Formation pressure prediction method and device suitable for small well area

Abstract

The invention provides a formation pressure prediction method and device suitable for a small well area. The corresponding method comprises the following steps: determining fracture attribute data according to post-stack seismic data in a target work area; predicting the whole-area formation pressure of the target work area according to the logging data of the target work area and a rock physical model of a reservoir; the formation pressure of the small well area in the target work area is corrected according to the fracture attribute data and the formation pressure of the whole area so as to predict the formation pressure of the small well area, and the target work area is composed of a plurality of small well areas. According to the method, the formation pressure of a complex structure area is precisely predicted through crack attribute correction, the prediction precision of formation pressure change of a small well area is improved, and powerful technical support is provided for engineering dessert prediction and drilling engineering.

Description

Technical Field

[0001] The present application belongs to the field of oil and gas exploration technology, in particular to the field of oil and gas exploration seismic data processing technology, and specifically relates to a formation pressure prediction method and device suitable for small well areas. Background Art

[0002] It is understandable that formation pressure prediction is extremely important in the exploration and development of oil and gas fields, and is very important for oil and gas exploration, drilling engineering, shale engineering sweet spot prediction, and improving reservoir properties. The practice of shale gas exploration and development in recent years has shown that shale gas production is positively correlated with the pressure coefficient, and the prediction and research of the pressure coefficient is crucial to the successful exploration of shale gas.

[0003] Seismic prediction is an effective means to achieve pre-drilling prediction of formation pressure, but it has many influencing factors and is difficult to accurately predict. The matrix of shale gas reservoirs has the characteristics of low porosity and ultra-low permeability, and natural fractures are developed locally. When drilling into natural fractures during drilling, problems such as pressure relief and leakage occur. The impact of fractures of different scales on shale gas varies greatly. It is generally believed that large natural open fractures are unfavorable for shale gas, while small-scale fractures and micro-fractures, if they do not destroy the accumulation of shale gas, can improve the permeability of low-permeability shale gas layers and are important storage spaces and main seepage channels for shale gas reservoirs. The development of fractures to a certain degree of development affects the formation pressure. If larger fractures are encountered during drilling, it often causes pressure relief, which reduces the measured pressure. Accurately predicting formation pressure is of great significance for safe drilling.

[0004] In the prior art, there is a lack of a method that can accurately predict the formation pressure in the entire area and the small well area. Summary of the invention

[0005] The present invention belongs to the technical field of seismic data processing. One purpose of the present invention is to achieve precise prediction of formation pressure in complex structural areas with multi-information fusion, and to support the optimization and comprehensive evaluation of sweet spots in shale reservoir engineering.

[0006] Another object of the present invention is to provide a formation pressure prediction device suitable for small well areas. Another object of the present invention is to provide an electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps of the above-mentioned formation pressure prediction method suitable for small well areas when executing the computer program. Another object of the present invention is to provide a readable medium, on which a computer program is stored, and when the computer program is executed by the processor, the steps of the above-mentioned formation pressure prediction method suitable for small well areas are implemented.

[0007] In order to solve the technical problems in the background technology of this application, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a formation pressure prediction method suitable for small well areas, comprising:

[0009] Determine fracture attribute data based on post-stack seismic data in the target work area;

[0010] Predicting the formation pressure of the entire target work area based on the well logging data of the target work area and the rock physics model of the reservoir;

[0011] The formation pressure of the small well area in the target work area is corrected according to the fracture attribute data and the formation pressure of the whole area to predict the formation pressure of the small well area, wherein the target work area is composed of multiple small well areas.

[0012] In one embodiment of the present invention, the fracture attribute data includes: coherence volume, structural curvature, AFE fault enhancement parameters and ant volume.

[0013] In one embodiment of the present invention, the coherence volume is used to identify cracks of a first scale;

[0014] The structural curvature and the AFE fault enhancement parameter are used to identify fractures of a second scale;

[0015] The ant body is used to identify cracks of a third scale, wherein the first scale is larger than the second scale and the third scale, and the second scale is larger than the third scale.

[0016] In one embodiment of the present invention, predicting the formation pressure of the entire target work area according to the well logging data of the target work area and the rock physics model of the reservoir includes:

[0017] Constructing the wave impedance of the target work area according to the well logging data;

[0018] The formation pressure of the entire area is predicted based on the wave impedance and the rock physics model.

[0019] In one embodiment of the present invention, the components of the rock physics model include: wet clay, sandy mixture and organic matter.

[0020] In one embodiment of the present invention, a formation pressure prediction method suitable for small well areas further includes:

[0021] The target work area is divided into a plurality of small well areas according to the structural units of the target work area.

[0022] In one embodiment of the present invention, the formation pressure of the small well area in the target work area is corrected according to the fracture attribute data and the formation pressure of the whole area, including:

[0023] constructing a formation pressure correction coefficient according to the first-scale fractures, the second-scale fractures, and the third-scale fractures;

[0024] The formation pressure of the small well area is corrected according to the formation pressure correction coefficient.

[0025] In a second aspect, the present invention provides a formation pressure prediction device suitable for small well areas, the device comprising:

[0026] A fracture attribute data determination module, used to determine fracture attribute data based on post-stack seismic data in a target work area;

[0027] A whole-area formation pressure prediction module, used to predict the whole-area formation pressure of the target work area according to the well logging data of the target work area and the rock physics model of the reservoir;

[0028] The formation pressure correction module is used to correct the formation pressure of the small well area in the target work area according to the fracture attribute data and the formation pressure of the whole area to predict the formation pressure of the small well area, wherein the target work area is composed of multiple small well areas.

[0029] In one embodiment of the present invention, the fracture attribute data includes: coherence volume, structural curvature, AFE fault enhancement parameters and ant volume.

[0030] In one embodiment of the present invention, the coherence volume is used to identify cracks of a first scale;

[0031] The structural curvature and the AFE fault enhancement parameter are used to identify fractures of a second scale;

[0032] The ant body is used to identify cracks of a third scale, wherein the first scale is larger than the second scale and the third scale, and the second scale is larger than the third scale.

[0033] In one embodiment of the present invention, the whole-area formation pressure prediction module includes:

[0034] A wave impedance construction unit, used for constructing the wave impedance of the target work area according to the well logging data;

[0035] The whole-area formation pressure prediction unit is used to predict the whole-area formation pressure according to the wave impedance and the rock physics model.

[0036] In one embodiment of the present invention, the components of the rock physics model include: wet clay, sandy mixture and organic matter.

[0037] In one embodiment of the present invention, a formation pressure prediction device suitable for small well areas further includes:

[0038] The target work area division module is used to divide the target work area into a plurality of small well areas according to the structural units of the target work area.

[0039] In one embodiment of the present invention, the formation pressure correction module includes:

[0040] A correction coefficient construction unit, configured to construct a formation pressure correction coefficient according to the first-scale cracks, the second-scale cracks, and the third-scale cracks;

[0041] The formation pressure correction unit is used to correct the formation pressure of the small well area according to the formation pressure correction coefficient.

[0042] In a third aspect, the present invention provides a computer program product, comprising a computer program / instruction, which, when executed by a processor, implements the steps of a formation pressure prediction method suitable for small well areas.

[0043] In a fourth aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of a method for predicting formation pressure adapted to small well areas are implemented.

[0044] In a fifth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a formation pressure prediction method adapted to small well areas.

[0045] From the above description, it can be seen that an embodiment of the present invention provides a formation pressure prediction method and device suitable for small well areas. The corresponding formation pressure prediction method suitable for small well areas includes: first, determining fracture attribute data based on post-stack seismic data in a target work area; then, predicting the formation pressure of the entire target work area based on the logging data of the target work area and the rock physics model of the reservoir; finally, correcting the formation pressure of the small well area in the target work area based on the fracture attribute data and the formation pressure of the entire area to predict the formation pressure of the small well area, wherein the target work area is composed of multiple small well areas.

[0046] The corresponding formation pressure prediction device suitable for small well areas includes: a fracture attribute data determination module, which is used to determine fracture attribute data based on post-stack seismic data in the target work area; a full-area formation pressure prediction module, which is used to predict the full-area formation pressure of the target work area based on the logging data of the target work area and the rock physics model of the reservoir; a formation pressure correction module, which is used to correct the formation pressure of the small well area in the target work area based on the fracture attribute data and the full-area formation pressure to predict the formation pressure of the small well area, wherein the target work area is composed of multiple small well areas.

[0047] The formation pressure prediction method and device suitable for small well areas provided in the embodiments of the present invention can achieve precise prediction of formation pressure in structurally complex areas through nonlinear correction of fracture properties, improve the prediction accuracy of formation pressure changes in small well areas, and provide strong technical support for engineering sweet spot prediction and drilling engineering. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 A schematic diagram of a flow chart of a formation pressure prediction method suitable for a small well area in an embodiment of the present invention;

[0050] Figure 2 It is a flow chart of step 200 of the formation pressure prediction method suitable for small well areas in an embodiment of the present invention;

[0051] Figure 3 Another schematic flow chart of a formation pressure prediction method suitable for a small well area in an embodiment of the present invention;

[0052] Figure 4 It is a flow chart of step 300 of the formation pressure prediction method suitable for small well areas in an embodiment of the present invention;

[0053] Figure 5 It is a flow chart of a formation pressure prediction method suitable for small well areas in a specific embodiment of the present invention;

[0054] Figure 6 A mind map of a formation pressure prediction method suitable for small well areas in a specific embodiment of the present invention;

[0055] Figure 7 A schematic diagram of a fault-enhanced coherence body in a specific embodiment of the present invention;

[0056] Figure 8 A schematic diagram of negative curvature is constructed in a specific embodiment of the present invention;

[0057] Fig. 9 It is a schematic diagram of an ant body in a specific embodiment of the present invention;

[0058] Fig.10 In the specific implementation of the present invention Fig. 9 The enlarged schematic diagram of the ant body is shown;

[0059] Fig.11 It is a flow chart of a formation pressure seismic prediction method based on an improved RT method in a specific embodiment of the present invention;

[0060] Fig.12 It is a schematic diagram of the formation pressure prediction results before and after fracture correction in a small well area in a specific embodiment of the present invention;

[0061] Fig.13 It is a block diagram of a formation pressure prediction device suitable for small well areas in a specific embodiment of the present invention;

[0062] Fig.14 It is a schematic diagram of the structure of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0065] It should be noted that the terms "including" and "having" in the specification and claims of the present application and the above-mentioned drawings and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. In the absence of conflict, the embodiments in the present application and the features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0066] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of laws and regulations.

[0067] Embodiment 1:

[0068] The embodiment of the present invention provides a specific implementation method of a formation pressure prediction method suitable for a small well area, see Figure 1 , specifically including the following:

[0069] Step 100: determining fracture attribute data according to post-stack seismic data in the target work area;

[0070] Step 200: predicting the formation pressure of the entire target work area according to the well logging data of the target work area and the rock physics model of the reservoir;

[0071] Step 300: Correct the formation pressure of the small well area in the target work area according to the fracture attribute data and the formation pressure of the entire area to predict the formation pressure of the small well area, wherein the target work area is composed of multiple small well areas.

[0072] From the above description, it can be seen that an embodiment of the present invention provides a formation pressure prediction method suitable for small well areas, including: first, determining fracture attribute data based on post-stack seismic data in a target work area; then, predicting the formation pressure of the entire target work area based on the well logging data of the target work area and the rock physics model of the reservoir; finally, correcting the formation pressure of the small well area in the target work area based on the fracture attribute data and the formation pressure of the entire area to predict the formation pressure of the small well area, wherein the target work area is composed of multiple small well areas.

[0073] The formation pressure prediction method suitable for small well areas provided in the embodiment of the present invention realizes the precise prediction of formation pressure in structurally complex areas through nonlinear correction of fracture attributes, improves the prediction accuracy of formation pressure changes in small well areas, and provides strong technical support for engineering sweet spot prediction and drilling engineering.

[0074] Embodiment 2:

[0075] It is understood that the post-stack seismic data in step 100 refers to the seismic data formed by superposition of the data received by the common shot geophone after dynamic correction and static correction. Furthermore, the post-stack seismic data refers to the result obtained after a series of seismic data processing steps. These processing steps include noise removal, correction, superposition, etc. Post-stack data usually has a higher signal-to-noise ratio and better underground structure analysis capability, and can be better used in applications such as seismic interpretation and geological model construction.

[0076] Correspondingly, pre-stack seismic data refers to the raw data of seismic records before any processing. These data are usually seismic wave signals recorded by seismic instruments, containing information about underground structures and possible noise and interference.

[0077] When step 100 is implemented, specifically: first, by analyzing certain attributes in the post-stack seismic data, such as amplitude, frequency, phase, etc., features reflecting the existence of underground fractures can be identified. Commonly used methods include amplitude envelope extraction, frequency spectrum analysis, phase analysis, etc.

[0078] Then, according to the geological characteristics and seismic response of the fracture, some characteristic parameters related to the fracture can be extracted, such as the width, length, direction, cross-sectional shape, etc. These characteristic parameters can be calculated and determined by various seismic interpretation and analysis methods.

[0079] Finally, based on the extracted crack attribute data, three-dimensional modeling can be performed to visualize the spatial distribution and geometric morphology of the cracks, which can better understand and analyze the distribution law and impact of cracks.

[0080] It is understood that the logging data in step 200 refers to the data of underground rock and fluid properties obtained by using various measurement tools during the drilling process. These logging tools are usually installed in logging instruments, which are lowered into the well and perform corresponding measurements to obtain information about formation properties, fluid types and contents, etc. It mainly has the following applications:

[0081] Formation identification and description: Well logging data can provide information about the physical properties of underground rocks, such as density, resistivity, acoustic wave velocity, etc. By analyzing these data, the type, thickness and properties of the formation can be identified and described, helping to determine the boundaries and distribution of the formation.

[0082] Oil and gas exploration and development: Well logging data can provide information about underground oil and gas reservoirs, such as porosity, permeability, oil saturation, etc. These data are of great guiding significance for oil and gas exploration and development, and can help evaluate the production capacity and economic value of reservoirs.

[0083] Wellbore evaluation and completion design: Well logging data can be used to evaluate the quality and integrity of the wellbore, determine the stability and suitability of the downhole environment, and design and optimize well completion plans.

[0084] Geological model construction and reservoir simulation: Well logging data, as an important input for geological modeling and reservoir simulation, can provide parameters of underground rock and fluid properties for building geological models and predicting reservoir behavior.

[0085] Preferably, the logging data in step 200 include density logging, resistivity logging, acoustic logging, nuclear magnetic resonance logging, cable logging, etc. These data are usually presented in the form of curves, and detailed information about the formation and fluid can be obtained through the changes and mutual relationships of the curves.

[0086] A rock physics model is a model that mathematically or physically describes the physical properties and behavior of underground rocks. It is used to help understand and interpret seismic data, well logging data, and other geophysical data to infer the composition, structure, and fluid properties of rocks. Rock physics models have important applications in oil and gas exploration and development, geological disaster prediction, and groundwater resource evaluation. The establishment of a rock physics model is usually based on the following aspects:

[0087] Rock physics experiments: Through laboratory measurements and tests of the physical properties of different rock samples, such as density, acoustic wave velocity, resistivity, etc., actual observation data is obtained to establish rock physics models.

[0088] Rock physics theory: Based on the basic theories of rock composition, structure and physical properties, mathematical modeling and physical equation derivation are used to obtain equations and relationships that describe the physical behavior of rocks.

[0089] Geophysical data interpretation: Using seismic data, well logging data and other geophysical data, through inversion and simulation methods, the physical properties and structure of rocks are inferred, and then a rock physics model is constructed.

[0090] Applications of rock physics models include:

[0091] Seismic interpretation and underground structure research: Rock physics models can be used to explain reflection, propagation and attenuation phenomena in seismic data, and further infer the properties, interfaces and structures of underground rocks.

[0092] Oil and gas exploration and development: Rock physics models can help evaluate reservoir physical properties and fluid saturation, optimize oil and gas development plans, and predict reserves and production capacity.

[0093] Geological disaster prediction and prevention: Through rock physics models, we can study the mechanical properties and deformation characteristics of underground rocks, predict the potential risks of geological disasters, and guide prevention and control work.

[0094] Groundwater resource assessment: Rock physics models can be used to assess the storage and flow characteristics of groundwater and to study the distribution, recharge and sustainable use of groundwater resources.

[0095] Regarding step 300, in view of the fact that the formation pressure coefficient obtained by the method in the prior art cannot well reflect the microscopic characteristics of the large pressure changes in the small well area where local fractures are developed. For this reason, step 300 carries out small-scale fracture correction to solve the problem of complex local formation pressure changes in complex structural areas. The specific implementation process is based on the previous two steps of fracture attribute optimization and formation pressure prediction, and carries out fine prediction of formation pressure with fault and fracture correction. The threshold value of the fracture attribute data is adjusted based on the fracture density curve interpreted by well logging, and the weight of the pressure correction factor is adjusted according to the size of the attribute threshold value. The convolution algorithm is used to obtain multi-information fused formation pressure data to finely characterize the complex changes in the formation pressure in the small well area of ​​the target layer.

[0096] In some embodiments of the present invention, the fracture attribute data in step 100 includes: coherence volume, structural curvature, AFE fault enhancement parameters and ant volume.

[0097] In seismic data processing, a coherent volume refers to seismic signals with similar waveforms and phases in a set of seismic traces. By calculating the coherence of seismic data, underground objects or interfaces corresponding to seismic signals with similar amplitudes and phases can be identified.

[0098] Structural curvature refers to the rate of change of geological structures and is used to describe the curvature changes of strata or underground structures. By calculating the curvature in seismic data, structural features such as bends and fractures in strata can be identified.

[0099] AFE fault enhancement parameter is a parameter used in seismic interpretation and seismic data processing to enhance the characteristics of faults in seismic data. It can highlight the energy and amplitude related to faults in seismic signals by filtering and transforming seismic data to help identify and analyze underground faults.

[0100] Ant body is a commonly used term in seismic interpretation, which is used to describe small-scale, high-frequency anomalies on seismic sections. Ant bodies usually represent local anomalies, fine faults or folds in underground rocks, and can provide important underground structural information.

[0101] In some embodiments of the invention, the coherence volume is used to identify cracks at a first scale;

[0102] The structural curvature and the AFE fault enhancement parameter are used to identify fractures of a second scale;

[0103] The ant body is used to identify cracks of a third scale, wherein the first scale is larger than the second scale and the third scale, and the second scale is larger than the third scale.

[0104] Specifically, the coherence body is used to effectively identify large-scale faults, including the main faults and the surrounding small secondary faults; the AFE fault enhancement parameters and structural curvature attributes can identify medium-scale faults that the coherence body cannot accurately identify, and the ant body can identify small-scale cracks.

[0105] In some embodiments of the present invention, see Figure 2 , step 200 comprises:

[0106] Step 201: constructing the wave impedance of the target work area according to the well logging data;

[0107] Specifically, first, collect logging data from the target area, including density logging, acoustic logging, and resistivity logging. These data are usually presented in the form of curves, recording the physical properties of different formations. Preprocess the collected logging data, including data cleaning, correction, and alignment. Ensure the accuracy and consistency of the data so that the measurement depth and sampling rate of different logging curves are consistent. Based on the collected logging data, calculate the wave impedance of each formation in the target area.

[0108] Wave impedance refers to the acoustic wave velocity of the formation multiplied by the density of the formation. The wave impedance can be obtained according to the following formula:

[0109] Wave impedance = sound wave speed × density

[0110] Based on the calculated wave impedance data, a wave impedance model of the target work area can be established. This model can be used to describe the wave impedance distribution of underground strata. According to the wave impedance model, wave impedance interpretation and analysis can be performed. By analyzing the changes and distribution of wave impedance, information about the strata, such as lithology, fluid properties, etc., can be obtained.

[0111] Step 202: Predict the formation pressure of the entire area based on the wave impedance and the rock physics model.

[0112] It is understandable that the formation pressure prediction method based on wave impedance is an effective means to solve the pre-drilling prediction of formation pressure in shale reservoirs. The implementation of this method is based on the effective stress principle. The method of directly predicting pressure based on impedance information has the characteristics of high resolution of seismic data. At the same time, it can ignore the problem that the density parameters obtained based on prestack inversion are usually not accurate, and can obtain stable and reliable formation pressure prediction results. In this study, the single well formation pore pressure coefficient prediction will be carried out based on the rock physics model, and the acoustic time difference under the normal compaction trend will be constructed. Then, based on the pseudo-acoustic impedance inversion or Kriging interpolation method, the background trend impedance information under normal pressure conditions will be constructed. Then, the formation pressure and pressure coefficient of the target layer in the work area will be calculated based on the impedance information to obtain the global formation pressure prediction results.

[0113] In some embodiments of the present invention, the components of the rock physics model include: wet clay, sandy mixture and organic matter.

[0114] It is understandable that wet clay has a greater resistance to the flow of oil due to its viscosity and plasticity. Wet clay has adsorption capacity and can absorb oil and gas, thereby reducing the effective porosity and permeability in the reservoir. In addition, wet clay will expand or contract with changes in water content, which will have a certain impact on oil reservoir storage and production.

[0115] In addition, organic matter in the oil field usually refers to the organic matter mass abundance (TOC, Total Organic Carbon), which refers to the content of organic matter in sediments. Organic matter is the source rock of oil and natural gas, and can be decomposed to produce oil and natural gas through pyrolysis or pressure. The abundance and maturity of organic matter are of great significance to oil exploration and production. Organic rocks with high abundance and maturity can be used as high-quality oil and gas source rocks.

[0116] In recent years, Pervukhina, Han and others proposed a mud shale rock physics model called CPS (clay plus silt), and proved through practical application that the model can be used to obtain the acoustic time difference under normal compaction conditions, and has higher pressure prediction accuracy than the regional compaction trend line obtained based on data fitting. However, the above model is established for ordinary mud shale, and the influence of hydrocarbon-generating substances (organic matter) is not considered in the model. The presence of organic matter will not only greatly reduce the hardness of the rock, but also its directional spatial distribution form will often further enhance the anisotropy of the rock. In order to apply this idea of ​​using rock physics models to construct compaction trend lines in organic-rich shale gas reservoirs, the above model needs to be improved to make it suitable for pressure prediction problems in actual work areas.

[0117] It can be understood that the theoretical basis for the CPS model to calculate the normal acoustic wave time difference is that the model assumes that the rock pores are all related to clay, and the pore fluid and clay particles constitute a "wet clay" mixture.

[0118] The applicant has obtained the following conclusion through a large amount of experimental data: the elastic tensor of the wet clay mixture is linearly negatively correlated with the wet clay porosity (the volume content of the fluid in the wet clay), and has nothing to do with the mineral components of the clay. Therefore, the elastic tensor of wet clay can be determined by the wet clay porosity alone, and the elastic tensor of the entire shale can be determined by the wet clay porosity and the volume content of the sandy mixture (other mineral components except clay). At the same time, changes in formation pressure will only affect the opening and closing of soft pores in the rock, and the effect of soft pores on the total porosity can be ignored. Therefore, the two factors that affect the elastic tensor of shale are not affected by abnormal pressure, and the model can calculate the velocity or time difference of the rock under normal compaction.

[0119] In the rock physics model of shale improved in the present application, it is still assumed that clay and pore fluid constitute a wet clay mixture, while hard minerals such as quartz, feldspar, calcite, pyrite, etc. constitute a sandy mixture. The difference is that the components of the model are changed from the original two-phase wet clay-sand mixture to a three-phase wet clay-sand mixture-organic matter. Compared with the original model that uses the differential equivalent medium theory (DEM model) to obtain the equivalent elastic tensor of the two-phase mixture, the improved model uses the Backus average formula to obtain the equivalent medium composed of the three phases of wet clay-sand mixture-organic matter. The Backus average formula, due to its explicit expression form, has higher computational efficiency than the DEM model that requires iterative solution, so it has greater advantages in the rock physics modeling process of organic-rich shale.

[0120] In some embodiments of the present invention, see Figure 3 , a formation pressure prediction method suitable for small well areas, further comprising:

[0121] Step 400: Divide the target work area into a plurality of small well areas according to the structural units of the target work area.

[0122] In view of the complex structural characteristics of the study area, the influence of fractures on formation pressure is further considered. According to the structural units, the study area is divided into n different pressure systems according to the development of fractures and cracks of different scales in the local small well area.

[0123] In some embodiments of the present invention, see Figure 4 , step 300 comprises:

[0124] Step 301: constructing a formation pressure correction coefficient according to the first-scale cracks, the second-scale cracks, and the third-scale cracks;

[0125] Step 302: Correct the formation pressure of the small well area according to the formation pressure correction coefficient.

[0126] In step 301 and step 302, according to the development of fractures and cracks of different scales in the local small well area, the fracture attribute threshold value is adjusted, and the distance between the fault and the crack is considered to construct a correction coefficient to realize the fracture correction of the formation pressure and improve the prediction accuracy of the formation pressure in the complex structural area.

[0127] From the above description, it can be seen that an embodiment of the present invention provides a formation pressure prediction method suitable for small well areas, including: first, determining fracture attribute data based on post-stack seismic data in a target work area; then, predicting the formation pressure of the entire target work area based on the well logging data of the target work area and the rock physics model of the reservoir; finally, correcting the formation pressure of the small well area in the target work area based on the fracture attribute data and the formation pressure of the entire area to predict the formation pressure of the small well area, wherein the target work area is composed of multiple small well areas.

[0128] On the one hand, based on the seismic prediction of formation pressure, the present invention considers the influence of faults and cracks on pressure and develops a fine prediction method for formation pressure in small well areas with crack correction, providing a favorable basis for the prediction of sweet spots in shale reservoir engineering and geological evaluation.

[0129] On the other hand, in view of the large changes in formation pressure in complex tectonic areas and the low accuracy of conventional seismic prediction of formation pressure, the present invention, based on the optimization of post-stack fracture attributes and seismic prediction of formation pressure in the whole area, realizes the precise prediction of formation pressure in complex tectonic areas by multi-information fusion through local fracture correction, and supports the optimization and comprehensive evaluation of sweet spots in shale reservoir engineering.

[0130] Embodiment three:

[0131] In a specific embodiment, the present invention also provides a specific embodiment of a formation pressure prediction method suitable for a small well area, see Figure 5 as well as Figure 6 , specifically including the following steps.

[0132] The present invention belongs to the technical field of petroleum geophysical exploration, and specifically aims at the local difference changes in formation pressure of shale reservoirs in complex structures and fracture development areas. On the basis of post-stack fracture prediction attribute optimization, fracture correction is carried out on the formation pressure of the entire area predicted by seismic prediction, so as to improve the prediction accuracy of complex changes in formation pressure in small well areas.

[0133] S1: Optimize fracture attributes of different scales based on post-stack seismic data;

[0134] See also Figures 7 to 10In order to predict the large, medium and small scale faults and cracks in the work area, high-precision coherence, structural curvature, AFE fault enhancement, ant body and other technologies are used to predict their distribution range and direction. Combined with the measured drilling data, through the joint analysis of multiple attributes, combined with the seismic reflection characteristics such as the fault and distortion of the seismic phase axis of the seismic profile, large-scale faults, medium-scale and small-scale faults can be accurately identified, and the basic distribution characteristics of cracks can be qualitatively analyzed. High-precision coherence can identify the main fault and a small part of the secondary faults around it, and guide the analysis and description of structural characteristics; AFE and structural curvature can identify medium-scale cracks that cannot be identified by coherence attributes, and predict the development characteristics of cracks associated with local structural deformation; ant body can identify small-scale cracks, predict the direction of crack distribution in different areas, and guide the deployment of horizontal well drilling trajectory direction.

[0135] S2: Seismic prediction of formation pressure in the whole region;

[0136] The method of directly predicting pressure based on impedance information has the characteristics of high resolution of seismic data, and can also ignore the problem that density parameters obtained based on pre-stack inversion are usually not very accurate, and can obtain stable and reliable formation pressure prediction results. In this study, the single-well formation pore pressure coefficient prediction will be carried out based on the CPS model (short for Clay-Plus-Silt model), and the acoustic time difference under normal compaction trend will be constructed. Then, based on the pseudo-acoustic impedance inversion or Kriging interpolation method, the background trend impedance information under normal pressure conditions will be constructed, and then the improved Eaton formula will be combined to calculate the formation pressure and pressure coefficient of the target layer in the work area to obtain the global seismic prediction results. (The improved RT method and the improved Eaton formula are as follows: Fig.11 shown).

[0137] Due to the lack of measured pressure data in the research area, the establishment of compaction trend lines by traditional methods has great uncertainty, and it is also impossible to use measured pressure data to regress the empirical coefficients in other empirical formula methods, which brings great difficulties to the prediction of formation pressure. The single-well formation pressure prediction process based on the improved CPS model (is used to predict the pressure in the target area, which can reduce the dependence on measured pressure data and ensure the accuracy of pressure prediction.

[0138] Note: The CPS model is a rock physics model for mudstone. The model still does not consider the influence of organic matter in shale. The improved CPS model still assumes that clay and pore fluid constitute a wet clay mixture, while hard minerals such as quartz, feldspar, calcite, and pyrite constitute a sandy mixture. The difference is that the components of the model have changed from the original wet clay-sand mixture to a wet clay-sand mixture-organic matter three-phase. Compared with the original model that uses the differential equivalent medium theory (DEM model, Differential Equivalent Medium Model) to obtain the equivalent elastic tensor of the two-phase mixture, the improved model uses the Backus average formula to obtain the equivalent medium composed of the three phases of wet clay-sand mixture-organic matter.

[0139] S3: Detailed prediction of formation pressure in small well areas based on fracture correction.

[0140] The formation pressure coefficient obtained based on the above-mentioned Eaton method and Philips method cannot well reflect the microscopic characteristics of the large pressure changes in small well areas with local fracture development. For this reason, this patent carries out small-scale fracture correction to solve the problem of complex local formation pressure changes in complex structural areas. The specific implementation process is based on the previous two steps of fracture attribute optimization and formation pressure prediction, and carries out fine prediction of formation pressure for fault and fracture correction. The threshold value of the fault enhancement coherence and ant body attributes is adjusted based on the fracture density curve interpreted by well logging, and the weight of the pressure correction factor is adjusted according to the size of the attribute threshold value. The convolution algorithm is used to obtain multi-information fused formation pressure data to finely characterize the complex changes in formation pressure in small well areas of the target layer.

[0141] Based on the single well formation pressure prediction, the formation pressure prediction model and correlation coefficient of the work area are determined, and then the pressure of the whole area is predicted based on the velocity field obtained by prestack inversion. The formation pressure coefficient in the structurally complex area varies greatly, and the formation pressure coefficient in the core area of ​​the anticline is low. From the middle to the slope and the south and north ends, the formation pressure coefficient gradually increases with the increase of burial depth.

[0142] Furthermore, on the basis of formation pressure prediction, aiming at the complex structural characteristics of the study area, the influence of cracks on formation pressure is further considered. According to the structural units, the study area is divided into n different pressure systems. According to the development of fractures and cracks of different scales in local small well areas, the fracture attribute threshold value is adjusted. At the same time, considering the distance between faults and cracks, a correction coefficient is constructed to realize fracture correction of formation pressure and improve the prediction accuracy of formation pressure in complex structural areas.

[0143] Fig.12 The middle left part is the uncorrected formation pressure distribution predicted in the local well area (i.e., the plane distribution of high-quality shale formation pressure coefficient (before fault-fracture correction)). Fig.12In the middle SZa well area, the large-scale fractures predicted by fault enhancement coherence are not developed, while the small-scale fractures predicted by the ant body are developed. Fracture correction is carried out based on the ant body attributes and the formation pressure prediction results of the whole area. Before correction, the predicted formation pressure of this well area does not change much between the wellhead and the bottom of the well. The wellhead formation pressure coefficient is 1.58, and the bottom of the horizontal well is 1.6, which is very different from the actual drilling situation. After correction, the formation pressure prediction accuracy is greatly improved ( Fig.12 The middle right part is the plane distribution of the high-quality shale formation pressure coefficient (after fault-crack correction). The wellhead formation pressure coefficient is 1.1, and the bottom hole pressure coefficient is 1.6, which is consistent with the measured formation pressure. It also shows that the pressure is reduced due to the wellhead fractures that are very developed, resulting in wellhead pressure relief.

[0144] From the above description, it can be seen that a formation pressure prediction method suitable for small well areas provided by a specific application example of the present invention includes: first, determining fracture attribute data based on post-stack seismic data in a target work area; then, predicting the formation pressure of the entire target work area based on the well logging data of the target work area and the rock physics model of the reservoir; finally, correcting the formation pressure of the small well area in the target work area based on the fracture attribute data and the formation pressure of the entire area to predict the formation pressure of the small well area, wherein the target work area is composed of multiple small well areas.

[0145] The present invention is based on the optimization of shale reservoir fracture attributes and the seismic prediction of formation pressure in the whole area, and realizes the fine prediction of shale reservoir pressure in complex structural areas by multi-information fusion through fracture correction.

[0146] The beneficial effect of a formation pressure prediction method suitable for small well areas provided by a specific application example of the present invention is that based on the advantageous attributes of fracture and crack prediction and the improved RT formation pressure prediction, the fine prediction of formation pressure in structurally complex areas is achieved through nonlinear correction of fracture attributes, thereby improving the prediction accuracy of formation pressure changes in small well areas and providing strong technical support for engineering sweet spot prediction and drilling projects.

[0147] In summary, the present invention provides a method for fine prediction of formation pressure in small well areas based on fracture correction, which belongs to the field of geophysical exploration. Specifically, the method optimizes the advantageous attributes of fracture prediction of different scales based on post-stack seismic data, obtains the formation pressure distribution of the whole area based on the improved RT method seismic prediction, analyzes the local pressure system of the work area on this basis, and the fracture attributes that affect the pressure change, and carries out fracture correction in local small well areas, and obtains the fine prediction results of formation pressure in complex change areas, which provides strong technical support for the prediction and comprehensive evaluation of sweet spots in shale reservoir engineering.

[0148] Embodiment 4:

[0149] Based on the same inventive concept, the embodiments of the present application also provide a formation pressure prediction device adapted to small well areas, which can be used to implement the methods described in the above embodiments, such as the following embodiments. Since the principle of solving the problem by the formation pressure prediction device adapted to small well areas is similar to that of the formation pressure prediction method adapted to small well areas, the implementation of the formation pressure prediction device adapted to small well areas can refer to the implementation of the formation pressure prediction method adapted to small well areas, and the repeated parts will not be repeated. As used below, the terms "unit" or "module" can be a combination of software and / or hardware that implements predetermined functions. Although the system described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0150] The embodiment of the present invention provides a specific implementation of a formation pressure prediction device adapted to a small well area, which can realize a formation pressure prediction method adapted to a small well area, see Fig.13 , a formation pressure prediction device suitable for small well areas comprises:

[0151] A fracture attribute data determination module 10, for determining fracture attribute data according to post-stack seismic data in a target work area;

[0152] The whole-area formation pressure prediction module 20 is used to predict the whole-area formation pressure of the target work area according to the well logging data of the target work area and the rock physics model of the reservoir;

[0153] The formation pressure correction module 30 is used to correct the formation pressure of the small well area in the target work area according to the fracture attribute data and the formation pressure of the whole area to predict the formation pressure of the small well area, wherein the target work area is composed of multiple small well areas.

[0154] In one embodiment of the present invention, the fracture attribute data includes: coherence volume, structural curvature, AFE fault enhancement parameters and ant volume.

[0155] In one embodiment of the present invention, the coherence volume is used to identify cracks of a first scale;

[0156] The structural curvature and the AFE fault enhancement parameter are used to identify fractures of a second scale;

[0157] The ant body is used to identify cracks of a third scale, wherein the first scale is larger than the second scale and the third scale, and the second scale is larger than the third scale.

[0158] In one embodiment of the present invention, the whole-area formation pressure prediction module includes:

[0159] A wave impedance construction unit, used for constructing the wave impedance of the target work area according to the well logging data;

[0160] The whole-area formation pressure prediction unit is used to predict the whole-area formation pressure according to the wave impedance and the rock physics model.

[0161] In one embodiment of the present invention, the components of the rock physics model include: wet clay, sandy mixture and organic matter.

[0162] In one embodiment of the present invention, a formation pressure prediction device suitable for small well areas further includes:

[0163] The target work area division module is used to divide the target work area into a plurality of small well areas according to the structural units of the target work area.

[0164] In one embodiment of the present invention, the formation pressure correction module includes:

[0165] A correction coefficient construction unit, configured to construct a formation pressure correction coefficient according to the first-scale cracks, the second-scale cracks, and the third-scale cracks;

[0166] The formation pressure correction unit is used to correct the formation pressure of the small well area according to the formation pressure correction coefficient.

[0167] From the above description, it can be seen that a formation pressure prediction device suitable for a small well area provided by a specific application example of the present invention includes: a fracture attribute data determination module, which is used to determine fracture attribute data according to post-stack seismic data in a target work area;

[0168] A whole-area formation pressure prediction module, used to predict the whole-area formation pressure of the target work area according to the well logging data of the target work area and the rock physics model of the reservoir;

[0169] The formation pressure correction module is used to correct the formation pressure of the small well area in the target work area according to the fracture attribute data and the formation pressure of the whole area to predict the formation pressure of the small well area, wherein the target work area is composed of multiple small well areas.

[0170] The present invention is based on the optimization of shale reservoir fracture attributes and the seismic prediction of formation pressure in the whole area, and realizes the fine prediction of shale reservoir pressure in complex structural areas by multi-information fusion through fracture correction.

[0171] The beneficial effect of a formation pressure prediction device suitable for small well areas provided by a specific application example of the present invention is: based on the advantageous attributes of fracture and crack prediction and the improved RT formation pressure prediction, the fine prediction of formation pressure in structurally complex areas is achieved through nonlinear correction of fracture attributes, thereby improving the prediction accuracy of formation pressure changes in small well areas and providing strong technical support for engineering sweet spot prediction and drilling projects.

[0172] In summary, the present invention provides a fine prediction device for formation pressure in small well areas based on fracture correction, which belongs to the field of geophysical exploration. Specifically, the device optimizes the advantageous attributes of fracture prediction of different scales based on post-stack seismic data, obtains the formation pressure distribution of the whole area based on the improved RT method seismic prediction, analyzes the local pressure system of the work area on this basis, and the fracture attributes that affect the pressure change, and carries out fracture correction in local small well areas, and obtains the fine prediction results of formation pressure in complex change areas, which provides strong technical support for the prediction and comprehensive evaluation of sweet spots in shale reservoir engineering.

[0173] Embodiment five:

[0174] The embodiment of the present application also provides a specific implementation of an electronic device capable of implementing all steps in the formation pressure prediction method adapted to a small well area in the above embodiment, see Fig.14 , electronic equipment specifically includes the following:

[0175] Processor (processor) 1201, memory (memory) 1202, communication interface (CommunicationsInterface) 1203 and bus 1204;

[0176] The processor 1201, the memory 1202, and the communication interface 1203 communicate with each other through the bus 1204; the communication interface 1203 is used to realize information transmission between the server device and the client device and other related devices;

[0177] The processor 1201 is used to call the computer program in the memory 1202. When the processor executes the computer program, all the steps in the formation pressure prediction method suitable for small well areas in the above embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0178] Determine fracture attribute data based on post-stack seismic data in the target work area;

[0179] Predicting the formation pressure of the entire target work area based on the well logging data of the target work area and the rock physics model of the reservoir;

[0180] The formation pressure of the small well area in the target work area is corrected according to the fracture attribute data and the formation pressure of the whole area to predict the formation pressure of the small well area, wherein the target work area is composed of multiple small well areas.

[0181] In one embodiment, the fracture attribute data includes: coherence volume, structural curvature, AFE fault enhancement parameters and ant volume.

[0182] In one embodiment, the coherence volume is used to identify cracks of a first scale;

[0183] The structural curvature and the AFE fault enhancement parameter are used to identify fractures of a second scale;

[0184] The ant body is used to identify cracks of a third scale, wherein the first scale is larger than the second scale and the third scale, and the second scale is larger than the third scale.

[0185] In one embodiment, predicting the formation pressure of the entire target work area according to the well logging data of the target work area and the rock physics model of the reservoir includes:

[0186] Constructing the wave impedance of the target work area according to the well logging data;

[0187] The formation pressure of the entire area is predicted based on the wave impedance and the rock physics model.

[0188] In one embodiment, the components of the rock physics model include: wet clay, sandy mixture and organic matter.

[0189] In one embodiment, a formation pressure prediction method suitable for small well areas further includes:

[0190] The target work area is divided into a plurality of small well areas according to the structural units of the target work area.

[0191] In one embodiment, the formation pressure of the small well area in the target work area is corrected according to the fracture attribute data and the formation pressure of the whole area, including:

[0192] constructing a formation pressure correction coefficient according to the first-scale fractures, the second-scale fractures, and the third-scale fractures;

[0193] The formation pressure of the small well area is corrected according to the formation pressure correction coefficient.

[0194] Embodiment six:

[0195] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps of the formation pressure prediction method adapted to a small well area in the above-mentioned embodiment. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, all the steps of the formation pressure prediction method adapted to a small well area in the above-mentioned embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0196] Determine fracture attribute data based on post-stack seismic data in the target work area;

[0197] Predicting the formation pressure of the entire target work area based on the well logging data of the target work area and the rock physics model of the reservoir;

[0198] The formation pressure of the small well area in the target work area is corrected according to the fracture attribute data and the formation pressure of the whole area to predict the formation pressure of the small well area, wherein the target work area is composed of multiple small well areas.

[0199] In one embodiment, the fracture attribute data includes: coherence volume, structural curvature, AFE fault enhancement parameters and ant volume.

[0200] In one embodiment, the coherence volume is used to identify cracks of a first scale;

[0201] The structural curvature and the AFE fault enhancement parameter are used to identify fractures of a second scale;

[0202] The ant body is used to identify cracks of a third scale, wherein the first scale is larger than the second scale and the third scale, and the second scale is larger than the third scale.

[0203] In one embodiment, predicting the formation pressure of the entire target work area according to the well logging data of the target work area and the rock physics model of the reservoir includes:

[0204] Constructing the wave impedance of the target work area according to the well logging data;

[0205] The formation pressure of the entire area is predicted based on the wave impedance and the rock physics model.

[0206] In one embodiment, the components of the rock physics model include: wet clay, sandy mixture and organic matter.

[0207] In one embodiment, a formation pressure prediction method suitable for small well areas further includes:

[0208] The target work area is divided into a plurality of small well areas according to the structural units of the target work area.

[0209] In one embodiment, the formation pressure of the small well area in the target work area is corrected according to the fracture attribute data and the formation pressure of the whole area, including:

[0210] constructing a formation pressure correction coefficient according to the first-scale fractures, the second-scale fractures, and the third-scale fractures;

[0211] The formation pressure of the small well area is corrected according to the formation pressure correction coefficient.

[0212] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the hardware + program embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0213] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0214] Although the present application provides method operation steps such as embodiments or flow charts, more or fewer operation steps may be included based on conventional or non-creative labor. The order of steps listed in the embodiments is only one way of executing the order of many steps and does not represent the only execution order. When the actual device or client product is executed, it can be executed in the order of the method shown in the embodiments or the drawings or in parallel (for example, in a parallel processor or multi-threaded processing environment).

[0215] For the convenience of description, the above devices are described in various modules according to their functions. Of course, when implementing the embodiments of this specification, the functions of each module can be implemented in the same or more software and / or hardware, or the module implementing the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0216] Those skilled in the art also know that, in addition to implementing the controller in a purely computer-readable program code, the controller can be made to implement the same function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered as a hardware component, and the devices for implementing various functions included therein can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software modules for implementing the method and structures within the hardware component.

[0217] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0218] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0219] Each embodiment in this specification is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. In the description of this specification, the description of the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of this specification. In this specification, the schematic representation of the above terms does not necessarily target the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, in the absence of contradiction, a person skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0220] The above is only an example of the embodiment of the present specification and is not intended to limit the embodiment of the present specification. For those skilled in the art, the embodiment of the present specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiment of the present specification shall be included in the scope of the claims of the embodiment of the present specification.

Claims

1. A formation pressure prediction method suitable for small well areas, It is characterized in that include: Determine fracture attribute data based on post-stack seismic data in the target work area; Predicting the formation pressure of the entire target work area based on the well logging data of the target work area and the rock physics model of the reservoir; The formation pressure of the small well area in the target work area is corrected according to the fracture attribute data and the formation pressure of the whole area to predict the formation pressure of the small well area, wherein the target work area is composed of multiple small well areas.

2. The formation pressure prediction method according to claim 1, It is characterized in that The fracture attribute data include: coherence volume, structural curvature, AFE fault enhancement parameters and ant volume.

3. The formation pressure prediction method according to claim 2, It is characterized in that The coherence volume is used to identify cracks at a first scale; The structural curvature and the AFE fault enhancement parameter are used to identify fractures of a second scale; The ant body is used to identify cracks of a third scale, wherein the first scale is larger than the second scale and the third scale, and the second scale is larger than the third scale.

4. The formation pressure prediction method according to claim 1, It is characterized in that Predicting the formation pressure of the entire target work area based on the well logging data of the target work area and the rock physics model of the reservoir includes: Constructing the wave impedance of the target work area according to the well logging data; The formation pressure of the entire area is predicted based on the wave impedance and the rock physics model.

5. The formation pressure prediction method according to claim 1, It is characterized in that The components of the rock physics model include wet clay, sandy mixture and organic matter.

6. The formation pressure prediction method according to claim 3, It is characterized in that Also includes: The target work area is divided into a plurality of small well areas according to the structural units of the target work area.

7. The formation pressure prediction method according to claim 6, It is characterized in that Correcting the formation pressure of the small well area in the target work area according to the fracture attribute data and the formation pressure of the whole area includes: constructing a formation pressure correction coefficient according to the first-scale fractures, the second-scale fractures, and the third-scale fractures; The formation pressure of the small well area is corrected according to the formation pressure correction coefficient.

8. A formation pressure prediction device suitable for small well areas, It is characterized in that include: A fracture attribute data determination module, used to determine fracture attribute data based on post-stack seismic data in a target work area; A whole-area formation pressure prediction module, used to predict the whole-area formation pressure of the target work area according to the well logging data of the target work area and the rock physics model of the reservoir; The formation pressure correction module is used to correct the formation pressure of the small well area in the target work area according to the fracture attribute data and the formation pressure of the whole area to predict the formation pressure of the small well area, wherein the target work area is composed of multiple small well areas.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, It is characterized in that When the processor executes the program, the steps of the formation pressure prediction method suitable for small well areas described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, It is characterized in that When the computer program is executed by a processor, the steps of the formation pressure prediction method suitable for small well areas as described in any one of claims 1 to 7 are implemented.

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