Carbonate rock identification method and system based on element logging analysis

Abstract

This invention provides a method and system for identifying carbonate rocks based on elemental logging analysis. The method involves performing elemental spectral analysis on samples already identified as carbonate rocks to obtain the Ca and Mg content; establishing a carbonate rock identification model, which includes a carbonate rock classification index calculation model, a calcium-magnesium index calculation model, and a carbonate rock discrimination model; inputting the obtained Ca and Mg content into the carbonate rock discrimination model; and using the carbonate rock classification index calculation model and the calcium-magnesium index calculation model to obtain the carbonate rock classification index value and the calcium-magnesium index value. The carbonate rock discrimination model determines the lithology of the carbonate rock sample based on the intersection range of the carbonate rock classification index value and the calcium-magnesium index value. This invention establishes a carbonate rock classification and identification model based on elemental characteristics, thereby guiding the rapid and accurate quantitative identification of carbonate rocks in the field, solving the problem of in-situ carbonate rock lithology identification.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas exploration and development technology, and particularly relates to the field of well logging technology in the process of oil and gas exploration and development. Specifically, it relates to a method and system for identifying carbonate rocks based on elemental well logging analysis. Background Technology

[0002] In the process of oil and gas exploration and development, it is necessary to first conduct well logging to detect the structural properties of underground oil and gas reservoirs and make a preliminary judgment on the exploration value and methods of oil and gas, so as to improve the accuracy and economic benefits of exploration. Therefore, the identification and judgment of the physical structure of the reservoir has important guiding significance for oil and gas extraction. Carbonate reservoirs are one of the main reservoirs of oil and gas, mainly composed of calcite and dolomite. According to the different contents of these two materials, carbonate rocks are further divided into different lithologies such as limestone, dolomite, dolomitic limestone, and calcareous dolomite. Carbonate rocks are rich in oil and gas resources and are becoming a new direction for oil and gas exploration and development. In order to accurately discover oil and gas resources and guide the accurate and rational extraction of oil and gas resources, it is necessary to make accurate judgments on the physical properties of carbonate reservoirs. Due to the extensive use of PDC drill bits or gas drilling in the current oil and gas drilling technology, carbonate rock cuttings form fine particles or dust, making it impossible to accurately determine the lithology of carbonate rocks by manual visual analysis. Therefore, in order to change the current situation where the lithology of carbonate rocks cannot be accurately determined by the naked eye, some other alternative methods have gradually emerged.

[0003] For example, the traditional method for identifying carbonate lithology at well logging sites involves manually selecting a certain amount of carbonate rock samples, grinding them, and then placing them in a sealed container to react with a certain amount of dilute hydrochloric acid. The resulting gas pressure is used to determine the content of calcium carbonate and magnesium carbonate in the sample, thereby describing the lithology of the sample. However, this method of carbonate rock lithology identification is affected by the quality of the manually selected samples, and the identification process is also greatly influenced by human factors. Furthermore, the testing cycle is long, making it unsuitable for rapid and accurate identification of carbonate rock lithology. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a method and system for identifying carbonate rocks based on elemental logging analysis. By establishing a carbonate rock classification and identification model through elemental characteristics, it guides the rapid and accurate quantitative identification of carbonate rocks in the field, thus solving the problem of lithological identification of carbonate rocks in the field.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for identifying carbonate rocks based on elemental logging analysis, the specific steps of which are as follows:

[0006] S1 performed elemental spectral analysis on the sample that had been identified as carbonate rock to obtain the contents of Ca and Mg elements;

[0007] S2 establishes a carbonate rock identification model, which includes: a carbonate rock classification index calculation model, a calcium-magnesium index calculation model, and a carbonate rock discrimination model.

[0008] S3 inputs the Ca and Mg element contents obtained from S1 into the carbonate rock discrimination model. In the carbonate rock discrimination model, the carbonate rock classification index value and the calcium-magnesium index value are obtained through the carbonate rock classification index calculation model and the calcium-magnesium index calculation model. The carbonate rock discrimination model determines the lithology of the carbonate rock sample based on the intersection range of the carbonate rock classification index value and the calcium-magnesium index value.

[0009] Furthermore, in S2, a calculation model for the carbonate rock classification index is established using the chemical differentiation intensity index of Ca and Mg elements.

[0010] Furthermore, in S2, the specific calculation model for the carbonate rock classification index is as follows:

[0011] TZS = H Ca *HZ Ca +H Mg *HZ Mg

[0012] In the formula:

[0013] TZS—Carbonate Rock Classification Index;

[0014] HZ Ca —The chemical differentiation intensity index of Ca element;

[0015] HZ Mg —Index of chemical differentiation intensity of Mg element;

[0016] H Ca —Percentage content of Ca element;

[0017] H Mg —Percentage content of Mg element.

[0018] Furthermore, in S2, a calcium-magnesium index calculation model is established based on the percentage content of Ca and Mg elements.

[0019] Furthermore, in S2, the calcium-magnesium index calculation model is specifically as follows:

[0020] GMZS=lg(H Ca / H Mg )

[0021] In the formula:

[0022] GMZS—Calcium Magnesium Index;

[0023] H Ca —Percentage content of Ca element;

[0024] H Mg —Percentage content of Mg element.

[0025] Furthermore, in S3, in the carbonate rock discrimination model, a scatter plot is established with the carbonate rock classification index as the abscissa and the calcium-magnesium index as the ordinate to determine the intersection interval of the carbonate rock classification index curve and the calcium-magnesium index curve. Different intersection intervals represent the lithology of different carbonate rock samples.

[0026] Furthermore, in S3, different intersection zones represent the lithology of different carbonate rock samples, as detailed below:

[0027] When the carbonate rock classification index is ≤-13.5 and the calcium-magnesium index is ≤0.25, the carbonate rock is pure dolomite;

[0028] When -13.5 < carbonate rock classification index ≤ -5.5 and 0.25 < calcium-magnesium index ≤ 0.4, the carbonate rock is a gray-white dolomite.

[0029] When -5.5 < carbonate rock classification index ≤ 4 and 0.4 < calcium-magnesium index ≤ 0.7, the carbonate rock is a calcareous dolomite.

[0030] When 4 < carbonate rock classification index ≤ 9 and 0.7 < calcium-magnesium index ≤ 1.0, the carbonate rock is dolomitic limestone;

[0031] When 9 < carbonate rock classification index ≤ 18 and 1.0 < calcium-magnesium index ≤ 1.8, the carbonate rock is a dolomitic limestone.

[0032] When 18 ≤ carbonate rock classification index and 1.8 ≤ calcium-magnesium index, the carbonate rock is pure limestone.

[0033] This invention also provides a carbonate rock discrimination system based on elemental logging analysis, including...

[0034] The content detection module is used to perform elemental spectral analysis on samples that have been identified as carbonate rocks to obtain the content of Ca and Mg elements.

[0035] The model building module is used to build a carbonate rock identification model, which includes: a carbonate rock classification index calculation model, a calcium-magnesium index calculation model, and a carbonate rock discrimination model.

[0036] The lithology discrimination module is used to input the obtained Ca and Mg element contents into the carbonate rock discrimination model. In the carbonate rock discrimination model, the carbonate rock classification index value and the calcium-magnesium index value are obtained through the carbonate rock classification index calculation model and the calcium-magnesium index calculation model. The carbonate rock discrimination model determines the lithology of the carbonate rock sample based on the intersection range of the carbonate rock classification index and the calcium-magnesium index value.

[0037] The present invention also provides a terminal device, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in the above method, or to implement the functions of each module in the above system.

[0038] The present invention also provides a computer-readable medium, including a computer program stored in the computer-readable storage medium, wherein the computer program, when executed by a processor, implements the steps in the above method, or implements the functions of each module in the above system.

[0039] Compared with the prior art, the present invention has at least the following beneficial effects:

[0040] This invention provides a carbonate rock identification method based on elemental logging analysis. It rapidly and quantitatively obtains the Ca and Mg content in carbonate rocks through elemental spectral analysis and establishes a carbonate rock identification model to obtain a carbonate rock classification index and a calcium-magnesium index. Carbonate rocks are sedimentary rocks mainly composed of carbonate minerals, the most common being calcite (CaCO3) and dolomite (MgCO3), with transitional lithologies between them. Due to the different chemical differentiation intensities of Ca and Mg, the lithology formed at different stages of carbonate rock formation varies, resulting in different enrichment levels of Ca and Mg, and also different ratios of the two elements. Dividing different intervals allows for accurate lithology identification. Therefore, this invention combines the carbonate rock classification index and the calcium-magnesium index for precise identification of carbonate rock lithology. This method is highly efficient, low-cost, and can quickly and accurately determine stratigraphic lithology, providing effective guidance for the discovery and exploration of oil and gas reservoirs and improving the development efficiency of oil and gas. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in this invention, the accompanying drawings used in the description of the invention and embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 Schematic diagram of carbonate rock discrimination based on elemental logging analysis;

[0043] Figure 2 Flowchart of a method for identifying carbonate rocks based on elemental logging analysis. Detailed Implementation

[0044] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0045] This invention provides a method for identifying carbonate rocks based on elemental logging analysis. Utilizing chemical differentiation theory and quantitative analysis of the Ca / Mg content ratio, it enables rapid identification of carbonate rock lithology. The specific steps are as follows:

[0046] Step 1: Identify sensitive elements. There are two main constituent elements of carbonate rocks: Mg and Ca.

[0047] Step 2: Perform elemental spectral analysis on the sample that has been identified as carbonate rock to obtain the Ca and Mg content.

[0048] Step 3: Establish a carbonate rock identification model, which includes: a carbonate rock classification index calculation model, a calcium-magnesium index calculation model, and a carbonate rock discrimination model;

[0049] Step 4: Input the Ca and Mg elemental contents obtained in Step 2 into the carbonate rock discrimination model. The carbonate rock classification index value and the calcium-magnesium index value are obtained in the carbonate rock discrimination model through the carbonate rock classification index calculation model and the calcium-magnesium index calculation model. Therefore, as... Figure 1 As shown, the carbonate rock discrimination model determines the lithology of the carbonate rock in the sample to be tested based on the specific carbonate rock classification index and calcium-magnesium index value, thereby achieving accurate identification of carbonate rocks.

[0050] In step 2, the Ca and Mg elemental contents are obtained based on elemental logging technology using X-ray fluorescence spectrometry (XRF). XRF equipment is an instrument that can analyze elemental content by measuring the characteristic X-rays of elements in a substance. It allows for rapid and non-destructive quantitative analysis.

[0051] The following are the steps for rapid quantitative analysis of elemental content using XRF equipment:

[0052] 1) Sample preparation: Prepare the sample to be analyzed into an appropriate form. For example, solid samples can be ground into powder, and liquid samples can be measured directly.

[0053] 2) Instrument calibration: Use standard samples with known content to calibrate the instrument, and establish a calibration curve based on the relationship between the standard samples and the instrument measurement results.

[0054] 3) Sample Measurement: The sample is placed in the measurement chamber of the XRF device, where X-rays are generated by exciting the sample. Elements in the sample absorb some of the excited X-rays and emit characteristic X-rays. The XRF device determines the elemental content by detecting and analyzing these characteristic X-rays.

[0055] 4) Data processing: The XRF device calculates the content of each element in the sample based on the calibration curve and measurement results.

[0056] XRF equipment quantitatively and rapidly analyzes elemental content by measuring characteristic X-rays in samples. It is non-destructive, highly efficient, and fast, and is widely used in materials science, geology, environmental monitoring, and other fields.

[0057] Thirty-one national standard samples were selected to establish a standard working curve. A standard working curve is a method used to establish a relationship between the measurement signal of an XRF instrument and the known chemical composition of a sample. It is established by measuring a series of standard samples with known concentrations and recording their corresponding XRF measurement signals. These standard samples typically have known elemental concentrations and can be pure elements or mixtures. By measuring these standard samples and establishing a calibration curve, the measurement signal of the XRF instrument can be converted into the concentration of each element in the sample.

[0058] The stability and repeatability of the standard working curve are less than or equal to 5%. This technology performs analysis while drilling, offering timely, rapid, accurate, reliable, and quantitative analysis capabilities. It has strong technical support, and the method used in this invention has been practically verified with significant results.

[0059] In step 3, the carbonate rock classification index calculation model is established based on the different chemical differentiation intensities of Ca and Mg elements, as detailed below:

[0060] Chemical differentiation refers to the process by which a system with a definite geochemical composition undergoes a geochemical process that causes the exchange of elements between the system and the external environment, resulting in the outflow or inflow of elements and thus altering the system's elemental composition. Due to the varying intensity of chemical differentiation among elements, the lithology formed at different stages of carbonate rock formation will differ due to variations in the enrichment levels of Ca and Mg.

[0061] The specific calculation formula is as follows:

[0062] TZS = H Ca *HZ Ca +H Mg *HZ Mg

[0063] In the formula:

[0064] TZS—Carbonate Rock Classification Index;

[0065] HZ Ca —The chemical differentiation intensity index of Ca element;

[0066] HZ Mg —Index of chemical differentiation intensity of Mg element;

[0067] H Ca —Percentage content of Ca element;

[0068] H Mg—Percentage content of Mg element.

[0069] element Chemical differentiation intensity index (HZ) Mg -1.5 Ca 0.4

[0070] In step 3, the calcium-magnesium index calculation model is established based on the ratio of the percentage content of Ca to the percentage content of Mg, and then by taking the logarithm of the ratio. The specific calculation formula is as follows:

[0071] GMZS=lg(H Ca / H Mg )

[0072] In the formula:

[0073] GMZS—Calcium Magnesium Index;

[0074] H Ca —Percentage content of Ca element;

[0075] H Mg —Percentage content of Mg element.

[0076] In step 3, a carbonate rock discrimination model is established using the obtained carbonate rock classification index and calcium-magnesium index. These two indices explain the formation of carbonate rocks from different perspectives: the carbonate rock classification index considers the rate of chemical differentiation, while the calcium-magnesium index considers atomic composition. The lithology of carbonate rocks is determined by the scatter plot of the two indices. Using a large amount of carbonate rock lithology identification data, points are plotted in reverse, resulting in an interval for each index, as detailed below:

[0077] When TZS≤-13.5 and GMZS≤0.25, the carbonate rock is pure dolomite;

[0078] When -13.5 < TZS ≤ -5.5 and 0.25 < GMZS ≤ 0.4, the carbonate rock is a gray-white dolomite.

[0079] When -5.5 < TZS ≤ 4 and 0.4 < GMZS ≤ 0.7, the carbonate rock is a calcareous dolomite.

[0080] When 4 < TZS ≤ 9 and 0.7 < GMZS ≤ 1.0, the carbonate rock is a dolomitic limestone.

[0081] When 9 < TZS ≤ 18 and 1.0 < GMZS ≤ 1.8, the carbonate rock is a dolomitic limestone.

[0082] When 18≤TZS and 1.8≤GMZS, the carbonate rock is pure limestone.

[0083] This invention also provides a carbonate rock discrimination system based on elemental logging analysis, including...

[0084] The content detection module is used to perform elemental spectral analysis on samples that have been identified as carbonate rocks to obtain the content of Ca and Mg elements.

[0085] The model building module is used to build a carbonate rock identification model, which includes: a carbonate rock classification index calculation model, a calcium-magnesium index calculation model, and a carbonate rock discrimination model.

[0086] The lithology discrimination module is used to input the obtained Ca and Mg element contents into the carbonate rock discrimination model. In the carbonate rock discrimination model, the carbonate rock classification index value and the calcium-magnesium index value are obtained through the carbonate rock classification index calculation model and the calcium-magnesium index calculation model. The carbonate rock discrimination model determines the lithology of the carbonate rock sample based on the intersection range of the carbonate rock classification index and the calcium-magnesium index value.

[0087] Based on the above ideas and solutions, the present invention also provides a terminal device, which includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the above method; or, when the processor executes the computer program, it implements the functions of each module in the above system.

[0088] The computer program described above can be divided into one or more modules, one or more modules are stored in the memory described above, and executed by the processor to complete the present invention.

[0089] Terminal devices can be computing devices such as desktop computers, laptops, handheld computers, and cloud servers.

[0090] Terminal devices may include, but are not limited to, processors and memory.

[0091] The processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0092] The memory can be used to store the aforementioned computer programs and / or modules. The processor implements the various functions of the aforementioned terminal device by running or executing the computer programs and / or modules stored in the memory, and by calling the data stored in the memory.

[0093] If the modules integrated into the terminal device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0094] Based on this understanding, all or part of the processes in the above-described method can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the above-described method steps. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form.

[0095] The aforementioned computer-readable media may include: any entity or device capable of carrying the aforementioned computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the aforementioned computer-readable media may be appropriately added to or subtracted from the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media may not include electrical carrier signals and telecommunication signals.

[0096] Example 1:

[0097] like Figure 2 As shown, the specific steps of the carbonate rock discrimination method based on elemental logging analysis of the present invention are as follows:

[0098] (1) Equipment calibration

[0099] The national standard samples were selected; the equipment was calibrated and a standard curve was developed; repeatability and stability tests were conducted.

[0100] (2) Sensitive element analysis

[0101] Elemental spectroscopic analysis was performed on core samples 1#-32#, which were identified as carbonate rocks, to obtain the contents of Ca and Mg.

[0102] (3) Establishing classification indices

[0103] The carbonate rock classification index (TZS) and the calcium-magnesium index (GMZS) were calculated based on the Ca and Mg content values. The detection and calculation data and core lithology judgment results of this embodiment are shown in the table below:

[0104] Table 1. Carbonate Rock Classification Index (TZS) and Calcium-Magnesium Index (GMZS)

[0105]

[0106]

[0107] Explanation results

[0108] The lithology of core samples #1 to #32 was identified using the thin section identification method. The results are shown in the table below:

[0109] Table 2. Lithology Identification of Core Samples

[0110] Thin film naming Thin plate serial number pure limestone 2.3.11.12.13.15.16.17.18.19 Dolomitic limestone 4.5.6.28 Pure white dolomite 1.7.8.9.10.14.20.21.22.23.24.25.26.27.29.30.31.32

[0111] As shown in Table 1 above, the core lithology determined by the lithology identification method of carbonate rocks of the present invention is consistent with the identification results of the traditional thin section identification method, indicating that the identification method of the present invention can achieve a fine description of carbonate rock lithology and can automatically, quickly and accurately determine the lithology of strata.

Claims

1. A method for identifying carbonate rocks based on elemental logging analysis, characterized in that, The specific steps are as follows: S1 performed elemental spectral analysis on the sample that had been identified as carbonate rock to obtain the contents of Ca and Mg elements; S2 establishes a carbonate rock identification model, which includes: a carbonate rock classification index calculation model, a calcium-magnesium index calculation model, and a carbonate rock discrimination model. S3 inputs the Ca and Mg element contents obtained from S1 into the carbonate rock discrimination model. In the carbonate rock discrimination model, the carbonate rock classification index value and the calcium-magnesium index value are obtained through the carbonate rock classification index calculation model and the calcium-magnesium index calculation model. The carbonate rock discrimination model judges the lithology of the carbonate rock sample based on the intersection range of the carbonate rock classification index value and the calcium-magnesium index value. In S3, the carbonate rock discrimination model establishes a scatter plot with the carbonate rock classification index as the abscissa and the calcium-magnesium index as the ordinate to determine the intersection interval of the carbonate rock classification index curve and the calcium-magnesium index curve. Different intersection intervals represent the lithology of different carbonate rock samples.

2. The method for identifying carbonate rocks based on elemental logging analysis according to claim 1, characterized in that, In S2, a model for calculating the classification index of carbonate rocks is established using the chemical differentiation intensity index of Ca and Mg elements.

3. The method for identifying carbonate rocks based on elemental logging analysis according to claim 2, characterized in that, In S2, the specific calculation model for the carbonate rock classification index is as follows: TZS = H Ca *HZ Ca + H Mg *HZ Mg In the formula: TZS—Carbonate Rock Classification Index; HZ Ca —The chemical differentiation intensity index of Ca element; HZ Mg —Index of chemical differentiation intensity of Mg element; H Ca —Percentage content of Ca element; H Mg —Percentage content of Mg element.

4. The method for identifying carbonate rocks based on elemental logging analysis according to claim 1, characterized in that, In S2, a calcium-magnesium index calculation model is established based on the percentage content of Ca and Mg elements.

5. The method for identifying carbonate rocks based on elemental logging analysis according to claim 4, characterized in that, In S2, the calcium-magnesium index calculation model is specifically as follows: GMZS = lg(H Ca / H Mg ) In the formula: GMZS—Calcium Magnesium Index; H Ca —Percentage content of Ca element; H Mg —Percentage content of Mg element.

6. The method for identifying carbonate rocks based on elemental logging analysis according to claim 1, characterized in that, In S3, different intersection zones represent the lithology of different carbonate rock samples, as detailed below: When the carbonate rock classification index is ≤-13.5 and the calcium-magnesium index is ≤0.25, the carbonate rock is pure dolomite; When -13.5 < carbonate rock classification index ≤ -5.5 and 0.25 < calcium-magnesium index ≤ 0.4, the carbonate rock is a gray-white dolomite. When -5.5 < carbonate rock classification index ≤ 4 and 0.4 < calcium-magnesium index ≤ 0.7, the carbonate rock is a calcareous dolomite. When 4 < carbonate rock classification index ≤ 9 and 0.7 < calcium-magnesium index ≤ 1.0, the carbonate rock is dolomitic limestone; When 9 < carbonate rock classification index < 18 and 1.0 < calcium-magnesium index < 1.8, the carbonate rock is a dolomitic limestone. When 18 < carbonate rock classification index and 1.8 < calcium-magnesium index, the carbonate rock is pure limestone.

7. A carbonate rock discrimination system based on elemental logging analysis, characterized in that, include The content detection module is used to perform elemental spectral analysis on samples that have been identified as carbonate rocks to obtain the content of Ca and Mg elements. The model building module is used to build a carbonate rock identification model, which includes: a carbonate rock classification index calculation model, a calcium-magnesium index calculation model, and a carbonate rock discrimination model. The lithology discrimination module is used to input the obtained Ca and Mg element contents into the carbonate rock discrimination model. In the carbonate rock discrimination model, the carbonate rock classification index value and the calcium-magnesium index value are obtained through the carbonate rock classification index calculation model and the calcium-magnesium index calculation model. The carbonate rock discrimination model judges the lithology of the carbonate rock sample based on the intersection range of the carbonate rock classification index and the calcium-magnesium index value. In the carbonate rock discrimination model, a scatter plot is established with the carbonate rock classification index as the abscissa and the calcium-magnesium index as the ordinate to determine the intersection interval of the carbonate rock classification index curve and the calcium-magnesium index curve. Different intersection intervals represent the lithology of different carbonate rock samples.

8. A terminal device, characterized in that, include: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any one of the methods of claims 1 to 6, or to implement the functions of each module in the system of claim 7.

9. A computer-readable medium, characterized in that, It includes a computer program stored in a computer-readable storage medium, which, when executed by a processor, implements the steps of any one of the methods of claims 1 to 6, or implements the functions of each module in the system of claim 7.

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