Method and system for predicting and identifying gypsum and salt rocks of complex mountain front zone with well-seismic combination

Abstract

The invention relates to the technical field of oil-gas exploration, in particular to a method and a system for predicting and identifying gypsum and salt rocks of complex mountain front zone well-seismic combination. The method comprises the steps of collecting VSP data, geological data and seismic data, preprocessing the collected initial data to obtain preprocessed data, then calculating an error between the geological data and the seismic data based on the preprocessed data, analyzing changes of a sedimentary environment, determining a spatial transverse change rule, establishing an anisotropic field model, and establishing a velocity field model based on the VSP data. According to the method, VSP data, geological data and seismic data are acquired, parameters of a velocity field model are continuously adjusted, an optimized velocity field model is obtained, the depth of gypsum and salt rock is predicted through the optimized velocity field model and small-layer geological comparison, finally, a sample at the predicted depth is acquired through a time-controlled drilling method, and whether the sample is gypsum and salt rock or not is identified. Therefore, whether the gypsum salt rock exists under the complex mountain front zone or not is judged, and the prediction precision can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration, and in particular to a method and system for predicting and identifying gypsum-salt rocks in a complex piedmont zone by combining well-seismic exploration. Background Art

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

[0003] A Chinese patent with announcement number CN115793053B discloses a comprehensive prediction method for Cambrian gypsum-salt rock based on seismic, drilling and logging. The method calculates the comprehensive change rate of drilling parameters of Cambrian gypsum-salt rock, further calibrates the top surface position of the gypsum-salt rock according to the comprehensive change rate of drilling parameters of Cambrian gypsum-salt rock, and finally conducts logging parameter cuttings observation and X-ray element analysis within the depth range of Cambrian gypsum-salt rock predicted by seismic and drilling, so as to realize the comprehensive prediction of Cambrian gypsum-salt rock. With the continuous breakthroughs in deep-ultra-deep carbonate oil and gas exploration, carbonate oil and gas exploration in complex piedmont belts has attracted more and more attention. However, it is difficult to accurately predict and identify gypsum-salt rock in complex piedmont belts in the existing technology, resulting in great difficulty in carbonate oil and gas exploration in complex piedmont belts. Summary of the invention

[0004] The purpose of the present invention is to address the problems existing in the background technology and to propose a method and system for predicting and identifying gypsum-salt rocks in complex piedmont zones by combining well-seismic data.

[0005] The technical solution of the present invention:

[0006] On the one hand, the present application provides a method for predicting and identifying gypsum-salt rock in a complex piedmont zone by combining well-seismic data, including:

[0007] Collect VSP data, geological data and seismic data, and pre-process the collected initial data to obtain pre-processed data;

[0008] Calculate the errors between geological data and seismic data based on preprocessed data, analyze the changes in sedimentary environment, determine the spatial lateral variation law, and establish 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 the gypsum-salt rock is predicted by the optimized velocity field model and the small-layer geological comparison respectively; the depth predicted by the optimized velocity field model is recorded as the first depth, and the depth predicted by the small-layer geological comparison is recorded as the second depth;

[0011] Through the time-controlled drilling method, samples at the predicted depth are obtained and it is identified whether the samples are gypsum salt rock.

[0012] Preferably, collecting VSP data, geological data and seismic data, and preprocessing the collected initial data to obtain preprocessed data include:

[0013] Create a basic data table;

[0014] respectively collecting a plurality of VSP data, geological data and a plurality of seismic data, and putting the collected VSP data, geological data and seismic data into a basic data table;

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

[0016] De-noising the seismic data to obtain pre-processed seismic data, and putting the pre-processed seismic data into a basic data table;

[0017] Normalize the logging curves in the geological data to obtain pre-processed geological data, and put the pre-processed geological data into the basic data table;

[0018] Return to randomly select an initial data from the basic data table until all the initial data in the basic data table are selected, and obtain preprocessed geological data and preprocessed seismic data.

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

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

[0021] Analyze the errors between the positive drilling layer and the seismic profile layer based on the pre-processed geological data and determine the error type;

[0022] Obtain sedimentary environment characteristics based on preprocessed seismic data;

[0023] Obtain the changing rules of lithology based on preprocessed geological data;

[0024] Based on the variation law of lithologic bodies and the characteristics of sedimentary environment, the anisotropic field model is constructed by formula 1;

[0025]

[0026] Where C is the yield function of the anisotropic field, q is the average principal stress, q0 is the initial average principal stress, N is the slope of the critical state line, λ is the slope of the normal consolidation curve, κ is the slope of the rebound curve, is the plastic volume strain of soil.

[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] Extracting initial depth migration velocities based on preprocessed seismic data;

[0029] Obtain VSP velocity, and determine the lateral and longitudinal velocity patterns by combining the VSP velocity with the initial depth migration velocity;

[0030] The velocity field model is constructed by model tomography based on the lateral velocity law and the longitudinal velocity law.

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

[0032] The angle of incidence between the source reflection wave and the ground is calculated by formula 2;

[0033]

[0034] in, is the travel time function of the surface observation in isotropic formations, P E is the exit point, β0 is the ray exit angle, v1 is the exit velocity corresponding to the exit point of the first layer, x s is the horizontal coordinate of the coordinate system, y s is the ordinate of the coordinate system;

[0035] The radius of curvature of the imaginary wavefront is calculated by formula 3;

[0036]

[0037] Where R0 is the radius of curvature of the imaginary wavefront generated at the NIP and emitted at the CDP, t(0) is the two-way time of the normal reflection wave corresponding to the coincidence of the source and receiver at the CDP point, β0 is the ray exit angle, and v1 is the exit velocity corresponding to the first layer exit point;

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

[0039] Preferably, a velocity field model is established based on the VSP data, and the parameters of the velocity field model are continuously adjusted to obtain an optimized velocity field model, which further includes:

[0040] Calculate the Delta value and update the lateral velocity field and longitudinal velocity field using the Delta value;

[0041] Fitting formation dips by quadratic polynomials;

[0042] The residual time difference correction is performed on the CRP gathers by using formula 4 to determine the leveling of the far-track gathers;

[0043]

[0044] in, is the corrected CRP gather, G(t,x) is the original gather, Δm(t,x) is the correction function, t is time, and x is the channel number.

[0045] Preferably, the depth of gypsum salt rock is predicted by respectively optimizing the velocity field model and comparing the geological conditions of the small layers, including:

[0046] Obtain small-layer geological data;

[0047] The sub-layer geological data is compared with the pre-processed geological data, and 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, obtaining a sample at a predicted depth by a time-controlled drilling method and identifying whether the sample is gypsum salt rock includes:

[0049] Get a first depth and a second depth;

[0050] setting a warning depth based on the first depth and the second depth;

[0051] Obtain samples at the warning depth by time-controlled drilling method;

[0052] Determine whether the sample is gypsum salt rock by observing the rock cuttings.

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

[0054] On the other hand, the present application also provides a system for predicting and identifying gypsum-salt rocks in complex piedmont zones by combining well-seismic data, including a data acquisition component, a data processing component and an identification component. The data acquisition component is used to collect VSP data, geological data and seismic data. The data processing component is used to execute the method for predicting and identifying gypsum-salt rocks in complex piedmont zones by combining well-seismic data as described in any of the preceding claims. The identification component is used to analyze sample parameters.

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

[0056] By collecting VSP data, geological data and seismic data, the collected initial data are preprocessed to obtain preprocessed data, and then the errors of geological data and seismic data are calculated based on the preprocessed data, and the changes in the sedimentary environment are analyzed to determine the spatial lateral change law, establish an anisotropic field model, and establish a velocity field model based on the 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 small-layer geological comparison. Finally, a time-controlled drilling method is used to obtain samples at the predicted depth and identify whether the samples are gypsum salt rock. The present application combines VSP data, geological data and seismic data to determine whether gypsum salt rock exists under a complex piedmont belt and can effectively improve the prediction accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A schematic diagram of the flow of a method for predicting and identifying gypsum-salt rocks in a complex piedmont zone by combining well-seismic analysis proposed by the present invention;

[0058] Figure 2 This is a schematic diagram of the structure of a well-seismic combined gypsum-salt rock prediction and identification system in a complex piedmont zone proposed by the present invention;

[0059] Figure 3 A schematic diagram of seismic wave propagation for a method for predicting and identifying gypsum-salt rocks in a complex piedmont zone by combining well-seismic analysis proposed by the present invention;

[0060] Figure 4 This is a schematic diagram of the prediction of gypsum-salt rock depth by small-layer geological comparison according to a method for predicting and identifying gypsum-salt rock in a complex piedmont zone by combining well-seismic analysis proposed by the present invention.

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

[0062] Embodiment 1, as Figure 1 As shown, the present invention proposes a method for predicting and identifying gypsum-salt rock in a complex piedmont zone by combining well-seismic data, including:

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

[0064] Specifically, before collecting VSP data, VSP needs to be conducted in the target complex foreland zone, while seismic data is related data on the properties and morphology of underground rock formations inferred by observing and analyzing the response of the earth to artificially excited seismic waves;

[0065] S200, calculating the errors between geological data and seismic data based on the preprocessed data, analyzing the changes in the sedimentary environment, determining the spatial lateral change law, and establishing an anisotropic field model;

[0066] S300, establishing a velocity field model based on the VSP data, and continuously adjusting the parameters of the velocity field model to obtain an optimized velocity field model;

[0067] S400, predicting the depth of the gypsum-salt rock by using the optimized velocity field model and the small-layer geological comparison respectively; recording the depth predicted by the optimized velocity field model as the first depth, and recording the depth predicted by the small-layer geological comparison as the second depth;

[0068] S500, obtaining samples at the predicted depth through a time-controlled drilling method, and identifying whether the samples are gypsum salt rock;

[0069] Specifically, the complex foreland zone refers to a weak tectonic deformation zone between the orogenic belt and the foreland basin, with typical characteristics including dramatic fluctuations in surface elevation, bedrock outcropping at high angles, large lateral spans of stratigraphic age, and large variations in near-surface longitudinal and lateral velocities;

[0070] Gypsum salt rock coexists with carbonate rock, has extremely low porosity and permeability, and has a high breakthrough pressure, which makes it difficult for fluids to pass through, making it easier to form high-abundance oil and gas reservoirs in places with gypsum salt rock. For this reason, it is necessary to increase the prediction of gypsum salt rock in complex piedmont zones;

[0071] On the other hand, gypsum salt rock has strong plasticity and fluidity, which leads to large formation dip angles during drilling, especially in complex piedmont areas. Complex accidents such as casing deformation and drill sticking caused by plastic creep of gypsum salt rock are often encountered, resulting in wellbore scrapping and ultimate drilling failure. In addition, the gypsum in gypsum salt rock will lose a large amount of crystalline water during the transformation process. This water is rich in organic acids and has a dissolving effect, which enhances the reaction between fluids and rocks, dissolves 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 under-compaction of itself and its adjacent formations, directly leading to abnormally high formation pressure, posing a great safety risk to well control.

[0072] In the present invention, by collecting VSP data, geological data and seismic data, the collected initial data are preprocessed to obtain preprocessed data, and then the errors of geological data and seismic data are calculated based on the preprocessed data, and the changes in the sedimentary environment are analyzed to determine the spatial lateral change law, establish an anisotropic field model, establish a velocity field model based on the VSP data, and continuously adjust the parameters of the velocity field model to obtain an optimized velocity field model, and predict the depth of gypsum salt rock through the optimized velocity field model and small-layer geological comparison, and finally obtain samples at the predicted depth through a time-controlled drilling method, and identify whether the samples are gypsum salt rock. The present application combines VSP data, geological data and seismic data to determine whether gypsum salt rock exists under a complex piedmont belt, and can effectively improve the prediction accuracy.

[0073] In an optional embodiment, the S100 includes:

[0074] S110, creating a basic data table;

[0075] S120, respectively collecting a plurality of VSP data, geological data and a plurality of seismic data, and putting 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 amplitudes and frequencies;

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

[0078] S140, performing denoising processing on the seismic data to obtain pre-processed seismic data, and putting the pre-processed seismic data into a basic data table;

[0079] S150, normalizing the well logging curves in the geological data to obtain pre-processed geological data, and putting the pre-processed geological data into a basic data table;

[0080] Specifically, normalization is performed by formula 5;

[0081]

[0082] Among them, 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, returning to step S130, until all the initial data in the basic data table are 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 of exciting seismic waves at some points near the wellhead and observing at some multi-level and multi-component detection points arranged at different depths along the wellbore. In vertical seismic profiling, because the detectors are placed inside the formation, they can not only receive the upward P-waves and upward converted waves propagating from bottom to top, but also the downward P-waves and downward converted waves propagating from top to bottom, and even the S-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 to avoid affecting the processing in subsequent steps.

[0086] In an optional embodiment, the S200 includes:

[0087] S210, obtaining preprocessed geological data and preprocessed seismic data from a basic data table;

[0088] S220, analyzing the error between the positive drilling layer and the seismic profile layer based on the preprocessed geological data and determining the error type;

[0089] Specifically, the S220 includes:

[0090] S221, aligning the layered data of the well being drilled with the layered data of the seismic profile to ensure that the two are spatially corresponding;

[0091] S222, calculating 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) Stratum misalignment: Check whether there is stratigraphic misalignment, that is, the position of the wellbore layer and the seismic profile layer is inconsistent. This may be caused by complex geological structure, changes in stratigraphic dip angle or measurement errors.

[0094] (2) Thickness inconsistency: Analyze the thickness difference between the drilling layer and the seismic profile layer. This may be caused by changes in formation thickness, measurement errors, or improper data processing methods.

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

[0096] Specifically, the S230 includes:

[0097] S231, conduct full 3D structural and sequence stratigraphic interpretation and establish sequence models;

[0098] S232, conduct seismic attribute analysis and paleo-geomorphology restoration within the sequence framework, interpret the sedimentary environment with the modern sedimentary environment as a guide, and further understand the sedimentary evolution history of the target strata;

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

[0100] S234, using attribute dimension reduction and optimization methods, screen out seismic attributes that are sensitive to sedimentary facies;

[0101] S235, analysis of the spatial structure of seismic attributes using tools such as variograms;

[0102] S236, establish a sedimentary facies identification model through the integration of well point and seismic data;

[0103] S237, using the established recognition model, predict sedimentary facies.

[0104] S240, obtaining the lithologic body variation law based on the preprocessed geological data;

[0105] Specifically, the S240 includes:

[0106] S241, by observing the lithological characteristics, color, structure, and texture of the core, combined with field outcrop observations and geological profiles, preliminary identification and classification of the lithological bodies; the core samples record in detail the lithological changes, thickness, depth, and other data during the drilling process;

[0107] S242, using the characteristics of the logging curve, such as natural gamma, resistivity, acoustic time difference, etc., by establishing a relationship model between lithology and logging parameters, the lithology body is identified and classified;

[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 are relatively low. The resistivity curve can reflect the pore structure and water content of the rock. Generally, the resistivity of sandstone is higher and the resistivity of mudstone is lower. The acoustic time difference curve can reflect the porosity and compaction degree of the rock. The acoustic time difference of mudstone is usually higher, while that of sandstone is lower.

[0109] S250, based on the lithologic body variation law and sedimentary environment characteristics, the anisotropic field model is constructed by formula 1;

[0110]

[0111] Where C is the yield function of the anisotropic field, q is the average principal stress, q0 is the initial average principal stress, N is the slope of the critical state line, λ is the slope of the normal consolidation curve, κ is the slope of the rebound curve, is the plastic volume strain of soil;

[0112] Specifically, seismological observations show that the structure of the Earth's inner core is complex, with seismic waves traveling faster along the North and South Poles and slower along the equatorial plane. The anisotropy of seismic wave speed also changes with depth. There are isotropic areas 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. There are also east-west differences in the anisotropy of the Earth's inner core, with the anisotropy being stronger in the Western Hemisphere.

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

[0114] In an optional embodiment, the S300 includes:

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

[0116] S320, obtaining the VSP velocity, and determining the lateral velocity law and the longitudinal velocity law by combining the VSP velocity and the initial depth migration velocity;

[0117] Specifically, the S320 includes:

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

[0119] S322, velocity analysis is performed through common scattering point gathers to determine the lateral velocity pattern;

[0120] S323, performing velocity spectrum analysis on each common scattering point gather to obtain a velocity function curve at each position;

[0121] S324, comparing these speed function curves horizontally to analyze the speed change trend;

[0122] S325, obtaining a continuous variation law of the lateral velocity by an interpolation or fitting method;

[0123] S326, VSP velocity analysis method based on hyperbola correction, extrapolates VSP records into ground seismic records along the reflection wave ray path according to time through formula 6, and then uses up-going waves, according to the ground hyperbola formula, to give a series of stacking velocities, perform velocity scanning in the common shot point gather, and make a stacking velocity spectrum;

[0124]

[0125] Among them, t is the propagation time of the reflected wave, x is the shot distance, v is the velocity of the seismic wave, and t0 is the self-excitation and self-reception time, that is,

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

[0127] It should be noted that the VSP common scattering point gather has a larger coverage number and a higher offset range. Its velocity is not affected by the formation dip angle and can adapt to other vertical observation systems such as inclined wells. Since the time-distance relationship of the common scattering point gather has nothing to do with the interface dip angle and is only related to the scattering point position, the velocity analysis of the formed common scattering point gather can be performed, and the obtained velocity is the offset velocity.

[0128] Velocity analysis through common scattering point gathers can improve the imaging accuracy of VSP data and enhance the ability of VSP data to describe the subtle structure around the well.

[0129] In the common scattering point gather, fix a certain t0, let the shot offset x change, and give an arbitrary stacking velocity v1, the dynamic correction amount can be calculated, and then the dynamic correction is performed after calculation, and then a stacking energy A1 is obtained by horizontal stacking. By analogy, the curve of the change of the stacking energy A with the stacking velocity V can be obtained, and this curve is called the velocity spectrum curve. Then find the maximum value of the stacking energy from the velocity spectrum curve. This velocity is the dynamic correction velocity at a certain t0. For another t0, according to the above method, a velocity spectrum curve can also be obtained. By analogy, a series of velocity spectrum lines that change with t0 can be obtained, and the arrangement from shallow to deep is the seismic velocity spectrum.

[0130] Specifically, before collecting VSP data, it is necessary to perform VSP processing on the target location, and continuously collect VSP data during the VSP process. The VSP velocity includes downhole velocity and wellside velocity, and the initial depth migration velocity comes from pre-processed seismic data.

[0131] S330, constructing a velocity field model through a model tomography method based on the transverse velocity law and the longitudinal velocity law.

[0132] It should be noted that since the present application is mainly used in complex piedmont zones, a velocity field model is constructed by the model tomography method. The model tomography method is suitable for areas with high and steep piedmont structures, large stratum bending dips, and non-horizontal stratum media. It supports areas with developed reverse faults and is suitable for three-dimensional seismic work areas. At the same time, stratigraphic constraints are added to support velocity field construction in areas with developed reverse faults. The layer velocity of each layer is calculated based on the interpreted time model, and then the layer velocity is smoothed. The stratigraphic surface is used as a section for multi-dimensional spatial gridding to establish a layer velocity field model.

[0133] In an optional embodiment, the S330 includes:

[0134] S331, calculating the angle of incidence between the earthquake source reflection wave and the ground using formula 2;

[0135]

[0136] in, is the travel time function of the surface observation in isotropic formations, P E is the exit point, β0 is the ray exit angle, v1 is the exit velocity corresponding to the exit point of the first layer, x s is the horizontal coordinate of the coordinate system, y s is the ordinate of the coordinate system;

[0137] S332, calculating the radius of curvature of an imaginary wavefront using Formula 3;

[0138]

[0139] Where R0 is the radius of curvature of the imaginary wavefront generated at the NIP and emitted at the CDP, t(0) is the two-way time of the normal reflection wave corresponding to the coincidence of the source and receiver at the CDP point, β0 is the ray exit angle, and v1 is the exit velocity corresponding to the first layer exit point;

[0140] S333, substituting the data of the 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 if Figure 3 As shown, the normal ray is traced 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 end point B is close to the travel time of the reflected CDP between the source A and the end point B. Therefore, the various data of the CDP point can be substituted into Formula 3 to obtain the wavefront curvature radius.

[0143] When executing 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 upwardly expanding NIP wavefront radius in the forward problem, and when tracing the normal ray downward, a positive travel time can be measured.

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

[0145] S340, calculating the Delta value, and updating the lateral velocity field and the longitudinal velocity field according to the Delta value;

[0146] Specifically, the S340 includes:

[0147] S341, establishing a linear relationship between the model space and the data space through formula 7;

[0148]

[0149] Formula 7;

[0150] Among them, θ is the angle between the transmitted ray and the longitudinal axis when it is emitted, s is the propagation surface element, v is the surface element velocity, σ is the current position, Π is the continuous multiplication matrix, η is the derivative matrix, and w is the propagation operator;

[0151] S342, checking whether the gather is flattened based on the offset, and calculating the data residual by formula 8;

[0152]

[0153] Among them, θ is the angle between the transmitted ray and the longitudinal axis when it is emitted, x and z are the horizontal and vertical plane elements respectively, and Δv is the velocity change;

[0154] S343, solving the inversion equations based on the least square method, and calculating the change in slowness by using Formula 9;

[0155] L(s)=||AΔs-Δt|| 2 +||μΓΔs|| 2 Formula 9;

[0156] Among them, Δs is the change of slowness, μ is the number of ray coverage in the grid, and Γ is the regularization matrix of the first-order lateral derivative;

[0157] S350, fitting the formation dip by quadratic polynomial;

[0158] Specifically, the quadratic polynomial is shown in Formula 10;

[0159] f(d)=ad 2 +bd+c Formula 10;

[0160] Among them, a, b and c are all constants, and abc is not equal to 0;

[0161] S360, perform residual moveout correction on the CRP gathers by using formula 4 to determine the leveling of the far-track gathers;

[0162]

[0163] in, is the corrected CRP gather, G(t,x) is the original gather, Δm(t,x) is the correction function, t is time, and x is the channel number;

[0164] Specifically, the receiving record of a single seismic detector is a seismic trace, which can be superimposed into one trace after processing. The collection of multiple seismic traces is a trace gather, and each trace represents a unique trace gather. All trace gathers displayed according to certain rules are seismic profiles. CRP is the common reflection point.

[0165] It should be noted that in the present application, the quadratic polynomial fitting coefficients of each sample point in the CRP gather are accurately scanned to determine the event axis direction of the sample point and median filtering is performed in the event axis direction, thereby retaining the formation edge characteristics, and residual time difference correction is performed on the stacked section, and the residual correction amount is estimated on a track-by-track basis according to the optimization principle to obtain the residual correction amount model for each common reflection point gather, thereby enhancing the stability of the CRP gather, and because the residual correction amount is controlled, new errors are avoided, and ultimately the quality of the stacked section and the CRP gather is improved.

[0166] Since CRP gathers play an important role in accurate imaging and prestack inversion, they are continuously optimized in this application. During the optimization process, it is necessary to ensure that the residual correction amount Δm(t,x) in the CRP gathers is not too large, otherwise new errors will be introduced into the CRP gathers.

[0167] In an optional embodiment, the S400 includes:

[0168] S410, predicting the depth of gypsum salt rock by using the optimized velocity field model;

[0169] Specifically, the depth of the gypsum salt rock is predicted by using the corrected velocity model through the time-depth conversion method (multiplying the velocity of the velocity field by the reflection time of the target layer to obtain the structural depth value), including:

[0170] S411, calculate the average velocity of each layer segment;

[0171] S412, using the average velocity and reflection time, calculate the depth of each layer;

[0172] S413, draw a depth map to determine the specific location of the gypsum salt rock;

[0173] S420, obtaining small layer geological data;

[0174] S430, comparing the small layer geological data with the pre-processed geological data, and predicting the depth of the gypsum salt rock to be measured based on the gypsum salt rock depth of the pre-processed geological data.

[0175] It should be noted that if Figure 4As shown, the sub-layer geological data are derived from the wells drilled around the complex foreland zone under test. When making a comparison, it is necessary to compare the sub-layer geological data with the pre-processed geological data item by item, so as to predict the depth of the tested gypsum-salt rock through the depth of the gypsum-salt rock in the sub-layer geological data.

[0176] In an optional embodiment, the S500 includes:

[0177] S510, obtaining a first depth and a second depth;

[0178] S520, setting a warning depth based on the first depth and the second depth;

[0179] S530, obtaining samples at the warning depth by a time-controlled drilling method;

[0180] S540, judging whether the sample is gypsum salt rock by observing the rock cuttings;

[0181] Specifically, the actual depth of the gypsum salt rock should be between the first depth and the second depth.

[0182] It should be noted that the warning depth is higher than the first depth. For example, if the first depth is 50 meters, 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 cuttings can be discovered in time.

[0183] After each forward advancement, a rock chip observation is required, during which the composition and some characteristics of the sample at that depth are judged.

[0184] Since the main components of carbonate rocks are calcium carbonate and magnesium carbonate, and the main components of gypsum-salt rocks are gypsum and halite, the changing trend of carbonate content in the stratum at that depth can be determined based on the carbonate content. Similarly, since halite is generally soluble in drilling fluid, halite can be observed in the cuttings only when the drilling fluid is saturated.

[0185] When executing the aforementioned embodiment, 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 Drilling Monitoring Table.

[0186] Table-1

[0187]

[0188] In an optional embodiment, the sub-layer geological data includes formation thickness, formation lithology and logging curve characteristics.

[0189] It should be noted that the formation thickness is used to determine the continuity and changes of the formation.

[0190] Stratigraphic lithology includes the type, composition and structure of rocks, which can reveal the differences in geological history and geological processes.

[0191] The characteristics of the logging curve reflect the physical characteristics of rocks at different depths to determine the distribution and changes of underground rocks.

[0192] like Figure 2 As shown, the present application also provides a system for predicting and identifying gypsum-salt rocks in complex piedmont zones by combining well-seismic analysis, including a data acquisition component 100, a data processing component 200 and an identification component 300. The data acquisition component 100 collects VSP data, geological data and seismic data. The data processing component 200 executes a method for predicting and identifying gypsum-salt rocks in complex piedmont zones by combining well-seismic analysis as described in any one of the claims in Example 1. The identification component 300 analyzes sample parameters.

[0193] It should be noted that the data acquisition component 100 collects VSP data, geological data and seismic data, and all the collected data are transmitted to the data processing component 200. The data is processed by the data processing component 200, and a velocity field model and an anisotropy field model are established. After the depth of the gypsum salt rock is predicted, the collected samples are identified by the identification component 300 to determine whether gypsum salt rock exists in the complex piedmont zone.

[0194] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited thereto, and various changes can be made within the knowledge scope of technicians in the relevant technical field without departing from the purpose of the present invention.

Claims

1. A method for predicting and identifying gypsum-salt rock in complex piedmont zones by combining well-seismic data, characterized in that: include: Collect VSP data, geological data and seismic data, and pre-process the collected initial data to obtain pre-processed data; Calculate the errors between geological data and seismic data based on preprocessed data, analyze the changes in sedimentary environment, determine the spatial lateral variation law, and establish anisotropic field model; 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; The depth of the gypsum-salt rock is predicted by the optimized velocity field model and the small-layer geological comparison respectively; the depth predicted by the optimized velocity field model is recorded as the first depth, and the depth predicted by the small-layer geological comparison is recorded as the second depth; Through the time-controlled drilling method, samples at the predicted depth are obtained and it is identified whether the samples are gypsum salt rock.

2. The method for predicting and identifying gypsum-salt rock in complex piedmont zones by combining well-seismic data according to claim 1 is characterized in that: Collect VSP data, geological data and seismic data, and pre-process the collected initial data to obtain pre-processed data, including: Create a basic data table; respectively collecting a plurality of VSP data, geological data and a plurality of seismic data, and putting 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; Randomly select an initial data from the basic data table; De-noising the seismic data to obtain pre-processed seismic data, and putting the pre-processed seismic data into a basic data table; Normalize the logging curves in the geological data to obtain pre-processed geological data, and put the pre-processed geological data into the basic data table; Return to randomly select an initial data from the basic data table until all the initial data in the basic data table are selected, and obtain preprocessed geological data and preprocessed seismic data.

3. The method for predicting and identifying gypsum-salt rock in complex piedmont zones by combining well-seismic data according to claim 2 is characterized in that: Based on the preprocessed data, the errors between geological data and seismic data are calculated, and the changes in the sedimentary environment are analyzed to determine the spatial lateral variation law and establish an anisotropic field model, including: Get a preprocessed geological data and preprocessed seismic data from the basic data table; Analyze the errors between the positive drilling layer and the seismic profile layer based on the pre-processed geological data and determine the error type; Obtain sedimentary environment characteristics based on preprocessed seismic data; Obtain the changing rules of lithology based on preprocessed geological data; Based on the variation law of lithologic bodies and the characteristics of sedimentary environment, the anisotropic field model is constructed by formula 1; Where C is the yield function of the anisotropic field, q is the average principal stress, q0 is the initial average principal stress, N is the slope of the critical state line, λ is the slope of the normal consolidation curve, κ is the slope of the rebound curve, is the plastic volume strain of soil.

4. The method for predicting and identifying gypsum-salt rock in complex piedmont zones by combining well-seismic data according to claim 3 is characterized in that: The velocity field model is established based on VSP data, and the parameters of the velocity field model are continuously adjusted to obtain the optimized velocity field model, including: Extracting initial depth migration velocities based on preprocessed seismic data; Obtain VSP velocity, and determine the lateral and longitudinal velocity patterns by combining the VSP velocity with the initial depth migration velocity; The velocity field model is constructed by model tomography based on the lateral velocity law and the longitudinal velocity law.

5. The method for predicting and identifying gypsum-salt rock in complex piedmont zones by combining well-seismic data according to claim 4 is characterized in that: Based on the lateral velocity law and the longitudinal velocity law, the velocity field model is constructed by the model tomography method, including: The angle of incidence between the source reflection wave and the ground is calculated by formula 2; in, is the travel time function of the surface observation in isotropic formations, P E is the exit point, β0 is the ray exit angle, v1 is the exit velocity corresponding to the exit point of the first layer, x s is the horizontal coordinate of the coordinate system, y s is the ordinate of the coordinate system; The radius of curvature of the imaginary wavefront is calculated by formula 3; Where R0 is the radius of curvature of the imaginary wavefront generated at the NIP and emitted at the CDP, t(0) is the two-way time of the normal reflection wave corresponding to the coincidence of the source and receiver at the CDP point, β0 is the ray exit angle, and v1 is the exit velocity corresponding to the first layer exit point; Substitute the data at the CDP point into Equation 3 to determine the wavefront curvature radius.

6. The method for predicting and identifying gypsum-salt rock in complex piedmont zones by combining well-seismic data according to claim 5 is characterized in that: The velocity field model is established based on the VSP data, and the parameters of the velocity field model are continuously adjusted to obtain the optimized velocity field model, which also includes: Calculate the Delta value and update the lateral velocity field and longitudinal velocity field using the Delta value; Fitting formation dips by quadratic polynomials; The residual time difference correction is performed on the CRP gathers by using formula 4 to determine the leveling of the far-track gathers; in, is the corrected CRP gather, G(t,x) is the original gather, Δm(t,x) is the correction function, t is time, and x is the channel number.

7. The method for predicting and identifying gypsum-salt rock in complex piedmont zones by combining well-seismic data according to claim 6, characterized in that: The depth of gypsum salt rock is predicted by optimizing the velocity field model and comparing the geological conditions of small layers, including: Predict the depth of gypsum salt rock through the optimized velocity field model; Obtaining small-layer geological data; The geological data of the small layer is compared with the pre-processed geological data, and the depth of the gypsum-salt rock to be measured is predicted based on the depth of the gypsum-salt rock of the pre-processed geological data.

8. The method for predicting and identifying gypsum-salt rock in complex piedmont zones by combining well-seismic data according to claim 7 is characterized in that: Through the time-controlled drilling method, samples at the predicted depth are obtained and whether the samples are gypsum salt rock is identified, including: Get a first depth and a second depth; setting a warning depth based on the first depth and the second depth; Obtain samples at the warning depth by time-controlled drilling method; Determine whether the sample is gypsum salt rock by observing the rock cuttings.

9. The method for predicting and identifying gypsum-salt rock in complex piedmont zones by combining well-seismic data according to claim 8, characterized in that: The sub-layer geological data include formation thickness, formation lithology and logging curve characteristics.

10. A gypsum-salt rock prediction and identification system combining well-seismic data in complex piedmont zones, characterized in that: include: A data acquisition component, through which VSP data, geological data and seismic data are collected; A data processing component, through which the method for predicting and identifying gypsum-salt rocks in complex piedmont zones by combining well-seismic analysis as described in any one of claims 1 to 9 is executed; and an identification component, through which sample parameters are analyzed.

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