A synchronous seismic inversion method for lithofacies and impedance based on MCMC

Abstract

The present invention discloses a method and system for synchronous seismic inversion of lithofacies and impedance based on MCMC, and the steps are as follows: based on actual logging data, statistically calculate the lithofacies transition probability matrix and the mean and variance of impedance parameters corresponding to different lithofacies; calculate the vertical variogram of impedance parameters based on well data; randomly generate an initial lithofacies combination based on the statistically calculated lithofacies transition probability matrix, and calculate the impedance corresponding to the lithofacies by using the statistically calculated mean, variance, and variogram; based on the randomly generated lithofacies and impedance, generate the lithofacies and impedance parameters again, design an objective loss function and introduce the L1 norm, calculate the loss values corresponding to the two sets of lithofacies and impedance parameters respectively, and use the Markov chain acceptance criterion to determine whether to accept the newly generated lithofacies and impedance parameters; after the iteration is completed, statistically calculate the distribution of lithofacies and impedance parameters in the Markov chain at the stable stage, and evaluate the uncertainty information of the prediction results. The present invention improves the resolution and accuracy of impedance prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of seismic exploration, and particularly to a method for synchronous seismic inversion of lithofacies and impedance based on MCMC. Background Art

[0002] As exploration activities continue to advance, the geological conditions faced become increasingly complex, the target horizons gradually deepen underground, and the requirements for exploration accuracy also increase accordingly. Seismic inversion technology plays a crucial role in the exploration and development of oil and gas resources. It provides a solid foundation for accurately assessing reservoir characteristics, calculating oil and gas reserves, and formulating reasonable production plans. By comprehensively analyzing seismic data, downhole measurements, and existing geological knowledge, seismic inversion can not only reveal the spatial layout and structural morphology of oil and gas reservoirs at a macroscopic level but also effectively predict the changing trends of reservoir parameters, thus supporting more refined resource management and development decisions. Elastic parameter prediction is an important part of seismic inversion. It involves estimating rock physical properties such as velocity, density, impedance, etc., which reflect the response characteristics of rocks to external forces. Different types of rocks and the fluids (oil, gas, or water) they contain will result in different elastic responses. Therefore, by accurately predicting elastic parameters, potential oil and gas reservoir locations can be effectively identified, and the mechanical properties of the reservoir can be understood. This information is crucial for optimizing drilling decisions as it helps reduce the risk of dry wells and increases the likelihood of discovering commercial-scale oil and gas fields. Lithofacies prediction is another key technology, aiming to infer the distribution of rock types in unknown areas based on existing data. Lithofacies refers to a detailed description of the internal structure and composition of rocks, which is directly related to the effectiveness of the reservoir - that is, whether the reservoir has good storage capacity and the ability to allow fluid flow. Through lithofacies prediction, geologists can obtain valuable insights into reservoir heterogeneity, which is of great significance for selecting the best well locations, designing effective completion plans, and adopting advanced enhanced oil recovery techniques. In addition, lithofacies prediction also supports a deeper understanding of reservoir connectivity and permeability, thus facilitating the formulation of more scientific and reasonable oilfield development strategies. Seismic inversion is generally divided into deterministic inversion and stochastic inversion. Deterministic inversion focuses on finding one or a set of optimal models that can best explain the observed data. This method is widely used in practical projects, especially in cases where geological conditions are relatively simple. However, as exploration targets gradually shift to more complex and deeper geological environments, relying solely on a single solution may not be sufficient to comprehensively understand reservoir characteristics and their potential risks. In contrast, stochastic inversion has received increasing attention in recent years, especially under the Bayesian framework. Bayesian stochastic inversion not only aims to find the best model but, more importantly, provides a probability distribution of all possible models, enabling researchers to evaluate the reliability of results under different assumptions. By integrating prior knowledge (such as existing geological information) with currently collected seismic data, the Bayesian method can generate a posterior probability density function that details the likelihood of various models. The Markov Chain Monte Carlo (MCMC) method is an effective technique for solving seismic inversion problems under the Bayesian framework, especially suitable for dealing with high-dimensional and non-linear inverse problems.In seismic inversion, MCMC generates a series of model samples by sampling from the posterior probability distribution. It can not only identify the most likely geological model but also comprehensively evaluate the uncertainty of model parameters. This method is particularly suitable for applications under complex geological conditions because it can naturally handle multimodal posterior distributions and avoid getting trapped in local optimal solutions, thereby increasing the likelihood of finding the global optimal solution. In addition, MCMC allows the integration of rich prior information (such as geological knowledge, logging data, etc.) into the inversion process, enhancing the reliability and accuracy of the results. After obtaining the elastic parameters, supervised or unsupervised data-driven methods can be used to achieve lithofacies prediction.

[0003] In summary, the current research on reservoir lithofacies and impedance prediction methods has the following problems: (1) Deterministic inversion methods can only give a unique solution to the prediction result. However, affected by noise, seismic forward modeling assumptions, etc., when conducting reservoir evaluation, if only relying on a deterministic model, other potential geological scenarios may be ignored, leading to misunderstandings or incorrect estimations of reservoir characteristics; (2) Seismic inversion based on the Bayesian framework can obtain analytical solutions when dealing with linear problems, but in the face of non-linear situations, solving the posterior distribution becomes extremely complex, and the non-linear relationship makes it difficult to obtain the posterior distribution through direct calculation; (3) The MCMC inversion method has a large degree of randomness in the solving process, which results in poor stability of the predicted elastic parameters. Since MCMC relies on random sampling to explore the posterior distribution, especially when the sampling is insufficient or the chain is not fully mixed, different results may be produced each time it runs, and this instability affects the reliable estimation of the subsurface structure; (4) The conventional method of predicting impedance step by step and then predicting lithofacies itself has cumulative errors because the error of each step will accumulate in subsequent steps. In addition, the impedance value itself depends on the combination of different lithofacies, and ignoring the interdependence between lithofacies and impedance leads to a decrease in the accuracy of lithofacies and impedance prediction. Summary of the Invention

[0004] The purpose of the present invention is to provide a synchronous seismic inversion method for lithofacies and impedance based on MCMC. By establishing an impedance iteration optimization algorithm dependent on lithofacies, it effectively avoids the cumulative errors of step-by-step prediction of impedance and lithofacies, and simultaneously estimates the impedance and lithofacies of the reservoir, meeting the requirements of high-precision seismic exploration and fine reservoir characterization.

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

[0006] A synchronous seismic inversion method for lithofacies and impedance based on MCMC, comprising the following steps:

[0007] Step 1: Based on the actual logging data, statistically analyze the lithofacies transition probability matrix for subsequent generation of reasonable lithofacies combinations. By analyzing the logging data, construct a transition probability matrix that reflects the spatial distribution law of subsurface lithofacies, and accurately calculate the mean and variance of the impedance parameters corresponding to different lithofacies. Provide key basic data for simulation and prediction to ensure a high degree of agreement between the lithofacies model and the actual geological situation.

[0008] Step 2: Calculate the vertical variogram of the impedance parameters based on well data and establish a vertical continuity model of the impedance parameters. By analyzing the variation law of the impedance parameters with depth in well data, construct a variogram that reflects the spatial correlation of these parameters, reduce the randomness introduced in the process of generating the Markov chain, and enhance the stability and reliability of the impedance prediction results.

[0009] Step 3: Based on the statistically analyzed lithofacies transition probability matrix, first randomly generate an initial lithofacies combination. Then, use the mean, variance of the impedance parameters of different lithofacies and the vertical variogram obtained in the previous two steps to calculate the impedance values corresponding to each lithofacies.

[0010] Step 4: Based on the randomly generated initial lithofacies and impedance parameters, further iteratively generate new lithofacies and impedance data. Design an objective loss function to quantify the difference between the prediction results and the actual data. At the same time, introduce the L1 norm constraint in the loss function to promote sparsity and ensure the simplicity and effectiveness of the model. Calculate the loss values corresponding to the initially generated and regenerated lithofacies and impedance parameters respectively, and evaluate the improvement effect of the model by comparing the two loss values.

[0011] Step 5: According to the acceptance criterion of the Markov chain, determine whether to accept the new parameter set by comparing the newly generated lithofacies and impedance parameters with the current state of the model. If the new parameter set can significantly improve the loss function values in the old and new states, directly accept it; otherwise, accept or reject it according to a certain probability.

[0012] Step 6: Repeat Step 4 and Step 5 until the preset number of iterations is reached. During this process, continuously update the lithofacies and impedance parameters according to the acceptance criterion of the Markov chain to ensure that each iteration moves towards the optimization goal. When the iteration is completed, statistically analyze the distribution of lithofacies and impedance parameters in the Markov chain during the stable stage to determine the final prediction result. At the same time, evaluate the uncertainty information of the prediction result by analyzing the variability and convergence in the chain.

[0013] In some embodiments, in Step 1, the lithofacies transition probability matrix is:

[0014] (1);

[0015] Where, each row is the current state lithofacies, and each column is the probability of the current state lithofacies converting to other lithofacies.

[0016] In some embodiments, in step 2, the variogram is:

[0017] (2);

[0018] Where, is the variogram value calculated based on the well data; is the lag distance; is the length of logging data; For a certain location Impedance parameter value.

[0019] In some embodiments, step 3 includes the following steps:

[0020] S31. Randomly generate initial lithofacies combination:

[0021] (3);

[0022] Where, for The state of the lithofacies at the moment; for The state of the lithofacies at the moment; is the lithofacies transition probability matrix; given the lithofacies state at time 0, a lithofacies sequence of any length can be obtained according to formula (3);

[0023] S32. By performing a Kronecker product between the matrix constructed by the variogram and the impedance variance matrix of each lithofacies, an impedance curve that satisfies the characteristics of the well logging curve is simulated and generated:

[0024] (4);

[0025] Where, It is the current lithofacies state; for The corresponding impedance distribution under the lithofacies state; for The mean value of the corresponding impedance under the lithofacies state; is the covariance obtained by Kronecker product of impedance variance and variogram; It is the possible lithofacies space.

[0026] In some embodiments, the calculation expression for the loss value corresponding to the lithofacies and impedance parameters generated twice in step 4 is:

[0027] (5);

[0028] Where, For observing seismic data; is the forward operator for obtaining the synthetic seismic record from impedance ; is the difference operator.

[0029] In some embodiments, in step 5, by comparing the newly generated lithofacies and impedance parameters with the state of the current model, it is determined whether to accept the new parameter set; if the new parameter set can improve the loss function values in the new and old states, it is directly accepted; otherwise, it is accepted or rejected according to a certain probability;

[0030] The loss function values in the new and old states are:

[0031] (6);

[0032] In the formula, is the logarithm of the loss value in the new state; is the logarithm of the loss value in the old state. It should be noted that the smaller the loss value, the larger the logarithm.

[0033] In some embodiments, if the loss value in the new state is less than the loss value in the old state, is greater than 1, and vice versa is less than 1; at the same time, a random number between 0 and 1 is generated , if is greater than , then the lithofacies and impedance in the new state are received, otherwise the original state is retained.

[0034] A lithofacies and impedance synchronous seismic inversion system based on MCMC, comprising:

[0035] A statistical module: based on actual logging data, used to statistically calculate the lithofacies transition probability matrix for subsequent generation of lithofacies combinations, and at the same time statistically calculate the mean and variance of the impedance parameters corresponding to different lithofacies;

[0036] An impedance parameter calculation module: based on well data, used to calculate the vertical variogram of impedance parameters and establish the vertical continuity of impedance parameters;

[0037] A random generation module: using the lithofacies transition probability matrix, used to randomly generate an initial lithofacies combination, and calculate the impedance corresponding to the lithofacies using the statistically calculated mean, variance, and variogram;

[0038] A re-generation module: based on the randomly generated lithofacies and impedance, used to re-generate lithofacies and impedance parameters, and calculate the loss values corresponding to the lithofacies and impedance parameters generated twice using the target loss function and L1 norm constraint;

[0039] A judgment module: according to the Markov chain acceptance criterion, used to judge whether to accept the newly generated lithofacies and impedance parameters;

[0040] Iterative output module: Repeat steps S4 - S5 until a given number of iterations is reached. Statistically analyze the distributions of lithofacies and impedance in the stable Markov chain to obtain the final lithofacies and impedance prediction results and their uncertainty information.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] 1. The present invention considers the coupling relationship between lithofacies and impedance, and simultaneously predicts reservoir lithofacies and impedance parameters, avoiding the cumulative error caused by the step-by-step prediction method;

[0043] 2. Based on the Bayesian framework, the present invention uses the MCMC method to solve the inverse problem, and gives the uncertainty information of the results while presenting the results, realizing the reliability evaluation of the results;

[0044] 3. In the MCMC iteration process, the present invention considers the vertical variogram of the geological sedimentation process, making there be a coupling relationship between adjacent points, reducing the randomness of the generated lithofacies and impedance parameters, and accelerating the convergence of the Markov chain;

[0045] 4. Add the L1 norm constraint to the objective loss to promote the sparsity of impedance parameters and improve the resolution and accuracy of impedance prediction. Description of the Drawings

[0046] Figure 1 It is a schematic diagram of the overall process of Embodiment 1 of the present invention;

[0047] Figure 2 It is a schematic diagram of well data tested in Embodiment 1 of the present invention; among them, Figure (a) is lithofacies data; Figure (b) is impedance data; Figure (c) is seismic record data;

[0048] Figure 3 It is a schematic diagram of the distribution of impedance corresponding to each lithofacies statistically based on well data in Embodiment 1 of the present invention;

[0049] Figure 4 It is a schematic diagram of the inversion result in Embodiment 1 of the present invention; among them, Figure (a) is the iterative update process of lithofacies; Figure (b) is the finally predicted lithofacies; Figure (c) is the probability of each lithofacies; Figure (d) is the impedance prediction result and the random realization of the iterative process;

[0050] Figure 5 It is a schematic diagram of the structure of the system in Embodiment 2 of the present invention. Detailed Embodiments

[0051] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0052] Embodiment 1

[0053] Please refer to Figures 1-4 , a synchronous seismic inversion method for lithofacies and impedance based on MCMC, which is proposed based on the following problems:

[0054] 1. Although the deterministic inversion method can provide a unique solution for the prediction result, this method is easily affected by data noise and the accuracy of seismic forward modeling assumptions. In the practical application of reservoir evaluation, if completely relying on a single deterministic model, various potential geological scenarios and the complex and changeable characteristics inside the reservoir may be ignored. This limitation may lead to a one-sided understanding or misjudgment of the reservoir properties, and further affect a series of decision-making processes such as resource assessment and development planning. Therefore, when conducting reservoir analysis, combining probabilistic methods and other auxiliary information and considering more possibilities are important ways to improve the accuracy and reliability of evaluation.

[0055] 2. The seismic inversion method based on the Bayesian framework can provide a direct analytical solution when dealing with linear problems, which makes parameter estimation relatively simple and intuitive. However, when encountering non-linear situations, solving the posterior probability distribution becomes extremely complex. Non-linear relationships not only increase the computational difficulty but may also cause the posterior distribution to exhibit multi-modal or multi-peak characteristics, and accurate results cannot be obtained through simple mathematical operations. At this time, traditional direct calculation methods are no longer applicable, and numerical methods such as Monte Carlo simulation and Markov chain are usually needed to approximately estimate the posterior distribution to obtain more reliable geological parameter estimates.

[0056] 3. The MCMC inversion method introduces significant random elements in the solution process, which makes the stability of the predicted elastic parameters relatively low. Since this method relies on random sampling to explore the complex posterior probability distribution, in the case of insufficient sampling or the Markov chain not being fully mixed, different results may be generated each time it runs. This variability of the results reduces the reliability and consistency of the estimation of underground structure characteristics.

[0057] 4. The conventional method of gradually predicting impedance and then predicting lithofacies is prone to error accumulation because the small deviations in each step will gradually amplify in subsequent analyses, ultimately affecting the accuracy of the overall result. In addition, this method often ignores the intrinsic interdependence between impedance values and lithofacies - impedance is the result of different lithofacies combinations, and lithofacies in turn affect impedance characteristics. This neglect leads to the failure to fully reflect the complex interaction between the two during the prediction process, thus reducing the accuracy of lithofacies and impedance prediction.

[0058] Specifically, it includes the following steps:

[0059] S1. As Figure 2 shown, based on actual logging data, statistically calculate the lithofacies transition probability matrix for subsequent generation of lithofacies combinations. By analyzing logging data, construct a lithofacies transition probability matrix that reflects the spatial distribution law of subsurface lithofacies, and accurately statistically calculate the mean and variance of impedance parameters corresponding to different lithofacies, as Figure 3 shown. This provides key basic data for simulation and prediction, ensuring a high degree of agreement between the lithofacies model and the actual geological situation.

[0060] Different lithofacies Corresponding impedance mean And variance Are respectively denoted as: 、 、 . This statistical method helps to more accurately understand the conversion relationship between lithofacies and their physical properties, improving the reliability and accuracy of reservoir evaluation and prediction.

[0061] The lithofacies transition probability matrix is:

[0062] (1);

[0063] In the formula, each row represents lithofacies 1, lithofacies 2, and lithofacies 3 in the current state, and each column represents the probability of the lithofacies in the current state converting to other lithofacies in the next state. For example: The second column in the first row represents the probability of converting from lithofacies 1 to lithofacies 2 is 0.05.

[0064] S2. Calculate the vertical variogram of impedance parameters based on well data, and effectively establish a vertical continuity model of impedance parameters. By analyzing the variation law of impedance parameters with depth in well data, construct a variogram that reflects the spatial correlation of these parameters, reducing the randomness introduced during the generation of the Markov chain and enhancing the stability and reliability of the impedance prediction result.

[0065] The variogram is:

[0066] (2);

[0067] In the formula, is the variogram value calculated based on well data; is the lag distance; is the length of the logging data; is a certain position at the impedance parameter value.

[0068] S3. Based on the statistically-based lithofacies transition probability matrix, first randomly generate an initial lithofacies combination.

[0069] (3);

[0070] In the formula, is the state of the lithofacies at time is the state of the lithofacies at time is the lithofacies transition probability matrix; Given the lithofacies state at time 0, according to formula (3), a lithofacies sequence of any length can be obtained.

[0071] Then, using the mean, variance, and vertical variogram of the impedance parameters of different lithofacies statistically obtained in the first two steps, by performing a Kronecker product (formula 4) on the matrix constructed by the variogram and the impedance variance matrix of each lithofacies to simulate and generate an impedance curve that meets the characteristics of the logging curve:

[0072] (4);

[0073] In the formula, is the current lithofacies state; is the impedance distribution corresponding to the lithofacies state; is the mean value of the impedance corresponding to the lithofacies state; is the covariance obtained by performing a Kronecker product on the impedance variance and the variogram; is the possible lithofacies space.

[0074] S4. Based on the randomly generated lithofacies and impedance, further iteratively generate new lithofacies and impedance data. Design an objective loss function to quantify the difference between the prediction result and the actual data. At the same time, introduce the L1 norm constraint to promote sparsity and ensure the simplicity and effectiveness of the model (formula 5). Calculate the loss values corresponding to the lithofacies and impedance parameters generated initially and regenerated respectively ( and ), and evaluate the improvement effect of the model by comparing the loss values of the two.

[0075] (5);

[0076] In the formula, is the observed seismic data; is the impedance to obtain the forward operator of the synthetic seismogram; is the difference operator.

[0077] S5. According to the Markov chain acceptance criterion, by comparing the newly generated lithofacies and impedance parameters with the state of the current model, determine whether to accept the new parameter set. If the new parameter set can significantly improve the loss function values in the new and old states (Equation 6), directly accept it; otherwise, accept or reject it according to a certain probability.

[0078] (6);

[0079] In the formula, is the logarithm of the loss value in the new state; is the logarithm of the loss value in the old state. It should be noted that the smaller the loss value, the larger the logarithm.

[0080] If the loss value of the new state is less than that of the old state, then is greater than 1, otherwise it is less than 1; at the same time, randomly generate a number between 0 and 1 , if is greater than , then accept the lithofacies and impedance of the new state, otherwise retain the original state.

[0081] S6. Repeat steps S4 - S5 until the given number of iterations is reached. During this process, continuously update the lithofacies and impedance parameters according to the Markov chain acceptance criterion to ensure that each iteration moves towards the optimization goal. When the iteration is completed, count the distribution of the lithofacies and impedance parameters in the Markov chain during the stable stage to determine the final prediction result. At the same time, evaluate the uncertainty information of the prediction result by analyzing the variability and convergence in the chain.

[0082] As Figure 4 shown, Figure 4 (a) is the random realization of the lithofacies during the iteration process. As the number of iterations increases, the lithofacies simulation results gradually tend to be stable; Figure 4 (b) is the mean lithofacies result obtained after the lithofacies tends to be stable, Figure 4 (c) is the probability corresponding to the predicted lithofacies; Figure 4 (d) is the impedance prediction result, where the background represents the possible impedance result range. By analyzing the variability and convergence in the chain, the uncertainty information of the prediction result can be well evaluated.

[0083] Embodiment 2

[0084] A lithofacies and impedance synchronous seismic inversion system based on MCMC, as Figure 5 shown, includes:

[0085] Statistics module: Based on actual logging data, it is used to statistically calculate the lithofacies transition probability matrix for subsequent generation of lithofacies combinations, and at the same time statistically calculate the mean and variance of impedance parameters corresponding to different lithofacies;

[0086] Impedance parameter calculation module: Based on well data, it is used to calculate the vertical variogram of impedance parameters and establish the vertical continuity of impedance parameters;

[0087] Random generation module: Using the lithofacies transition probability matrix, it is used to randomly generate an initial lithofacies combination, and calculate the impedance corresponding to the lithofacies using the statistically calculated mean, variance, and variogram;

[0088] Regeneration module: Based on the randomly generated lithofacies and impedance, it is used to regenerate lithofacies and impedance parameters again, and calculate the loss values corresponding to the lithofacies and impedance parameters generated twice using the target loss function and L1 norm constraint;

[0089] Judgment module: According to the Markov chain acceptance criterion, it is used to judge whether to accept the newly generated lithofacies and impedance parameters;

[0090] Iterative output module: Repeat the methods in the regeneration module and the judgment module until the given number of iterations is reached, statistically calculate the distribution of lithofacies and impedance in the stable Markov chain, and obtain the final lithofacies and impedance prediction results and their uncertainty information.

[0091] The MCMC-based synchronous seismic inversion system for lithofacies and impedance of the present invention can be installed in a computer device. The computer device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a program for generating an MCMC-based synchronous seismic inversion system for lithofacies and impedance. Among them, the memory includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (such as: SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The processor is the control core of the electronic device, connects various components of the entire computer device through various interfaces and lines, and executes various functions of the computer device and processes data by running or executing programs or modules stored in the memory, and calling data stored in the memory.

[0092] The module of the present invention refers to a series of computer program segments that can be executed by the processor of a computer device and can complete fixed functions, and are stored in the memory of the computer device.

Claims

1. A method for synchronous seismic inversion of lithofacies and impedance based on MCMC, characterized in that It includes the following steps: S1. Based on the actual logging data, statistically calculate the lithofacies transition probability matrix for subsequent generation of lithofacies combinations, and at the same time statistically calculate the mean and variance of the impedance parameters corresponding to different lithofacies; S2. Calculate the vertical variogram of the impedance parameters based on the well data to establish the vertical continuity of the impedance parameters; S3. Randomly generate an initial lithofacies combination using the lithofacies transition probability matrix, and calculate the impedance corresponding to the lithofacies using the statistically calculated mean, variance, and variogram; S4. Based on the randomly generated lithofacies and impedance, generate the lithofacies and impedance parameters again, design the target loss function, and at the same time introduce the L1 norm constraint, and calculate the loss values corresponding to the lithofacies and impedance parameters generated twice respectively; S5. According to the Markov chain acceptance criterion, judge whether to accept the newly generated lithofacies and impedance parameters; S6. Repeat steps S4 - S5 until a given number of iterations is reached, statistically calculate the distribution of lithofacies and impedance in the stable Markov chain, and obtain the final lithofacies and impedance prediction results and their uncertainty information.

2. The method for synchronous seismic inversion of lithofacies and impedance based on MCMC according to claim 1, characterized in that, In S1, the lithofacies transition probability matrix is: （1）； In the formula, each row is the lithofacies of the current state, and each column is the probability of the lithofacies of the current state being converted into other lithofacies.

3. A method for synchronous seismic inversion of lithofacies and impedance based on MCMC according to claim 1, characterized in that In S2, the variogram is: （2）； Wherein, is the variogram value calculated based on well data; is the lag distance; is the length of well logging data; is a certain position at the impedance parameter value.

4. A method for synchronous seismic inversion of lithofacies and impedance based on MCMC according to claim 1, characterized in that, S3 includes the following steps: S31. Randomly generate an initial lithofacies combination: （3）； In the formula, is the state of the lithofacies at time is the state of the lithofacies at time is the lithofacies transition probability matrix; given the lithofacies state at time 0, a lithofacies sequence of any length can be obtained according to formula (3). S32. By performing a Kronecker product of the matrix constructed by the variogram and the impedance variance matrix of each lithofacies, simulate and generate an impedance curve that meets the characteristics of the logging curve: （4）； Wherein, is the current lithofacies state; is the impedance distribution corresponding to the lithofacies state; is the mean value of the impedance corresponding to the lithofacies state; is the covariance obtained by the Kronecker product of the impedance variance and the variogram; is the possible lithofacies space.

5. A method for synchronous seismic inversion of lithofacies and impedance based on MCMC according to claim 1, characterized in that, The calculation expression for calculating the loss values corresponding to the lithofacies and impedance parameters generated twice in S4 is: （5）； wherein, is the observed seismic data; is the forward operator for obtaining the synthetic seismic record from the impedance ; and is the difference operator.

6. A method for synchronous seismic inversion of lithofacies and impedance based on MCMC according to claim 1, characterized in that, In S5, by comparing the newly generated lithofacies and impedance parameters with the state of the current model, decide whether to accept the new parameter set; if the new parameter set can improve the loss function values in the old and new states, directly accept it; otherwise, accept or reject it according to a certain probability; The loss function values in the old and new states are: （6）； In the formula, is the logarithm of the loss value in the new state; is the logarithm of the loss value in the old state.

7. A method for synchronous seismic inversion of lithofacies and impedance based on MCMC according to claim 6, characterized in that If the new state loss value is less than the old state loss value, it is greater than 1; otherwise, it is less than 1. At the same time, a random number between 0 and 1 is generated , if is greater than , then accept the lithofacies and impedance of the new state; otherwise, retain the original state.

8. A lithofacies and impedance synchronous seismic inversion system based on MCMC, using a lithofacies and impedance synchronous seismic inversion method based on MCMC according to any one of claims 1-7, characterized in that, It includes: Statistics module: Based on the actual logging data, used to statistically calculate the lithofacies transition probability matrix for subsequent generation of lithofacies combinations, and at the same time statistically calculate the mean and variance of the impedance parameters corresponding to different lithofacies; Impedance parameter calculation module: Based on the well data, used to calculate the vertical variogram of the impedance parameters to establish the vertical continuity of the impedance parameters; Random generation module: Using the lithofacies transition probability matrix, used to randomly generate an initial lithofacies combination, and calculate the impedance corresponding to the lithofacies using the statistically calculated mean, variance, and variogram; Regeneration module: Based on the randomly generated lithofacies and impedance, used to regenerate the lithofacies and impedance parameters again, and calculate the loss values corresponding to the lithofacies and impedance parameters generated twice respectively using the target loss function and the L1 norm constraint; Judgment module: According to the Markov chain acceptance criterion, used to judge whether to accept the newly generated lithofacies and impedance parameters; Iterative output module: Repeat steps S4 - S5 until a given number of iterations is reached, statistically calculate the distribution of lithofacies and impedance in the stable Markov chain, and obtain the final lithofacies and impedance prediction results and their uncertainty information.

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