Deep carbonate rock lithology identification method based on gamma-ray spectrum logging

Abstract

The invention relates to the technical field of geological rock type identification, and discloses a deep carbonate rock lithology identification method based on gamma-ray spectrum logging, and the method comprises the steps: S1, collecting a large number of rock cores, slices and the like as verification data; s2, performing statistics on slice identification results of 4, 6, 7 and 8 well carbonate rock coring layer sections of the ancient city in the research area, and dividing the carbonate rock in the research area into five types, namely grit limestone, mud crystal, bright crystal grit limestone, mud crystal limestone, cloud limestone and dolomite; and S3, calibrating on a single well histogram, and counting gamma-ray energy spectrum (U, K, TH, PE) values corresponding to the slice samples. According to the method provided by the invention, the lithology of the carbonate rocks in the fourth, sixth, seventh and eighth wells of the ancient city is identified with relatively high accuracy, fine lithology changes are further identified in the originally explained large set of limestone, the accuracy is far higher than that of logging and debris logging through rock core inspection, and the accuracy of identifying the lithology of the carbonate rocks is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of geological rock type identification, and particularly to a method for identifying the lithology of deep carbonate rocks based on gamma ray spectrometry logging. Background Art

[0002] At present, logging technology is widely applied in the identification and evaluation of carbonate reservoirs. Methods such as using imaging logging, BP network (Luo Li, Ren Xingguo, 1999), energy spectrum logging (Lu Yingzhong et al., 2006), and the combination of conventional logging and imaging logging to identify carbonate reservoir types (Zhang Weifeng et al., 2006; Zhang Bing et al., 2010) have achieved good results. However, the logging identification of the lithology combination of carbonate sedimentary microfacies has always been a difficult problem in research. At present, there is less research on this problem at home and abroad. The relevant research mainly uses the stepwise regression method to establish a sedimentary facies model (Luo Li, Chen Xintang, 1997), establishes an identification chart of sedimentary microfacies through imaging logging, and uses a fuzzy recognition system to identify sedimentary microfacies and other methods (You Zhanglong et al., 1998). For deep carbonate rocks, due to strong tectonic, diagenetic effects and special sedimentary environments, the lithology information measured by logging is interfered (Han Changwei, 2009). Identifying the lithology combination is relatively complex, resulting in a certain problem of multiple solutions in identifying the lithology of carbonate rocks by conventional methods. It is very difficult to determine sedimentary microfacies, reservoir evaluation and sedimentary environment.

[0003] How to identify the lithology of carbonate rocks by combining multiple means based on limited data is the key to carrying out research on sedimentary microfacies and reservoir evaluation. Through the comparison of the effects of various methods, we use natural gamma ray spectrometry logging data to distinguish the rock types of carbonate reservoirs. Natural gamma ray spectrometry has high application value in lithology identification and reservoir evaluation in carbonate strata with complex structures. For example, TH is easily adsorbed by clay minerals, has good stability and weak migration ability. The higher the content of clay minerals, the higher the TH value (Guo Yufeng 1996); when the sediment grain size is finer and the shale content is higher, the corresponding TH and K values are higher (Guo Yufeng 1995, 1996, Johann Schnyder 2006); U is easily dissolved in water and migrates with the water flow. The stronger the water flow action, the higher the enrichment degree of U (Xing Zhou, 2005, Liu Xianhong 2007, Guo Yufeng 1996, Johann Schnyder 2006); PE (lithology density logging) can simultaneously measure the formation bulk density and the effective photoelectric absorption cross section index, and can well reflect the changes in lithology.

[0004] Marching towards the deep Earth has become the main direction of oil and gas exploration. In the Paleozoic, there are extremely thick marine carbonate strata developed in the Sichuan Basin, Tarim Basin and Ordos Basin. In recent years, many major breakthroughs have been achieved in exploration. Carbonate rocks have experienced complex and long diagenesis, and the reservoirs formed have strong heterogeneity. At the same time, there are many classification schemes for carbonate rocks, which can generally be divided into two basic types: limestone and dolomite. In addition, there are several transitional lithologies, including limestone, dolomite-bearing limestone, dolomitic limestone, sandy limestone, muddy limestone, argillaceous limestone, micritic limestone, calcareous dolomite, dolomite-bearing dolomite, sandy dolomite, dolomite, etc. During the actual drilling process, continuous coring cannot be carried out in the main target interval. Especially for deep carbonate reservoirs, due to their strong brittleness and well-developed fractures, the coring recovery rate is generally low, and the complete information of the deep underground reservoir cannot be obtained, which limits the geological understanding of the wellbore longitudinally and even regionally, and is not conducive to the in-depth exploration of oil and gas.

[0005] Therefore, the present invention provides a method for identifying deep carbonate lithology based on gamma-ray spectrometry logging to solve the deficiencies of the prior art. Summary of the Invention

[0006] Aiming at the deficiencies of the prior art, the present invention provides a method for identifying deep carbonate lithology based on gamma-ray spectrometry logging to achieve a more accurate means of identifying carbonate lithology and improve the accuracy of identifying carbonate lithology.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: A method for identifying deep carbonate lithology based on gamma-ray spectrometry logging, comprising the following steps:

[0008] S1. Collect a large number of cores, thin sections, etc. as calibration data;

[0009] S2. Statistically analyze the thin-section identification results of the carbonate coring intervals of Wells 4, 6, 7, and 8 in the study area, and divide the carbonate rocks in the study area into five types: sandy limestone, micrite, sparitic sandy limestone, micritic limestone, dolomitic limestone, and dolomite.

[0010] S3. Calibrate on the single-well histogram and statistically analyze the gamma-ray spectrometry (U, K, TH, PE) values corresponding to the thin-section samples.

[0011] Preferably, in the step S2, the identification result parameters are TH / U, U / (U + K + TH), TH / KTH, TH / K, U / K, U / (TH + K), TH / (U + K + TH), among which the parameters PE and U / (TH + K) - TH / KTH are the most sensitive to lithology changes.

[0012] Preferably, a method for identifying deep carbonate lithology based on gamma-ray spectrometry logging further includes using the normalized parameters PE and U / (TH+K)-TH / KTH for lithology identification, where limestone and dolomite are identified through PE, and the types of limestone are identified through U / (TH+K)-TH / KTH.

[0013] Preferably, a method for identifying deep carbonate lithology based on gamma-ray spectrometry logging further includes making cross plots of the PE and U / (TH+K)-TH / KTH data of gamma-ray spectrometry sensitive parameters to establish a gamma-ray spectrometry identification chart and a lithology identification table for deep carbonate lithology.

[0014] Preferably, in the step S2, the method for thin section identification of the carbonate core section is to determine the identification criteria for five lithologies of sandy limestone, micrite limestone, micrite, sparitic sandy limestone, and dolomitic limestone by calibrating a large number of core thin sections and analyzing the variation laws of parameters, and using the cluster analysis method:

[0015] a. For the PE value, less than 0.89 is dolomite, 0.89 - 0.98 is the transitional lithology between dolomite and limestone, and greater than 0.98 is limestone;

[0016] b. For the value of U / (TH+K)-TH / KTH, greater than 3 is sandy limestone, 1 - 3 is micrite and sparitic sandy limestone, and less than 1 is micrite limestone.

[0017] The present invention provides a method for identifying deep carbonate lithology based on gamma-ray spectrometry logging. It has the following beneficial effects:

[0018] 1. The method provided by the present invention can identify the carbonate lithology of Wells Gucheng 4, 6, 7, and 8 with high accuracy, and further identify the fine lithology changes in the originally interpreted large thick limestone. Taking Well Gucheng 7 as an example ( Figure 2 ), the lithology is mainly sandy limestone and dolomite. The lithology identification profile is finer than the original logging profile and cuttings profile. Through core inspection, the accuracy is much higher than that of logging and cuttings logging, and the accuracy of identifying carbonate lithology is greatly improved. At the same time, through a large number of core thin sections to test the lithology identification results, the overall accuracy rate of the lithology identification results reaches 83%.

[0019] 2. The present invention comprehensively applies a variety of logging methods and technical means, including imaging logging, spectrometry logging, conventional logging, etc., to identify and evaluate the complexity and heterogeneity of carbonate reservoirs, improving the understanding and knowledge of carbonate strata. At the same time, in the logging identification process, normalized parameters are used, and combined with the methods of calibration and cross plots, a gamma-ray spectrometry identification chart and a lithology identification table for deep carbonate lithology are established, providing an intuitive and convenient lithology interpretation and identification tool. Description of the Drawings

[0020] Figure 1 It is a schematic diagram of the intersection of PE and parameter U / (TH + K)-TH / KTH of the present invention;

[0021] Figure 2 It is a schematic diagram of the comprehensive columnar gamma ray spectrometry logging of Well Gucheng 7 of the present invention. Detailed Embodiments

[0022] Next, in combination with the accompanying drawings of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0023] Embodiment:

[0024] Please refer to the attached Figure 1 - attached Figure 2 The embodiment of the present invention provides a method for identifying the lithology of deep carbonate rocks based on gamma ray spectrometry logging, including the following steps:

[0025] S1. Collect a large number of cores, thin sections, etc. as calibration data. Through collecting a large amount of calibration data and statistical analysis, the accuracy and reliability of lithology identification can be improved by this method;

[0026] S2. Statistically study the thin section identification results of the carbonate core sections of Wells Gucheng 4, 6, 7, and 8 in the study area, and divide the carbonate rocks in the study area into five types: sandy limestone, micrite, sparitic sandy limestone, micritic limestone, dolomitic limestone, and dolomite. This step can provide a basis and reference for subsequent lithology identification;

[0027] S3. Calibrate on the single well columnar diagram, and statistically analyze the gamma ray spectrometry (U, K, TH, PE) values corresponding to the thin section samples. By analyzing the gamma ray spectrometry data, the characteristic spectrometry curves of different types of carbonate rocks can be obtained. These characteristic spectrometry curves can be compared and matched with the actual logging data to determine the rock types and lithologies in the formation.

[0028] In step S2, the identification result parameters are TH / U, the ratio of thorium (TH) to uranium (U), which is used to evaluate the content of uranium series radioactive elements in rocks; U / (U + K + TH), the ratio of thorium to the total amount of thorium series radioactive elements (thorium, uranium, and potassium), which is used to evaluate the thorium content in rocks; TH / KTH, the ratio of thorium to the total amount of thorium series radioactive elements (thorium, uranium, and potassium), which is used to evaluate the thorium content in rocks; TH / K, the ratio of thorium to potassium, which is used to evaluate the relationship between the thorium and potassium contents in rocks; U / K, the ratio of uranium to potassium, which is used to evaluate the relationship between the uranium and potassium contents in rocks; U / (TH + K), the ratio of uranium to the sum of the total amount of thorium series radioactive elements and potassium, which is used to evaluate the relationship between the uranium content and the total amount of thorium series radioactive elements and potassium content in rocks; TH / (U + K + TH), the ratio of thorium to the sum of the total amount of thorium series radioactive elements and potassium, which is used to evaluate the relationship between the thorium content and the total amount of thorium series radioactive elements and potassium content in rocks. Among them, the parameters PE and U / (TH + K) - TH / KTH are the most sensitive to lithology changes.

[0029] It also includes using the normalized parameters PE and U / (TH + K) - TH / KTH for lithology identification. Among them, limestone and dolomite are identified by PE, and then the type of limestone is identified by U / (TH + K) - TH / KTH.

[0030] Using the parameter PE for the identification of limestone and dolomite:

[0031] When the PE value is greater than a certain threshold, it can be determined as dolomite.

[0032] When the PE value is less than a certain threshold, it can be determined as limestone.

[0033] For limestone, the parameter U / (TH + K) - TH / KTH is used for type identification:

[0034] When the value of U / (TH + K) - TH / KTH is greater than a certain threshold, it can be determined as sparite limestone.

[0035] When the value of U / (TH + K) - TH / KTH is less than a certain threshold, it can be determined as micrite limestone.

[0036] It also includes making a cross plot of the PE and U / (TH + K) - TH / KTH data of gamma energy spectrum sensitive parameters to establish a gamma energy spectrum identification chart for deep carbonate lithology, such as Figure 1 , and a lithology identification table, as shown in Table 1.

[0037] Making crossplots of the PE and U / (TH+K)-TH / KTH data of gamma energy spectrum sensitive parameters is a common method, which can help establish the gamma energy spectrum identification chart and lithology identification table for deep carbonate lithology. Such charts and tables can be used for lithology interpretation and identification, thus better understanding the properties of subsurface rocks.

[0038] The following are the general steps to establish the gamma energy spectrum identification chart and lithology identification table for deep carbonate lithology:

[0039] Collect gamma energy spectrum logging data and extract the PE and U / (TH+K)-TH / KTH parameters.

[0040] Normalize the data to ensure the comparability of parameters between different wells.

[0041] Draw a crossplot of the PE and U / (TH+K)-TH / KTH parameters. The horizontal axis is the PE value and the vertical axis is the U / (TH+K)-TH / KTH value. Each point represents a logging point or sample point.

[0042] Mark the areas of different lithologies on the crossplot. Based on the known geological background and the verified lithology, mark the data points of different lithologies on the graph. Different symbols or colors can be used to represent different lithologies.

[0043] Based on the marked data points, draw a lithology identification table. The rows of the table represent the range of PE values and the columns represent the range of U / (TH+K)-TH / KTH values. In each cell, fill in the corresponding lithology type.

[0044] According to the crossplot and lithology identification table, identify the lithology of unknown areas or new samples. Determine the lithology category to which it belongs based on the values of its PE and U / (TH+K)-TH / KTH parameters.

[0045] In step S2, the way of thin section identification for the carbonate core section is to determine the identification criteria for five lithologies of sandy limestone, micrite, micrite, sparitic sandy limestone, and dolomitic limestone through the calibration of a large number of core thin sections and the analysis of the variation law of parameters, using the cluster analysis method:

[0046] a. For the PE value, less than 0.89 is dolomite, 0.89 - 0.98 is the transitional lithology between dolomite and limestone, and greater than 0.98 is limestone;

[0047] b. For the U / (TH+K)-TH / KTH value, greater than 3 is sandy limestone, 1 - 3 is micrite and sparitic sandy limestone, and less than 1 is micrite.

[0048] Table 1 Gamma energy spectrum identification criteria for carbonate lithology

[0049]

[0050]

[0051] Taking the ancient city well 7 as an example ( Figure 2 ), the lithology is mainly composed of sand debris limestone and dolomite. The lithology identification profile is finer than both the original logging profile and the cuttings profile. Through core inspection, the accuracy is much higher than that of logging and cuttings logging. The sedimentary environment where the ancient city well 7 is located exactly matches the platform edge, indicating that the lithology identification conforms to geological laws.

[0052] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for identifying deep carbonate lithology based on gamma-ray spectrometry logging, characterized in that, it includes the following steps: S1. Collect a large number of cores, thin sections, etc. as calibration data; S2. Statistically analyze the thin-section identification results of the carbonate core-taking intervals of Wells 4, 6, 7, and 8 in the ancient city of the study area, and divide the carbonate rocks in the study area into five types: sandy limestone, micrite, sparitic sandy limestone, micritic limestone, dolomitic limestone, and dolomite; S3. Calibrate on the single-well columnar diagram and statistically analyze the gamma-ray spectrometry (U, K, TH, PE) values corresponding to the thin-section samples.

2. A method for identifying deep carbonate lithology based on gamma-ray spectrometry logging according to claim 1, characterized in that, in step S2, the identification result parameters are TH / U, U / (U + K + TH), TH / KTH, TH / K, U / K, U / (TH + K), TH / (U + K + TH), where the parameters PE and U / (TH + K) - TH / KTH are the most sensitive to lithology changes.

3. A method for identifying deep carbonate lithology based on gamma-ray spectrometry logging according to claim 2, characterized in that, it also includes using the normalized parameters PE and U / (TH + K) - TH / KTH for lithology identification, where limestone and dolomite are identified by PE, and then the types of limestone are identified by U / (TH + K) - TH / KTH.

4. A method for identifying deep carbonate lithology based on gamma-ray spectrometry logging according to claim 3, characterized in that, it also includes making cross plots of the PE and U / (TH + K) - TH / KTH data of the gamma-ray spectrometry sensitive parameters, and establishing a gamma-ray spectrometry identification chart and a lithology identification table for deep carbonate lithology.

5. A method for identifying deep carbonate lithology based on gamma-ray spectrometry logging according to claim 1, characterized in that, in step S2, the method for thin-section identification of the carbonate core-taking intervals is to determine the identification criteria for five lithologies of sandy limestone, micritic limestone, micrite, sparitic sandy limestone, and dolomitic limestone through the calibration of a large number of core thin sections and the analysis of the variation laws of the parameters, using the cluster analysis method: a. For the PE value, less than 0.89 is dolomite, 0.89 - 0.98 is the transitional lithology between dolomite and limestone, and greater than 0.98 is limestone; b. For the value of U / (TH + K) - TH / KTH, greater than 3 is sandy limestone, 1 - 3 is micrite and sparitic sandy limestone, and less than 1 is micritic limestone.

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