.

[0142] The oil reservoir utilization prediction method provided in this application, in addition to predicting the utilization level of oil reservoir reserves, also predicts the reservoir's fluid supply capacity by calculating the reservoir's elastic production capacity index, providing more reference data for oil reservoir development. Furthermore, the reservoir's utilization depth is also an important piece of information for oil reservoir development. The oil reservoir utilization prediction method provided in this application also includes the prediction of the reservoir's utilization depth, which will be discussed in detail below.

[0143] In one implementation, the reservoir reserve utilization depth is determined based on the reservoir's static temperature-depth relationship curve and the corresponding flow temperature-depth relationship curve.

[0144] The static temperature-depth curve reflects the relationship between static temperature and depth, while the flow temperature-depth curve reflects the relationship between flow temperature and depth. Activated depth refers to the vertical distance from the bottom of the well to the point where fluid begins to flow upwards during reservoir production. Obtaining activated depth data can further predict oil column height and reservoir reserves to formulate reasonable production plans.

[0145] like Figure 8 As shown, during oil production, fluids from the reservoir flow towards the well. However, beyond a certain depth, the fluids no longer flow towards the well. Figure 8 As shown in the diagram, above the dynamic-static boundary line, the fluid flows towards the oil well; below the boundary line, the fluid is stationary. The operational depth refers to the distance from the dynamic-static boundary line to the bottom of the well. Figure 8 As shown in D in the diagram.

[0146] When the diameter of the nozzle is fixed, the oil well is in a stable fluid supply production state for a period of time. Stable flow temperature-depth relationship curves and static temperature-depth relationship curves can be obtained through testing. It can be understood that at the operating depth, the flow temperature and static temperature are equal. Therefore, the depth at the intersection of the flow temperature-depth relationship curve and the static temperature-depth relationship curve is the operating depth.

[0147] Therefore, using the test data, the formula for fitting the static temperature-depth curve is as follows:

[0148] T i =T oi +G ti D

[0149] Among them, T i For static temperature, T oi At initial static temperature, G ti Let be the slope of the static temperature fitting, and D be the depth at a certain moment.

[0150] The flow temperature-depth fitting formula is:

[0151] T f =T of +G tf D

[0152] If the static temperature and the flow temperature are equal at the operating depth, then combining the above two formulas yields...

[0153]

[0154] Among them, D p To utilize depth.

[0155] The method for predicting the exploitability of oil reservoir reserves provided in this application, in addition to predicting the exploitability of oil reservoir reserves based on dynamic and static reserves, also predicts the exploitability depth of the oil reservoir based on static temperature-depth and flow temperature-depth curves, providing more comprehensive basis for the production and exploitation of oil reservoirs.

[0156] Figure 9 A schematic diagram of the reservoir dynamic reserve prediction device provided in this application is shown below. Figure 9As shown, the reservoir utilization prediction device 90 provided in this embodiment includes:

[0157] The acquisition module 901 is used to acquire formation pressure, surface crude oil density, natural gas relative density, and production gas-oil ratio measured during the oil production process.

[0158] The processing module 902 is used to determine the crude oil volume factor under the original formation pressure based on the formation pressure, surface crude oil density, natural gas relative density and production gas-oil ratio; and to determine the real-time crude oil volume factor during the oil extraction process based on the crude oil volume factor.

[0159] The processing module 902 is also used to determine the dynamic reserves of the reservoir based on the crude oil volume factor, the real-time crude oil volume factor, and the reservoir's comprehensive compressibility factor.

[0160] In one possible implementation, the processing module 902 is further configured to:

[0161] By substituting formation pressure, surface crude oil density, natural gas relative density, and production gas-oil ratio into the prediction model, the crude oil volume factor under the original formation pressure is obtained. The prediction model is obtained by regression analysis of the crude oil volume factor under the original formation pressure based on experimental data.

[0162] In one possible implementation, the processing module 902 is further configured to:

[0163] Based on the pressure-volume-temperature (PVT) experimental data of reservoirs with similar fluid properties to the described reservoir, a curve showing the relationship between the crude oil volume factor and formation pressure for reservoirs with similar fluid properties to the described reservoir was plotted.

[0164] Based on the crude oil volume factor under the original formation pressure, the original formation pressure, and the relationship curve between the crude oil volume factor and formation pressure of reservoirs with similar fluid properties to the reservoir, the relationship curve between the crude oil volume factor and formation pressure of the reservoir is reconstructed.

[0165] Figure 10 A schematic diagram of the reservoir reserve utilization prediction device provided in this application is shown below. Figure 10 As shown, the reservoir utilization prediction device 100 provided in this application embodiment includes:

[0166] The dynamic reserve prediction module 1001 is used to predict the dynamic reserves of oil reservoirs.

[0167] The static reserves acquisition module 1002 is used to acquire the static reserves of the reservoir based on the geostatistical inversion method and the volumetric integral method.

[0168] The utilization degree prediction module 1003 is used to predict the utilization degree of the oil reservoir reserves based on the dynamic reserves and the static reserves of the oil reservoir.

[0169] In one possible implementation, the utilization degree prediction module 1003 is used to: predict the utilization degree of reservoir reserves based on the ratio of dynamic reserves to static reserves of the reservoir.

[0170] In one possible implementation, the static storage acquisition module 1002 is used for:

[0171] The porosity distribution in the reservoir was obtained using geostatistical inversion.

[0172] The volume of the reservoir is obtained by volume integration of the pores in the reservoir.

[0173] A calibration template is created using three-dimensional forward modeling. The volume of the reservoir is then corrected using the calibration template to obtain the static reserves of the reservoir.

[0174] In one possible implementation, the reservoir reserve utilization prediction device 100 further includes: an elastic production capacity index prediction module 1004, which is used to predict the elastic production capacity index of the reservoir based on dynamic reserves, crude oil volume coefficient, real-time crude oil volume coefficient and reservoir comprehensive compressibility coefficient. The elastic production capacity index reflects the changes in the internal liquid supply unit of the reservoir during the utilization of reservoir reserves.

[0175] In one possible implementation, the reservoir reserve utilization prediction device 100 further includes: a utilization depth prediction module 1005, which is used to determine the reservoir reserve utilization depth based on the static temperature-depth relationship curve of the reservoir and the flow temperature-depth relationship curve at the corresponding time. The static temperature-depth relationship curve reflects the relationship between static temperature and depth, and the flow temperature-depth relationship curve reflects the relationship between flow temperature and depth.

[0176] The oil reservoir utilization prediction device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0177] Figure 11 A schematic diagram of the computing device provided in this application. Figure 11 As shown, the computing device 110 provided in this embodiment includes at least one processor 1101 and a memory 1102. Optionally, the device 110 further includes a communication component 1103. The processor 1101, memory 1102, and communication component 1103 are connected via a bus 1104.

[0178] In a specific implementation, at least one processor 1101 executes computer execution instructions stored in memory 1102, causing at least one processor 1101 to perform the above-described method.

[0179] The specific implementation process of processor 1101 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0180] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0181] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0182] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0183] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0184] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0185] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0186] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an application-specific integrated circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0187] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0188] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0189] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0190] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0191] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0192] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for predicting dynamic oil reservoir reserves, characterized in that, include: To obtain formation pressure, surface crude oil density, natural gas relative density, and production gas-oil ratio measured during the oil production process; The crude oil volume factor under the original formation pressure is determined based on the formation pressure, the surface crude oil density, the natural gas relative density, and the production gas-oil ratio. Based on the crude oil volume factor under the original formation pressure, the real-time crude oil volume factor during the oil production process is determined. The dynamic reserves of the oil reservoir are determined based on the crude oil volume factor under the original formation pressure, the real-time crude oil volume factor, and the comprehensive compressibility factor of the oil reservoir.

2. The method for predicting dynamic oil reservoir reserves according to claim 1, characterized in that, The step of determining the crude oil volume factor under the original formation pressure based on the formation pressure, the surface crude oil density, the relative density of natural gas, and the production gas-oil ratio includes: The formation pressure, the surface crude oil density, the relative density of natural gas, and the production gas-oil ratio are substituted into the prediction model to obtain the crude oil volume coefficient under the original formation pressure. The prediction model is obtained by regression analysis of the crude oil volume coefficient under the original formation pressure based on experimental data.

3. The method for predicting dynamic oil reservoir reserves according to claim 1 or 2, characterized in that, The step of determining the real-time crude oil volume factor during oil production based on the crude oil volume factor under the original formation pressure includes: Based on the pressure-volume-temperature (PVT) experimental data of reservoirs with similar fluid properties to the described reservoir, a curve showing the relationship between the crude oil volume factor and formation pressure for reservoirs with similar fluid properties to the described reservoir was plotted. Based on the crude oil volume factor under the original formation pressure, the original formation pressure, and the relationship curve between the crude oil volume factor and formation pressure of reservoirs with similar fluid properties to the reservoir, the relationship curve between the crude oil volume factor and formation pressure of the reservoir is reconstructed.

4. A method for predicting the degree of utilization of oil reservoir reserves, characterized in that, include: The dynamic reserves of the oil reservoir are obtained by any one of claims 1 to 3; Obtain the static reserves of the reservoir; Based on the dynamic reserves and the static reserves of the reservoir, the degree of reservoir reserve utilization is predicted.

5. The method for predicting the degree of oil reservoir utilization according to claim 4, characterized in that, The step of predicting the degree of reservoir utilization based on the dynamic reserves and the static reserves of the reservoir includes: The utilization rate of the reservoir reserves is predicted based on the ratio of the dynamic reserves to the static reserves of the reservoir.

6. The method for predicting the degree of oil reservoir utilization according to claim 4, characterized in that, The static reserves of the reservoir were obtained through the following methods: The porosity distribution in the reservoir was obtained using geostatistical inversion. The volume of the reservoir is obtained by volume integration of the pores in the reservoir; A calibration template is created using three-dimensional forward modeling. The volume of the reservoir is then corrected using the calibration template to obtain the static reserves of the reservoir.

7. The method for predicting the degree of oil reservoir utilization according to any one of claims 4 to 6, characterized in that, Also includes: Based on the dynamic reserves, the crude oil volume coefficient, the real-time crude oil volume coefficient, and the reservoir comprehensive compressibility coefficient, the elastic production capacity index of the reservoir is predicted and obtained. The elastic production capacity index reflects the changes in the internal liquid supply unit of the reservoir during the utilization of reservoir reserves.

8. The method for predicting the degree of oil reservoir utilization according to any one of claims 4 to 6, further comprising: Based on the static temperature-depth relationship curve and the corresponding flow temperature-depth relationship curve of the reservoir, the reservoir reserve utilization depth is determined. The static temperature-depth relationship curve reflects the relationship between static temperature and depth, while the flow temperature-depth relationship curve reflects the relationship between flow temperature and depth.

9. A device for predicting the dynamic reserves of an oil reservoir, characterized in that, include: The acquisition module is used to acquire formation pressure, surface crude oil density, natural gas relative density, and production gas-oil ratio measured during the oil production process. The processing module is used to determine the crude oil volume factor under the original formation pressure based on the formation pressure, the surface crude oil density, the relative density of natural gas, and the production gas-oil ratio; and to determine the real-time crude oil volume factor during the oil extraction process based on the crude oil volume factor. The processing module is also used to determine the dynamic reserves of the oil reservoir based on the crude oil volume factor, the real-time crude oil volume factor, and the comprehensive compressibility factor of the oil reservoir.

10. A device for predicting the degree of oil reservoir utilization, characterized in that, include: The dynamic reserves prediction module is used to predict the dynamic reserves of oil reservoirs. The static reserves acquisition module is used to acquire the static reserves of the reservoir based on the geostatistical inversion method and the volumetric integral method. The utilization degree prediction module is used to predict the utilization degree of the oil reservoir reserves based on the dynamic reserves and the static reserves of the oil reservoir.

11. A computing device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes the computer execution instructions, causing the processor to perform the method as described in any one of claims 1 to 8.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed, are used to implement the method as described in any one of claims 1 to 8.

13. A computer program product, characterized in that, Includes a computer program, which, when executed, implements the method of any one of claims 1 to 8.

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