Volcanic rock lithology prediction and identification method and device and storage medium

Abstract

The invention discloses a volcanic rock lithology prediction and identification method and device and a storage medium, and relates to the technical field of oil-gas exploration, and the method is characterized in that the method comprises the steps: obtaining the seismic data of a target well, and the logging data and lithology logging data of a known drilling well around the target well; analyzing according to logging data and the lithologic logging data, and determining a plurality of lithologic sensitive logging curves; performing well-seismic calibration by using the logging data and the seismic data so as to project the target well on a seismic section; performing waveform difference inversion on the lithologic sensitive logging curve based on a well-to-seismic calibration result to obtain waveform difference inversion bodies of a plurality of sensitive logging curves; and analyzing and identifying the plurality of waveform difference inversion bodies, and predicting the distribution result of each lithology of the volcanic rock. Through the prediction identification method, the volcanic rocks can be accurately predicted before well drilling, the lithology of the volcanic rocks can be accurately distinguished, the probability of occurrence of well drilling accidents in oil-gas exploration and development is reduced, and the manpower and material resource cost is saved.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration, and in particular to a method, a device and a storage medium for predicting and identifying volcanic rock lithology. Background Art

[0002] This section is intended to provide a background or context to the embodiments recited in the claims. No admission is made that the description herein is prior art by inclusion in this section.

[0003] As a special type of gas reservoir, volcanic gas reservoirs have gradually become an important exploration target and a growth point for oil and gas reserves in China. Due to the rapid changes in lithology and lithofacies of volcanic reservoirs and the complex controlling factors of oil and gas accumulation, the identification of favorable reservoir lithofacies of volcanic rocks has become a research problem at present.

[0004] The existing methods of identifying volcanic rocks by geophysical means mainly include: logging response characteristics, seismic response characteristics, seismic attributes and seismic inversion. Due to the different rock composition, porosity and microfracture development of different volcanic rocks, different logging curve responses are also different. Usually, by analyzing the seismic attributes of the volcanic rock formations that need to be drilled and developed, a variety of seismic attributes that are sensitive to the lithology of the volcanic rocks are obtained, such as amplitude attributes, coherence attributes, sweet spot attributes, etc.; thus, based on the seismic attributes and logging curves that are sensitive to the lithology of the volcanic rocks, and then through seismic inversion methods, a comprehensive pre-drilling prediction of the volcanic rocks is achieved. However, the accuracy of seismic prediction is poor, and it is difficult to distinguish volcanic rock geological bodies of several meters or more than ten meters, resulting in most of the prediction and identification results remaining at the distinction between volcanic rocks and sedimentary rocks, lacking the prediction and identification of volcanic rock types, further resulting in insufficient identification of the lithology of volcanic rocks, which can easily lead to drilling and development accidents. Summary of the invention

[0005] In response to the above technical problems, the present invention proposes a method, device and storage medium for predicting and identifying volcanic rock lithology, which can accurately predict volcanic rocks before drilling and accurately distinguish the lithology of volcanic rocks, reduce the probability of drilling accidents in oil and gas exploration and development, and save manpower and material costs.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention includes four aspects.

[0007] In a first aspect, a method for predicting and identifying lithology of volcanic rocks is provided, comprising the following steps:

[0008] Obtain seismic data of the target well and well logging data and lithology logging data of known wells around the target well;

[0009] Analyze the well logging data and the lithology logging data to determine a plurality of lithology-sensitive logging curves that are sensitive to lithology;

[0010] Using the well logging data and seismic data to perform well-seismic calibration, so as to project the target well onto the seismic section;

[0011] Based on the well seismic calibration result, waveform difference inversion is performed on the lithology sensitive logging curve to obtain waveform difference inversion bodies of multiple sensitive logging curves;

[0012] The plurality of waveform difference inversion bodies are analyzed and identified to predict the distribution results of various lithologies of the volcanic rock.

[0013] In some embodiments, the analyzing and identifying the plurality of waveform difference inversion bodies to predict the distribution results of various lithologies of volcanic rocks includes:

[0014] sorting the plurality of waveform difference inversion bodies according to the magnitude of the inversion values ​​of the respective lithologies;

[0015] The distribution of various lithologies of volcanic rocks is predicted based on the sorting results.

[0016] In some embodiments, the analyzing and identifying the plurality of waveform difference inversion bodies to predict the distribution results of various lithologies of volcanic rocks includes:

[0017] The waveform difference inversion body is fused by RGB fusion technology to obtain an inversion attribute fusion map;

[0018] The distribution results of various lithologies of volcanic rocks are predicted according to the inversion attribute fusion map.

[0019] In some embodiments, the lithology sensitive logging curve includes a gamma curve, an acoustic time difference curve and a wave impedance curve; fusing the waveform difference inversion body through RGB fusion technology to obtain an inversion attribute fusion map includes:

[0020] The inversion value of the gamma curve is set to R, the inversion value of the wave impedance curve is set to G, and the inversion value of the acoustic time difference curve is set to B;

[0021] Assign corresponding RGB color values ​​to each point of multiple lithology-sensitive logging curves that are sensitive to lithology;

[0022] Based on the RGB color values ​​of multiple lithology-sensitive logging curves, the three colors of R, G, and B are fused in different proportions to obtain the inversion attribute fusion map.

[0023] In some embodiments, the analyzing the well logging data and the lithology logging data to determine a plurality of lithology-sensitive logging curves that are sensitive to lithology includes:

[0024] Establishing a cross plot of logging curves and lithology and a histogram according to the logging data and lithology logging data;

[0025] According to the crossplot and the histogram, a lithology-sensitive logging curve that is sensitive to lithology is selected.

[0026] In some embodiments, establishing a cross plot of well logging curves and lithology and a histogram based on the well logging data and lithology logging data includes:

[0027] The well logging data and lithology logging data are standardized and normalized to obtain a standardized well logging curve;

[0028] A cross plot of the well logging curve and lithology and a histogram are established according to the well logging curve.

[0029] In some embodiments, selecting a lithology-sensitive logging curve that is sensitive to lithology according to the crossplot and the histogram includes:

[0030] According to the cross plot and the histogram, a gamma curve is established as a lithology sensitive logging curve;

[0031] According to the cross-plot and the histogram, an acoustic time difference curve is established as a lithology sensitive logging curve;

[0032] According to the cross plot and the histogram, a wave impedance curve is established as a lithology sensitive logging curve.

[0033] In some embodiments, the waveform difference inversion is performed on the sensitivity curves of various lithologies to obtain waveform difference inversion bodies of various sensitivity curves, including:

[0034] Determine the relationship between lithology and sensitivity curves based on sensitivity curves of various lithologies;

[0035] Determine the relationship between the sensitivity curve and the seismic waveform according to the seismic data and the lithology sensitivity curve;

[0036] According to the relationship between the lithology and the sensitivity curve and the relationship between the sensitivity curve and the seismic waveform, a relationship between the seismic waveform and the lithology is established;

[0037] According to the relationship between seismic waveform and lithology, the waveform difference inversion volume is determined.

[0038] In a second aspect, a volcanic rock lithology prediction and identification device is provided, comprising:

[0039] An acquisition unit, used to acquire seismic data of a target well and well logging data and lithology logging data of known wells around the target well;

[0040] A first processing unit is used to analyze the well logging data and the lithology logging data to determine a plurality of lithology-sensitive logging curves that are sensitive to lithology;

[0041] A second processing unit is used to perform well-seismic calibration using the well logging data and seismic data to project the target well on the seismic section;

[0042] An inversion unit, used for performing waveform difference inversion on the lithology sensitive logging curve based on the well seismic calibration result to obtain waveform difference inversion bodies of multiple sensitive logging curves;

[0043] The identification and prediction unit is used to analyze and identify the plurality of waveform difference inversion bodies and predict the distribution results of various lithologies of the volcanic rocks.

[0044] In a third aspect, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the aforementioned prediction and recognition method when executing the computer program.

[0045] In a fourth aspect, a computer-readable storage medium is provided, and when the computer program is executed by a processor, the steps of the aforementioned prediction and recognition method are implemented.

[0046] Compared with the prior art, one or more embodiments of the above scheme may have the following advantages or beneficial effects:

[0047] The present application provides a method, device, equipment and storage medium for predicting and identifying volcanic rock lithology. The prediction and identification method comprises the following steps: acquiring seismic data of a target well and well logging data and lithology logging data of known wells around the target well; analyzing the well logging data and the lithology logging data to determine a plurality of lithology-sensitive logging curves that are sensitive to lithology; performing well-seismic calibration using the well logging data and seismic data to project the target well on a seismic profile; performing waveform difference inversion on the lithology-sensitive logging curve based on the well-seismic calibration result to obtain waveform difference inversion bodies of a plurality of sensitive logging curves; analyzing and identifying a plurality of the waveform difference inversion bodies to predict the distribution results of various lithologies of the volcanic rock. Through this prediction and identification method, by integrating the logging and seismic prediction methods, the drilled wells are used to constrain the sensitive logging parameters of the undrilled area, and at the same time, the distribution of the volcanic rock body is predicted before the target well is drilled through the difference in seismic waveforms, and the lithology of the volcanic rock is predicted and identified, so as to predict the risk sections prone to accidents in the project before drilling, make early predictions, reduce the probability of drilling accidents in oil and gas exploration and development, and save manpower and material costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Hereinafter, the present application will be described in more detail based on embodiments and with reference to the accompanying drawings;

[0049] Figure 1 A schematic diagram of a flow chart of a method for predicting and identifying volcanic rock lithology provided in an embodiment of the present invention;

[0050] Figure 2 In the embodiment of the present invention, Figure 1 An exemplary flow chart of step S2 shown in;

[0051] Figure 3 In the embodiment of the present invention, Figure 2 An exemplary flow chart of step S21 shown in FIG.

[0052] Figure 4 In the embodiment of the present invention, Figure 2 An exemplary flow chart of step S22 shown in FIG.

[0053] Figure 5 In the embodiment of the present invention, Figure 1 An exemplary flow chart of step S4 shown in;

[0054] Figure 6 In the embodiment of the present invention, Figure 1 An exemplary flow chart of step S5 shown in;

[0055] Figure 7 In the embodiment of the present invention, Figure 1 An exemplary flow chart of another embodiment of step S5 shown in FIG.

[0056] Figure 8 In the embodiment of the present invention, Figure 7 An exemplary flow chart of step S53 shown in FIG.

[0057] Fig. 9 It is a schematic diagram of an exemplary inversion attribute fusion diagram after waveform difference inversion is performed in an embodiment of the present invention;

[0058] Fig.10 A schematic diagram of the connection of a device for predicting and identifying lithology of volcanic rocks provided in an embodiment of the present invention;

[0059] Fig.11 A schematic block diagram of an electronic device provided in an embodiment of the invention;

[0060] Fig.12 A schematic diagram of a computer-readable storage medium provided in an embodiment of the invention.

[0061] In the drawings, the same reference numerals are used for the same components, and the drawings are not drawn to scale. DETAILED DESCRIPTION

[0062] The present disclosure is further described below in conjunction with the embodiments shown in the accompanying drawings.

[0063] Embodiment 1:

[0064] The present application embodiment discloses a method for predicting and identifying volcanic rock lithology, such as Figure 1 As shown, the following steps are included: obtaining the seismic data of the target well and the logging data and lithology logging data of known wells around the target well; analyzing the logging data and the lithology logging data to determine multiple lithology-sensitive logging curves that are sensitive to lithology; using the logging data and seismic data to perform well-seismic calibration to project the target well on the seismic profile; performing waveform difference inversion on the lithology-sensitive logging curve based on the well-seismic calibration result to obtain waveform difference inversion bodies of multiple sensitive logging curves; analyzing and identifying multiple waveform difference inversion bodies to predict the distribution results of various lithologies of volcanic rocks. Through this prediction and identification method, volcanic rocks can be accurately predicted before drilling, and the lithology of volcanic rocks can be accurately distinguished, reducing the probability of drilling accidents in oil and gas exploration and development, and saving manpower and material costs.

[0065] At least one embodiment of the present disclosure provides a method for predicting and identifying lithology of volcanic rocks. The method can be implemented in the form of software, hardware, firmware or any combination thereof, and loaded and executed by a processor in a device such as a mobile phone, a tablet computer, a laptop computer, a desktop computer, a network server, etc., so as to accurately predict volcanic rocks before drilling and accurately distinguish the lithology of volcanic rocks, thereby reducing the probability of drilling accidents in oil and gas exploration and development.

[0066] Reference below Figure 1 As shown, a method for predicting and identifying volcanic rock lithology provided by at least one embodiment of the present disclosure is described. The method for predicting and identifying volcanic rock lithology includes steps S1 to S5.

[0067] S1. Obtain the seismic data of the target well and the well logging data and lithology logging data of known wells around the target well.

[0068] In some embodiments, the logging data is derived from some recorded data generated during the drilling process, such as time, drill bit pressure, drill bit speed, mud density, etc.; the lithology logging data is the measurement and recording of rock debris, rock lithology, and rock fluid brought out during the drilling process. Specifically, the logging data includes gamma logging curves, sonic logging curves, and wave impedance curves.

[0069] In some embodiments, the lithology of volcanic rocks includes dacite, tuff, basalt, sandstone, mudstone, limestone, etc. The prediction and identification method provided by at least one embodiment of the present disclosure can clearly distinguish basalt, dacite, tuff and sedimentary rock. In the drilling engineering of oil and gas exploration and development, the well wall of the basalt section is prone to collapse, the mud of the dacite section is prone to leakage, and the sedimentary rock and tuff are not prone to engineering accidents. Therefore, the present invention can predict the risk section prone to accidents in the engineering before drilling, so as to achieve early prediction, reduce the probability of drilling accidents, and reduce drilling time and cost.

[0070] S2. Analyze the well logging data and the lithology logging data to determine a plurality of lithology-sensitive logging curves that are sensitive to lithology.

[0071] In some embodiments, step S2, such as Figure 2 As shown, including:

[0072] S21, establishing a cross plot of well logging curves and lithology and a histogram according to the well logging data and lithology logging data.

[0073] In some embodiments, step S21, such as Figure 3 As shown, specifically including:

[0074] S211, performing standardization and normalization correction on the well logging data and lithology logging data to obtain a standardized well logging curve;

[0075] S212: establishing a cross plot and a histogram of the well logging curve and lithology according to the well logging curve.

[0076] In some specific embodiments, for the sensitivity curves of different lithologies, after the logging curves are standardized and normalized, the outliers are eliminated, and a gamma (hereinafter referred to as GR)-acoustic time difference (hereinafter referred to as AC)-lithology cross-plot is established. In the cross-plot, different lithologies of volcanic rocks are presented in different colors, so that the gamma values ​​and acoustic time difference values ​​of different lithologies of volcanic rocks can be easily obtained through the cross-plot, so as to determine the sensitive logging curves corresponding to each lithology.

[0077] Furthermore, a histogram of wave impedance curve (hereinafter referred to as AI) and lithology was produced. In the histogram, different lithologies of volcanic rocks were also presented in different colors, so that the wave impedance values ​​of different lithologies can be obtained according to the histogram, so as to determine the sensitive logging curves corresponding to each lithology.

[0078] S22. According to the crossplot and the histogram, a lithology-sensitive logging curve that is sensitive to lithology is selected.

[0079] In some embodiments, step S22, such as Figure 4 As shown, including:

[0080] S221, establishing a gamma curve as a lithology sensitive logging curve according to the cross plot and the histogram;

[0081] S222, establishing an acoustic time difference curve as a lithology sensitive logging curve according to the crossplot and the histogram;

[0082] S223. According to the cross plot and the histogram, a wave impedance curve is established as a lithology sensitive logging curve.

[0083] In some embodiments, when the following situations are known through the cross plot and histogram, that is, when dacite and tuff correspond to high GR, and basalt corresponds to low GR, and the AC values ​​of dacite, tuff and basalt are low; the AI ​​values ​​of sandstone, mudstone, siltstone and sedimentary rock are low, and the AI ​​values ​​of dacite, basalt, tuff and volcanic rock are high, the gamma curve, the acoustic time difference curve and the wave impedance curve are used as lithology sensitive logging curves. Therefore, in this embodiment, the GR, AC and AI curves are determined as lithology sensitive logging curves.

[0084] S3. Perform well-seismic calibration using the well logging data and seismic data to project the target well onto the seismic profile.

[0085] S4. Performing waveform difference inversion on the lithology sensitive logging curve based on the well seismic calibration result to obtain waveform difference inversion volumes of multiple sensitive logging curves.

[0086] In some embodiments, step S4, such as Figure 5 As shown, including:

[0087] S41. Determine the relationship between lithology and sensitivity curve based on the sensitivity curves of various lithologies;

[0088] S42, determining the relationship between the sensitivity curve and the seismic waveform according to the seismic data and the lithology sensitivity curve;

[0089] S43, establishing a relationship between the seismic waveform and the lithology according to the relationship between the lithology and the sensitivity curve and the relationship between the sensitivity curve and the seismic waveform;

[0090] S44. Determine the waveform difference inversion volume based on the relationship between the seismic waveform and the lithology.

[0091] In some embodiments, the propagation process of seismic wavelets in geological bodies is equivalent to a filtering process. In different strata or multiphase media in the same stratum, due to different stratum media, different pore sizes, and different phases of materials contained in the pores, seismic waves propagate in the stratum, and the attenuation degree of their amplitude is different. Conversely, the different media in the stratum can be predicted by the difference in the change of the amplitude of the seismic wave at different positions. Therefore, we first identify the relationship between lithology and sensitivity curves and the relationship between sensitivity curves and seismic waveforms, and then establish the relationship between seismic waveforms and lithology, so as to predict and classify volcanic rocks.

[0092] S5. Analyze and identify the plurality of waveform difference inversion bodies to predict the distribution results of various lithologies of the volcanic rock.

[0093] In some embodiments, step S5, such as Figure 6 As shown, including:

[0094] S51, sorting the plurality of waveform difference inversion bodies according to the magnitude of the inversion values ​​of the respective lithologies;

[0095] S52. Predict the distribution results of various lithologies of volcanic rocks based on the sorting results.

[0096] In some embodiments, after waveform difference inversion is performed on the GR, AC and AI curves, a waveform difference inversion map of the GR curve, a waveform difference inversion map of the AC curve and a waveform difference inversion map of the AI ​​curve are obtained respectively. According to the waveform difference inversion map of the GR curve, the waveform difference inversion map of the C curve and the waveform difference inversion map of the AI ​​curve, the GR value, AC value and AI value of different lithologies of volcanic rocks can be obtained respectively, and after further sorting the GR value, AC value and AI value of each lithology, the distribution of different lithologies of volcanic rocks in the target area can be analyzed, and the distribution of different lithologies of volcanic rocks can be predicted and identified, so as to predict the risk section prone to accidents in the project before the drilling project, make early predictions and reduce the probability of drilling accidents.

[0097] In some embodiments, step S5, such as Figure 7 As shown, including:

[0098] S53, fusing the waveform difference inversion volume through RGB fusion technology to obtain an inversion attribute fusion map;

[0099] In some embodiments, RGB attribute fusion processing is performed based on the inversion, step S53, such as Figure 8 As shown, including:

[0100] S531, setting the inversion value of the gamma curve to R, the inversion value of the wave impedance curve to G, and the inversion value of the acoustic wave time difference curve to B;

[0101] S532, assigning corresponding RGB color values ​​to each point of a plurality of lithology-sensitive logging curves that are sensitive to lithology;

[0102] S533. Based on the RGB color values ​​of multiple lithology-sensitive logging curves, the three colors of R, G, and B are fused in different proportions to obtain an inversion attribute fusion map.

[0103] S54, predicting the distribution results of various lithologies of volcanic rocks according to the inversion attribute fusion map.

[0104] In some embodiments, it can be seen from the inversion attribute fusion map that different lithologies of volcanic rocks correspond to different colors, so that the lithologies can be finely distinguished in the same map, without the need to analyze the inversion conditions of multiple logging curves sensitive to different lithologies one by one. The lithology of volcanic rocks can be predicted and identified more conveniently, efficiently and accurately, and accurate distinction can be made to meet the requirements of engineering construction, predict the risk sections prone to accidents in the project in advance, avoid risks in advance, reduce the probability of drilling accidents, and reduce drilling time and costs.

[0105] In a specific embodiment, if Fig. 9 As shown in the figure, an inversion attribute fusion diagram of the inversion results of the target well area is given as an example. From the inversion attribute fusion diagram, it can be seen that the distribution of basalt, dacite and sedimentary rocks, and tuff are difficult to judge. For drilling engineering, such a distinction is sufficient to meet engineering needs. During the drilling process, the well wall of the basalt section is prone to collapse, and the mud in the dacite section is prone to leakage, while sedimentary rocks and tuff are not prone to accidents. Therefore, in the drilling project, the risk sections prone to accidents can be predicted in advance, and risks can be avoided in advance, reducing the probability of drilling accidents, and reducing drilling time and costs.

[0106] The volcanic rock lithology prediction and identification method provided by the embodiment of the present disclosure integrates the logging and seismic prediction methods, uses the drilled wells to constrain the sensitive logging parameters of the undrilled area, and predicts the distribution of the volcanic rock body before drilling the target well through the difference in seismic waveforms, and predicts and identifies the volcanic rock lithology, rather than simply separating the volcanic rock from the surrounding sedimentary rock, and actually distinguishes the basalt, dacite and sedimentary rock in the volcanic rock. In the drilling engineering of oil and gas exploration and development, the well wall of the basalt section is prone to collapse, the mud of the dacite section is prone to leakage, and the sedimentary rock and tuff are not prone to engineering accidents. Therefore, the present invention can predict the risk section prone to accidents in the engineering before drilling, so as to achieve early prediction, reduce the probability of drilling accidents, and reduce drilling time and cost.

[0107] Embodiment 2:

[0108] At least one embodiment of the present disclosure further provides a volcanic rock lithology prediction and identification device, wherein each unit in the device corresponds to the steps in the above method, and the effects achieved and the problems solved have been described above and will not be repeated here. Fig.10 As shown, including:

[0109] An acquisition unit 11 is used to acquire seismic data of a target well and well logging data and lithology logging data of known wells around the target well;

[0110] A first processing unit 12 is used to analyze the well logging data and the lithology logging data to determine a plurality of lithology-sensitive logging curves that are sensitive to lithology;

[0111] A second processing unit 13 is used to perform well-seismic calibration using the well logging data and seismic data to project the target well on the seismic section;

[0112] An inversion unit 14 is used to perform waveform difference inversion on the lithology sensitive logging curve based on the well seismic calibration result to obtain waveform difference inversion bodies of multiple sensitive logging curves;

[0113] The identification and prediction unit 15 is used to analyze and identify the plurality of waveform difference inversion bodies, and predict the distribution results of various lithologies of the volcanic rocks.

[0114] In some embodiments, the output end of the acquisition unit 11 is connected to the input ends of the first processing unit 12 and the second processing unit 13, the output end of the first processing unit 12 is connected to the first input end of the inversion unit 14; the output end of the second processing unit 13 is connected to the second input end of the inversion unit 14, and the output end of the inversion unit 14 is connected to the input end of the recognition prediction unit 15. The output end of the recognition prediction unit 15 can be connected to a display device (such as a display) to present the prediction result to the staff through the screen.

[0115] In some embodiments, the device may be a part of the software, or may be implemented in combination with corresponding hardware, which will not be described in detail here.

[0116] Embodiment three:

[0117] At least some embodiments of the present disclosure also provide an electronic device, such as Fig.11 As shown, the electronic device includes a memory 21 and a processor 22, wherein the memory 21 stores a computer program, and when the computer program is executed by the processor, the steps of the prediction and recognition method provided in any embodiment of the present disclosure are performed.

[0118] In some embodiments, the processor 22 is used to execute all or part of the steps in the prediction and recognition method of any embodiment of the present disclosure. The memory 21 is used to store various types of data, which may include instructions of any application or method in the electronic device, and data related to the application.

[0119] The processor 22 can be an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components, and is used to execute the prediction and recognition method in the above-mentioned embodiment 1.

[0120] The memory 21 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0121] Embodiment 4:

[0122] At least some embodiments of the present disclosure also provide a computer-readable storage medium, such as Fig.12 As shown, the readable storage medium stores a computer program 31, and when the computer program 31 is executed by a processor, the steps of the prediction and recognition method provided by any embodiment of the present disclosure are implemented.

[0123] In some embodiments, the storage medium can be a tangible medium that can contain or store a program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the above. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0124] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0125] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0126] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0127] An embodiment of the present invention further provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the prediction and recognition method provided in any embodiment of the present disclosure.

[0128] In summary, the present application provides a method, device and storage medium for predicting and identifying the lithology of volcanic rocks, which integrates the methods of well logging and seismic prediction, and uses the drilled wells to constrain the sensitive logging parameters of the undrilled areas. At the same time, Yuci predicts the distribution of the volcanic rock body before drilling the target well through the difference in seismic waveforms, and predicts and identifies the lithology of the volcanic rock, rather than simply separating the volcanic rock from the surrounding sedimentary rock, and actually distinguishes the basalt, dacite and sedimentary rock in the volcanic rock. In the drilling engineering of oil and gas exploration and development, the well wall of the basalt section is prone to collapse, the mud of the dacite section is prone to leakage, and the sedimentary rock and tuff are not prone to engineering accidents. Therefore, the present invention can predict the risk section prone to accidents in the engineering before drilling, so as to achieve early prediction, reduce the probability of drilling accidents, and reduce drilling time and cost.

[0129] The various embodiments in the present disclosure are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0130] The protection scope of the present disclosure is not limited to the above-mentioned embodiments. Obviously, those skilled in the art can make various changes and modifications to the present disclosure without departing from the scope and spirit of the present disclosure. If these changes and modifications fall within the scope of the claims of the present disclosure and their equivalents, the intention of the present disclosure also includes these changes and modifications.

Claims

1. A method for predicting and identifying volcanic rock lithology, characterized in that: The following steps are involved: Obtain seismic data of the target well area and well logging data and lithology logging data of known wells around the target well; Analyze the well logging data and the lithology logging data to determine a plurality of lithology-sensitive logging curves that are sensitive to lithology; Using the well logging data and seismic data to perform well-seismic calibration, so as to project the target well onto the seismic section; Based on the well seismic calibration result, waveform difference inversion is performed on the lithology sensitive logging curve to obtain waveform difference inversion bodies of multiple sensitive logging curves; The plurality of waveform difference inversion bodies are analyzed and identified to predict the distribution results of various lithologies of the volcanic rock.

2. A method for predicting and identifying volcanic rock lithology according to claim 1, characterized in that: The analyzing and identifying the plurality of waveform difference inversion bodies to predict the distribution results of various lithologies of volcanic rocks includes: sorting the plurality of waveform difference inversion bodies according to the magnitude of the inversion values ​​of the respective lithologies; The distribution of various lithologies of volcanic rocks is predicted based on the sorting results.

3. A method for predicting and identifying volcanic rock lithology according to claim 1, characterized in that: The analyzing and identifying the plurality of waveform difference inversion bodies to predict the distribution results of various lithologies of volcanic rocks includes: The waveform difference inversion body is fused by RGB fusion technology to obtain an inversion attribute fusion map; The distribution results of various lithologies of volcanic rocks are predicted according to the inversion attribute fusion map.

4. A method for predicting and identifying volcanic rock lithology according to claim 3, characterized in that: The lithology sensitive logging curve includes a gamma curve, an acoustic time difference curve and a wave impedance curve; the waveform difference inversion body is fused by RGB fusion technology to obtain an inversion attribute fusion map including: The inversion value of the gamma curve is set to R, the inversion value of the wave impedance curve is set to G, and the inversion value of the acoustic time difference curve is set to B; Assign corresponding RGB color values ​​to each point of multiple lithology-sensitive logging curves that are sensitive to lithology; Based on the RGB color values ​​of multiple lithology-sensitive logging curves, the three colors of R, G, and B are fused in different proportions to obtain the inversion attribute fusion map.

5. A method for predicting and identifying volcanic rock lithology according to claim 1, characterized in that: The step of analyzing the well logging data and the lithology logging data to determine a plurality of lithology-sensitive well logging curves that are sensitive to lithology includes: Establishing a cross plot of logging curves and lithology and a histogram according to the logging data and lithology logging data; According to the crossplot and the histogram, a lithology-sensitive logging curve that is sensitive to lithology is selected.

6. A method for predicting and identifying volcanic rock lithology according to claim 5, characterized in that: The step of establishing a cross plot of well logging curves and lithology and a histogram based on the well logging data and lithology logging data includes: The well logging data and lithology logging data are standardized and normalized to obtain a standardized well logging curve; A cross plot of the well logging curve and lithology and a histogram are established according to the well logging curve.

7. A method for predicting and identifying volcanic rock lithology according to claim 5, characterized in that: According to the cross plot and the histogram, a lithology-sensitive logging curve that is sensitive to lithology is selected, including: According to the cross plot and the histogram, a gamma curve is established as a lithology sensitive logging curve; According to the crossplot and the histogram, an acoustic time difference curve is established as a lithology sensitive logging curve; According to the cross plot and the histogram, a wave impedance curve is established as a lithology sensitive logging curve.

8. A method for predicting and identifying volcanic rock lithology according to claim 1, characterized in that: The waveform difference inversion is performed on the sensitivity curves of various lithologies to obtain waveform difference inversion bodies of various sensitivity curves, including: Determine the relationship between lithology and sensitivity curves based on sensitivity curves of various lithologies; Determine the relationship between the sensitivity curve and the seismic waveform according to the seismic data and the lithology sensitivity curve; According to the relationship between the lithology and the sensitivity curve and the relationship between the sensitivity curve and the seismic waveform, a relationship between the seismic waveform and the lithology is established; According to the relationship between seismic waveform and lithology, the waveform difference inversion volume is determined.

9. A device for predicting and identifying volcanic rock lithology, characterized in that: include: An acquisition unit, used to acquire seismic data of a target well and well logging data and lithology logging data of known wells around the target well; A first processing unit analyzes the well logging data and the lithology logging data to determine a plurality of lithology-sensitive logging curves that are sensitive to lithology; A second processing unit performs well-seismic calibration using the well logging data and seismic data to project the target well onto the seismic section; An inversion unit performs waveform difference inversion on the lithology sensitive logging curve based on the well seismic calibration result to obtain waveform difference inversion bodies of multiple sensitive logging curves; The identification and prediction unit is used to analyze and identify the plurality of waveform difference inversion bodies and predict the distribution results of various lithologies of the volcanic rocks.

10. An electronic 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 the prediction and recognition method according to any one of claims 1 to 8 are implemented.

11. A computer-readable storage medium, characterized in that: When the computer program is executed by a processor, the steps of the prediction and recognition method according to any one of claims 1 to 8 are implemented.

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