Rock facies prediction method and apparatus based on indicator curves

Abstract

The application provides a lithofacies prediction method and device based on an indicator curve. According to the application, the effective combination of seismic data information and logging data information is realized by using the indicator curve, the accuracy and delicacy of lithofacies prediction are significantly improved, the prediction result has clear geological significance, the actual lithofacies distribution of volcanic rocks can be reflected, the prediction of the three-dimensional space distribution rule of lithofacies, especially the lithofacies of volcanic rocks, is realized, and the volcanic rock exploration has important value.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geophysical exploration, and more particularly to a lithofacies prediction method and device based on an indicator curve. BACKGROUND

[0002] According to statistical analysis of a large number of researchers, it is found that the volcanic rock reservoir is obviously controlled by lithology, and therefore, volcanic rock lithofacies prediction is very important. The volcanic rock has strong heterogeneity, and in particular, the volcanic rock lithofacies belt changes very quickly. This brings great challenges to accurately predict the spatial distribution of the volcanic rock lithofacies. At present, the lithofacies prediction mainly includes two ideas based on post-stack seismic attributes and well logging data interpretation, but both methods have certain defects and deficiencies.

[0003] The lithofacies prediction based on post-stack seismic attributes mainly relies on extracting a plurality of post-stack seismic attributes reflecting different lithofacies and performing attribute fusion to identify the lithofacies, and the seismic facies formed by the seismic attributes is not the real lithofacies. However, the difference in seismic response characteristics of different lithofacies is usually not very large, and it is difficult to effectively distinguish from the seismic attributes. In addition, the resolution limit of the seismic method cannot reflect the rapid change characteristics of the lithofacies in detail. These factors all lead to the fact that it is difficult to clearly and accurately identify and depict the change of the volcanic rock facies belt only by relying on seismic data.

[0004] And the method of relying on well logging data for lithofacies interpretation is mainly to identify various well logging curves that can reflect the characteristics of different lithofacies, such as acoustic, density, electrical property, etc., to complete the lithofacies division in the well section. However, this method is limited in the well section range and is difficult to extend horizontally to realize regional prediction. In addition, in some cases, the indicating effect of the well logging curve characteristics on the lithofacies also has certain limitations.

[0005] Overall, the existing lithofacies prediction methods rely on either seismic data or well logging data, but both have their own limitations, and it is difficult to accurately and reliably predict the high-resolution three-dimensional spatial distribution of the complex change of the volcanic rock lithofacies. Therefore, a technology for more accurately predicting lithofacies is needed to better guide the effective development of volcanic rock oil and gas reservoirs. SUMMARY

[0006] Therefore, the present application aims to provide a technical scheme for predicting the three-dimensional distribution of lithofacies by integrating multi-source data to improve the accuracy of lithofacies interpretation.

[0007] According to an aspect of the present application, a lithofacies prediction method based on an indicator curve is provided, which comprises:

[0008] Collecting well logging data of a plurality of wells in a target block, and selecting an electrical property parameter D for indicating different lithofacies;

[0009] collecting three-dimensional seismic data of multiple wells in a target block, and selecting an elastic parameter T for indicating different lithofacies;

[0010] constructing a lithofacies indicating curve L=D / T according to the selected electric parameter D and the elastic parameter T;

[0011] predicting the three-dimensional distribution of the lithofacies in the target block based on waveform indicating simulation by the constraint of the constructed lithofacies indicating curve L.

[0012] In some embodiments, the method further comprises:

[0013] If the constructed lithofacies indicating curve L cannot distinguish different lithofacies, a second electric parameter D2 is selected, a modified indicating curve L2=D*D2 / T is constructed, and the three-dimensional distribution of the lithofacies in the target block is predicted based on waveform indicating simulation by the constraint of the modified indicating curve L2.

[0014] In some embodiments, the selected electric parameter D for indicating different lithofacies is a gamma ray parameter, resistivity, natural potential, electromagnetic wave absorption rate or phase.

[0015] In some embodiments, the target block is volcanic rock, and the selected electric parameter D for indicating different lithofacies is a gamma ray parameter.

[0016] In some embodiments, the selected elastic parameter for indicating different lithofacies is acoustic travel time, shear wave velocity, longitudinal wave velocity, density, Poisson's ratio, rigidity parameter or stress parameter.

[0017] In some embodiments, the target block is volcanic rock, and the selected elastic parameter for indicating different lithofacies is acoustic travel time.

[0018] In some embodiments, the predicted three-dimensional distribution of the lithofacies is compared with the lithofacies geological results in the target block to verify the predicted three-dimensional distribution of the lithofacies.

[0019] According to another aspect of the present application, a lithofacies prediction device based on an indicating curve is provided, which comprises:

[0020] an electric parameter selecting unit for collecting logging data of multiple wells in a target block and selecting an electric parameter D for indicating different lithofacies;

[0021] an elastic parameter selecting unit for collecting three-dimensional seismic data of multiple wells in the target block and selecting an elastic parameter T for indicating different lithofacies;

[0022] a lithofacies indicating curve constructing unit for constructing a lithofacies indicating curve L=D / T according to the selected electric parameter D and the elastic parameter T;

[0023] a lithofacies distribution prediction unit configured to predict a three-dimensional distribution of lithofacies in the target block based on the waveform-indication simulation, with the lithofacies indicator curve L constructed as a constraint.

[0024] In some embodiments, the apparatus further comprises a second electrical parameter selecting unit and a modified indicator curve constructing unit, in particular, if the constructed lithofacies indicator curve L fails to distinguish different lithofacies, the second electrical parameter selecting unit is configured to select a second electrical parameter D2, and the modified indicator curve constructing unit is configured to construct a modified indicator curve L2=D*D2 / T, and the lithofacies distribution prediction unit is further configured to predict a three-dimensional distribution of lithofacies in the target block based on the waveform-indication simulation, with the modified indicator curve L2 as a constraint.

[0025] In some embodiments, the electrical parameter D selected to indicate different lithofacies is a gamma ray parameter, resistivity, spontaneous potential, electromagnetic wave absorption rate or phase.

[0026] In some embodiments, the target block is volcanic rock, and the electrical parameter D selected to indicate different lithofacies is a gamma ray parameter.

[0027] In some embodiments, the elastic parameter selected to indicate different lithofacies is acoustic travel time, shear wave velocity, longitudinal wave velocity, density, Poisson's ratio, rigidity parameter or stress parameter.

[0028] In some embodiments, the target block is volcanic rock, and the elastic parameter selected to indicate different lithofacies is acoustic travel time.

[0029] In some embodiments, the predicted three-dimensional distribution of lithofacies is verified by comparing it with lithofacies geological results in the target block.

[0030] According to another aspect of the present application, an electronic device is also provided, which comprises:

[0031] a memory storing executable instructions;

[0032] a processor running the executable instructions in the memory to implement the lithofacies prediction method based on indicator curves as described above.

[0033] According to another aspect of the present application, a computer readable storage medium storing a computer program is also provided, which, when executed by a processor, implements the lithofacies prediction method based on indicator curves as described above.

[0034] The present application has the following advantages:

[0035] 1. The indication curve is used to realize the effective combination of seismic data information and logging data information, and improve the accuracy of lithofacies prediction.

[0036] 2. The prediction result has clear geological significance, and can reflect the actual lithofacies distribution of volcanic rocks.

[0037] 3. The three-dimensional space distribution rule of lithofacies, especially volcanic rock lithofacies, is realized, and the volcanic rock exploration has important value.

[0038] The method and device of the present application have other characteristics and advantages, which will be apparent or will be described in detail in the accompanying drawings and subsequent detailed description incorporated herein, which together serve to explain the specific principles of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0039] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings, in which like reference characters refer to like parts throughout the figures, and wherein:

[0040] Figure 1 A flow chart of a lithofacies prediction method based on an indication curve according to one embodiment of the present application is shown.

[0041] Figure 2 A histogram of the relationship between a plurality of parameters and different lithofacies according to one embodiment of the present application is shown.

[0042] Figure 3 A histogram of the relationship between a lithofacies indication curve and different lithofacies according to one embodiment of the present application is shown.

[0043] Figure 4 A comparison chart of a lithofacies prediction result and a lithofacies geological result according to one embodiment of the present application is shown. DETAILED DESCRIPTION

[0044] The preferred embodiments of the present application will be described in detail with reference to the drawings. Although the preferred embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to make the present application more thorough and complete, and to fully convey the scope of the present application to those skilled in the art.

[0045] Example 1

[0046] Figure 1A flowchart of the lithofacies prediction method based on indicator curves according to one embodiment of the present application is shown. As shown in the figure, the example includes steps 1-4.

[0047] Step 1, collect logging data of multiple wells in the target block, and select electrical parameters D for indicating different lithofacies.

[0048] Logging data is an important data in the field of petroleum engineering, which is used to evaluate the properties and characteristics of underground oil and gas reservoirs. Logging is a process of measuring and recording physical data of the formation around the wellbore during drilling using various logging tools to obtain information related to the underground reservoir.

[0049] Logging data provides information about important parameters such as formation composition, pore structure, porosity, permeability, fluid saturation, which are crucial for evaluating the potential of oil and gas reservoirs, determining oil production plans and predicting production. By analyzing logging data, engineers can understand the properties of the reservoir, lithology, fluid type and fluid distribution in the reservoir.

[0050] Common logging tools include logging cables, logging heads, resistivity logging instruments, natural gamma logging instruments, acoustic logging instruments, density logging instruments, etc. These tools provide information about different properties and characteristics of the formation by measuring resistivity, natural gamma rays, acoustic wave propagation speed, density, etc.

[0051] Logging data is usually presented in the form of curves, where each curve represents a different measured parameter. Engineers use geological models and mathematical methods to interpret and interpret the data by comprehensively analyzing these curves to obtain quantitative information about the properties of the formation and the characteristics of the reservoir.

[0052] In summary, logging data is obtained by physically measuring and recording data of the formation during drilling using logging tools to evaluate the properties and characteristics of oil and gas reservoirs. It plays a crucial role in oil and gas exploration and development, helping engineers make decisions and optimize oil and gas production.

[0053] According to the logging data, a plurality of electrical parameters can be obtained, and then the relationship between different electrical parameter curves and lithofacies can be analyzed, for example, a histogram of the relationship between different electrical parameter curves and lithofacies can be drawn, and an electrical parameter that can effectively distinguish different lithofacies is selected as the electrical parameter D used to construct the lithofacies indicator curve.

[0054] The following is a brief description of several electrical parameters that can be obtained from logging data:

[0055] Gamma ray parameter (GR): represents the natural radioactivity intensity of rock, reflecting the content of radioactive elements in rock;

[0056] Resistivity (R): Reflects the rock's electrical conductivity, related to porosity, water content and salinity of the rock;

[0057] Spontaneous Potential (SP): Spontaneous potential is measured using the principle of natural battery, which reflects the corrosion, permeability and saturation characteristics of the rock.

[0058] Electromagnetic wave absorption (A): Indicates the degree of attenuation of electromagnetic waves passing through the rock, related to the electrical conductivity and magnetic permeability of the rock.

[0059] Phase (P): Indicates the phase change of electromagnetic waves in the rock, reflecting the electrical conductivity distribution of the rock.

[0060] In addition, the electrical parameters obtained from well logging data can also include rock density (DEN) and the like.

[0061] These electrical parameters play an important role in identifying lithology and determining stratigraphic position, and are an important basis for facies prediction. The inventors need to analyze the relationship between different electrical parameters and facies in order to select electrical parameters that can effectively reflect the differences between facies.

[0062] In some embodiments, the electrical parameters selected to indicate different facies are gamma ray parameters, resistivity, spontaneous potential, electromagnetic wave absorption or phase, etc. In particular, in some examples according to the present application, the target block is volcanic rock, and the selected elastic parameter T is acoustic travel time.

[0063] Step 2, collect three-dimensional seismic data of multiple wells in the target block, and select elastic parameter T for indicating different facies.

[0064] Seismic data is an important data obtained by using seismic measurement technology in the field of seismic exploration, which is used to study underground structure, detect oil and gas resources and assess seismic risk, etc.

[0065] Seismic exploration is achieved by placing seismic instruments on the surface or underwater, sending controlled seismic energy (such as explosive sources or vibrators) to the underground, and recording the returned seismic wave signals. These seismic wave signals propagate at different speeds in the underground rocks and media, and then are received and recorded by the seismic instruments. By analyzing these recorded seismic wave signals, information about the properties, structure, sequence and underground structure of the underground rocks can be inferred.

[0066] Seismic data is usually presented in the form of seismic profiles, where the horizontal axis represents distance or time, and the vertical axis represents the amplitude or energy of the seismic wave signals. Seismic profiles can be two-dimensional (2D) or three-dimensional (3D), providing detailed information about the underground structure.

[0067] Seismic data plays an important role in oil and gas exploration. By analyzing seismic data, seismologists and geologists can determine the location, shape, and properties of potential oil and gas reservoirs to help explorers choose appropriate drilling locations. In addition, seismic data can also be used to assess seismic risk, predict seismic activity, and provide data support for geological disaster research.

[0068] In summary, seismic data is a record of seismic wave signals obtained through seismic exploration techniques, used to study underground structures, detect oil and gas resources, and assess seismic risk. It plays an important role in oil and gas exploration, geological research, and geological disaster prediction.

[0069] According to the seismic data, multiple elastic parameters can be obtained, and then the relationship between different elastic parameter curves and lithofacies can be analyzed. For example, the histogram of the relationship between different elastic parameter curves and lithofacies can be drawn, and the elastic parameter that can effectively distinguish different lithofacies is selected as the elastic parameter T used for constructing the lithofacies indicator curve in the subsequent process.

[0070] The following is a brief description of several elastic parameters that can be obtained from well logging data:

[0071] Acoustic travel time (DT): reflects the travel time characteristics of acoustic waves, closely related to the P-wave velocity Vp;

[0072] S-wave velocity (Vs): S-wave refers to the particle vibration perpendicular to wave propagation, and S-wave velocity is used to describe the speed of acoustic waves as S-waves propagating in rock;

[0073] P-wave velocity (Vp): P-wave refers to the particle vibration direction consistent with the wave propagation direction, and P-wave velocity is used to describe the speed of acoustic waves as P-waves propagating in rock;

[0074] Density (DEN): represents the mass density of rock, related to the mineral composition and porosity of rock;

[0075] Poisson's ratio: represents the relationship between lateral strain and longitudinal strain, reflecting the pore characteristics of rock;

[0076] Rigidity: reflects the ability of rock to resist deformation, related to the hardness of rock;

[0077] Stress: stress state in rock, reflecting the fracture and aggregation state of the formation;

[0078] Dispersion: difference in velocity of acoustic waves of different frequencies, reflecting the absorption and scattering characteristics of rock;

[0079] Attenuation: reflects the degree of attenuation of acoustic waves in the rock, related to the porosity, water content, mineral composition, etc. of the rock.

[0080] These elastic parameters can be extracted by analyzing seismic records, and are important reflections of petrophysical characteristics, and can provide effective reference for lithofacies prediction. The inventors need to analyze the relationship between different elastic parameters and lithofacies in order to select elastic parameters that can effectively reflect the differences between lithofacies.

[0081] In some embodiments, the selected elastic parameters for indicating different lithofacies are acoustic travel time, shear wave velocity, compressional wave velocity, density, Poisson's ratio, rigidity parameter or stress parameter, etc. In particular, in some examples according to the present application, the target block is volcanic rock, and the selected elastic parameter for indicating different lithofacies is acoustic travel time.

[0082] Step 3: Construct a lithofacies indicating curve L = D / T according to the selected electrical parameter D and elastic parameter T.

[0083] The inventors have found through in-depth research that, according to the present application, the elastic parameter can serve as a low-frequency background in lithofacies prediction, and the electrical parameter D can well reflect the characteristics of the lithofacies, so that the obtained lithofacies indicating curve L can well distinguish between different lithofacies. In application, the constructed lithofacies indicating curve can be subjected to intersection analysis and histogram analysis to ensure the discrimination of the constructed indicating curve, i.e. to accurately predict the three-dimensional distribution of lithofacies.

[0084] In some embodiments, the predicted three-dimensional distribution of lithofacies can be compared with the lithofacies geological results in the target block to verify the predicted three-dimensional distribution of lithofacies.

[0085] In some embodiments, if the constructed lithofacies indicating curve L cannot distinguish between different lithofacies, a second electrical parameter D2 is selected, a modified indicating curve L2 = D*D2 / T is constructed, and the three-dimensional distribution of lithofacies in the target block is predicted based on waveform indicating simulation by using the modified indicating curve L2 for constraint.

[0086] According to the present embodiment, when the constructed lithofacies indicating curve can effectively distinguish between different lithofacies, the indicating curve uses a single electrical parameter, i.e. a single electrical parameter D is used as the numerator of the indicating curve; when the single electrical indicating curve cannot effectively distinguish between different lithofacies, a second electrical parameter D2 is added to modify the indicating curve until the modified indicating curve can effectively distinguish between different lithofacies, further improving the accuracy and adaptability of lithofacies prediction according to the present application.

[0087] If the target block is volcanic rock, the identified lithofacies can generally include eruption facies, sedimentary facies, sedimentary rock facies, effusion facies, and the like, and in some blocks, can also include dike facies, tube facies, pyroclastic facies, and volcanic mud flow facies. The following briefly introduces the lithofacies of volcanic rock.

[0088] From the perspective of the genesis and formation process of volcanic rock, the lithofacies of the wellhead volcanic rock can be divided into:

[0089] Eruption facies: indicates the magma phase ejected during volcanic eruption, and this part of magma rapidly cools to form a glassy pyroclastic layer;

[0090] Sedimentary facies: refers to the sequence formed by the accumulation of volcanic clasts under the action of gravity after the end of eruption, such as volcanic tuff and volcanic gravel;

[0091] Sedimentary rock facies: refers to the sedimentary volcanic rock layer formed by re-deposition and diagenesis of water flow, such as volcanic siltstone;

[0092] Effusion facies: refers to the rock mass formed by the cooling of magma flowing in a gentle manner, such as porphyry and rhyolite;

[0093] Dike facies: indicates the dike formed by the intrusion of magma through fissures into other rocks, often having a pore structure;

[0094] Tube facies: refers to the narrow rock mass such as volcanic neck formed by the eruption of magma through a channel;

[0095] Pyroclastic facies: contains various pyroclastic deposits, volcanic bombs, and the like;

[0096] Volcanic mud flow facies: mud flow deposits formed by the mixture of volcanic clasts and water.

[0097] The above classification emphasizes the genetic process and is helpful for analyzing the volcanic eruption and deposition history, and has guiding significance for the study of volcanic rock accumulation rules.

[0098] According to the present application, the lithofacies of volcanic rock can be effectively predicted, different lithofacies represent different genesis and formation process of volcanic rock, and accurate identification of lithofacies is helpful for explaining the volcanic eruption and deposition history, and the identification result has extremely important significance for the accumulation rules of volcanic rock.

[0099] Step 4, using the constructed lithofacies indication curve L to constrain the seismic data, and based on waveform indication simulation, predicting the three-dimensional distribution of lithofacies in the target block.

[0100] Waveform indication simulation (Waveform Modeling) can be used to simulate the propagation process of seismic waves in underground medium and the generated waveform response.

[0101] Waveform indication modeling is a method of calculating and predicting the propagation path and waveform response of seismic waves in the subsurface based on known subsurface medium models and seismic instrument parameters through mathematical modeling and computational methods.

[0102] Through waveform indication modeling, geophysicists can predict the propagation path and waveform response of seismic waves under different subsurface conditions. This is of great significance for understanding the characteristics of subsurface media, predicting the distribution and interference of seismic waves in the subsurface, interpreting seismic data, and optimizing exploration and development strategies.

[0103] Waveform indication modeling is usually based on numerical methods such as the Finite Difference Method (FDM), the Finite Element Method (FEM), or the Radiation Modeling Method (RMM). These methods use numerical algorithms to solve the seismic wave equation and calculate the propagation path and waveform response of seismic waves in the subsurface.

[0104] In summary, waveform indication modeling is a geophysical exploration technique that simulates the propagation path and waveform response of seismic waves in subsurface media through mathematical modeling and computational methods. It plays an important role in seismic wave interpretation, subsurface structure research, and exploration and development.

[0105] In the present invention, the inventors can effectively predict the three-dimensional distribution of lithofacies in the target block based on waveform indication modeling by constructing the lithofacies indication curve L (in some examples, the modified indication curve L2).

[0106] In summary, according to the technical solution of the present embodiment, the indication curve is used to effectively combine seismic data information and logging data information, significantly improving the accuracy and detail of lithofacies prediction, and the prediction results have clear geological significance, reflecting the actual lithofacies distribution of volcanic rocks, realizing the prediction of three-dimensional spatial distribution of lithofacies, especially volcanic rock lithofacies, and having important value for volcanic rock exploration.

[0107] Example 2

[0108] The present invention also proposes a lithofacies prediction device based on an indication curve, which comprises:

[0109] An electrical parameter selection unit is configured to collect logging data of multiple wells in a target block and select electrical parameters D for indicating different lithofacies;

[0110] An elastic parameter selection unit is configured to collect three-dimensional seismic data of multiple wells in the target block and select elastic parameters T for indicating different lithofacies.

[0111] a lithofacies indicator curve constructing unit configured to construct a lithofacies indicator curve L=D / T according to the selected electrical parameter D and the selected elastic parameter T;

[0112] a lithofacies distribution predicting unit configured to predict a three-dimensional distribution of lithofacies in the target block based on waveform-indication modeling, with the constructed lithofacies indicator curve L as a constraint.

[0113] In some embodiments, the apparatus further comprises a second electrical parameter selecting unit and a modified indicator curve constructing unit, in particular, if the constructed lithofacies indicator curve L cannot distinguish different lithofacies, the second electrical parameter selecting unit is configured to select a second electrical parameter D2, the modified indicator curve constructing unit is configured to construct a modified indicator curve L2=D*D2 / T, and the lithofacies distribution predicting unit is further configured to predict a three-dimensional distribution of lithofacies in the target block based on waveform-indication modeling, with the modified indicator curve L2 as a constraint.

[0114] In some embodiments, the selected electrical parameter D for indicating different lithofacies is a gamma ray parameter, resistivity, spontaneous potential, electromagnetic wave absorption rate or phase.

[0115] In some embodiments, the target block is volcanic rock, and the selected electrical parameter D for indicating different lithofacies is a gamma ray parameter.

[0116] In some embodiments, the selected elastic parameter for indicating different lithofacies is acoustic travel time, shear wave velocity, longitudinal wave velocity, density, Poisson's ratio, rigidity parameter or stress parameter.

[0117] In some embodiments, the target block is volcanic rock, and the selected elastic parameter for indicating different lithofacies is acoustic travel time.

[0118] In some embodiments, the predicted three-dimensional distribution of lithofacies is compared with lithofacies geological results in the target block to verify the predicted three-dimensional distribution of lithofacies.

[0119] Other detailed descriptions and advantages related to the present embodiments can be referred to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0120] Example 3

[0121] According to another aspect of the present application, an electronic device is also provided. The electronic device comprises:

[0122] a memory storing executable instructions;

[0123] a processor running the executable instructions in the memory to implement a lithofacies prediction method based on an indicator curve according to the present application.

[0124] The method comprises steps 1-4 as follows.

[0125] The method comprises:

[0126] Step 1, collecting well logging data of multiple wells in a target block, and selecting an electrical parameter D for indicating different lithofacies;

[0127] Step 2, collecting three-dimensional seismic data of multiple wells in the target block, and selecting an elastic parameter T for indicating different lithofacies;

[0128] Step 3, constructing a lithofacies indicator curve L=D / T according to the selected electrical parameter D and elastic parameter T;

[0129] Step 4, predicting a three-dimensional distribution of lithofacies in the target block based on waveform indicator simulation with the constraint of the constructed lithofacies indicator curve L.

[0130] In some embodiments, the method further comprises:

[0131] If the constructed lithofacies indicator curve L cannot distinguish different lithofacies, a second electrical parameter D2 is selected, a modified indicator curve L2=D*D2 / T is constructed, and a three-dimensional distribution of lithofacies in the target block is predicted based on waveform indicator simulation with the constraint of the modified indicator curve L2.

[0132] In some embodiments, the selected electrical parameter D for indicating different lithofacies is a gamma ray parameter, resistivity, spontaneous potential, electromagnetic wave absorption rate, or phase.

[0133] In some embodiments, the target block is volcanic rock, and the selected electrical parameter D for indicating different lithofacies is a gamma ray parameter.

[0134] In some embodiments, the selected elastic parameter for indicating different lithofacies is acoustic travel time, shear wave velocity, longitudinal wave velocity, density, Poisson's ratio, rigidity parameter, or stress parameter.

[0135] In some embodiments, the target block is volcanic rock, and the selected elastic parameter for indicating different lithofacies is acoustic travel time.

[0136] In some embodiments, the predicted three-dimensional distribution of lithofacies is compared with lithofacies geological results in the target block to verify the predicted three-dimensional distribution of lithofacies.

[0137] In particular, the memory can include one or more computer program products that can include various forms of computer-readable storage media, such as volatile and / or non-volatile memory. The volatile memory, for example, can include random access memory (RAM), and / or a cache, and / or the like. The non-volatile memory, for example, can include read-only memory (ROM), hard disk, flash memory, and / or the like.

[0138] The processor can be a central processing unit (CPU) or other form of processing unit that has data processing and / or instruction execution capabilities, and can control other components in the electronic device to perform desired functions. In one embodiment of the present application, the processor is used to run the computer readable instructions stored in the memory.

[0139] Detailed descriptions of the present embodiments can refer to the corresponding descriptions in the foregoing embodiments, and will not be repeated here.

[0140] Example 4

[0141] According to another aspect of the present application, there is also provided a computer readable storage medium storing a computer program, which, when executed by a processor, implements the lithofacies prediction method based on an indicator curve according to the present application.

[0142] The method comprises the following steps 1-4.

[0143] The method comprises:

[0144] Logging data of multiple wells in a target block is collected, and an electrical parameter D indicating different lithofacies is selected;

[0145] Three-dimensional seismic data of multiple wells in the target block is collected, and an elastic parameter T indicating different lithofacies is selected;

[0146] According to the selected electrical parameter D and elastic parameter T, a lithofacies indicator curve L=D / T is constructed;

[0147] Based on waveform indicator simulation, the three-dimensional distribution of lithofacies in the target block is predicted by constraining the constructed lithofacies indicator curve L.

[0148] In some embodiments, the method further comprises:

[0149] If the constructed lithofacies indicator curve L cannot distinguish different lithofacies, a second electrical parameter D2 is selected, a modified indicator curve L2=D*D2 / T is constructed, and based on waveform indicator simulation, the three-dimensional distribution of lithofacies in the target block is predicted by constraining the modified indicator curve L2.

[0150] In some embodiments, the electrical parameter D selected to indicate different lithofacies is a gamma ray parameter, resistivity, spontaneous potential, electromagnetic wave absorption, or phase.

[0151] In some embodiments, the target block is a volcanic rock, and the electrical parameter D selected to indicate different lithofacies is a gamma ray parameter.

[0152] In some embodiments, the elastic parameter selected to indicate different lithofacies is acoustic travel time, shear wave velocity, compressional wave velocity, density, Poisson's ratio, rigidity parameter, or stress parameter.

[0153] In some embodiments, the target block is a volcanic rock, and the elastic parameter selected to indicate different lithofacies is acoustic travel time.

[0154] In some embodiments, the predicted three-dimensional distribution of lithofacies is compared with a lithofacies geological result in the target block to verify the predicted three-dimensional distribution of lithofacies.

[0155] The computer readable storage medium according to the embodiments of the present application has non-transitory computer readable instructions stored thereon. When the non-transitory computer readable instructions are run by a processor, all or part of the steps of the method of the embodiments of the present application described above are performed.

[0156] The computer readable storage medium described above includes, but is not limited to, an optical storage medium (for example, CD-ROM and DVD), a magneto-optical storage medium (for example, MO), a magnetic storage medium (for example, magnetic tape or a mobile hard disk), a medium with a built-in rewritable non-volatile memory (for example, a memory card), and a medium with a built-in ROM (for example, a ROM cartridge).

[0157] Those skilled in the art should understand that, in order to solve the technical problem of how to obtain a good user experience effect, the embodiments can also include well-known structures such as a communication bus, an interface, and the like, which should also be included in the protection scope of the present application.

[0158] The detailed description of the embodiments can refer to the corresponding description in the foregoing embodiments, which will not be described here again.

[0159] Example 5

[0160] The present example is illustrated by comparing a lithofacies interpretation result with a volcanic rock lithofacies phase predicted based on an indicator curve.

[0161] Figure 2Histograms of the seismic response characteristics and the electrical property curve characteristics of different volcanic rock facies and the rock correlation according to the present application are shown. Among them, DT represents the acoustic travel time curve; CNL is a logging curve for measuring the hydrogen content in the formation, which estimates the hydrogen content by measuring the neutron scattering in the formation, thereby providing information about the formation porosity and fluid saturation; DEN represents the density curve; GR represents the gamma ray parameter curve; and LLD is a logging curve combining density logging and lithology logging, which uses two long-interval detectors to measure the density and natural gamma ray intensity of the formation respectively, and the density and radioactivity characteristics of the formation can be obtained by analyzing the LLD curve, thereby inferring the rock type and composition.

[0162] In the present example, the radioactive gamma parameter is selected as the electrical property parameter D for constructing the curve, and the acoustic travel time is selected as the elastic parameter T for constructing the curve, which both have significantly different characteristics on different rock facies.

[0163] Then the rock facies indicator curve L=T / D of three test wells in the target block is calculated, and histogram analysis is performed on the indicator curve and the volcanic rock facies, so as to clearly distinguish different volcanic rock facies by the indicator curve, as shown in Figure 3 .

[0164] The rock facies indicator curve of multiple wells is constrained, and the spatial prediction of the entire rock facies three-dimensional body is realized based on waveform indicator simulation. The volcanic rock facies interpretation results obtained according to the geological data are compared with the prediction results of the volcanic rock facies body predicted based on the rock facies indicator curve L according to the present application, as shown in Figure 4 , it can be found that they have a good degree of coincidence, which fully illustrates that the prediction of the volcanic rock facies body can be realized by constructing the rock facies indicator curve to predict the volcanic rock facies according to the present application, and the spatial distribution of the volcanic rock facies body can be well predicted, and the prediction results of the facies body have consistent geological significance with the actual results.

[0165] Other detailed descriptions related to the present embodiment can be referred to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0166] The embodiments of the present application have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes are obvious to those skilled in the art without departing from the scope and spirit of the described embodiments. The selection of the terms used herein is intended to best explain the principles, practical applications, or technical improvements of the technology in the market, or to enable other ordinary skilled persons in the art to understand the embodiments disclosed herein.

Claims

1. A method of lithofacies prediction based on an indicator curve, characterized by, The method comprises: collecting logging data of multiple wells in a target block, and selecting an electrical parameter D for indicating different lithofacies; collecting three-dimensional seismic data of multiple wells in the target block, and selecting an elastic parameter T for indicating different lithofacies; constructing a lithofacies indicating curve L=D / T according to the selected electrical parameter D and the elastic parameter T; predicting a three-dimensional distribution of the lithofacies in the target block by waveform indication simulation under the constraint of the constructed lithofacies indicating curve L.

2. The method of claim 1, wherein, The method further comprises: if the constructed lithofacies indicating curve L cannot distinguish different lithofacies, selecting a second electrical parameter D2, constructing a modified indicating curve L2=D*D2 / T, predicting a three-dimensional distribution of the lithofacies in the target block by waveform indication simulation under the constraint of the modified indicating curve L2.

3. The method of claim 1, wherein, The selected electrical parameter D for indicating different lithofacies is a gamma ray parameter, resistivity, spontaneous potential, electromagnetic wave absorption rate or phase.

4. The method of claim 3, wherein, The target block is volcanic rock, and the selected electrical parameter D for indicating different lithofacies is a gamma ray parameter.

5. The method of claim 1, wherein, The selected elastic parameter for indicating different lithofacies is acoustic travel time, shear wave velocity, longitudinal wave velocity, density, Poisson's ratio, rigidity parameter or stress parameter.

6. The method of claim 5, wherein, The target block is volcanic rock, and the selected elastic parameter for indicating different lithofacies is acoustic travel time.

7. The method of claim 1, wherein, The predicted three-dimensional distribution of the lithofacies is compared with a lithofacies geological result in the target block to verify the predicted three-dimensional distribution of the lithofacies.

8. A lithofacies prediction device based on an indicator curve, characterized by, The device comprises: an electrical parameter selecting unit configured to collect logging data of multiple wells in a target block, and select an electrical parameter D for indicating different lithofacies; an elastic parameter selecting unit configured to collect three-dimensional seismic data of multiple wells in the target block, and select an elastic parameter T for indicating different lithofacies; a lithofacies indicating curve constructing unit configured to construct a lithofacies indicating curve L=D / T according to the selected electrical parameter D and the elastic parameter T; a lithofacies distribution predicting unit configured to predict a three-dimensional distribution of the lithofacies in the target block by waveform indication simulation under the constraint of the constructed lithofacies indicating curve L.

9. The apparatus of claim 8, wherein, The device further comprises a second electrical parameter selecting unit and a modified indicating curve constructing unit, specifically, if the constructed lithofacies indicating curve L cannot distinguish different lithofacies, the second electrical parameter selecting unit is configured to select a second electrical parameter D2, the modified indicating curve constructing unit is configured to construct a modified indicating curve L2=D*D2 / T, and the lithofacies distribution predicting unit is further configured to predict a three-dimensional distribution of the lithofacies in the target block by waveform indication simulation under the constraint of the modified indicating curve L2.

10. An electronic device, comprising: The electronic device comprises: a memory storing executable instructions; a processor running the executable instructions in the memory to implement the method in any one of claims 1-7.

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