A method for characterizing and predicting internal instable interlayer in thick reservoir

Abstract

The application discloses a kind of thick reservoir internal unstable interlayer characterization and prediction method, comprising the following steps: 1) according to coring well, the well logging interpretation standard of different types of interlayer is determined, the whole area is interpreted, and a set of high-precision lithofacies basic data is generated;Fine interpretation is carried out on lithofacies on single well by actual drilling data;2) using petrel software variogram analysis function, multiple variogram analysis is carried out on different phase types, the space and distance relationship of the predicted point and the drilled point is characterized, the correlation is established, and the variogram is generated;3) based on the principle of two-point geostatistics, a plurality of sedimentary facies models are randomly simulated using sequential indicator simulation algorithm;4) the probability distribution characteristics of each lithofacies in the plurality of sedimentary facies models in step 3) are calculated, the model is refitted, and a high-precision lithofacies model is obtained.The application establishes a high-precision model, aims to finely characterize the distribution law of unstable interlayer, and simultaneously improves the interlayer distribution prediction accuracy of three-dimensional geological model between wells.

Description

Technical Field

[0001] This invention relates to geological exploration technology, and more particularly to a method for characterizing and predicting unstable interlayers within thick reservoirs. Background Technology

[0002] Currently, there are three main approaches to simulating and predicting lithofacies: First, target-based modeling. This method typically selects a background facies, generates facies units based on spatial relationships, and then fits them into a model grid to fit well data. While this method can reproduce the geometry of the target facies, it struggles with parameterizing complex facies and fitting multiple data points within a single target area, sometimes even failing to converge. Second, two-point geostatistical facies modeling. This method uses Kriging to determine the conditional cumulative probability distribution function (CCDF) and applies sequential simulation to obtain the simulation result. This method can simulate complex anisotropic geological phenomena; since each type of variable corresponds to an indicator variogram, an anisotropic simulation image can be established. However, this method cannot accurately recover the geometry of the target facies. Third, multi-point geostatistical modeling. This method uses training images instead of variograms to characterize the facies at multiple spatial points. This method simulates conditions faithfully, and the simulation results have relatively ideal morphology and distribution characteristics. However, it is difficult to obtain training images suitable for the target reservoir, and it requires high stability of geological conditions in the simulation area; the simulation results still need further optimization. The method of this invention mainly improves the data source and simulation method based on the two-point geostatistical stochastic modeling method, fully considers the geological characteristics of the actual work area, and realizes accurate prediction of the spatial distribution of unstable interlayers inside thick reservoirs. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method for characterizing and predicting unstable interlayers inside reservoirs, addressing the deficiencies in the prior art.

[0004] The technical solution adopted by this invention to solve its technical problem is: a method for characterizing and predicting unstable interlayers inside thick reservoirs, comprising the following steps:

[0005] 1) Determine the logging interpretation standards for different types of interlayers based on the core wells, perform full-area and full-well interpretation, and generate a set of high-precision lithofacies basic data; perform fine interpretation of lithofacies on single wells using actual drilling data;

[0006] 2) Use the variogram analysis function of Petrel software to perform multiple variogram analyses on different phase types to characterize the spatial and distance relationship between the point to be predicted and the actual drilling point, establish the correlation, and generate the variogram.

[0007] 3) Based on the two-point geostatistical principle, a sequential indicator simulation algorithm was used to randomly simulate multiple sedimentary facies models;

[0008] 4) Calculate the probability distribution characteristics of each lithofacies in multiple sedimentary facies models in step 3), refit the models to obtain a high-precision lithofacies model, accurately characterize the distribution law of interlayers and accurately predict the distribution of well-interlayers.

[0009] According to the above scheme, in step 4), the algorithm is written into the Petrel simulation module based on the principle of probability statistics to calculate the probability distribution characteristics of each lithofacies in multiple sedimentary facies models in step 3).

[0010] According to the above scheme, in step 4), the Property calculator module is used to calculate the probability of the three rock facies appearing in the same rock facies model one by one, and then combined with the distribution ratio of rock facies from the actual drilling logging interpretation results to obtain a high-precision rock facies model.

[0011] The beneficial effects of this invention are: through steps such as detailed interpretation of actual drilling data, multiple variogram analysis, multiple sedimentary facies simulation, statistical analysis of lithofacies probability distribution characteristics, and high-precision model fitting, it aims to accurately characterize the distribution law of unstable interlayers, while improving the prediction accuracy of inter-well interlayer distribution in three-dimensional geological models. Attached Figure Description

[0012] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings:

[0013] Figure 1 This is a lithofacies distribution map based on actual drilling data according to an embodiment of the present invention;

[0014] Figure 2 This is a graph illustrating the multiple variation function analysis of an embodiment of the present invention;

[0015] Figure 3 This is an initial lithofacies model diagram of an embodiment of the present invention;

[0016] Figure 4 This is a statistical diagram of the lithofacies probability distribution characteristics according to an embodiment of the present invention;

[0017] Figure 5 This is the final lithofacies model diagram of an embodiment of the present invention;

[0018] Figure 6 This is a flowchart of a method according to an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0020] like Figure 6As shown, this application provides a method for characterizing and predicting unstable interlayers within thick reservoirs, including the following steps:

[0021] 1) Determine the logging interpretation standards for different types of interbeds based on the cored wells, perform full-area and full-well interpretation, and generate a set of high-precision lithofacies basic data; conduct detailed lithofacies interpretation on individual wells using actual drilling data; such as Figure 1 ;

[0022] 2) Utilize the variogram analysis function of Petrel software to perform multiple variogram analyses on different phase types, characterizing the spatial and distance relationship between the predicted point and the actual drilled point, establishing correlation, and generating variograms; such as Figure 2 ;

[0023] 3) Based on the two-point geostatistical principle, a sequential indicator simulation algorithm was used to randomly simulate multiple sedimentary facies models; such as... Figure 3 ;

[0024] 4) Based on the principles of probability statistics, the algorithm is written into the Petrel simulation module to calculate the probability distribution characteristics of each lithofacies in multiple sedimentary facies models; such as... Figure 4 ;

[0025] Based on the principles of probability and statistics, the algorithm for multiple sedimentary facies models in step 3) is written using the Petrel workflow module: The for loop...End loop statement is used to simulate and calculate the target facies and background facies HHTFn = If(New_folder\FACIES[$i] = 1,HHTFn+1,HHTFn) (n takes the value 1,2,3; HHTF1 represents the sandstone facies model, HHTF2 represents the mudstone facies model, HHTF3 represents the intermontane subfacies model, and FACIES[$i] represents the i-th sedimentary facies model) respectively, thereby realizing multiple sandstone facies, mudstone facies, and intermontane subfacies models respectively.

[0026] 5) The model is refitted to obtain a high-precision lithofacies model, which accurately characterizes the distribution pattern of interlayers and precisely predicts the distribution of well-interlayers; such as Figure 5 ;

[0027] The Property calculator module is used to calculate the probability of the three rock facies appearing in the same rock facies model one by one. Then, the distribution ratio of rock facies is obtained by combining the interpretation results of actual drilling well logging. This results in a high-precision rock facies model that accurately represents the distribution law of interlayers and accurately predicts the distribution of interlayers between wells.

[0028] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A method for characterizing and predicting unstable interlayers within thick reservoirs, characterized in that, Includes the following steps: 1) Determine the logging interpretation standards for different types of interlayers based on the core wells, perform full-area and full-well interpretation, and generate a set of high-precision lithofacies basic data; Detailed interpretation of lithofacies was performed on a single well using actual drilling data; 2) Use the variogram analysis function of Petrel software to perform multiple variogram analyses on different phase types to characterize the spatial and distance relationship between the point to be predicted and the actual drilling point, establish the correlation, and generate the variogram. 3) Based on the two-point geostatistical principle, a sequential indicator simulation algorithm was used to randomly simulate multiple sedimentary facies models; 4) Calculate the probability distribution characteristics of each lithofacies in multiple sedimentary facies models in step 3), refit the models to obtain a high-precision lithofacies model, and use the high-precision lithofacies model to characterize the distribution law of interlayers and predict the distribution of well-interlayers. In step 4), the algorithm is integrated into the Petrel simulation module based on the principles of probability statistics to calculate the probability distribution characteristics of each lithofacies in the multiple sedimentary facies models in step 3). The algorithm is as follows: It uses loop statements to simulate and calculate the target facies and background facies separately, realizing multiple sandstone facies, mudstone facies, and intermontane subfacies models; the calculation function is: HHTFn=If(New_folder\FACIES[$i]=1, HHTFn+1,HHTFn); Where n takes values ​​of 1, 2, 3; HHTF1 represents the sandstone facies model, HHTF2 represents the mudstone facies model, HHTF3 represents the intermontane subfacies model, and FACIES[$i] represents the i-th sedimentary facies model; A high-precision lithofacies model is obtained by using the Property calculator module to calculate the probability of the three lithofacies appearing in the same lithofacies model one by one, and then combining the distribution ratio of lithofacies from the actual drilling logging interpretation results to obtain a high-precision lithofacies model.

2. A server, 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, it implements the method for characterizing and predicting unstable interlayers within thick reservoirs as described in claim 1.

3. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are used to cause a processor to execute the method for characterizing and predicting unstable interlayers within thick reservoirs as described in claim 1.

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