Formation pressure prediction method and system, electronic equipment and storage medium

Abstract

The invention relates to a formation pressure prediction method and system, electronic equipment and a storage medium, and the prediction method comprises the steps: building a pressure prediction model according with the geological features of a region based on formation pressure data collected by a deployed adjacent well, and then obtaining seismic interval velocity data for distinguishing different formations; seismic velocity quasi-impedance data of different stratums are obtained through seismic layer velocity data inversion, the well-side velocity of the target well point is obtained through seismic velocity quasi-impedance data back calculation, and the formation pressure condition of the target well point is determined based on the well-side velocity of the target well point and the pressure prediction model. According to the formation pressure prediction method based on seismic velocity constraint quasi-impedance inversion, the combined ultra-deep formation pressure prediction precision is effectively improved.

Description

Technical Field

[0001] This disclosure belongs to the field of geological exploration technology, and in particular relates to a method, system, electronic device and storage medium for predicting formation pressure. Background Technology

[0002] The southern margin of the Junggar Basin is a regenerated foreland basin formed during the Himalayan orogeny, characterized by intense north-south trending compressional tectonic stress. This region is characterized by dramatic tectonic activity, multiple fault developments, and rapid variations in formation pressure both longitudinally and laterally. Since 2019, Xinjiang Oilfield has intensified its exploration efforts in the lower strata of the southern margin, deploying and implementing numerous risk and exploratory wells. These efforts have yielded significant breakthroughs in natural gas exploration across several areas, demonstrating the immense exploration potential of natural gas in the lower strata of the southern margin.

[0003] However, due to the deep drilling depth of the southern margin subsurface, the unclear formation pressure mechanism, the low signal-to-noise ratio of seismic data, and the limited number of deep wells drilled, current pressure prediction methods mainly include the equivalent depth method, the Eaton formula method, the Bowers method, and the Fillippone method. These are essentially empirical formulas, primarily seeking the relationship between pressure and velocity for formation pressure prediction. Most of these traditional prediction methods target anomalous formation pressure caused by vertical overlying strata, mainly considering the relationship between overlying stress, vertical effective stress, and formation pressure for analysis and calculation. Under tectonic compression, anomalous formation pressure caused by tectonic stress exhibits more complex variations in velocity and lateral effective stress due to the influence of formation sealing properties. Conventional methods for predicting anomalous formation pressure caused by overlying strata cannot meet the needs of predicting formation pressure caused by tectonic stress under tectonic compression. Summary of the Invention

[0004] To address the aforementioned issues, this disclosure provides a formation pressure prediction method, system, electronic device, and storage medium. The formation pressure prediction method based on seismic velocity-constrained pseudo-impedance inversion effectively improves the prediction accuracy of combined ultra-deep formation pressure.

[0005] To address the aforementioned technical problems, the first aspect of this invention provides a method for predicting formation pressure, the method comprising:

[0006] Obtain formation pressure data and layer velocity data of adjacent wells from the target well point, and establish a pressure prediction model based on the formation pressure data and layer velocity data of the adjacent wells;

[0007] Seismic layer velocity data is obtained by interpolation along the layers using the interpolation method. Seismic velocity pseudo-impedance data is then obtained by pseudo-impedance inversion based on the seismic layer velocity data.

[0008] The target wellpoint velocity is calculated by back-calculating the seismic velocity pseudo-impedance data, and then the target wellpoint velocity is substituted into the pressure prediction model to obtain the formation pressure value of the target wellpoint.

[0009] According to a preferred embodiment of the present invention, obtaining seismic layer velocity data by interpolation along the layers includes:

[0010] The layer velocity obtained by seismic root mean square velocity conversion determines the velocity at the grid center point of each acquisition point along the layer;

[0011] The seismic layer velocity data for each layer is determined by interpolation based on the velocity at the center point of the grid in each layer.

[0012] According to a preferred embodiment of the present invention, the determination of the velocity at the grid center point of each acquisition point along the layer by using the layer velocity obtained through seismic root mean square velocity conversion includes:

[0013] Obtain the root mean square velocity of the seismic data points, and convert the root mean square velocity of the seismic data to obtain the layer velocity;

[0014] The velocity at the grid center point of each acquisition point is determined by interpolation based on the layer velocity.

[0015] According to a preferred embodiment of the present invention, the method further includes:

[0016] The logging velocity and seismic velocity of the adjacent well are obtained, and the correction velocity ratio is calculated based on the logging velocity and the seismic velocity.

[0017] The seismic layer velocity data is corrected using the corrected velocity ratio.

[0018] According to a preferred embodiment of the present invention, the step of obtaining seismic velocity pseudo-impedance data by performing pseudo-impedance inversion based on the seismic layer velocity data includes:

[0019] Wave impedance was obtained by fitting the seismic layer velocity data.

[0020] The time-depth correspondence is determined based on the relationship between the synthetic records of adjacent wells and the seismic wave group, and the seismic wavelet is optimized accordingly.

[0021] By interpreting horizons, faults, and the relationships between horizons through earthquakes, a seismic framework model is constructed.

[0022] Using the wave impedance, time-depth correspondence, and seismic wavelet information as inputs, pseudo-impedance inversion is performed in the seismic frame model to obtain seismic velocity pseudo-impedance data.

[0023] According to a preferred embodiment of the present invention, the step of establishing a pressure prediction model based on the adjacent well formation pressure data and the formation velocity includes:

[0024] Acquire the overlying formation pressure, formation hydrostatic pressure, normal compaction velocity, and measured formation velocity values ​​from the formation pressure data of adjacent wells.

[0025] Based on the overlying strata pressure value, formation hydrostatic pressure value, normal compaction strata velocity value, and measured strata velocity value, combined with the lateral pressure coefficient and tectonic factor, the pressure prediction model is constructed.

[0026] According to a preferred embodiment of the present invention, the method further includes:

[0027] Obtain the actual formation pressure value from the adjacent well formation pressure data;

[0028] Based on the overlying formation pressure value, formation hydrostatic pressure value, normal compaction velocity value, and measured formation velocity value, the lateral pressure coefficient and tectonic factor in the pressure prediction model are adjusted, and the predicted formation pressure value is obtained through the pressure prediction model.

[0029] When the predicted formation pressure and the actual formation pressure value meet the preset conditions, the optimized pressure prediction model is obtained.

[0030] To address the aforementioned technical problems, a second aspect of the present invention provides a formation pressure prediction system, the system comprising:

[0031] The data acquisition module is used to acquire formation pressure data and formation velocity data of adjacent wells at the target well point;

[0032] The model building module is used to build a pressure prediction model based on the adjacent well formation pressure data and the formation velocity.

[0033] The layer velocity acquisition module is used to obtain seismic layer velocity data by interpolating along the layers using an interpolation method.

[0034] The pseudo-impedance inversion module is used to perform pseudo-impedance inversion based on the seismic layer velocity data to obtain seismic velocity pseudo-impedance data.

[0035] The formation pressure prediction module is used to back-calculate the target wellpoint velocity based on the seismic velocity pseudo-impedance data, and then substitute the target wellpoint velocity into the pressure prediction model to obtain the formation pressure value of the target wellpoint.

[0036] To address the aforementioned technical problems, a third aspect of the present invention provides an electronic device, comprising:

[0037] Processor; and

[0038] A memory storing computer-executable instructions, which, when executed, cause the processor to perform the method described in any of the above embodiments.

[0039] To address the aforementioned technical problems, a fourth aspect of the present invention provides a computer storage medium, wherein the computer storage medium stores one or more programs, which, when executed by a processor, implement the method described in any of the above embodiments.

[0040] Compared with existing technologies, this disclosure has the following advantages: This disclosure establishes a pressure prediction model that conforms to the geological characteristics of the region based on formation pressure data collected from adjacent wells. Then, it acquires seismic layer velocity data that distinguishes different strata, and obtains seismic velocity pseudo-impedance data for different strata through seismic layer velocity data inversion. The wellbore velocity of the target well is then calculated using the seismic velocity pseudo-impedance data. Based on the wellbore velocity of the target well and the pressure prediction model, the formation pressure at the target well is determined. This formation pressure prediction method based on seismic velocity-constrained pseudo-impedance inversion effectively improves the accuracy of combined ultra-deep formation pressure prediction.

[0041] Other features and advantages of this disclosure will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the disclosure. The objects and other advantages of this disclosure may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 A schematic flowchart of a formation pressure prediction method according to an embodiment of the present disclosure is shown;

[0044] Figure 2 A schematic diagram illustrating the determination of adjacent well formation pressure model parameters according to an embodiment of this disclosure is shown;

[0045] Figure 3 A second schematic flowchart of a formation pressure prediction method according to an embodiment of the present disclosure is shown.

[0046] Figure 4 A schematic diagram of seismic layer velocity interpolation according to an embodiment of the present disclosure is shown;

[0047] Figure 5 A third schematic flowchart of a formation pressure prediction method according to an embodiment of the present disclosure is shown;

[0048] Figure 6A block diagram of a formation pressure prediction system according to an embodiment of the present disclosure is shown;

[0049] Figure 7 A schematic diagram of an electronic device structure according to an embodiment of the present disclosure is shown. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0051] The same reference numerals in the accompanying drawings denote the same or similar elements, components, or parts, and therefore, repeated descriptions of the same or similar elements, components, or parts may be omitted below. It should also be understood that although terms such as first, second, third, etc., indicating numbers may be used herein to describe various devices, elements, components, or parts, these devices, elements, components, or parts should not be limited by these terms. That is, these terms are only used to distinguish one from another. For example, a first device may also be referred to as a second device, without departing from the essential technical solution of the invention. Furthermore, the terms "and / or" and "and / or" refer to all combinations including any one or more of the listed items.

[0052] Please see Figure 1 , Figure 1 This is a schematic diagram of a formation pressure prediction method provided by the present invention, as shown below. Figure 1 As shown, the method includes:

[0053] S11. Obtain formation pressure data and layer velocity data of adjacent wells at the target well point, and establish a pressure prediction model based on the formation pressure data and layer velocity data of adjacent wells.

[0054] In this embodiment, due to the deep drilling depth of the southern margin subsurface, the unclear formation pressure mechanism, the low signal-to-noise ratio of seismic data, and the limited number of deep wells already drilled, current pressure prediction methods mainly include the equivalent depth method, the Eaton formula method, the Bowers method, and the Fillippone method. These are essentially empirical formulas, primarily seeking the relationship between pressure and velocity to predict formation pressure. Most of these traditional prediction methods target abnormal formation pressure caused by vertical overburden strata, mainly considering the relationship between overburden stress, vertical effective stress, and formation pressure for analysis and calculation.

[0055] In this embodiment, formation pressure data and layer velocity of each neighboring well around the target well point are acquired. A pressure prediction model is constructed using the formation pressure data and layer velocity. The model is used to learn the relationship between layer velocity and formation pressure in the formation data, thereby improving the efficiency of analyzing known data when the formation pressure mechanism is unclear, the signal-to-noise ratio of seismic data is low, and there are few deep wells drilled.

[0056] Specifically, such as Figure 2 As shown, Figure 2 This is a parameter determination diagram for the adjacent well formation pressure model, involving various parameters, established in an embodiment of the present invention. Adjacent well formation pressure data is analyzed to obtain formation pressure prediction parameters such as lateral pressure coefficient and tectonic factor. The influence of increased open formation velocity and unchanged closed formation velocity but changing compressive stress on formation pressure prediction is fully considered. Therefore, a reasonable pressure prediction model is established for factors such as the compressive background and undercompaction causes. The overlying formation pressure value, formation hydrostatic pressure value, normally compacted formation velocity value, and measured formation velocity value are obtained from the adjacent well formation pressure data. Based on the overlying formation pressure value, formation hydrostatic pressure value, normally compacted formation velocity value, and measured formation velocity value, combined with the lateral pressure coefficient and tectonic factor, a pressure prediction model is constructed.

[0057] In this embodiment, the pressure prediction model is as follows:

[0058]

[0059] P p Calculate the formation pore pressure value, i.e., the formation pressure value, in MPa. V Overlying formation pressure, MPa. w Formation hydrostatic pressure, MPa. V represents the measured velocity trend of the mudstone formation, i.e., the measured formation velocity value, m / s. n Normal compaction velocity value, i.e., normal compaction velocity value, m / s.

[0060] α is the lateral pressure coefficient, which increases with increasing tectonic stress in open formations. The trend of normal compaction velocity V is determined based on the conventional pressure zone. n The pressure will increase accordingly. If the exponent remains unchanged, the calculated formation pressure P will be... p The result will be too large, so it needs to be adjusted by changing the value of α. Let β = 1, and by adjusting α, make the formula for calculating formation pressure P more accurate. p The value of α can be obtained when it is equal to the actual formation pressure, and usually 1 < α < 3.

[0061] β is a tectonic factor. In closed strata, the formation velocity remains essentially constant with increasing tectonic stress. This is because, as tectonic stress increases, due to the relatively closed strata, the increased stress primarily acts on the fluids, leading to a corresponding increase in formation pressure, while the stress in the rock skeleton remains essentially unchanged, meaning the formation velocity remains essentially constant. Therefore, the velocity is not sensitive to increased tectonic stress in this case, and the magnitude of formation pressure needs to be adjusted by increasing the value of β. After α is determined, β is adjusted to make the formula for calculating formation pressure P... p The β value can be obtained when it is equal to the actual formation pressure, and usually 1 < β < 5.

[0062] Specifically, the actual formation pressure value is obtained from the formation pressure data of adjacent wells; based on the overlying formation pressure value, formation hydrostatic pressure value, normal compaction velocity value, and measured formation velocity value, the lateral pressure coefficient and structural factor in the pressure prediction model are adjusted, and the predicted formation pressure value is obtained through the pressure prediction model; when the predicted formation pressure and the actual formation pressure value meet the preset conditions, the optimized pressure prediction model is obtained.

[0063] S12. Seismic layer velocity data is obtained by interpolation along the layers using the interpolation method. Based on the seismic layer velocity data, pseudo-impedance inversion is performed to obtain seismic velocity pseudo-impedance data.

[0064] In this embodiment, interpolation, also known as interpolation, utilizes the known function values ​​of a function at several points within a certain interval to construct an appropriate specific function. The known values ​​are then used at these points, and the values ​​of this specific function are used as approximations of the function at other points within the interval. Interpolation is an important method for approximating discrete functions, often used to fill gaps between pixels during image transformations, and to estimate approximate values ​​of unknown functions in mathematics, computer graphics, and other fields.

[0065] In this embodiment, seismic velocity, i.e., the propagation speed of seismic waves in rock, is generally related to rock type, confining pressure, rock structure, and other geological factors. Seismic layer velocity refers to the propagation speed of seismic waves in layered strata. It directly reflects the lithology of the strata and can be used to delineate strata. Layer velocity can be calculated using methods such as sonic logging data, seismic logging data, stacked velocity spectrum data, and synthetic velocity logging data. The concept of layer velocity applies to both P-waves and S-waves, but their values ​​differ.

[0066] In this embodiment, the seismic layer velocity is obtained by combining the seismic velocities in the neighboring wells of the target well point based on the interpolation method. Specifically, the seismic velocities of the neighboring wells around the target well point are obtained, and the seismic velocities of the neighboring wells are interpolated to obtain the seismic layer velocity of the target well point.

[0067] Quasi-impedance inversion is a widely used technique in seismic exploration. It primarily involves processing and analyzing seismic data to infer the physical properties of subsurface media, such as wave impedance. Wave impedance is the product of rock density and seismic wave velocity, reflecting the impedance characteristics of seismic waves propagating through the subsurface medium. Quasi-impedance inversion technology can utilize information from seismic data, combined with well logging, geological, and other data, to provide a relatively accurate estimate of the wave impedance of subsurface media, thus offering crucial geological information for oil and gas exploration and mineral development.

[0068] In this embodiment, the wave impedance of the subsurface medium is indirectly inferred or simulated using seismic wave velocity information. In seismic exploration, seismic wave velocity and density (or wave impedance) are two closely related parameters. When a seismic wave propagates through a subsurface medium, its velocity is affected by the medium's density and elastic modulus. Therefore, by measuring the seismic wave velocity and combining it with the density information of the subsurface medium (usually obtained through other geophysical methods), the wave impedance of the subsurface medium can be calculated.

[0069] S13. The target wellpoint velocity is calculated by back-calculating the seismic velocity-impedance data, and the target wellpoint velocity is substituted into the pressure prediction model to obtain the formation pressure value of the target wellpoint.

[0070] In this embodiment, the Gardner formula in geophysics is used to estimate the relationship between formation density and P-wave velocity, i.e., wave impedance (Z) is the product of medium density (ρ) and seismic wave velocity (V), i.e., Z = ρ × V. Therefore, given the wave impedance data and medium density, the seismic wave velocity can be calculated using simple mathematical operations. In this scheme, the pseudo-impedance data of seismic velocity is obtained through the above comprehensive calculation. After obtaining the pseudo-impedance data of seismic velocity for each formation, the corresponding seismic layer velocity can be calculated.

[0071] In this embodiment, the well-side velocity of the target well point is the seismic layer velocity of each stratum at the target well point. After back-calculating the corresponding seismic layer velocities, the seismic layer velocities are analyzed using a pressure prediction model to predict the formation pressure values ​​of each stratum at the target well point. This invention proposes a formation pressure prediction method based on seismic velocity-constrained pseudo-impedance inversion, which effectively improves the prediction accuracy of combined ultra-deep formation pressure and provides important support for safe drilling and drilling speed-up of ultra-deep wells.

[0072] This disclosure establishes a pressure prediction model that conforms to the geological characteristics of the region based on formation pressure data collected from adjacent wells. Then, it acquires seismic layer velocity data that distinguishes different strata, and uses this data to invert and obtain pseudo-impedance data for different strata. The wellbore velocity at the target well is then calculated using this pseudo-impedance data. Based on the wellbore velocity at the target well and the pressure prediction model, the formation pressure at the target well is determined. This formation pressure prediction method based on seismic velocity-constrained pseudo-impedance inversion effectively improves the accuracy of combined ultra-deep formation pressure prediction.

[0073] Please see Figure 3 , Figure 3 This is a schematic diagram of a formation pressure prediction method provided by the present invention, as shown below. Figure 3 As shown, and as Figure 1 Compared to the prediction method shown, this method includes the following steps:

[0074] S21. The layer velocity obtained by converting the seismic root mean square velocity determines the velocity at the grid center point of each acquisition point along the layer.

[0075] In this embodiment, the layer velocity of a specific layer is calculated from the root mean square velocity (RMS) of the seismic data using the DIX formula. The DIX formula, also known as the Dix formula, has wide applications in geophysical exploration. It is primarily used for various calculations and analyses, depending on the specific application scenario. The DIX formula is a formula used in geophysical exploration that implements the method of calculating layer velocities using the RMS velocity. This formula is particularly important in seismology because it can reflect and obtain multifaceted information about a specific stratum. The application of the DIX formula is based on a fundamental idea: expanding the RMS velocity formula, reducing one term, squaring it, expanding again, and subtracting it from another RMS velocity formula to obtain the layer velocity of a specific layer. This method is similar to using the concept of the sum of the first n terms of a sequence, providing an effective tool for geophysical exploration.

[0076] Specifically, the root mean square velocity of the seismic data points is obtained, and the layer velocity is converted from the root mean square velocity of the seismic data points. The velocity at the grid center point of each data point is determined based on the layer velocity using an interpolation method.

[0077] In this embodiment, as Figure 4 As shown, Figure 4 This is a schematic diagram of seismic layer velocity interpolation. The small black dots in the diagram represent the seismic root mean square velocity acquisition points, and the circles represent the first interpolation points. First, the layer velocity obtained by converting the root mean square velocity is used to interpolate the velocity at the center point (circle) of each pick-up point grid along the layer. Then, the velocity at the circle is used to interpolate the entire layer velocity volume. The lateral variation of the seismic layer velocity after rationalization is more natural and reasonable.

[0078] The seismic root mean square velocity is converted using the DIX formula, and then interpolated along the layers to obtain the seismic layer velocity. Since the seismic root mean square velocity is mostly manually collected by jumper grids (such as 50*50), there are many singular points in the collected values. The directly interpolated layer velocity varies rapidly in both the longitudinal and lateral directions, and there is a large difference from the actual stratum velocity. Therefore, in the interpolation process, the velocity value of each picked grid center point is interpolated first by using an inverse distance weighting method to reduce the interference of singular points on the overall data and improve the accuracy of the data.

[0079] S22. Using interpolation, determine the seismic layer velocity data for each layer based on the velocity at the center point of the grid in each layer.

[0080] In this embodiment, the center point velocity value is interpolated using a natural adjacency method to obtain more natural and reasonable layer velocity data.

[0081] In this embodiment, reasonable seismic layer velocity data is obtained by using the DIX formula and the "two-step interpolation method". Compared with the layer velocity obtained by direct interpolation, this method can effectively reduce the influence of interference and error parameters on the seismic layer velocity data and obtain more accurate seismic layer velocities.

[0082] In this embodiment, after calculating the seismic layer velocity data, the seismic layer velocity can be corrected using the velocity of adjacent wells.

[0083] Specifically, it involves obtaining the logging velocity and seismic velocity of adjacent wells. Logging velocity is a petroleum term published in 1994. It involves using the geophysical properties of rock formations, such as electrochemical, electrical, acoustic, and radioactive properties, to obtain physical parameters about the rock formations by measuring geophysical parameters. Seismic velocity is the speed at which seismic waves propagate in rocks.

[0084] Then, the corrected velocity ratio is calculated based on the logging velocity and the seismic velocity. Interpolation is then performed to obtain the corrected velocity ratio for each formation, and this ratio is used to correct the seismic layer velocity data. The corrected seismic layer velocity data is more accurate, resulting in more precise predictions.

[0085] Please see Figure 5 , Figure 5 This is a schematic diagram of a formation pressure prediction method provided by the present invention, as shown below. Figure 5 As shown, and as Figure 1 Compared to the prediction method shown, this method includes the following steps:

[0086] S31. Wave impedance is obtained by fitting seismic layer velocity data.

[0087] In this embodiment, conventional seismic inversion mainly involves well-constrained seismic inversion. However, well-constrained seismic inversion with few adjacent wells cannot fully reflect the lateral variations in formation velocity, leading to uncertainties in formation pressure prediction. Therefore, in areas with few adjacent wells, it is necessary to incorporate seismic velocity information as a constraint condition for seismic inversion to obtain more accurate and reasonable velocity inversion results. The specific velocity inversion process is as follows:

[0088] ① Well impedance velocity fitting. Since formation pressure prediction requires reasonable layer velocities, while seismic inversion can only obtain wave impedance data, it is necessary to fit the wave impedance through velocity fitting.

[0089] The density is calculated using the Gardner formula based on the velocity: ρ = 0.32 × v 0.25 ;

[0090] Next, calculate the wave impedance: Z = ρ × v.

[0091] Where Z is wave impedance, ρ is density, and v is seismic layer velocity.

[0092] S32. Determine the time-depth correspondence based on the relationship between the synthetic records of adjacent wells and the seismic wave group, and optimize the seismic wavelet accordingly.

[0093] In this embodiment, ② time-depth relationship calibration. The time-depth correspondence is determined by the relationship between well synthetic records and seismic wave groups. Simultaneously, during the time-depth calibration process, wavelets are extracted and continuously optimized to ultimately obtain a reasonable time-depth relationship and the optimal wavelet. Seismic synthetic records are seismic records (seismic traces) artificially synthesized from sonic logging or vertical seismic profile data. They are a widely used technique in seismic modeling and form the basis for stratigraphic calibration, reservoir description, and other work, serving as an intermediate medium for converting geological models into seismic information. Seismic wave group relationships are mainly reflected in the type of seismic waves, their propagation speed, and their relationship with the Earth's internal materials. When an earthquake occurs, the resulting waves propagate in the form of elastic waves from the hypocenter in all directions. The propagation path of seismic waves is a complex curve, and their propagation speed is related to the density and elasticity of the Earth's internal materials, generally increasing with depth.

[0094] S33. By interpreting the horizons, faults, and the relationships between horizons through earthquakes, a seismic framework model is constructed.

[0095] In this embodiment, ③ a geological framework model is established. Using seismic interpretation horizons and faults, a reasonable geological framework model is established from deep to shallow, and from the footwall to the hanging wall, based on the contact relationships between horizons and between horizons and faults. Seismic interpretation horizons are an important component of seismic geological interpretation. They involve tracing and interpreting seismic reflection characteristics such as amplitude, phase, morphology, continuity, and characteristic combinations on the seismic data volume to obtain seismic horizon data. A fault is a structural feature where the Earth's crust fractures under stress, resulting in significant relative displacement of rock blocks on either side of the fault surface.

[0096] S34. Using wave impedance, time-depth correspondence, and seismic wavelet information as inputs, pseudo-impedance inversion is performed in the seismic frame model to obtain seismic velocity pseudo-impedance data.

[0097] In this embodiment, step ④ is to establish a low-frequency model. The low-frequency information of the seismic layer data volume is fitted with the pseudo-impedance using the Gardner formula to obtain the low-frequency pseudo-impedance model required for inversion. Step ⑤ is to perform seismic velocity pseudo-impedance inversion. The velocity pseudo-impedance, seismic and wavelet information are used as input data to perform seismic velocity pseudo-impedance inversion in the established geological framework model, resulting in the seismic velocity pseudo-impedance data volume.

[0098] Please see Figure 6 , Figure 6 This is a block diagram of a formation pressure prediction system provided by the present invention, such as... Figure 6 As shown, the system includes: a data acquisition module 11, a model building module 12, a layer velocity acquisition module 13, a pseudo-impedance inversion module 14, and a formation pressure prediction module 15.

[0099] In this embodiment, the data acquisition module 11 is used to acquire formation pressure data and formation velocity of adjacent wells of the target well point.

[0100] In this embodiment, the model building module 12 is used to build a pressure prediction model based on the formation pressure data and formation velocity of adjacent wells.

[0101] In this embodiment, the layer velocity acquisition module 13 is used to obtain seismic layer velocity data by interpolation along the layers.

[0102] In this embodiment, the pseudo-impedance inversion module 14 is used to perform pseudo-impedance inversion based on seismic layer velocity data to obtain seismic velocity pseudo-impedance data.

[0103] In this embodiment, the formation pressure prediction module 15 is used to back-calculate the target wellpoint velocity based on the seismic velocity pseudo-impedance data, and substitute the target wellpoint velocity into the pressure prediction model to obtain the formation pressure value of the target wellpoint.

[0104] In this embodiment, the layer velocity acquisition module 13 is specifically used to determine the velocity at the grid center point of each acquisition point along the layer by using the layer velocity obtained by converting the seismic root mean square velocity; and to determine the seismic layer velocity data of each layer based on the velocity at the grid center point of each layer by using an interpolation method.

[0105] In this embodiment, the layer velocity acquisition module 13 is specifically used to acquire the root mean square velocity of the seismic data acquisition points, and to obtain the layer velocity by converting the root mean square velocity of the seismic data acquisition points; and to determine the velocity at the grid center point of each data acquisition point based on the layer velocity by using an interpolation method.

[0106] In this embodiment, the system also includes a correction module, which is used to obtain the logging velocity and seismic velocity of adjacent wells, and calculate the correction velocity ratio based on the logging velocity and seismic velocity; and correct the seismic layer velocity data by the correction velocity ratio.

[0107] In this embodiment, the pseudo-impedance inversion module 14 is specifically used to obtain wave impedance based on seismic layer velocity data fitting; determine the time-depth correspondence based on the relationship between the synthetic records of adjacent wells and the seismic wave group and optimize the seismic wavelet accordingly; construct a seismic frame model by interpreting the layers, faults and the relationship between layers through seismic interpretation; and perform pseudo-impedance inversion in the seismic frame model with wave impedance, time-depth correspondence and seismic wavelet information as input to obtain seismic velocity pseudo-impedance data.

[0108] In this embodiment, the model building module 12 is specifically used to acquire the overlying formation pressure value, formation hydrostatic pressure value, normal compaction formation velocity value, and measured formation velocity value from the formation pressure data of adjacent wells; and to construct a pressure prediction model based on the overlying formation pressure value, formation hydrostatic pressure value, normal compaction formation velocity value, and measured formation velocity value, combined with the lateral pressure coefficient and tectonic factor.

[0109] In this embodiment, the model building module 12 is specifically used to obtain the actual formation pressure value from the formation pressure data of adjacent wells; based on the overlying formation pressure value, formation hydrostatic pressure value, normal compaction velocity value and measured formation velocity value, the lateral pressure coefficient and structural factor in the pressure prediction model are adjusted, and the predicted formation pressure value is obtained through the pressure prediction model; when the predicted formation pressure and the actual formation pressure value meet the preset conditions, the optimized pressure prediction model is obtained.

[0110] like Figure 7 As shown, this embodiment of the invention provides an electronic device, including a processor 1110, a communication interface 1120, a memory 1130, and a communication bus 1140, wherein the processor 1110, the communication interface 1120, and the memory 1130 communicate with each other through the communication bus 1140.

[0111] Memory 1130 is used to store computer programs;

[0112] The processor 1110, when executing the program stored in the memory 1130, implements any of the above prediction methods.

[0113] The electronic device provided in this embodiment of the invention includes a processor 1110 that executes a program stored in a memory 1130 to obtain formation pressure data and layer velocity of adjacent wells at the target well point, and establishes a pressure prediction model based on the adjacent well formation pressure data and layer velocity; obtains seismic layer velocity data by interpolation along the layers, performs pseudo-impedance inversion based on the seismic layer velocity data to obtain seismic velocity pseudo-impedance data; calculates the well-side velocity of the target well point based on the seismic velocity pseudo-impedance data, and substitutes the well-side velocity of the target well point into the pressure prediction model to obtain the formation pressure value of the target well point.

[0114] The communication bus 1140 mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus 1140 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, it is shown in the figure with only one thick line, but this does not indicate that there is only one bus or one type of bus.

[0115] The communication interface 1120 is used for communication between the above-mentioned electronic device and other devices.

[0116] The memory 1130 may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory 1130 may also be at least one storage device located remotely from the aforementioned processor 1110.

[0117] The processor 1110 mentioned above can be a general-purpose processor 1110, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0118] This invention provides a computer-readable storage medium storing one or more programs that can be executed by one or more processors 1110 to implement the prediction method of any of the above embodiments.

[0119] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0120] Although the present disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure.

Claims

1. A method of predicting formation pressure, characterized by, The prediction method comprises: obtaining adjacent well formation pressure data and interval velocity of a target well point, and establishing a pressure prediction model based on the adjacent well formation pressure data and the interval velocity; obtaining seismic interval velocity data by interpolation along layers through an interpolation method, and obtaining seismic velocity pseudo impedance data through pseudo impedance inversion according to the seismic interval velocity data; obtaining wellside velocity of the target well point through back calculation according to the seismic velocity pseudo impedance data, and substituting the wellside velocity of the target well point into the pressure prediction model to obtain formation pressure value of the target well point.

2. The formation pressure prediction method of claim 1, wherein, The obtaining seismic interval velocity data by interpolation along layers through an interpolation method comprises: determining velocity at a grid center point of each acquisition point along layers through interval velocity converted from seismic root mean square velocity; determining seismic interval velocity data of each layer through an interpolation method based on the velocity at the grid center point of each layer.

3. The method of predicting formation pressure according to claim 2, wherein, The determining velocity at a grid center point of each acquisition point along layers through interval velocity converted from seismic root mean square velocity comprises: obtaining seismic root mean square velocity of each acquisition point, and converting interval velocity through the seismic root mean square velocity; determining velocity at a grid center point of each acquisition point based on the interval velocity through an interpolation method.

4. The method of predicting formation pressure according to claim 1, wherein, The method further comprises: obtaining logging velocity and seismic velocity of the adjacent well, and calculating a correction velocity ratio according to the logging velocity and the seismic velocity; correcting the seismic interval velocity data through the correction velocity ratio.

5. The prediction method of claim 4, wherein, The obtaining seismic velocity pseudo impedance data through pseudo impedance inversion according to the seismic interval velocity data comprises: fitting wave impedance based on the seismic interval velocity data; determining time-depth correspondence and corresponding optimizing seismic wavelet according to the adjacent well synthetic record and seismic wave group relationship; constructing a seismic framework model through seismic interpretation horizon, fault and relationship between horizons; taking the wave impedance, time-depth correspondence and seismic wavelet information as input, and performing pseudo impedance inversion in the seismic framework model to obtain seismic velocity pseudo impedance data.

6. The prediction method according to any one of claims 1 to 5, characterized in that, The establishing a pressure prediction model based on the adjacent well formation pressure data and the interval velocity comprises: obtaining overlying formation pressure value, formation hydrostatic pressure value, normal compaction formation velocity value and measured formation velocity value in the adjacent well formation pressure data; constructing the pressure prediction model in combination with lateral pressure coefficient and structure factor according to the overlying formation pressure value, the formation hydrostatic pressure value, the normal compaction formation velocity value and the measured formation velocity value.

7. The prediction method of claim 6, wherein, The method further comprises: obtaining real formation pressure value in the adjacent well formation pressure data; adjusting lateral pressure coefficient and structure factor in the pressure prediction model based on the overlying formation pressure value, the formation hydrostatic pressure value, the normal compaction formation velocity value and the measured formation velocity value, and obtaining predicted formation pressure value through the pressure prediction model; obtaining an optimized pressure prediction model when the predicted formation pressure and the real formation pressure value meet a preset condition.

8. A formation pressure prediction system characterized by, The system comprises: a data acquisition module configured to obtain adjacent well formation pressure data and interval velocity of a target well point; a model establishment module configured to establish a pressure prediction model based on the adjacent well formation pressure data and the interval velocity; a layer velocity acquisition module configured to obtain seismic layer velocity data by interpolation along layers; a pseudo impedance inversion module configured to perform pseudo impedance inversion according to the seismic layer velocity data to obtain seismic velocity pseudo impedance data; a formation pressure prediction module configured to inversely calculate a well point velocity near well of a target well point according to the seismic velocity pseudo impedance data, and substitute the well point velocity near well of the target well point into the pressure prediction model to obtain a formation pressure value of the target well point.

9. An electronic device, comprising: comprising: a processor; and a memory storing computer executable instructions that, when executed, cause the processor to perform the method of any one of claims 1-7.

10. A computer storage medium, characterized in that wherein, the computer storage medium stores one or more programs that, when executed by a processor, implement the method of any one of claims 1-7.

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