Carbonate formation pressure prediction method and device, electronic equipment and storage medium

Abstract

The application provides a carbonate rock stratum pressure prediction method and device, electronic equipment and a storage medium, wherein the method comprises the following steps: determining the interval velocity of the carbonate rock stratum in a target area according to the stacking velocity in the target area and the interval velocity in the shallow well logging data of the target area, to obtain a first interval velocity; determining the influence coefficient of the carbonate rock stratum in the target area according to the deep well logging data of the adjacent area of the target area and the seismic data of the target area; correcting the first interval velocity according to the influence coefficient to obtain a second interval velocity of the carbonate rock stratum in the target area; and determining the stratum pressure of the carbonate rock stratum in the target area according to the second interval velocity. The technical scheme can accurately predict the spatial distribution characteristics of the carbonate rock overpressure under the condition that the basic data is relatively scarce in the early exploration stage.

Description

Technical Field

[0001] This application relates to the field of geological and mineral exploration technology, and in particular to a method, apparatus, electronic device and storage medium for predicting pressure in carbonate rock formations. Background Technology

[0002] Currently, the proportion of marine carbonate oil and gas reservoirs in newly discovered oil and gas reservoir types is increasing year by year. Especially in the southern marine strata, from the Lower Paleozoic Sinian, Cambrian, and Silurian shale gas reservoirs to the Mesozoic Permian and Triassic conventional natural gas reservoirs, they are basically all related to basin overpressure. Formation pressure, or pore fluid pressure, is the pressure generated by geological fluids such as formation water, oil, and natural gas in the pores of rocks. Under any geological background, normal formation pressure is equal to the hydrostatic pressure from the surface to the target layer; pressure deviating from the normal pressure trend line is considered abnormal formation pressure. In the early stages of exploration, that is, in the process of oil and gas exploration for a specific exploration stratum within a study area, before drilling has reached the target stratum, the basic data available mainly include two-dimensional and three-dimensional seismic data of the study area, shallow drilling logging data, and deep drilling logging data from adjacent areas. Therefore, in the early stages of exploration, when basic data is scarce, how to effectively conduct research on the prediction of pressure in carbonate and acid rock formations has become a key research focus and challenge in the field of oil and gas exploration. Summary of the Invention

[0003] To address the aforementioned issues, this application provides a method, apparatus, electronic device, and storage medium for predicting pressure in carbonate formations.

[0004] This application provides a method for predicting pressure in carbonate rock formations, the method comprising:

[0005] The layer velocity of the carbonate formation in the target area is determined based on the superposition velocity in the target area and the layer velocity in the shallow drilling logging data in the target area, thus obtaining the first layer velocity;

[0006] Based on deep drilling and logging data from adjacent areas of the target area and seismic data from the target area, the influence coefficient of the carbonate rock formation in the target area is determined. The influence coefficient characterizes the degree of influence of porosity, fractures and fluids on the layer velocity within the carbonate rock formation in the target area.

[0007] The first layer velocity is corrected based on the influence coefficient to obtain the second layer velocity of the carbonate rock strata within the target area;

[0008] The formation pressure of the carbonate rock formation within the target area is determined based on the second layer velocity.

[0009] In some embodiments, determining the layer velocity of the carbonate formation in the target area based on the stacking velocity within the target area and the layer velocity in the shallow drilling logging data of the target area to obtain the first layer velocity includes:

[0010] Determine the stacking velocity of the target area based on the seismic data of the target area;

[0011] The superposition velocity is converted into a third-layer velocity using the Dix formula;

[0012] The third layer velocity is corrected using the layer velocity from the shallow drilling logging data to obtain the first layer velocity.

[0013] In some embodiments, the method for determining the stacking velocity of the target area based on seismic data of the target area includes any one of the following methods that can determine the stacking velocity: velocity spectrum picking, pre-stack time migration iteration, and post-stack migration iteration.

[0014] In some embodiments, correcting the third layer velocity using the layer velocity from the shallow drilling logging data to obtain the first layer velocity includes:

[0015] The layer velocity at the corresponding well location in the third layer velocity is replaced with the layer velocity at the well location in the shallow drilling logging data.

[0016] Based on the layer velocity at the well location in the shallow drilling logging data, the layer velocity of the area between wells in the target area is interpolated using an interpolation algorithm. The interpolated layer velocity of the area between wells in the target area is then used to replace the corresponding layer velocity of the area between wells in the third layer velocity to obtain the first layer velocity of the carbonate formation in the target area.

[0017] In some embodiments, determining the influence coefficient of carbonate formations within the target area based on deep drilling and logging data from adjacent areas of the target area and seismic data from the target area includes:

[0018] The thickness of carbonate strata within the target area is determined based on deep drilling and logging data from adjacent areas of the target area and seismic data from the target area.

[0019] Based on the pre-stack gather data within the target area, the anisotropic ellipse fitting technique is used to determine the anisotropic intensity of the carbonate strata within the target area.

[0020] The seismic properties of the carbonate strata within the target area are calculated based on seismic data from the target area.

[0021] The influence coefficient is determined based on the stratigraphic thickness, anisotropy intensity, and seismic properties of the carbonate rock strata.

[0022] In some embodiments, the method for determining the stratigraphic thickness of carbonate strata within the target area based on deep drilling and logging data from adjacent areas of the target area and seismic data from the target area includes any paleogeographic restoration method such as the residual thickness method or the impression method.

[0023] In some embodiments, determining the formation thickness of carbonate strata within the target area based on deep drilling and logging data from adjacent areas of the target area and seismic data from the target area includes:

[0024] The well-seismic calibration method is used to combine the time-domain seismic profile in the seismic data and the depth-domain logging curve in the deep drilling logging data to determine the top and bottom interfaces of the carbonate strata in the target area.

[0025] Based on the top and bottom interfaces of the carbonate rock formations within the target area, the formation thickness of the carbonate rock formations within the target area is determined using the residual thickness method.

[0026] In some embodiments, the method for determining the anisotropy intensity of carbonate strata within the target area based on pre-stack gather data within the target area includes either anisotropic ellipse fitting technology or anisotropic inversion, which can determine the anisotropic intensity.

[0027] In some embodiments, the seismic properties include any one or more of the following: post-stack high-frequency attenuation properties, post-stack low-frequency enhancement properties, and AVO seismic properties, which can characterize formation fluids.

[0028] In some embodiments, calculating the seismic properties of the carbonate strata within the target area based on seismic data within the target area includes:

[0029] Convert the time-domain seismic profiles in the seismic data of the target area into the corresponding spectrum;

[0030] Based on the spectrum, the post-stack high-frequency attenuation properties and post-stack low-frequency enhancement properties of the carbonate strata within the target area are calculated.

[0031] In some embodiments, determining the influence coefficient based on the formation thickness, anisotropy intensity, and seismic properties of the carbonate rock formation specifically includes the following steps:

[0032] The formation thickness, anisotropy intensity, and seismic properties of the carbonate rock formation are normalized. The normalization method includes any one of the following data processing methods that can achieve data normalization: extreme value normalization and mean standard deviation normalization.

[0033] The normalized stratum thickness, anisotropy intensity, and seismic attributes are calculated using the stratum comprehensive velocity influence coefficient calculation model to obtain the influence coefficient.

[0034] In some embodiments, determining the formation pressure of the carbonate rock formation within the target area based on the second layer velocity includes: calculating the formation pressure of the carbonate rock formation based on the second layer velocity using a formation pressure calculation method, wherein the formation pressure calculation method includes any one of the following: the Fillippone formula method, the equivalent depth method, the Eaton formula method and its improved method, and the improved Fillippone formula method.

[0035] This application provides another embodiment of a carbonate rock formation pressure prediction device, comprising:

[0036] The first calculation module is used to determine the layer velocity of the carbonate rock formation in the target area based on the stacking velocity in the target area and the layer velocity in the shallow drilling logging data of the target area, so as to obtain the first layer velocity;

[0037] The second calculation module is used to determine the influence coefficient of the carbonate rock formation in the target area based on the deep drilling and logging data of the adjacent areas of the target area and the seismic data of the target area. The influence coefficient characterizes the degree of influence of porosity, fractures and fluids on the layer velocity in the carbonate rock formation in the target area.

[0038] The third calculation module is used to correct the velocity of the first layer according to the influence coefficient to obtain the velocity of the second layer of carbonate rock formation in the target area;

[0039] The fourth calculation module is used to determine the formation pressure of the carbonate rock formation within the target area based on the second layer velocity.

[0040] This application provides an electronic device, including a memory and a processor. The memory stores a computer program, which, when executed by the processor, performs any of the carbonate formation pressure prediction methods described above.

[0041] This application provides a storage medium storing a computer program that can be executed by one or more processors and can be used to implement any of the carbonate formation pressure prediction methods described in this application.

[0042] This application provides a method, apparatus, electronic device, and storage medium for predicting carbonate formation pressure. The method determines the first layer velocity of the carbonate formation within the target area based on the stacking velocity and the layer velocity from shallow drilling and logging data. It then determines influence coefficients characterizing the impact of porosity, fractures, and fluids within the carbonate formation on the layer velocity based on deep drilling and logging data from adjacent areas and seismic data from the target area. The first layer velocity is corrected using these influence coefficients to obtain a more accurate second layer velocity, which is then used to determine the formation pressure. This method enables relatively accurate prediction of the spatial distribution characteristics of carbonate overpressure even when basic data is scarce in the early stages of exploration. This will benefit the exploration and development of carbonate overpressure oil and gas reservoirs and improve the economic efficiency of oil and gas exploration. Attached Figure Description

[0043] The present application will be described in more detail below based on embodiments and with reference to the accompanying drawings.

[0044] Figure 1 A flowchart illustrating a method for predicting pressure in carbonate formations as proposed in Example 1 is shown.

[0045] Figure 2 A schematic diagram of the 85% spectrum frequency attributes of Example 2 is shown;

[0046] Figure 3 A schematic diagram of the energy ratio properties at 35 Hz in Example 2 is shown.

[0047] Figure 4 A structural block diagram of the carbonate formation pressure prediction device provided in Example 3 is shown. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0049] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0050] If the application documents contain similar descriptions such as "first, second, third", the following explanation shall be added: In the following description, the terms "first, second, third" are used only to distinguish similar objects and do not represent a specific order of objects. It is understood that "first, second, third" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0052] Example 1

[0053] This application provides a method for predicting pressure in carbonate formations. The method is applied to an electronic device, such as a mobile phone, computer, or tablet computer. The function of the carbonate formation pressure prediction method can be implemented by a processor in the electronic device calling program code, which can be stored in a computer storage medium. Figure 1 This is a schematic diagram illustrating the implementation process of a carbonate formation pressure prediction method provided in an embodiment of this application, as shown below. Figure 1 As shown, the method includes:

[0054] Step S110: Determine the layer velocity of the carbonate rock formation in the target area based on the stacking velocity in the target area and the layer velocity in the shallow drilling logging data of the target area to obtain the first layer velocity.

[0055] In an optional implementation, step S110, determining the layer velocity of the carbonate formation within the target area based on the stacking velocity within the target area and the layer velocity in the shallow drilling logging data of the target area to obtain the first layer velocity, includes:

[0056] The stacking velocity of the target area is determined based on seismic data of the target area. Here, the method for determining the stacking velocity of the target area based on seismic data includes any method capable of determining the stacking velocity, such as velocity spectrum picking, pre-stack time migration iteration, or post-stack migration iteration. Taking velocity spectrum picking as an example, the stacking velocity of the target area is determined through the velocity spectrum in the seismic data of the target area. Here, the velocity spectrum refers to the curve showing the relationship between the energy of the seismic wave and its wave velocity. Depending on the velocity analysis criteria, the velocity spectrum can be divided into stacking velocity spectrum, correlation velocity spectrum, and similarity velocity spectrum. The principle of the stacking velocity spectrum is that the common-center reflection wave time-distance curve is a hyperbola. Assuming there is a reflection wave in the same direction on the set from the center point, according to the dynamic correction principle, at the same phase axis t0 value, with the corresponding velocity and the shot-receiver offset of each trace, the dynamic correction amount Δtx of each trace can be calculated. The stacking velocity spectrum represents the relationship between the amplitude of the stacked seismic waves and the stacking velocity. Therefore, the stacking velocity of the target area can be determined through the velocity spectrum within the target area.

[0057] The Dix formula is used to convert the stacking velocity into a third-layer velocity. Here, in the case of horizontally layered media, the stacking velocity is equal to the root-mean-square velocity; in the case of dipping layered media, the stacking velocity is equal to the root-mean-square velocity multiplied by the cosine of the dip angle. Taking the carbonate rock strata in the target area as an example of a horizontally layered medium, the Dix formula is as follows:

[0058]

[0059] Among them, v n v is the layer velocity of the nth layer. R,n v is the root mean square velocity of the nth layer. R,n-1 Let t be the root mean square velocity of the (n-1)th layer. 0,n Let t0 be the time of the nth layer, and t 0,n-1 Let t0 be the time of the (n-1)th layer.

[0060] The third layer velocity is corrected using the layer velocity from the shallow drilling logging data to obtain the first layer velocity.

[0061] Here, the layer velocity obtained directly from the root mean square velocity using the Dix formula only represents the layer velocity under the single scenario of the carbonate formation in the hypothetical target area being a horizontally layered or inclined layered medium, and still differs from the actual layer velocity of the carbonate formation in the target area. Furthermore, the layer velocity obtained from shallow drilling logging data at the well location within the target area is more accurate than the layer velocity obtained from the Dix formula. Therefore, the process of correcting the layer velocity obtained from the Dix formula includes:

[0062] First, the layer velocity at the well location in the shallow drilling logging data is used to replace the layer velocity at the corresponding well location in the third layer velocity. Second, based on the layer velocity at the well location in the shallow drilling logging data, the layer velocity between wells in the target area is interpolated using an interpolation algorithm. Finally, the interpolated layer velocity between wells in the target area is used to replace the layer velocity between wells in the corresponding well location in the third layer velocity to obtain the first layer velocity of the carbonate formation in the target area.

[0063] Step S120: Determine the influence coefficient of carbonate rock formations in the target area based on deep drilling and logging data of adjacent areas of the target area and seismic data of the target area. The influence coefficient characterizes the degree of influence of pores, fractures and fluids on the formation velocity of carbonate rock formations in the target area.

[0064] Step S130: Correct the first layer velocity according to the influence coefficient to obtain the second layer velocity of the carbonate rock strata within the target area. The specific calculation formula is as follows:

[0065] Pvel′=Pvel*Fluen vel ;

[0066] In the formula, Pvel′ is the second layer velocity considering the influence of factors such as porosity, fractures, and fluid in the carbonate rock formation, Pvel is the first layer velocity of the carbonate rock formation in the target area obtained in step S110, and Fluen vel This is an influence coefficient used to characterize the degree of influence of pores, fractures, and fluids on the layer velocity within the carbonate strata in the target area.

[0067] Step S140: Determine the formation pressure of the carbonate rock formation in the target area based on the second layer velocity.

[0068] In an optional embodiment, step S140 specifically involves: calculating the formation pressure of the carbonate formation based on the second layer velocity using a formation pressure calculation method. Here, the formation pressure calculation method includes any one of the following: the Fillippone formula method, the equivalent depth method, the Eaton formula method and its improved version, or the improved Fillippone formula method. In this embodiment, the Fillippone formula method is used as an example to calculate the formation pressure of the carbonate formation in the target area. The specific calculation formula is as follows:

[0069]

[0070]

[0071] In the formula, P oρ is the overlying formation pressure, i.e., static rock pressure; ρ is the average density of the strata in the adjacent drilled deep wells of the target area; g is the gravitational acceleration; h is the depth; T is the two-way travel time of the seismic data; Pvel′ is the second-layer velocity considering factors such as porosity, fractures, and fluids in the carbonate strata; P f Pvel′ represents the formation pressure of the carbonate strata within the target area. max Pvel′ is the maximum value of the second layer velocity. min This is the minimum speed of the second layer.

[0072] In this embodiment, the layer velocity calculated from seismic data is corrected using the layer velocity from shallow drilling logging data to obtain a more accurate layer velocity for the carbonate strata in the target area. The calculated layer velocity is then corrected using an influence coefficient characterizing the impact of porosity, fractures, and fluids in the carbonate strata on the layer velocity, resulting in a second layer velocity that comprehensively considers these factors. This second layer velocity is then used to calculate the formation pressure of the carbonate strata. This allows for the acquisition of formation pressure in the target area's carbonate strata during the initial exploration phase, before drilling reaches the target layer and before deep drilling logging data is available. This enables the prediction of the spatial distribution characteristics of overpressure in the target area during the initial exploration phase, which is beneficial for the exploration and development of overpressured carbonate oil and gas reservoirs and improves the economic efficiency of oil and gas exploration.

[0073] Example 2

[0074] Based on the above embodiments, Embodiment 2 of the present invention can also provide a method for predicting pressure in carbonate rock formations. The method includes:

[0075] Step S110: Determine the layer velocity of the carbonate rock formation in the target area based on the stacking velocity in the target area and the layer velocity in the shallow drilling logging data of the target area to obtain the first layer velocity.

[0076] In an optional implementation, step S110, determining the first layer velocity of the carbonate formation within the target area based on the stacking velocity within the target area and the layer velocity in the shallow drilling logging data of the target area, includes:

[0077] The stacking velocity of the target area is determined based on the seismic data of the target area; here, the method for determining the stacking velocity of the target area based on the seismic data of the target area includes any method that can determine the stacking velocity, such as velocity spectrum picking, pre-stack time migration iteration, or post-stack migration iteration.

[0078] In this embodiment, the velocity spectrum picking method is used as an example to determine the stacking velocity of the target area through the velocity spectrum in the seismic data of the target area. Here, the velocity spectrum refers to the curve showing the relationship between the energy of the seismic wave and its wave velocity. Depending on the velocity analysis criteria, the velocity spectrum can be divided into stacking velocity spectrum, correlation velocity spectrum, and similarity velocity spectrum. The principle of the stacking velocity spectrum is that the common-center reflection wave time-distance curve is a hyperbola. Assuming there is a reflection wave axis in the same direction on the set from the center point, according to the dynamic correction principle, at the same phase axis t0 value, with the corresponding velocity and the shot-receiver distance of each trace, the dynamic correction amount Δtx of each trace can be calculated. The stacking velocity spectrum represents the relationship between the amplitude of the stacked seismic waves and the stacking velocity. Therefore, the stacking velocity of the target area can be determined through the velocity spectrum within the target area.

[0079] The Dix formula is used to convert the stacking velocity into a third-layer velocity. Here, in the case of horizontally layered media, the stacking velocity is equal to the root-mean-square velocity; in the case of dipping layered media, the stacking velocity is equal to the root-mean-square velocity multiplied by the cosine of the dip angle. Taking the carbonate rock strata in the target area as an example of a horizontally layered medium, the Dix formula is as follows:

[0080]

[0081] Among them, v n v is the layer velocity of the nth layer. R,n v is the root mean square velocity of the nth layer. R,n-1 Let t be the root mean square velocity of the (n-1)th layer. 0,n Let t0 be the time of the nth layer, and t 0,n-1 Let t0 be the time of the (n-1)th layer.

[0082] The third layer velocity is corrected using the layer velocity from the shallow drilling logging data to obtain the first layer velocity.

[0083] Here, the layer velocity obtained directly from the root mean square velocity using the Dix formula only represents the layer velocity under the single scenario of the carbonate formation in the hypothetical target area being a horizontally layered or inclined layered medium, and still differs from the actual layer velocity of the carbonate formation in the target area. Furthermore, the layer velocity obtained from shallow drilling logging data at the well location within the target area is more accurate than the layer velocity obtained from the Dix formula. Therefore, the process of correcting the layer velocity obtained from the Dix formula includes:

[0084] First, the layer velocity at the well location in the shallow drilling logging data is used to replace the layer velocity at the corresponding well location in the third layer velocity. Second, based on the layer velocity at the well location in the shallow drilling logging data, the layer velocity between wells in the target area is interpolated using an interpolation algorithm. Finally, the interpolated layer velocity between wells in the target area is used to replace the layer velocity between wells in the corresponding well location in the third layer velocity to obtain the first layer velocity of the carbonate formation in the target area.

[0085] Step S120: Determine the influence coefficient of carbonate rock formations in the target area based on deep drilling and logging data of adjacent areas of the target area and seismic data of the target area. The influence coefficient characterizes the degree of influence of pores, fractures and fluids on the formation velocity of carbonate rock formations in the target area.

[0086] In carbonate formations, besides the influence of lithological variations on layer velocity, other factors affecting layer velocity mainly include three aspects: porosity, fractures, and fluids. While the stacking velocity determined by velocity spectra can generally characterize the impact of lithological variations on velocity, it cannot effectively characterize the influence of porosity, fractures, and fluids present in carbonate formations on layer velocity. Therefore, it is necessary to determine the influence coefficients of porosity, fractures, and fluids present in carbonate formations on layer velocity.

[0087] In an optional embodiment, step S120, determining the influence coefficient of carbonate formations within the target area based on deep drilling and logging data from adjacent areas of the target area and seismic data from the target area, includes:

[0088] The formation thickness of carbonate strata within the target area is determined based on deep drilling and logging data from adjacent areas and seismic data from the target area. It is worth noting that the porosity of carbonate strata is mainly classified into primary porosity and secondary porosity according to their genesis. Primary porosity refers to porosity controlled by the composition, structure, and tectonics of the carbonate strata, such as framework pores and intergranular pores; secondary porosity mainly refers to porosity formed after deposition by diagenesis and dissolution, such as dissolution pores. In the early stages of exploration, without deep drilling and logging data for the target area, secondary porosity cannot be effectively characterized. However, considering the genetic mechanism of primary porosity in carbonate rocks, porosity is more likely to develop in locations with greater stratum thickness and higher paleogeographic features within the carbonate strata. Therefore, the formation thickness of carbonate strata can be used to characterize the degree of porosity development in carbonate rocks. Here, the method for determining the stratigraphic thickness of carbonate strata within the target area based on deep drilling and logging data from adjacent areas and seismic data from the target area includes any paleogeographic reconstruction method such as the residual thickness method or the impression method. This embodiment uses the residual thickness method as an example, specifically including:

[0089] By employing well-seismic calibration, the time-domain seismic profiles from the seismic data and the depth-domain logging curves from the deep drilling logging data are combined to determine the top and bottom interfaces of the carbonate strata within the target area. Here, the top and bottom boundaries of the carbonate strata within the target area are typically large lithological interfaces, easily identifiable on both the time-domain seismic profiles and depth-domain logging curves. By combining the time-domain seismic profiles and depth-domain logging curves of the same lithological interface using well-seismic calibration, the top and bottom interfaces of the carbonate strata within the target area can be determined.

[0090] Based on the top and bottom interfaces of the carbonate strata within the target area, the formation thickness of the carbonate strata within the target area is determined using the residual thickness method. Specifically, this includes: subtracting the time-stratum of the top interface from the time-stratum of the bottom interface of the carbonate strata within the target area to obtain the time-stratum thickness of the carbonate strata within the target area; then, based on the layer velocity in the shallow drilling logging data and the time-stratum thickness of the carbonate strata in the target area, the formation thickness of the carbonate strata in the depth domain within the target area is calculated.

[0091] The anisotropic intensity of carbonate strata within the target area is determined based on pre-stack gather data within the target area. It is worth noting that fractures in carbonate rocks, based on their origin, mainly include tectonic fractures and dissolution fractures, and based on their orientation, are generally classified as vertical fractures (greater than 70 degrees), high-angle fractures (45-70 degrees), low-angle fractures (20-45 degrees), and horizontal fractures (less than 20 degrees). According to the principle of seismic wave propagation, high-angle fractures and vertical fractures have a greater impact on seismic velocity. Therefore, the anisotropic intensity of carbonate strata in the target area can be used to characterize the influence coefficient of carbonate strata fractures on layer velocity. The method for determining the anisotropic intensity of carbonate strata within the target area based on pre-stack gather data includes any method capable of determining anisotropic intensity, such as anisotropic ellipse fitting technology or anisotropic inversion. In this embodiment, anisotropic ellipse fitting technology is used as an example, specifically including:

[0092] The pre-stack gather data is then stacked and migrated by azimuth to obtain N seismic data sets containing azimuth information. Here, the pre-stack gather data refers to the unprocessed seismic data.

[0093] Calculate the frequency, attenuation, energy, and other seismic properties of these N seismic data points respectively;

[0094] The seismic attribute of the azimuth angle is projected onto the coordinate system of azimuth angle-attribute value to construct an azimuth anisotropic ellipse, the major axis of which represents the anisotropic intensity.

[0095] The seismic properties of the carbonate strata within the target area are calculated based on seismic data from the target area. In the early stages of exploration, before drilling reaches the target layer, well logging data is relatively scarce. For predicting formation fluids, pre-stack and post-stack seismic properties that characterize fluid development are used. These seismic properties include any one or more of the following: post-stack high-frequency attenuation properties, post-stack low-frequency enhancement properties, and AVO seismic properties. In this embodiment, post-stack high-frequency attenuation properties and post-stack low-frequency enhancement properties are used, as these two seismic properties characterize the influence coefficients of formation fluids on layer velocity. Specifically, this includes:

[0096] The time-domain seismic profiles in the seismic data of the target area are converted into corresponding spectra. Here, Fourier transform or wavelet transform formulas can be used to convert the time-domain seismic profiles into corresponding spectra.

[0097] Based on the aforementioned spectrum, the post-stack high-frequency attenuation and post-stack low-frequency enhancement properties of the carbonate strata within the target region were calculated. It is worth noting here that:

[0098] like Figure 2The post-stack high-frequency attenuation attribute refers to the 85% attenuation frequency attribute, defined as the frequency value corresponding to 85% energy attenuation within the effective frequency band. Theoretical experiments show that when the formation contains oil and gas, the high-frequency attenuation attribute exhibits low values. The calculation formula for the post-stack high-frequency attenuation attribute is as follows:

[0099]

[0100] In the formula, f l f is the lowest frequency within the effective frequency band. h The highest frequency within the effective frequency band. Let f be the frequency at which energy decays to 85%, and p(f) be the frequency-energy function.

[0101] like Figure 3 As shown, the post-stack low-frequency enhancement attribute refers to the energy percentage attribute at 35 Hz, defined as the energy percentage attribute at 35 Hz within the effective frequency band. Theoretical experiments show that when the formation contains oil and gas, the low-frequency energy attribute exhibits a high value. The calculation formula for the post-stack low-frequency enhancement attribute is as follows:

[0102]

[0103] In the formula, A represents the percentage energy attribute at 35 Hz, and f l f is the lowest frequency within the effective frequency band. h Let f be the highest frequency within the effective frequency band, and p(f) be the frequency-energy function.

[0104] In other embodiments, the seismic properties used to characterize the fluid are fluid factors. The fluid factors are specifically calculated as follows: based on seismic data of the target area, the shear wave velocity, p-wave velocity, and density of the carbonate strata in the target area are obtained using the pre-stack AOV (Amplitude Versus Offset) inversion method. Based on the shear wave velocity, p-wave velocity, and density of the carbonate strata, fluid factors for various carbonate strata are constructed.

[0105] In other embodiments, the seismic property used to characterize the fluid is the AVO (Amplitude VersusOffset) seismic property. In this embodiment, the AVO seismic property includes intercept and gradient properties, etc. The specific calculation process is as follows: the AVO property is determined based on pre-stack seismic gather data.

[0106] The influence coefficient is determined based on the formation thickness, anisotropy intensity, and seismic properties of the carbonate rock strata. Specifically, this includes: First, since the parameters characterizing the influence of pores, fractures, and fluids on layer velocity have different value ranges, each parameter needs to be normalized, i.e., the formation thickness, anisotropy intensity, and seismic properties of the carbonate rock strata need to be normalized. The normalization method includes any data processing method capable of data normalization, such as extreme value normalization or mean-standard deviation normalization. In this embodiment, extreme value normalization is used as an example, specifically:

[0107] The formula for normalizing the formation thickness of the carbonate rock strata is as follows:

[0108]

[0109] In the formula, por i ′ represents the normalized formation thickness used to characterize the influence of porosity on formation velocity. i Let por be the formation thickness at the i-th time position used to characterize the influence of porosity on formation velocity. max The maximum value of the formation thickness used to characterize the influence of porosity on formation velocity, por min This is the minimum formation thickness used to characterize the degree of influence of porosity on formation velocity.

[0110] The formula for normalizing the anisotropy intensity of the carbonate rock formation is as follows:

[0111]

[0112] In the formula, Frac i Frac is the normalized anisotropic intensity used to characterize the degree of influence of cracks on layer velocity. i Let Frac be the anisotropic intensity at the i-th time position, used to characterize the degree of influence of the fracture on the layer velocity. max Frac is the maximum value of the anisotropic strength used to characterize the extent to which cracks affect layer velocity. min This is the minimum value of the anisotropic strength used to characterize the degree of influence of cracks on layer velocity.

[0113] The formula for normalizing the seismic properties of the carbonate rock formation is as follows:

[0114]

[0115] In the formula, Fluid i′ is a normalized seismic attribute used to characterize the degree of influence of fluid on layer velocity; Fluid i Let be a seismic attribute representing the degree of influence of fluid on layer velocity at the i-th time position, and let Fluid be a seismic attribute representing the degree of influence of fluid on layer velocity. max Fluid is the maximum value of a seismic property used to characterize the extent to which fluid affects layer velocity. min This is the minimum value of the seismic property used to characterize the degree of influence of fluid on layer velocity.

[0116] Then, the normalized formation thickness, anisotropy intensity, and seismic attributes are calculated using the formation comprehensive velocity influence coefficient calculation model to obtain the influence coefficient. Here, the influence coefficient characterizes the degree of influence of porosity, fractures, and fluids within the carbonate formation on the formation velocity in the target area. The formation comprehensive velocity influence coefficient calculation model considers that the formation velocity will decrease when porosity, fractures, and fluids are present in the carbonate formation, and that porosity is the reservoir space for fluids, while fractures are channels constructed for fluid migration. The specific calculation formula is as follows:

[0117] Fluen vel =1-por i ′*Frac i Fluid i ′;

[0118] In the formula, por i Frac represents the normalized formation thickness used to characterize the influence of porosity on formation velocity. i ′ is the normalized anisotropic strength used to characterize the degree of influence of cracks on layer velocity; Fluid i ′ is a normalized seismic attribute used to characterize the degree of influence of fluid on layer velocity; Fluen vel The influence coefficient is used to characterize the degree of influence of pores, fractures, and fluids on the layer velocity within the carbonate strata in the target area.

[0119] Step S130: Correct the first layer velocity according to the influence coefficient to obtain the second layer velocity of the carbonate rock strata within the target area. The specific calculation formula is as follows:

[0120] Pvel′=Pvel*Fluen vel ;

[0121] In the formula, Pvel′ is the second layer velocity considering the influence of factors such as porosity, fractures, and fluid in the carbonate rock formation, Pvel is the first layer velocity of the carbonate rock formation in the target area obtained in step S110, and Fluen velThe influence coefficient is used to characterize the degree of influence of pores, fractures, and fluids on the layer velocity within the carbonate strata in the target area.

[0122] Step S140: Determine the formation pressure of the carbonate rock formation in the target area based on the second layer velocity.

[0123] In an optional embodiment, step S140 specifically involves: calculating the formation pressure of the carbonate formation based on the second layer velocity using a formation pressure calculation method. Here, the formation pressure calculation method includes any one of the following: the Fillippone formula method, the equivalent depth method, the Eaton formula method and its improved version, or the improved Fillippone formula method. In this embodiment, the Fillippone formula method is used as an example to calculate the formation pressure of the carbonate formation in the target area. The specific calculation formula is as follows:

[0124]

[0125]

[0126] In the formula, P o ρ is the overlying formation pressure, i.e., static rock pressure; ρ is the average density of the strata in the adjacent drilled deep wells of the target area; g is the gravitational acceleration; h is the depth; T is the two-way travel time of the seismic data; Pvel′ is the second-layer velocity considering factors such as porosity, fractures, and fluids in the carbonate strata; P f Pvel′ represents the formation pressure of the carbonate strata within the target area. max Pvel′ is the maximum value of the second layer velocity. min This is the minimum speed of the second layer.

[0127] In this embodiment, based on seismic data, shallow drilling data, and deep drilling and logging data from adjacent areas of the target region, the layer velocity of the carbonate strata in the target region is calculated, along with an influence coefficient characterizing the degree of influence of factors such as porosity, fractures, and fluids in the carbonate strata on the layer velocity. The calculated layer velocity is then corrected using this influence coefficient to obtain a layer velocity that considers factors such as porosity, fractures, and fluids in the carbonate strata, thereby calculating the formation pressure of the carbonate strata. This allows for the acquisition of formation pressure in the carbonate strata of the target region during the early stages of exploration, before drilling reaches the target layer and before deep drilling and logging data for the target region is available. This enables the prediction of the spatial distribution characteristics of overpressure in the carbonate strata within the target region during the early stages of exploration, which is beneficial for the exploration and development of overpressured carbonate oil and gas reservoirs and improves the economic efficiency of oil and gas exploration.

[0128] Example 3

[0129] According to an embodiment of the present invention, a device for predicting pressure in carbonate rock formations is also provided. Figure 4 A structural block diagram of the carbonate formation pressure prediction device provided in the embodiments of this application is shown below. Figure 4 As shown, it includes:

[0130] The first calculation module is used to determine the first layer velocity of the carbonate formation in the target area based on the stacking velocity in the target area and the layer velocity in the shallow drilling logging data of the target area.

[0131] The second calculation module is used to determine the influence coefficient of the carbonate rock formation in the target area based on the deep drilling and logging data of the adjacent areas of the target area and the seismic data of the target area. The influence coefficient characterizes the degree of influence of porosity, fractures and fluids on the layer velocity in the carbonate rock formation in the target area.

[0132] The third calculation module is used to correct the velocity of the first layer according to the influence coefficient to obtain the velocity of the second layer of carbonate rock formation in the target area;

[0133] The fourth calculation module is used to determine the formation pressure of the carbonate rock formation within the target area based on the second layer velocity.

[0134] In an optional implementation, the first calculation module is specifically used to determine the stacking velocity of the target region based on the seismic data of the target region. Here, the method for determining the stacking velocity of the target region based on the seismic data of the target region includes any method capable of determining the stacking velocity, such as velocity spectrum picking, pre-stack time migration iteration, or post-stack migration iteration. In this embodiment, the velocity spectrum picking method is taken as an example, and the stacking velocity of the target region is determined by the velocity spectrum in the seismic data of the target region.

[0135] Here, the velocity spectrum refers to the curve showing the relationship between the energy of a seismic wave and its wave velocity. Depending on the velocity analysis criteria, the velocity spectrum can be divided into stacking velocity spectrum, correlation velocity spectrum, and similarity velocity spectrum. The principle of the stacking velocity spectrum is that the time-distance curve of a common-center reflected wave is a hyperbola. Assuming there is a reflection wave axis in the same direction on the collection from the center point, according to the dynamic correction principle, at the value of t0 on the in-phase axis, the dynamic correction amount Δtx for each trace can be calculated using the corresponding velocity and the shot-receiver offset. The stacking velocity spectrum represents the relationship between the amplitude of the stacked seismic waves and the stacking velocity. Therefore, the stacking velocity to the target area can be determined through the velocity spectrum within the target area.

[0136] Optionally, the first calculation module is specifically used to convert the stacking velocity into a third-layer velocity using the Dix formula. Here, in the case of a horizontally layered medium, the stacking velocity is equal to the root-mean-square velocity; in the case of a dipping layered medium, the stacking velocity is equal to the root-mean-square velocity multiplied by the cosine of the dip angle. Taking the carbonate rock strata in the target area as an example of a horizontally layered medium, the Dix formula is:

[0137]

[0138] Among them, v n v is the layer velocity of the nth layer. R,n v is the root mean square velocity of the nth layer. R,n-1 Let t be the root mean square velocity of the (n-1)th layer. 0,n Let t0 be the time of the nth layer, and t 0,n-1 Let t0 be the time of the (n-1)th layer.

[0139] Optionally, the first calculation module is specifically used to correct the third layer velocity using the layer velocity in the shallow drilling logging data to obtain the first layer velocity.

[0140] Here, the layer velocity obtained directly from the root mean square velocity using the Dix formula only represents the layer velocity under the single condition that the carbonate formation in the target area is a horizontally layered or inclined layered medium, and still differs from the actual layer velocity of the carbonate formation in the target area. Furthermore, the layer velocity obtained from shallow drilling logging data at well locations within the target area is more accurate than the layer velocity obtained from the Dix formula. Therefore, the first calculation module is specifically used to replace the layer velocity at the corresponding well location in the third layer velocity with the layer velocity at the well location in the shallow drilling logging data; then, based on the layer velocity at the well location in the shallow drilling logging data, an interpolation algorithm is used to interpolate the layer velocity in the well-to-well region within the target area; finally, the interpolated layer velocity in the well-to-well region within the target area is used to replace the corresponding layer velocity in the well-to-well region in the third layer velocity to obtain the first layer velocity of the carbonate formation within the target area.

[0141] Optionally, the second calculation module is specifically used to determine the residual thickness of the carbonate strata within the target area based on deep drilling logging data from adjacent areas of the target area and seismic data from the target area. Here, the method for determining the thickness of the carbonate strata within the target area based on deep drilling logging data from adjacent areas of the target area and seismic data from the target area includes any paleogeographic reconstruction method such as the residual thickness method or the impression method. In this embodiment, the residual thickness method is used as an example, specifically including: using a well-seismic calibration method, combining the time-domain seismic profile in the seismic data and the depth-domain logging curve in the deep drilling logging data to determine the top and bottom interfaces of the carbonate strata within the target area. Then, based on the top and bottom interfaces of the carbonate strata within the target area, the residual thickness method is used to determine the thickness of the carbonate strata within the target area.

[0142] Optionally, the second calculation module is specifically used to determine the anisotropy intensity of carbonate strata within the target area based on pre-stack gather data within the target area. The method for determining the anisotropy intensity of carbonate strata within the target area based on pre-stack gather data includes any one of the following methods capable of determining anisotropy intensity: anisotropic ellipse fitting technique, anisotropic inversion, etc. In this embodiment, taking anisotropic ellipse fitting technique as an example, specifically, the second calculation module is used to perform azimuth stacking and migration processing on the pre-stack gather data to obtain N seismic data containing azimuth information. Here, the pre-stack gather data is unprocessed seismic data. The second calculation module is specifically used to calculate the frequency, attenuation, energy, and other seismic attributes of these N seismic data respectively. The second calculation module is specifically used to project the azimuth seismic attributes onto the azimuth-attribute value coordinate system, thereby constructing an azimuth anisotropy ellipse, where the major axis of the azimuth anisotropy ellipse represents the anisotropy intensity.

[0143] Optionally, the second calculation module is specifically used to calculate the seismic properties of the carbonate strata within the target area based on seismic data within the target area. These seismic properties include any one or more seismic properties capable of characterizing formation fluids, such as post-stack high-frequency attenuation properties, post-stack low-frequency enhancement properties, and AVO seismic properties. In this embodiment, post-stack high-frequency attenuation properties and post-stack low-frequency enhancement properties are used, as these two seismic properties characterize the influence coefficient of formation fluids on layer velocity. The post-stack high-frequency attenuation property refers to the 85% attenuation frequency property, defined as the frequency value corresponding to 85% energy attenuation within the effective frequency band. The calculation formula for the post-stack high-frequency attenuation property is as follows:

[0144]

[0145] In the formula, fl f is the lowest frequency within the effective frequency band. h f is the highest frequency within the effective frequency band. m2 Let f be the frequency at which energy decays to 85%, and p(f) be the frequency-energy function.

[0146] Post-stack low-frequency enhancement attribute refers to the energy percentage attribute at 35 Hz, defined as the percentage of energy at 35 Hz within the effective frequency band. The formula for calculating the post-stack low-frequency enhancement attribute is:

[0147]

[0148] In the formula, A represents the percentage energy attribute at 35 Hz, and f l f is the lowest frequency within the effective frequency band. h Let f be the highest frequency within the effective frequency band, and p(f) be the frequency-energy function.

[0149] In other embodiments, the seismic properties used to characterize the fluid are fluid factors. The fluid factors are specifically calculated as follows: based on seismic data of the target area, the shear wave velocity, p-wave velocity, and density of the carbonate strata in the target area are obtained using the pre-stack AOV (Amplitude Versus Offset) inversion method. Based on the shear wave velocity, p-wave velocity, and density of the carbonate strata, fluid factors for various carbonate strata are constructed.

[0150] In other embodiments, the seismic property used to characterize the fluid is the AVO (Amplitude VersusOffset) seismic property. In this embodiment, the AVO seismic property includes intercept and gradient properties, etc. The specific calculation process is as follows: the AVO property is determined based on pre-stack seismic gather data.

[0151] Optionally, the second calculation module is specifically used to determine the influence coefficient based on the formation thickness, anisotropy intensity, and seismic properties of the carbonate rock strata. Specifically, the second calculation module is used to normalize the formation thickness, anisotropy intensity, and seismic properties of the carbonate rock strata. The normalization method includes any data processing method capable of data normalization, such as extreme value normalization or mean-standard deviation normalization. In this embodiment, extreme value normalization is used as an example, specifically:

[0152] The formula for normalizing the formation thickness of the carbonate rock strata is as follows:

[0153]

[0154] In the formula, por i ′ represents the normalized formation thickness used to characterize the influence of porosity on formation velocity. i Let por be the formation thickness at the i-th time position used to characterize the influence of porosity on formation velocity. max The maximum value of the formation thickness used to characterize the influence of porosity on formation velocity, por min This is the minimum formation thickness used to characterize the degree of influence of porosity on formation velocity.

[0155] The formula for normalizing the anisotropy intensity of the carbonate rock formation is as follows:

[0156]

[0157] In the formula, Frac i Frac is the normalized anisotropic intensity used to characterize the degree of influence of cracks on layer velocity. i Let Frac be the anisotropic intensity at the i-th time position, used to characterize the degree of influence of the fracture on the layer velocity. max Frac is the maximum value of the anisotropic strength used to characterize the extent to which cracks affect layer velocity. min This is the minimum value of the anisotropic strength used to characterize the degree of influence of cracks on layer velocity.

[0158] The formula for normalizing the seismic properties of the carbonate rock formation is as follows:

[0159]

[0160] In the formula, Fluid i ′ is a normalized seismic attribute used to characterize the degree of influence of fluid on layer velocity; Fluid i Let be a seismic attribute representing the degree of influence of fluid on layer velocity at the i-th time position, and let Fluid be a seismic attribute representing the degree of influence of fluid on layer velocity. max Fluid is the maximum value of a seismic property used to characterize the extent to which fluid affects layer velocity. min This is the minimum value of the seismic property used to characterize the degree of influence of fluid on layer velocity.

[0161] The second calculation module calculates the influence coefficient by using the normalized stratum thickness, anisotropy intensity, and seismic attributes through a comprehensive stratum velocity influence coefficient calculation model. The specific calculation formula is as follows:

[0162] Fluen vel =1-por i ′*Frac i Fluid i ′;

[0163] In the formula, por i Frac represents the normalized formation thickness used to characterize the influence of porosity on formation velocity. i ′ is the normalized anisotropic strength used to characterize the degree of influence of cracks on layer velocity; Fluid i ′ is a normalized seismic attribute used to characterize the degree of influence of fluid on layer velocity, Fluen vel The influence coefficient is used to characterize the degree of influence of pores, fractures, and fluids on the layer velocity within the carbonate strata in the target area.

[0164] Optionally, the third calculation module is used to correct the first layer velocity based on the influence coefficient to obtain the second layer velocity of the carbonate strata within the target area. The specific calculation formula is as follows:

[0165] Pvel′=Pvel*Fluen vel ;

[0166] In the formula, Pvel′ represents the second-layer velocity considering factors such as porosity, fractures, and fluid in the carbonate strata, Pvel represents the first-layer velocity of the carbonate strata within the target area, and Fluen represents the second-layer velocity. vel The influence coefficient is used to characterize the degree of influence of pores, fractures, and fluids on the layer velocity within the carbonate strata in the target area.

[0167] Optionally, the fourth calculation module is used to calculate the formation pressure of the carbonate strata in the target area based on the second layer velocity using a formation pressure calculation method. Here, the formation pressure calculation method includes any one of the following: the Fillippone formula method, the equivalent depth method, the Eaton formula method and its improved version, or the improved Fillippone formula method. In this embodiment, the Fillippone formula method is used as an example, and the specific calculation formula is as follows:

[0168]

[0169]

[0170] In the formula, P oρ is the overlying formation pressure, i.e., static rock pressure; ρ is the average density of the strata in the adjacent drilled deep wells of the target area; g is the gravitational acceleration; h is the depth; T is the two-way travel time of the seismic data; Pvel′ is the second-layer velocity considering factors such as porosity, fractures, and fluids in the carbonate strata; P f Pvel′ represents the formation pressure of the carbonate strata within the target area. max Pvel′ is the maximum value of the second layer velocity. min This is the minimum speed of the second layer.

[0171] The specific process of predicting carbonate formation pressure using the above-mentioned functional modules has been described in detail in the above embodiments and will not be repeated here.

[0172] Example 4

[0173] According to an embodiment of the present invention, an electronic device is also provided, the electronic device including a memory and a processor, the memory storing a computer program, which, when executed by the processor, implements the carbonate formation pressure prediction method as described in any of the above embodiments.

[0174] Example 5

[0175] According to an embodiment of the present invention, a storage medium is also provided, wherein a computer program stored in the storage medium can be executed by one or more processors and can be used to implement the carbonate formation pressure prediction method described in any of the above embodiments.

[0176] The storage medium can be such as flash memory, hard disk, multimedia, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, server, app store, etc.

[0177] In summary, the carbonate formation pressure prediction method, apparatus, electronic equipment, and storage medium provided by this invention enable formation pressure prediction research to be conducted in the early stages of exploration—specifically, during the period before drilling reaches the target formation in a target area for oil and gas exploration of a particular stratigraphic system. The available basic data includes two-dimensional and three-dimensional seismic data of the target area, shallow drilling and logging data, and deep drilling and logging data from adjacent areas. This technical solution can calculate influence coefficients characterizing the degree of influence of porosity, fractures, and fluids within the carbonate formation on the formation velocity in the target area, based solely on the available basic data from the early exploration stage. These influence coefficients are then used to correct the formation velocity derived from seismic data, resulting in a formation velocity that comprehensively considers the influence of porosity, fractures, and fluids within the carbonate formation on the formation velocity in the target area. This allows for the calculation of formation pressure in the carbonate formation within the target area, enabling the prediction of the spatial distribution characteristics of overpressure in the carbonate formation during the early exploration stage. This is beneficial for the exploration and development of overpressured carbonate oil and gas reservoirs and improves the economic efficiency of oil and gas exploration.

[0178] It should be understood that the disclosed apparatus and methods can be implemented in other ways, as illustrated in the several embodiments provided in this application. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0179] 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 the embodiments of the present invention, depending on actual needs.

[0180] Furthermore, 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. The integrated unit can be implemented in hardware or as a software functional unit.

[0181] If the integrated unit 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, in essence, or the part that contributes to the prior art, or all or 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 an electronic 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.

[0182] While the embodiments disclosed in this invention are as described above, the content is merely for the purpose of facilitating understanding of the invention and is not intended to limit the invention. Any person skilled in the art to which this invention pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope disclosed herein; however, the scope of protection of this invention shall still be determined by the scope defined in the appended claims.

Claims

1. A method for predicting pressure in carbonate formations, characterized in that, The method is applied during the period when drilling in the target area has not yet reached the target layer and there is no deep drilling logging data for the target area. The method includes: The layer velocity of the carbonate formation in the target area is determined based on the superposition velocity within the target area and the layer velocity in the shallow drilling logging data of the target area, thus obtaining the first layer velocity; Based on the deep drilling and logging data of the adjacent areas of the target area and the seismic data of the target area, the influence coefficient of the carbonate rock formation in the target area is determined. The influence coefficient characterizes the degree of influence of porosity, fractures and fluids on the layer velocity in the carbonate rock formation in the target area. The first layer velocity is corrected based on the influence coefficient to obtain the second layer velocity of the carbonate rock strata within the target area; The formation pressure of the carbonate rock formation within the target area is determined based on the second layer velocity. The step of determining the layer velocity of the carbonate formation in the target area based on the stacking velocity within the target area and the layer velocity in the shallow drilling logging data of the target area to obtain the first layer velocity includes: Determine the stacking velocity of the target area based on the seismic data of the target area; The superposition velocity is converted into a layer velocity using the Dix formula to obtain the third layer velocity; The layer velocity at the corresponding well location in the third layer velocity is replaced with the layer velocity at the well location in the shallow drilling logging data. Based on the layer velocity at the well location in the shallow drilling logging data, the layer velocity between wells in the target area is interpolated using an interpolation algorithm. The interpolated layer velocity between wells in the target area is then used to replace the corresponding layer velocity between wells in the third layer velocity to obtain the first layer velocity of the carbonate formation in the target area. The determination of the influence coefficient of carbonate formations within the target area based on deep drilling and logging data from adjacent areas and seismic data from the target area includes: The thickness of carbonate strata within the target area is determined based on deep drilling and logging data from adjacent areas of the target area and seismic data from the target area. The anisotropy intensity of the carbonate strata in the target area is determined based on the pre-stack gather data within the target area; The seismic properties of the carbonate strata within the target area are calculated based on seismic data from the target area. The formation thickness, anisotropy intensity, and seismic properties of the carbonate rock formation are normalized. The normalization method includes any one of the following data processing methods that can achieve data normalization: extreme value normalization and mean standard deviation normalization. The normalized stratum thickness, anisotropy intensity, and seismic attributes are calculated using the stratum comprehensive velocity influence coefficient calculation model to obtain the influence coefficient.

2. The method according to claim 1, characterized in that, The method for determining the stacking velocity of the target area based on seismic data of the target area includes any one of the following methods that can determine the stacking velocity: velocity spectrum picking, pre-stack time migration iteration, and post-stack migration iteration.

3. The method according to claim 1, characterized in that, The method for determining the stratigraphic thickness of carbonate strata within the target area based on deep drilling and logging data from adjacent areas of the target area and seismic data from the target area includes any paleogeographic reconstruction method such as the residual thickness method or the impression method.

4. The method according to claim 3, characterized in that, The step of determining the formation thickness of carbonate strata within the target area based on deep drilling and logging data from adjacent areas and seismic data from the target area includes: The well-seismic calibration method is used to combine the time-domain seismic profile in the seismic data and the depth-domain logging curve in the deep drilling logging data to determine the top and bottom interfaces of the carbonate strata in the target area. Based on the top and bottom interfaces of the carbonate rock formations within the target area, the formation thickness of the carbonate rock formations within the target area is determined using the residual thickness method.

5. The method according to claim 1, characterized in that, Methods for determining the anisotropy intensity of carbonate strata within the target area based on pre-stack gather data within the target area include anisotropic ellipse fitting techniques and methods that can determine the anisotropy intensity using either anisotropic inversion.

6. The method according to claim 1, characterized in that: The seismic attributes include any one or more of the following: post-stack high-frequency attenuation attributes, post-stack low-frequency enhancement attributes, and AVO seismic attributes, which can characterize formation fluids.

7. The method according to claim 6, characterized in that, The calculation of the seismic properties of the carbonate strata within the target area based on seismic data within the target area includes: Convert the time-domain seismic profiles in the seismic data of the target area into the corresponding spectrum; Based on the spectrum, the post-stack high-frequency attenuation properties and post-stack low-frequency enhancement properties of the carbonate strata within the target area are calculated.

8. The method according to claim 1, characterized in that, Determining the formation pressure of the carbonate rock formation within the target area based on the second layer velocity includes: calculating the formation pressure of the carbonate rock formation based on the second layer velocity using a formation pressure calculation method, wherein the formation pressure calculation method includes any one of the following: the Fillippone formula method, the equivalent depth method, the Eaton formula method and its improved method, and the improved Fillippone formula method.

9. A device for predicting pressure in carbonate formations, characterized in that, The device is used during the period when drilling in the target area has not yet reached the target layer and there is no deep drilling logging data for the target area. The device includes: The first calculation module is used to determine the layer velocity of the carbonate rock formation in the target area based on the stacking velocity in the target area and the layer velocity in the shallow drilling logging data of the target area, so as to obtain the first layer velocity; The second calculation module is used to determine the influence coefficient of the carbonate rock formation in the target area based on the deep drilling and logging data of the adjacent areas of the target area and the seismic data of the target area. The influence coefficient characterizes the degree of influence of porosity, fractures and fluids on the layer velocity in the carbonate rock formation in the target area. The third calculation module is used to correct the velocity of the first layer according to the influence coefficient to obtain the velocity of the second layer of carbonate rock formation in the target area; The fourth calculation module is used to determine the formation pressure of the carbonate rock formation within the target area based on the velocity of the second layer; The first calculation module is used to determine the stacking velocity of the target area based on the seismic data of the target area; convert the stacking velocity into layer velocity using the Dix formula to obtain the third layer velocity; replace the layer velocity at the well location in the third layer velocity with the layer velocity at the well location in the shallow drilling logging data; interpolate the layer velocity between wells in the target area using an interpolation algorithm based on the layer velocity at the well location in the shallow drilling logging data, and replace the layer velocity between wells in the third layer velocity with the interpolated layer velocity between wells in the target area to obtain the first layer velocity of the carbonate strata in the target area; The second calculation module is used to determine the formation thickness of carbonate strata in the target area based on deep drilling and logging data from adjacent areas of the target area and seismic data from the target area; determine the anisotropy intensity of carbonate strata in the target area based on pre-stack gather data in the target area; calculate the seismic properties of carbonate strata in the target area based on seismic data from the target area; normalize the formation thickness, anisotropy intensity, and seismic properties of the carbonate strata, wherein the normalization method includes any data processing method that can achieve data normalization, such as extreme value normalization or mean-standard deviation normalization; and calculate the normalized formation thickness, anisotropy intensity, and seismic properties using a formation comprehensive velocity influence coefficient calculation model to obtain the influence coefficient.

10. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, performs the carbonate formation pressure prediction method as described in any one of claims 1 to 8.

11. A storage medium, characterized in that, The computer program stored in the storage medium can be executed by one or more processors and can be used to implement the carbonate formation pressure prediction method as described in any one of claims 1 to 8.

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