A method and system for predicting and identifying gypsum-salt rocks in complex foreland belts using a combination of well and seismic testing.

Abstract

This invention relates to the field of oil and gas exploration technology, specifically to a method and system for predicting and identifying gypsum-salt rocks in complex foreland belts using a combination of well and seismic data. The method involves acquiring VSP data, geological data, and seismic data, preprocessing the initial data to obtain preprocessed data, calculating the error between the geological and seismic data based on the preprocessed data, analyzing changes in the sedimentary environment, determining spatial lateral variation patterns, establishing an anisotropic field model, and building a velocity field model based on VSP data. The parameters of the velocity field model are continuously adjusted to obtain an optimized velocity field model. The depth of gypsum-salt rocks is predicted using the optimized velocity field model and small-layer geological correlation. Finally, samples are obtained at the predicted depth using a time-controlled drilling method, and the presence or absence of gypsum-salt rocks is identified. This application combines VSP data, geological data, and seismic data to determine the presence of gypsum-salt rocks in complex foreland belts and effectively improves prediction accuracy.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration technology, specifically to a method and system for predicting and identifying gypsum-salt rocks in complex foreland belts using a combination of well and seismic testing. Background Technology

[0002] Oil and gas exploration refers to the process of investigating, predicting, and evaluating underground oil and gas resources based on disciplines such as geology, geophysics, and geochemistry, using various technical means and methods; this process aims to determine the location, scale, quality, and development value of oil and gas reservoirs.

[0003] Chinese Patent No. CN115793053B discloses a comprehensive prediction method for Cambrian gypsum-salt rocks based on seismic, drilling, and logging data. This method calculates the comprehensive change rate of drilling parameters for Cambrian gypsum-salt rocks, further calibrates the top surface position of the gypsum-salt rocks based on this rate, and finally conducts logging parameter cuttings observation and X-ray elemental analysis within the depth range predicted by seismic and drilling data for Cambrian gypsum-salt rocks. This achieves comprehensive prediction of Cambrian gypsum-salt rocks. With continuous breakthroughs in deep-to-ultra-deep carbonate oil and gas exploration, the exploration of carbonate oil and gas in complex piedmont zones is attracting increasing attention. However, existing technologies struggle to accurately predict and identify gypsum-salt rocks in complex piedmont zones, leading to significant difficulties in carbonate oil and gas exploration in these zones. Summary of the Invention

[0004] The purpose of this invention is to address the problems existing in the background technology by proposing a method and system for predicting and identifying gypsum-salt rocks in complex foreland belts that combine well and seismic analysis.

[0005] The technical solution of the present invention:

[0006] On the one hand, this application provides a method for predicting and identifying gypsum-salt rocks in complex foreland belts involving both well and seismic activity, including:

[0007] Collect VSP data, geological data, and seismic data; preprocess the collected initial data to obtain preprocessed data.

[0008] The error between geological and seismic data is calculated based on preprocessed data, and the changes in sedimentary environment are analyzed to determine the spatial lateral variation law and establish an anisotropic field model.

[0009] A velocity field model is established based on VSP data, and the parameters of the velocity field model are continuously adjusted to obtain an optimized velocity field model.

[0010] The depth of gypsum-salt rock was predicted using the optimized velocity field model and the sub-layer geological correlation, respectively. The depth predicted by the optimized velocity field model was denoted as the first depth, and the depth predicted by the sub-layer geological correlation was denoted as the second depth.

[0011] By using time-controlled drilling, samples are obtained at the predicted depth, and it is identified whether the samples are gypsum-salt rocks.

[0012] Preferably, VSP data, geological data, and seismic data are acquired, and the acquired initial data are preprocessed to obtain preprocessed data, including:

[0013] Create the basic data table;

[0014] Multiple VSP data, geological data, and multiple seismic data were collected separately, and the collected VSP data, geological data, and seismic data were placed into a basic data table;

[0015] Randomly select a geological or seismic data point from the basic data table;

[0016] The seismic data is denoised to obtain preprocessed seismic data, which is then placed into the basic data table.

[0017] Normalize the well logging curves in the geological data to obtain preprocessed geological data, and put the preprocessed geological data into the basic data table.

[0018] Return to the initial data table and randomly select one data point until all initial data points in the initial data table have been selected, to obtain preprocessed geological data and preprocessed seismic data.

[0019] Preferably, the error between geological and seismic data is calculated based on preprocessed data, and changes in the sedimentary environment are analyzed to determine spatial lateral variation patterns and establish an anisotropic field model, including:

[0020] Obtain preprocessed geological data and preprocessed seismic data from the basic data table;

[0021] Based on the analysis of preprocessed geological data, the errors between drilling layering and seismic profile layering are determined and the error types are identified.

[0022] Sedimentary environment characteristics are obtained based on preprocessed seismic data;

[0023] The variation patterns of lithological bodies were obtained based on preprocessed geological data;

[0024] An anisotropic field model is constructed based on the lithological variation law and sedimentary environment characteristics using Formula 1;

[0025] Formula 1;

[0026] in, It is the yield function of an anisotropic field. It is the mean principal stress. It is the initial mean principal stress. It is the slope of the critical state line. It is the slope of the normal consolidation curve. It is the slope of the rebound curve. It is the strain of the soil plastic body.

[0027] Preferably, a velocity field model is established based on VSP data, and the parameters of the velocity field model are continuously adjusted to obtain an optimized velocity field model, including:

[0028] Initial depth migration velocity is extracted from preprocessed seismic data;

[0029] Obtain the VSP velocity, and combine the VSP velocity with the initial depth offset velocity to determine the lateral velocity pattern and the longitudinal velocity pattern;

[0030] A velocity field model is constructed based on the lateral and longitudinal velocity laws using the model tomography method.

[0031] Preferably, a velocity field model is constructed using model tomography based on the transverse and longitudinal velocity laws, including:

[0032] The angle of reflection of the seismic source relative to the ground is calculated using Formula 2.

[0033] Formula 2;

[0034] in, It is a travel time function of surface observations of isotropic strata. It is the launch point. It is the angle of ray emission. It is the launch velocity corresponding to the first launch point. It is the x-coordinate of the coordinate system. It is the ordinate of the coordinate system;

[0035] The hypothetical wavefront radius of curvature is calculated using Formula 3.

[0036] Formula 3;

[0037] in, It is the imaginary wavefront curvature radius generated at NIP and emitted from CDP. It is the two-way travel time of the normal reflected wave corresponding to the coincidence of the source and receiver at the CDP point. It is the angle of ray emission. It is the launch velocity corresponding to the first launch point;

[0038] Substitute the data from the CDP point into Formula 3 to determine the wavefront curvature radius.

[0039] Preferably, the process of establishing a velocity field model based on VSP data and continuously adjusting the parameters of the velocity field model to obtain an optimized velocity field model further includes:

[0040] Calculate the Delta value and update the transverse and longitudinal velocity fields using the Delta value;

[0041] The stratigraphic dip is fitted using a quadratic polynomial.

[0042] Formula 4 is used to correct the residual time difference of the CRP gather and determine the far-channel leveling of the gather.

[0043] Formula 4;

[0044] in, It is the corrected CRP gather. It is the original collection of Tao. It is the correction function, where t is time and x is the channel number.

[0045] Preferably, the depth of gypsum-salt rock is predicted using an optimized velocity field model and sub-layer geological correlation, including:

[0046] Obtain geological data of the subsurface;

[0047] By comparing the geological data of the sub-layer with the pre-processed geological data, the depth of the gypsum-salt rock to be measured is predicted based on the depth of the gypsum-salt rock in the pre-processed geological data; the depth of the gypsum-salt rock to be measured is recorded as the measured depth.

[0048] Preferably, a time-controlled drilling method is used to obtain samples at a predicted depth and identify whether the samples are gypsum-salt rocks, including:

[0049] Obtain the first and second depths;

[0050] Set the warning depth based on the first and second depths;

[0051] Samples were obtained at the warning depth using a time-controlled drilling method.

[0052] Determine whether a sample is gypsum-salt rock by observing rock fragments.

[0053] Preferably, the geological data of the sub-layer includes formation thickness, formation lithology, and well logging curve characteristics.

[0054] On the other hand, this application also provides a prediction and identification system for gypsum-salt rocks in complex piedmont areas with well-seismic interaction, including a data acquisition component, a data processing component, and an identification component. The data acquisition component acquires VSP data, geological data, and seismic data. The data processing component executes the above-mentioned prediction and identification method for gypsum-salt rocks in complex piedmont areas with well-seismic interaction. The identification component analyzes sample parameters.

[0055] Compared with the prior art, the above-mentioned technical solution of the present invention has the following beneficial technical effects:

[0056] By collecting VSP data, geological data, and seismic data, the initial data is preprocessed to obtain preprocessed data. Then, the error between the geological and seismic data is calculated based on the preprocessed data, and the changes in the sedimentary environment are analyzed to determine the spatial lateral variation law. An anisotropic field model is established, and a velocity field model is established based on VSP data. The parameters of the velocity field model are continuously adjusted to obtain an optimized velocity field model. The depth of gypsum-salt rock is predicted by the optimized velocity field model and the geological correlation of small layers. Finally, samples at the predicted depth are obtained by time-controlled drilling and the samples are identified as gypsum-salt rock. This application combines VSP data, geological data, and seismic data to determine whether gypsum-salt rock exists under complex piedmont zones and can effectively improve the prediction accuracy. Attached Figure Description

[0057] Figure 1 This is a flowchart illustrating a method for predicting and identifying gypsum-salt rocks in complex foreland belts using a combination of well and seismic testing, as proposed in this invention.

[0058] Figure 2 This is a schematic diagram of the structure of a complex gypsum-salt rock prediction and identification system combining well-seismic analysis in the foreland zone proposed in this invention.

[0059] Figure 3 This is a schematic diagram of seismic wave propagation in a method for predicting and identifying gypsum-salt rocks in complex foreland belts based on well-seismic integration proposed in this invention.

[0060] Figure 4 This is a schematic diagram illustrating the prediction of gypsum-salt rock depth using a layer geological comparison method proposed in this invention, which combines well-seismic analysis in complex piedmont zones.

[0061] Reference numerals: 100, data acquisition component; 200, data processing component; 300, identification component. Detailed Implementation

[0062] Example 1, as Figure 1 As shown, the present invention proposes a method for predicting and identifying gypsum-salt rocks in complex piedmont belts using a combination of well and seismic analysis, comprising:

[0063] S100 collects VSP data, geological data, and seismic data, and preprocesses the collected initial data to obtain preprocessed data;

[0064] Specifically, before collecting VSP data, VSP needs to be conducted in the complex piedmont area of ​​the target, while seismic data is obtained by observing and analyzing the earth's response to artificially induced seismic waves to infer the properties and morphology of underground rock strata.

[0065] S200 calculates the error between geological and seismic data based on preprocessed data, analyzes changes in sedimentary environment, determines spatial lateral variation patterns, and establishes an anisotropic field model.

[0066] S300: A velocity field model is established based on VSP data, and the parameters of the velocity field model are continuously adjusted to obtain an optimized velocity field model.

[0067] S400, the depth of gypsum-salt rock is predicted by the optimized velocity field model and the geological correlation of small layers respectively; the depth predicted by the optimized velocity field model is recorded as the first depth, and the depth predicted by the geological correlation of small layers is recorded as the second depth;

[0068] S500 uses time-controlled drilling to obtain samples at predicted depths and identify whether the samples are gypsum-salt rocks.

[0069] Specifically, the complex piedmont zone refers to the weak tectonic deformation zone between the orogenic belt and the foreland basin. Its typical characteristics include dramatic fluctuations in surface elevation, bedrock exposed at high angles, large lateral span of stratigraphic age, and large variations in longitudinal and lateral velocities near the surface.

[0070] Gypsum-salt rocks coexist with carbonate rocks, have extremely low porosity and permeability, and have very high breakthrough pressure, which makes it difficult for fluids to pass through. This makes it easier to form oil and gas reservoirs with high abundance in areas with gypsum-salt rocks. For this reason, it is necessary to increase the prediction of gypsum-salt rocks in complex foreland belts.

[0071] However, on the other hand, gypsum-salt rocks have strong plasticity and fluidity, which means that during drilling, especially in complex piedmont areas where the formation dip angle is usually large, complex accidents such as casing deformation and stuck pipe caused by the plastic creep of gypsum-salt rocks are frequently encountered, leading to wellbore abandonment and ultimately drilling failure. Moreover, during the transformation process, gypsum in gypsum-salt rocks loses a large amount of crystal water. This water is rich in organic acids, which have a dissolving effect, enhance the reaction between fluids and rocks, dissolve minerals to form secondary pores, and at the same time, this free water enters adjacent formations, increasing the pore fluid pressure in the formation, causing undercompaction of itself and adjacent formations, directly leading to abnormally high formation pressure, and posing a great safety risk to well control.

[0072] In this invention, VSP data, geological data, and seismic data are collected, and the initial data is preprocessed to obtain preprocessed data. Then, the error between the geological data and the seismic data is calculated based on the preprocessed data, and the changes in the sedimentary environment are analyzed to determine the spatial lateral variation law. An anisotropic field model is established, and a velocity field model is established based on VSP data. The parameters of the velocity field model are continuously adjusted to obtain an optimized velocity field model. The depth of gypsum-salt rock is predicted by the optimized velocity field model and the geological correlation of small layers. Finally, a time-controlled drilling method is used to obtain samples at the predicted depth and identify whether the samples are gypsum-salt rock. This application combines VSP data, geological data, and seismic data to determine whether gypsum-salt rock exists under complex piedmont zones and can effectively improve the prediction accuracy.

[0073] In an optional embodiment, S100 includes:

[0074] S110, Create the basic data table;

[0075] S120: Collect multiple VSP data, geological data, and multiple seismic data respectively, and put the collected VSP data, geological data, and seismic data into a basic data table; the VSP data, geological data, and seismic data are all initial data;

[0076] Specifically, the geological data includes core analysis data and well logging curves, and the seismic data includes seismic reflection waveforms, seismic amplitude, and frequency.

[0077] S130, randomly select an initial data point from the basic data table;

[0078] S140, Denoise the seismic data to obtain preprocessed seismic data, and put the preprocessed seismic data into the basic data table.

[0079] S150, normalize the logging curves in the geological data to obtain preprocessed geological data, and put the preprocessed geological data into the basic data table.

[0080] Specifically, normalization is performed using Formula 5;

[0081] Formula 5;

[0082] Where y refers to the normalized value, x refers to the original value, max refers to the maximum value of the original value, and min refers to the minimum value of the original value.

[0083] S160, return to step S130 until all initial data in the basic data table has been selected, and preprocessed geological data and preprocessed seismic data are obtained.

[0084] It should be noted that VSP, or Vertical Seismic Profiling, is a method that involves generating seismic waves at points near the wellhead and conducting observations at multiple levels and components of geophones arranged at different depths along the wellbore. In a vertical seismic profile, because the geophones are placed inside the formation, they can receive not only upward-propagating P-waves and upward-converted waves, but also downward-propagating P-waves and downward-converted waves, and even shear waves.

[0085] After collecting various initial data, VSP data, geological data, and multiple seismic data need to be preprocessed to remove noise contained in the initial data, so as to avoid affecting the processing of subsequent steps.

[0086] In an optional embodiment, S200 includes:

[0087] S210, retrieve preprocessed geological data and preprocessed seismic data from the basic data table;

[0088] S220, based on preprocessed geological data, analyze the error between drilling layering and seismic profile layering and determine the error type;

[0089] Specifically, S220 includes:

[0090] S221, Align the layered data of the drilling with the layered data of the seismic profile to ensure their spatial correspondence;

[0091] S222, calculate the error between the drilling layer and the seismic profile layer; the error includes depth error, thickness error and position error;

[0092] The error types include layer misalignment and thickness inconsistency;

[0093] (1) Stratigraphic misalignment: Check for stratigraphic misalignment, i.e., inconsistency in the location between the drilling strata and the seismic profile strata; this may be caused by complex geological structure, changes in stratum dip angle, or measurement errors.

[0094] (2) Inconsistent thickness: Analyze the thickness difference between the drilling strata and the seismic profile strata; this may be due to changes in formation thickness, measurement errors or improper data processing methods;

[0095] S230, obtaining sedimentary environment characteristics based on preprocessed seismic data;

[0096] Specifically, S230 includes:

[0097] S231, conduct full three-dimensional structural and sequence stratigraphic interpretation, and establish a sequence model;

[0098] S232 involves conducting seismic attribute analysis and paleogeomorphological reconstruction within a sequence stratigraphic framework, interpreting sedimentary environments guided by modern sedimentary environments, and further understanding the sedimentary evolution history of the target strata.

[0099] S233, Extract seismic attributes; the seismic attributes include amplitude, frequency, phase, coherence volume, and wave impedance;

[0100] S234 uses attribute dimensionality reduction and optimization methods to screen out seismic attributes that are sensitive to sedimentary facies.

[0101] S235, using tools such as variograms to analyze the spatial structure of seismic properties;

[0102] S236 establishes a sedimentary facies identification model by integrating well point and seismic data;

[0103] S237, using the established identification pattern to predict sedimentary facies;

[0104] S240, based on preprocessed geological data, to obtain the variation law of lithology;

[0105] Specifically, S240 includes:

[0106] S241, by observing the lithological characteristics, color, structure, and texture of the core samples, and combining this with field outcrop observations and geological profiles, the lithological bodies are preliminarily identified and classified; the core samples contain detailed records of lithological changes, thickness, depth, and other data during the drilling process;

[0107] S242 utilizes the characteristics of logging curves, such as natural gamma, resistivity, and sonic transit time, to identify and classify lithological bodies by establishing a relationship model between lithology and logging parameters.

[0108] It should be noted that the natural gamma curve is mainly used to identify the mud content. Mudstone usually has a higher natural gamma value, while sandstone and limestone have relatively lower values. The resistivity curve can reflect the pore structure and water content of the rock. Generally, sandstone has a higher resistivity and mudstone has a lower resistivity. The acoustic transit time curve can reflect the porosity and compaction of the rock. Mudstone usually has a higher acoustic transit time and sandstone has a lower acoustic transit time.

[0109] S250, based on the variation law of lithology and the characteristics of sedimentary environment, an anisotropic field model is constructed using Formula 1;

[0110] Formula 1;

[0111] in, It is the yield function of an anisotropic field. It is the mean principal stress. It is the initial mean principal stress. It is the slope of the critical state line. It is the slope of the normal consolidation curve. It is the slope of the rebound curve. It is the strain of the soil in a plastic state;

[0112] Specifically, seismological observations show that the Earth's inner core has a complex structure, exhibiting that seismic waves travel faster along the north and south poles and slower along the equatorial plane. The anisotropy of seismic wave velocity also changes with depth. There are isotropic regions in the outer layer of the inner core, especially in the deepest part of the inner core, where the slow axis is deflected by about 50° from the polar axis. The anisotropy of the Earth's inner core also shows east-west differences, with stronger anisotropy in the western hemisphere.

[0113] It should be noted that since sedimentary environment and changes in lithology can affect the propagation of seismic waves, it is necessary to determine the sedimentary environment and changes in lithology before creating an anisotropic field model, and then determine the spatial lateral velocity variation law, so as to establish an anisotropic field model.

[0114] In an optional embodiment, S300 includes:

[0115] S310, extracting initial depth migration velocity based on preprocessed seismic data;

[0116] S320, obtain the VSP velocity, and combine the VSP velocity with the initial depth offset velocity to determine the lateral velocity pattern and the longitudinal velocity pattern;

[0117] Specifically, S320 includes:

[0118] S321 introduces the concept of equivalent offset in ground earthquakes to form the common scattering point gather of VSP;

[0119] S322, velocity analysis is performed using common scattering point gathers to determine the transverse velocity characteristics;

[0120] S323, perform velocity spectrum analysis on each common scattering point gather to obtain the velocity function curve at each location;

[0121] S324, compare these velocity function curves horizontally to analyze the trend of velocity changes;

[0122] S325, through interpolation or fitting methods, the continuous variation law of lateral velocity is obtained;

[0123] S326, a VSP velocity analysis method based on hyperbolic correction, extrapolates the VSP record along the reflected wave ray path to the ground seismic record using Equation 6. Then, using the up-going wave, according to the ground hyperbolic formula, a series of stacking velocities are given, and velocity scanning is performed in the common shot gather to produce a stacking velocity spectrum.

[0124] Formula 6;

[0125] Where t is the propagation time of the reflected wave, x is the shot-receiver distance, v is the velocity of the seismic wave, and t0 is the self-excitation and self-reception time, i.e. ;

[0126] S327 can calculate layer velocity and average velocity in real time by picking up velocity on the velocity spectrum, thereby determining the longitudinal velocity pattern;

[0127] It should be noted that VSP coscattering point gathers have a larger coverage number and a wider offset range. Their velocity is not affected by the formation dip angle and can be adapted to other vertical observation systems such as deviated wells. Since the time-distance relationship of coscattering point gathers is independent of the interface dip angle and only depends on the location of the scattering point, velocity analysis can be performed on the formed coscattering point gathers, and the obtained velocity is the migration velocity.

[0128] Velocity analysis using common scattering point gathers can significantly improve the imaging accuracy of VSP data and enhance its ability to describe fine well-perimeter structures.

[0129] In a common-scattering point gather, with a fixed t0, the shot-receiver distance x is varied, and an arbitrary stacking velocity v1 is given, the dynamic correction can be calculated. After calculation, dynamic correction is performed again, and then horizontal stacking is performed to obtain a stacking energy A1. By analogy, the curve of the stacking energy A as a function of the stacking velocity V can be obtained, and this curve is called the velocity spectrum curve. Then, the maximum value of the stacking energy is found from the velocity spectrum curve. This velocity is the dynamic correction velocity at a certain t0. For another t0, the same method can be used to obtain a velocity spectrum curve. By analogy, a series of velocity spectrum lines as a function of t0 can be obtained. Arranged from shallow to deep, this is the seismic velocity spectrum.

[0130] Specifically, before acquiring VSP data, the target location needs to be processed by VSP, and VSP data is continuously acquired during the VSP process. The VSP velocity includes downhole velocity and wellside velocity, and the initial depth migration velocity comes from preprocessed seismic data.

[0131] S330, based on the transverse and longitudinal velocity laws, constructs a velocity field model using the model tomography method.

[0132] It should be noted that, since this application is mainly applied to complex piedmont zones, a velocity field model is constructed using model tomography. Model tomography is suitable for areas with steep piedmont structures, large strata bending angles, and non-horizontal strata, supports areas with reverse fault development, and is applicable to 3D seismic work areas. At the same time, layer constraints are added to support velocity field construction in areas with reverse fault development. The layer velocities of each layer are calculated based on the interpreted time model, and then the layer velocities are smoothed. The layer plane is used as a cross section for multi-dimensional spatial gridding to establish the layer velocity field model.

[0133] In an optional embodiment, S330 includes:

[0134] S331, calculate the angle of reflection of the source wave relative to the ground using Formula 2;

[0135] Formula 2;

[0136] in, It is a travel time function of surface observations of isotropic strata. It is the launch point. It is the angle of ray emission. It is the launch velocity corresponding to the first launch point. It is the x-coordinate of the coordinate system. It is the ordinate of the coordinate system;

[0137] S332, calculate the hypothetical wavefront radius of curvature using formula 3;

[0138] Formula 3;

[0139] in, It is the imaginary wavefront curvature radius generated at NIP and emitted from CDP. It is the two-way travel time of the normal reflected wave corresponding to the coincidence of the source and receiver at the CDP point. It is the angle of ray emission. It is the launch velocity corresponding to the first launch point;

[0140] S333, substitute the data of CDP point into formula 3 to determine the wavefront curvature radius;

[0141] Specifically, CDP is the common depth point, and NIP is the intermediate reflection point.

[0142] It should be noted that, as Figure 3 As shown, the normal ray traces from the CDP to the Nth reflection layer. The total time from the source A to the NIP point and then from the NIP point to the termination point B is close to the travel time of the reflected CDP between the source A and the termination point B. Therefore, the data of the CDP point can be substituted into Formula 3 to obtain the wavefront curvature radius.

[0143] When performing step S333, by performing inverse recursion on the NIP wavefront, the sign of the shrinking NIP wavefront radius is opposite to the sign of the upward expansion of the NIP wavefront radius in the forward modeling problem. When tracing the normal ray downward, the travel time can be measured as a positive number.

[0144] In an optional embodiment, S300 further includes:

[0145] S340, calculate the Delta value, and update the transverse and longitudinal velocity fields using the Delta value;

[0146] Specifically, S340 includes:

[0147] S341, establish a linear relationship between the model space and the data space using Formula 7;

[0148] Formula 7;

[0149] in, It is the angle between the transmitted ray and the longitudinal axis when it exits, s is the propagation surface element, and v is the surface element velocity. This is the current location. It is a chain multiplication matrix. It is a derivative matrix. It is a propagation operator;

[0150] S342, based on the offset check to see if the gather is flattened, calculate the data residual using formula 8;

[0151] Formula 8;

[0152] in, It is the angle between the transmitted ray and the longitudinal axis when it exits, where x and z are the horizontal and vertical plane elements, respectively. It is the change in velocity;

[0153] S343, based on the least squares method, solves the inversion equation system and calculates the change in slowness using formula 9;

[0154] Formula 9;

[0155] in, It is the change in slowness. It is the number of ray coverages within the grid. It is the horizontal first-order derivative regularization matrix;

[0156] S350, the stratigraphic dip is fitted by a quadratic polynomial;

[0157] Specifically, the quadratic polynomial is shown in Equation 10;

[0158] Formula 10;

[0159] Where a, b, and c are all constants, and abc is not equal to 0;

[0160] S360, using Formula 4, performs residual time difference correction on the CRP gather and determines the far-path leveling of the gather;

[0161] Formula 4;

[0162] in, It is the corrected CRP gather. It is the original collection of Tao. It is the correction function, where t is time and x is the channel number;

[0163] Specifically, the received record of a single seismic detector is a seismic trace. After processing, the seismic traces can be superimposed into one trace. The collection of multiple seismic traces is called a trace set. Each trace represents a unique trace set. All trace sets are displayed according to certain rules to form a seismic profile. CRP stands for common reflection point.

[0164] It should be noted that in this application, the quadratic polynomial fitting coefficients of each sample point in the CRP gather are precisely scanned to determine the in-phase axis direction of the sample point, and median filtering is performed in the in-phase axis direction to preserve the stratigraphic edge features. On the stacked profile, residual time difference correction is performed, and the residual correction amount is estimated one by one according to the optimization principle to obtain the residual correction amount model of each common reflection point gather, thereby enhancing the stability of the CRP gather. Furthermore, since the residual correction amount is controlled, new errors are avoided, ultimately improving the quality of the stacked profile and the CRP gather.

[0165] Since CRP gathers play a crucial role in accurate imaging and pre-stack inversion, this application continuously optimizes CRP gathers while ensuring the remaining correction amount within the CRP gathers during the optimization process. It cannot be too large, otherwise it will introduce new errors in the CRP gather.

[0166] In an optional embodiment, S400 includes:

[0167] S410, using an optimized velocity field model to predict the depth of gypsum-salt rock;

[0168] Specifically, using the corrected velocity model, the depth of gypsum-salt rocks is predicted through a time-depth conversion method (multiplying the velocity of the velocity field by the reflection time of the target layer to obtain the tectonic depth value), including:

[0169] S411, calculate the average velocity of each segment;

[0170] S412, using average velocity and reflection time, calculates the depth of each segment;

[0171] S413, Draw a depth map to determine the specific location of the gypsum-salt rock;

[0172] S420, acquire sub-layer geological data;

[0173] S430, by comparing the geological data of the small layer with the pre-processed geological data, the depth of the gypsum-salt rock under test is predicted based on the depth of the gypsum-salt rock under test.

[0174] It should be noted that, as Figure 4 As shown, the sub-layer geological data comes from the drilled wells around the complex piedmont zone being measured. When making comparisons, the sub-layer geological data needs to be compared with the pre-processed geological data item by item, so as to predict the depth of the gypsum-salt rock being measured by the depth of the gypsum-salt rock in the sub-layer geological data.

[0175] In an optional embodiment, S500 includes:

[0176] S510, obtain the first and second depths;

[0177] S520 sets the warning depth based on the first depth and the second depth;

[0178] S530, samples are obtained at the warning depth using a time-controlled drilling method;

[0179] S540, determine whether the sample is gypsum-salt rock by observing rock fragments;

[0180] Specifically, the actual depth of the gypsum-salt rock should be between the first and second depths.

[0181] It should be noted that the warning depth is located above the first depth. For example, if the first depth is 50 meters, then the warning depth can be set to 40 meters. After the drill bit reaches the warning depth, it is observed every 0.5 meters. As the depth is gradually predicted, the step distance can be continuously reduced to ensure that gypsum-salt rock cuttings can be detected in time.

[0182] After each advance, a rock cuttings observation is required. During the rock cuttings observation, the composition and some characteristics of the sample at that depth are judged.

[0183] Since the main components of carbonate rocks are calcium carbonate and magnesium carbonate, while the main components of gypsum-salt rocks are gypsum and halite, the trend of carbonate content variation in the formation at that depth can be determined based on the carbonate content. Similarly, since halite is generally soluble in drilling fluid, it can only be observed in rock cuttings when the drilling fluid is saturated.

[0184] When performing the aforementioned embodiments, it is also necessary to monitor the drilling status in real time to ensure construction safety. Specifically, the monitoring of the drilling status is shown in Table-1, the drilling monitoring table.

[0185] Table 1

[0186] .

[0187] In an optional embodiment, the sublayer geological data includes formation thickness, formation lithology, and well logging characteristics.

[0188] It should be noted that the thickness of the formation is used to determine the continuity and variation of the formation.

[0189] Stratigraphic lithology includes the types, composition, and structure of rocks, and is used to reveal differences in geological history and geological processes.

[0190] Well logging curves reflect the physical characteristics of rocks at different depths, thus determining the distribution and variation of underground rocks.

[0191] like Figure 2 As shown, this application also provides a prediction and identification system for gypsum-salt rocks in complex piedmont areas with well-seismic interaction, including a data acquisition component 100, a data processing component 200, and an identification component 300. The data acquisition component 100 acquires VSP data, geological data, and seismic data. The data processing component 200 executes the prediction and identification method for gypsum-salt rocks in complex piedmont areas with well-seismic interaction proposed in Example 1. The identification component 300 analyzes sample parameters.

[0192] It should be noted that VSP data, geological data, and seismic data are collected through the data acquisition component 100, and all the collected data are transmitted to the data processing component 200. The data processing component 200 processes the data and establishes a velocity field model and anisotropic field model. After predicting the depth of gypsum-salt rock, the collected samples are identified by the identification component 300 to determine whether gypsum-salt rock exists in the complex piedmont zone.

[0193] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. A method for predicting and identifying gypsum-salt rocks in complex piedmont belts using a combination of well and seismic analysis, characterized in that... include: Collect VSP data, geological data, and seismic data; preprocess the collected initial data to obtain preprocessed data. Based on preprocessed data, the errors between geological and seismic data are calculated, and the changes in sedimentary environment are analyzed to determine the spatial lateral variation patterns. An anisotropic field model is then established, including: Obtain preprocessed geological data and preprocessed seismic data from the basic data table; Based on the analysis of preprocessed geological data, the errors between drilling layering and seismic profile layering are determined and the error types are identified. Sedimentary environment characteristics are obtained based on preprocessed seismic data; The variation patterns of lithological bodies were obtained based on preprocessed geological data; An anisotropic field model is constructed based on the lithological variation law and sedimentary environment characteristics using Formula 1; Formula 1: in, It is the yield function of an anisotropic field. It is the mean principal stress. It is the initial mean principal stress. It is the slope of the critical state line. It is the slope of the normal consolidation curve. It is the slope of the rebound curve. It is the strain of the soil in a plastic state; A velocity field model is established based on VSP data, and the parameters of the velocity field model are continuously adjusted to obtain an optimized velocity field model, including: Initial depth migration velocity is extracted from preprocessed seismic data; Obtain the VSP velocity, and combine the VSP velocity with the initial depth offset velocity to determine the lateral velocity pattern and the longitudinal velocity pattern; A velocity field model is constructed based on the transverse and longitudinal velocity laws using the model tomography method. The depth of gypsum-salt rock was predicted using the optimized velocity field model and the sub-layer geological correlation, respectively. The depth predicted by the optimized velocity field model was denoted as the first depth, and the depth predicted by the sub-layer geological correlation was denoted as the second depth. By using time-controlled drilling, samples are obtained at the predicted depth, and it is identified whether the samples are gypsum-salt rocks.

2. The method for predicting and identifying gypsum-salt rocks in complex piedmont belts using a combination of well and seismic analysis, as described in claim 1, is characterized in that... VSP data, geological data, and seismic data are acquired. The initial data is preprocessed to obtain preprocessed data, including: Create the basic data table; Multiple VSP data, geological data, and multiple seismic data were collected separately, and the collected VSP data, geological data, and seismic data were placed into a basic data table; the VSP data, geological data, and seismic data were all initial data. Randomly select an initial data point from the basic data table; The seismic data is denoised to obtain preprocessed seismic data, which is then placed into the basic data table. Normalize the well logging curves in the geological data to obtain preprocessed geological data, and put the preprocessed geological data into the basic data table. Return to the initial data table and randomly select one data point until all initial data points in the initial data table have been selected, to obtain preprocessed geological data and preprocessed seismic data.

3. The method for predicting and identifying gypsum-salt rocks in complex piedmont belts based on well-seismic combined analysis, as described in claim 2, is characterized in that... A velocity field model is constructed based on the transverse and longitudinal velocity laws using the model tomography method, including: The angle of reflection of the seismic source relative to the ground is calculated using Formula 2. Formula 2: in, It is a travel time function of surface observations of isotropic strata. It is the launch point. It is the angle of ray emission. It is the launch velocity corresponding to the launch point of the first layer. It is the x-coordinate of the coordinate system. It is the ordinate of the coordinate system; The hypothetical wavefront radius of curvature is calculated using Formula 3. Formula 3: in, It is the imaginary wavefront curvature radius generated at NIP and emitted from CDP. It is the two-way travel time of the normal reflected wave corresponding to the coincidence of the source and receiver at the CDP point. It is the angle of ray emission. It is the launch velocity corresponding to the first launch point; It is the superposition speed; Substitute the data from the CDP point into Formula 3 to determine the wavefront curvature radius.

4. The method for predicting and identifying gypsum-salt rocks in complex piedmont belts using a combination of well and seismic analysis, as described in claim 3, is characterized in that... A velocity field model is established based on VSP data, and the parameters of the velocity field model are continuously adjusted to obtain an optimized velocity field model, which also includes: Calculate the Delta value and update the transverse and longitudinal velocity fields using the Delta value; The stratigraphic dip is fitted using a quadratic polynomial. Formula 4 is used to correct the residual time difference of the CRP gather and determine the far-channel leveling of the gather. Formula 4: in, It is the corrected CRP gather. It is the original collection of Tao. It is the correction function, where t is time and x is the channel number.

5. The method for predicting and identifying gypsum-salt rocks in complex piedmont belts using a combination of well and seismic analysis, as described in claim 4, is characterized in that... The depth of gypsum-salt rocks was predicted using the optimized velocity field model and sub-layer geological correlation, including: Predicting the depth of gypsum-salt rock using an optimized velocity field model; Obtain geological data of the subsurface; By comparing the geological data of the sub-layer with the pre-processed geological data, the depth of the gypsum-salt rock under test is predicted based on the depth of the gypsum-salt rock under test.

6. The method for predicting and identifying gypsum-salt rocks in complex piedmont belts based on well-seismic combined analysis, as described in claim 5, is characterized in that... By using time-controlled drilling, samples are obtained at the predicted depth, and it is identified whether the samples are gypsum-salt rocks, including: Obtain the first and second depths; Set the warning depth based on the first and second depths; Samples were obtained at the warning depth using a time-controlled drilling method. Determine whether a sample is gypsum-salt rock by observing rock fragments.

7. The method for predicting and identifying gypsum-salt rocks in complex piedmont belts based on well-seismic combined analysis, as described in claim 6, is characterized in that... The geological data of the sub-layers include formation thickness, formation lithology, and well logging curve characteristics.

8. A system for predicting and identifying gypsum-salt rocks in complex foreland belts using a combination of well and seismic analysis, characterized in that, include: A data acquisition component is used to acquire VSP data, geological data, and seismic data. A data processing component executes the method for predicting and identifying gypsum-salt rocks in complex foreland belts based on well-seismic combination as described in any one of claims 1 to 7. An identification component is used to analyze sample parameters.

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