Waveform-driven bedding diabase high-precision identification method and related equipment

Abstract

The invention discloses a waveform-driven bedding diabase high-precision identification method, which comprises the following steps of: screening seismic waveforms, obtaining a seismic medium-frequency impedance curve according to the seismic waveforms, screening well samples, obtaining a logging curve according to the well samples, processing the logging curve and analyzing sensitive attributes to obtain curve structure characteristics, establishing an initial model according to logging information, and identifying the bedding diabase according to the initial model. The initial model is corrected according to an earthquake intermediate-frequency impedance curve and curve structure characteristics, a probability result of existence of diabase is obtained, the initial model is inverted by utilizing waveform transverse difference driving logging high-frequency information, the influence of strong reflection and strong event of huge thick mudstone of a Santana group can be effectively avoided, bedding diabase on the roof surface of a workshop can be identified, and the probability result of existence of diabase is obtained. The transverse distribution range of diabase can be accurately predicted while the longitudinal resolution is improved, and the method has the incomparable advantages compared with a conventional inversion method.

Description

Technical Field

[0001] The invention belongs to the field of oil and gas exploration, and specifically relates to a waveform-driven high-precision identification method for bedding diabase and related equipment. Background Art

[0002] The oil and gas burial depth of a certain oil field is between 7,500 and 10,000 meters in the ultra-deep layer. The geological conditions of the formation are complex, and diabase is widely developed on the top surface of the Yijianfang Formation. Diabase has high rock brittleness, high strength and poor elastic-plastic mechanical characteristics. Microcracks can be seen under the microscope. The weak surface effect caused by it will reduce the overall strength of the diabase. In mild cases, the well wall will become unstable and fall off, causing the drill bit to wear. In severe cases, the well wall will collapse and backfill side drilling will occur. It costs a lot of money and time. At present, the diabase inside the Sangtamu Formation has strong reflection characteristics on the seismic profile, which can be clearly identified with high identification accuracy. The diabase on the top surface of a room has a large difference in wave impedance between the thick mudstone of the Sangtamu Formation and the top surface of a room, forming a strong reflection on the top surface of a room. Affected by its interference, the internal formation of the top surface of a room is a single peak reflection, and the diabase has no obvious seismic reflection characteristics. It fails to meet the expectations of accurate identification and early warning.

[0003] In summary, it is urgent to propose a new method and process to improve the accurate identification of bedding diabase and avoid risks. Summary of the invention

[0004] The present invention provides a waveform-driven high-precision identification method for bedding diabase to solve the problems existing in the background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A waveform-driven high-precision identification method for bedding diabase, comprising:

[0007] Screen seismic waveforms, analyze known wells based on seismic waveforms, screen well samples, and establish an initial model of the well samples;

[0008] According to the initial model of the well sample, the longitudinal wave impedance is statistically obtained as prior information;

[0009] Based on the initial model and the known seismic band impedance, the likelihood function is calculated;

[0010] The initial model was modified according to the prior information and likelihood function to obtain the probability result of the existence of diabase.

[0011] Preferably, the screening of well samples is specifically to select wells with similar medium and low frequency structures as spatial valuation samples in known wells using the dual variables of waveform similarity and spatial distance.

[0012] Preferably, the screening conditions for well samples also include a maximum spatial distance, and wells with a distance greater than the maximum spatial distance are not selected.

[0013] Preferably, the seismic waveform is selected from a densely distributed seismic waveform.

[0014] Preferably, the calculating of the likelihood function is specifically to perform matched filtering on the initial model and the seismic frequency band impedance, and then calculate the likelihood function.

[0015] Preferably, the initial model is corrected according to the seismic medium frequency impedance curve and the curve structure characteristics. Specifically, under the Bayesian framework, the seismic medium frequency impedance curve and the curve structure characteristics are combined to obtain the posterior probability distribution, and the posterior probability distribution is used to continuously perturb the model parameters, so that the solution when the posterior probability distribution function is maximum is used as the effective random realization, and the average of multiple effective realizations is taken as the expected value output.

[0016] Preferably, the formula for correcting the initial model based on prior information and likelihood function is:

[0017]

[0018] Among them, Z(x0) is the value of the unknown point; Z(x i ) is the value of the known sample point of the waveform; λ i is the weight of the i-th known sample point to the unknown sample point; n is the number of preferred sample points.

[0019] A waveform-driven high-precision identification system for bedding diabase, including

[0020] Model building module: used to screen seismic waveforms, analyze known wells based on seismic waveforms, screen well samples, and build an initial model for well samples;

[0021] Prior information acquisition module: used to screen well samples, obtain logging curves based on well samples, process logging curves and analyze sensitive attributes to obtain curve structural characteristics;

[0022] Likelihood function calculation module: used to calculate the likelihood function based on the initial model and the known seismic band impedance;

[0023] Diabase identification module: used to modify the initial model based on prior information and likelihood function to obtain the probability result of the existence of diabase.

[0024] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the waveform-driven high-precision identification method of bedding diabase are implemented.

[0025] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of a waveform-driven high-precision identification method for bedding diabase are implemented.

[0026] Compared with the prior art, the present invention has the following beneficial effects: the present invention provides a waveform-driven high-precision identification method for bedding diabase, which firstly screens the seismic waveform to obtain the seismic medium-frequency impedance curve, then screens the well samples, and obtains the structural characteristics of the logging curve after processing, and then establishes an initial model based on the logging data, and uses the lateral difference of the waveform to drive the high-frequency logging information to invert the initial model, which can effectively avoid the influence of the strong reflection and strong phase axis of the extremely thick mudstone of the Sangtamu Formation, identify the bedding diabase on the roof of a house, and accurately predict the lateral distribution range of the diabase while improving the vertical resolution, which has incomparable advantages over conventional inversion methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of a waveform-driven high-precision identification method for bedding diabase of the present invention;

[0028] Figure 2 This is a block diagram of a waveform-driven high-precision identification system for bedding diabase of the present invention;

[0029] Figure 3 This is a sensitivity curve attribute optimization diagram of an embodiment of the present invention;

[0030] Figure 4 The embodiment of the present invention effectively identifies the characteristic diagram of diabase;

[0031] Figure 5 The embodiment of the present invention effectively identifies the pinch-out diagram of diabase;

[0032] Figure 6 It is a plane diagram of abnormal impedance of the inversion body and a diagram of the actual drilling thickness of the bedding diabase in an embodiment of the present invention;

[0033] Figure 7 A schematic diagram of the module structure of an embodiment of the identification of bedding diabase provided by the present invention. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0035] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0037] In the description of the embodiments of the present invention, it should be noted that if the terms "upper", "lower", "horizontal", "inner", etc. indicate an orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the invention is usually placed when in use, it is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0038] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", which does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0039] In the description of the embodiments of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0040] like Figure 1 As shown, the present invention provides a waveform-driven high-precision identification method for bedding diabase, comprising:

[0041] S101 screens seismic waveforms, analyzes known wells based on the seismic waveforms, screens well samples, and establishes an initial model of the well samples;

[0042] S102 counts the longitudinal wave impedance of the well sample according to the initial model as prior information;

[0043] S103 calculates a likelihood function based on the initial model and known seismic band impedance;

[0044] S104 modifies the initial model according to the prior information and the likelihood function to obtain a probability result of the existence of diabase.

[0045] The screening well samples are specifically:

[0046] In known wells, waveform similarity and spatial distance are used as dual variables to select wells with similar low- and medium-frequency structures as spatial valuation samples.

[0047] The screening condition for well samples also includes a maximum spatial distance, and wells with a distance greater than the maximum spatial distance are not selected.

[0048] The seismic waveforms are selected to be densely distributed seismic waveforms.

[0049] The method of calculating the likelihood function specifically includes performing matching filtering on the initial model and the seismic frequency band impedance, and then calculating the likelihood function.

[0050] The initial model is modified according to the seismic medium-frequency impedance curve and the curve structure characteristics. Specifically, under the Bayesian framework, the posterior probability distribution is obtained by combining the seismic medium-frequency impedance curve and the curve structure characteristics, and the posterior probability distribution is used to continuously perturb the model parameters, so that the solution when the posterior probability distribution function is maximum is used as the effective random realization, and the average of multiple effective realizations is taken as the expected value output.

[0051] The formula for correcting the initial model based on prior information and likelihood function is:

[0052]

[0053] Among them, Z(x0) is the value of the unknown point; Z(x i ) is the value of the known sample point of the waveform; λ i is the weight of the i-th known sample point to the unknown sample point; n is the number of preferred sample points.

[0054] like Figure 2 As shown, the present invention also provides a waveform-driven high-precision identification system for bedding diabase, including

[0055] Model building module: used to screen seismic waveforms, analyze known wells based on seismic waveforms, screen well samples, and build an initial model for well samples;

[0056] Prior information acquisition module: used to screen well samples, obtain logging curves based on well samples, process logging curves and analyze sensitive attributes to obtain curve structural characteristics;

[0057] Likelihood function calculation module: used to calculate the likelihood function based on the initial model and the known seismic band impedance;

[0058] Diabase identification module: used to modify the initial model based on prior information and likelihood function to obtain the probability result of the existence of diabase.

[0059] Research process:

[0060] The purpose of the present invention is to use the waveform lateral difference to drive the high-frequency logging information for inversion on the basis of the existing seismic data, which can effectively avoid the influence of the strong reflection and strong phase axis of the thick mudstone of the Sangtamu Formation, identify the bedding diabase on the roof of a room, and accurately predict the lateral distribution range of the diabase while improving the vertical resolution. It has incomparable advantages over conventional inversion methods and provides a guarantee for the safe drilling of ultra-deep wells in the Fuman Oilfield.

[0061] The method and system for high-precision identification and prediction of bedding diabase guided by seismic guidance of the present invention are characterized by comprising the following steps (Attachment Figure 1 ):

[0062] Step 1: Analyze known wells according to seismic waveform characteristics, select well samples with high correlation with the waveform to be identified to establish an initial model, and count their longitudinal wave impedance as prior information. The traditional variogram is affected by the well location and it is difficult to accurately characterize the heterogeneity of the reservoir, while the densely distributed seismic waveform can accurately characterize the low-frequency changes in the spatial structure. Among the known wells, waveform similarity and spatial distance are used as dual variables to select wells with similar low-frequency structures as spatial valuation samples.

[0063] Step 2: Match the initial model with the seismic frequency band impedance and calculate the likelihood function. Perform multi-scale decomposition on the sample and gradually filter out the high-frequency components. The results show that when the waveform is similar, the closer the reservoir structure is to the low frequency, the stronger the certainty, and the closer it is to the high frequency, the stronger the randomness. Beyond the effective seismic frequency band, there is still a large deterministic component (basic configuration). Applying this rule can greatly increase the certainty of random inversion.

[0064] Step 3: Practice shows that the samples selected based on waveform indication have good spatial correlation. Therefore, the original data and spatial structure characteristics of the sample well are used to make linear unbiased and optimal estimates of unknown sample points. Under the support of Bayesian theory, the likelihood function distribution and prior distribution are combined to obtain the posterior probability distribution. The model parameters are continuously disturbed to make the solution when the posterior probability distribution function is maximized as an effective random realization, and the mean of multiple realizations is taken as the expected value output. That is, Where Z(x0) is the value of the unknown point; Z(x i ) is the value of the known sample point of the waveform; λ iis the weight of the i-th known sample point to the unknown sample point; n is the number of preferred sample points. Practice shows that the samples selected based on waveform indication have good spatial correlation, and can be unbiased and optimally estimated using Markov chain Monte Carlo random simulation to obtain expected and random solutions. Finally, by establishing the corresponding relationship between the drilling progress and the warning information, the risk warning results are obtained.

[0065] Invention application effect:

[0066] The invention has been promoted and applied in a certain oil field, and a total of 20 carbonate wells have been monitored, of which 15 were drilled into bedding intrusive rocks, 13 were identified, with an identification rate of 86.7% and an error of less than 10m. The results obtained by this invention reflect the constraints of sedimentary facies in space and are more in line with sedimentary laws and characteristics in plane: ① Effectively combining seismic, geological and logging information under the Bayesian framework, using seismic information to guide high-frequency simulation of well parameters, is a new way of combining wells and seismic, which effectively reduces the impact of seismic noise on inversion results; ② Using seismic waveform characteristics instead of variograms to analyze the spatial structure changes of the target layer, the lateral resolution is improved, and it is more in line with the plane geological laws and has phase control significance; ③ The global optimization algorithm is used to greatly enhance the inversion certainty (from completely random to step-by-step determination); ④ There are no strict requirements for the distribution of well locations, which is more applicable and improves the accuracy and reliability of drilling warning work.

[0067] Example:

[0068] Taking the early warning work of a well in a certain block of an oil field as an example (the target layers of carbonate rock drilling in this area are O2y, O1-2y, ∈), the risk of the layered diabase in this well is predicted by the method of the present invention. The process and results of this embodiment are as follows:

[0069] 1) First, the quality of seismic data was evaluated. The spectrum analysis was performed on the main target of a house roof. The results showed that the main frequency was 21Hz and the frequency band was in the range of 5-40Hz, which basically met the requirements of structural interpretation. In general, the event axis of the target layer is relatively continuous and clear, but the distribution of the bedding diabase cannot be distinguished. It is necessary to use waveform indication inversion technology to finely characterize the bedding diabase.

[0070] 2) Taking the top and bottom of the target layer as constraints, the acoustic time difference (AC), natural gamma (GR), and natural potential curve (SP) were statistically analyzed respectively. Only the GR curve has a relatively high sensitivity to diabase. Because the lowering of the conduit during the third opening has an impact on the logging results, it is finally found that the GR curve is sensitive to diabase and the curve is a low-amplitude box-shaped. Therefore, the GR waveform indication simulation can be used to predict the bedding diabase.

[0071] 3) Since the high-frequency part of the waveform indication inversion result is randomly inverted based on the structural sample, it is necessary to select a high-frequency cutoff frequency. As the optimal cutoff frequency increases, the resolution of the inversion result gradually increases, but the randomness of the profile also gradually increases. Through multiple tests, the optimal cutoff frequency suitable for this study area was found. By continuously debugging the above inversion parameters, the waveform indication simulation results based on the GR curve were finally obtained. In order to verify the reliability of the inversion results, a comparison was made with the GR curve of the drilled well that did not participate in the inversion ( Figure 4 ), it can be found that the bedding diabase layer predicted by inversion is in good agreement with the GR curve, and the pinch-out position verifies to a certain extent that the pinch-out position of the diabase inversion result is consistent with the actual drilling result ( Figure 5 ). The resolution on the profile is significantly improved, and the predicted intrusive rocks along the layers have good continuity, clear boundaries, and natural and reasonable lithology changes. On the plane, the root mean square amplitude attributes ( Figure 6 ).

[0072] 4) Based on the inversion results, the distribution range of the intrusive rocks in the well being drilled is predicted, and an early warning is made in the PPT to predict the range of the diabase ( Figure 4 ), the accuracy is greatly improved. On the other hand, the present invention also provides a device for identifying bedding diabase. Figure 7 This is a schematic diagram of the module structure of an embodiment of the identification of bedding diabase provided by the present invention, combined with the attached Figure 7 The device 60 may include: a seismic data analysis unit 61, a seismic waveform dynamic clustering analysis unit 62, a sample well curve multi-scale analysis unit 63, and a seismic waveform correction unit 64, wherein the seismic data analysis unit 61 evaluates the quality of the seismic data. The seismic waveform dynamic clustering analysis unit 62 establishes a mapping relationship between the seismic waveform and the well curve structural characteristics, and then establishes different sample sets. The sample well curve multi-scale analysis unit 63 determines the common structure in the statistical sample as the initial model. The seismic waveform correction unit 64 makes the inversion result conform to the medium-frequency seismic information and the well curve structural characteristics at the same time, and finally obtains a high-resolution waveform indication inversion result.

[0073] It can be seen that the technical solution of the method and device for high-precision identification of bedding diabase in the present invention has better overcome the shortcomings of the variation function with high requirements on well distribution and poor phase control. The thickness of the diabase in the study area is far lower than the resolution capability of earthquake. The random simulation technology of seismic waveform indication based on natural gamma is adopted to realize the quantitative characterization of bedding diabase. The post-test wells have confirmed the prediction effect of the method, which has better met the needs of safe production.

[0074] A terminal device is provided in one embodiment of the present invention. The terminal device of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned method embodiments are implemented. Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are implemented.

[0075] The computer program may be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to accomplish the present invention.

[0076] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0077] The processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0078] The memory may be used to store the computer program and / or module, and the processor implements various functions of the terminal device by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory.

[0079] If the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0080] Although the embodiments of the present invention are described above in conjunction with the accompanying drawings, the present invention is not limited to the above-mentioned specific embodiments and application fields, and the above-mentioned specific embodiments are only illustrative and instructive, rather than restrictive. Under the guidance of the specification, a person skilled in the art can also make many forms without departing from the scope of protection of the claims of the present invention, all of which belong to the scope of protection of the present invention.

Claims

1. A waveform-driven high-precision identification method for bedding diabase, characterized in that: include: Screen seismic waveforms, analyze known wells based on seismic waveforms, screen well samples, and establish an initial model of the well samples; According to the initial model of the well sample, the longitudinal wave impedance is statistically obtained as prior information; Based on the initial model and the known seismic band impedance, the likelihood function is calculated; The initial model was modified according to the prior information and likelihood function to obtain the probability result of the existence of diabase.

2. A waveform-driven high-precision identification method for bedding diabase according to claim 1, characterized in that: Specifically, the well sample screening method is to use the waveform similarity and spatial distance dual variables in the known wells to select wells with similar low- and medium-frequency structures as spatial valuation samples.

3. A waveform-driven high-precision identification method for bedding diabase according to claim 2, characterized in that: The screening conditions for well samples also include a maximum spatial distance, and wells with a distance greater than the maximum spatial distance are not selected.

4. A waveform-driven high-precision identification method for bedding diabase according to claim 1, characterized in that: The seismic waveforms are selected to be densely distributed seismic waveforms.

5. The waveform-driven high-precision identification method for bedding diabase according to claim 1 is characterized in that: The method of calculating the likelihood function specifically includes performing matching filtering on the initial model and the seismic frequency band impedance, and then calculating the likelihood function.

6. A waveform-driven high-precision identification method for bedding diabase according to claim 1, characterized in that: The initial model is modified according to the seismic medium-frequency impedance curve and the curve structure characteristics. Specifically, under the Bayesian framework, the posterior probability distribution is obtained by combining the seismic medium-frequency impedance curve and the curve structure characteristics, and the posterior probability distribution is used to continuously perturb the model parameters, so that the solution when the posterior probability distribution function is maximum is used as the effective random realization, and the average of multiple effective realizations is taken as the expected value output.

7. The waveform-driven high-precision identification method for bedding diabase according to claim 1 is characterized in that: The formula for correcting the initial model based on prior information and likelihood function is: Among them, Z(x0) is the value of the unknown point; Z(x i ) is the value of the known sample point of the waveform; λ i is the weight of the i-th known sample point to the unknown sample point; n is the number of preferred sample points.

8. A waveform-driven high-precision identification system for bedding diabase, characterized in that: include Model building module: used to screen seismic waveforms, analyze known wells based on seismic waveforms, screen well samples, and build an initial model for well samples; Prior information acquisition module: used to screen well samples, obtain logging curves based on well samples, process logging curves and analyze sensitive attributes to obtain curve structural characteristics; Likelihood function calculation module: used to calculate the likelihood function based on the initial model and the known seismic band impedance; Diabase identification module: used to modify the initial model based on prior information and likelihood function to obtain the probability result of the existence of diabase.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of a waveform-driven high-precision identification method for bedding diabase as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of a waveform-driven high-precision identification method for bedding diabase as described in any one of claims 1 to 7 are implemented.

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