Carbonate rock discrimination method and system based on element logging analysis

Abstract

The invention provides a carbonate rock discrimination method and system based on element logging analysis, and the method comprises the steps: carrying out the element spectrum analysis of a sample which is determined to be carbonate rock, and obtaining the contents of Ca and Mg elements; establishing a carbonate rock identification model, wherein the carbonate rock identification model comprises 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 element contents into a carbonate rock discrimination model, and obtaining a carbonate rock classification index value and a calcium magnesium index value in the carbonate rock discrimination model through a carbonate rock classification index calculation model and a calcium magnesium index calculation model; and the carbonate rock discrimination model judges the lithology of the carbonate rock sample according to the intersection interval of the carbonate rock classification index value and the calcium-magnesium index value. The carbonate rock classification and identification model is established through the element features, then on-site quantitative rapid and accurate identification of the carbonate rock is guided, and the problem of on-site carbonate rock lithology identification is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of oil and gas exploration and development, and in particular to the technical field of mud logging in the process of oil and gas exploration and development, and specifically to a carbonate rock identification method and system based on element mud logging analysis. Background Art

[0002] In the process of oil and gas exploration and development, it is necessary to first log the structural properties of the underground oil and gas reservoirs, and make a preliminary judgment on the exploration value and exploration methods of oil and gas 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 the exploitation of oil and gas. Carbonate reservoirs are one of the main reservoirs of oil and gas. They are mainly composed of calcite and dolomite. According to the different contents of the two, carbonate rocks are divided into different lithologies such as limestone, dolomite, dolomitic limestone, and gray 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 reasonable exploitation of oil and gas resources, it is necessary to make accurate judgments on the physical properties of carbonate reservoirs. Due to the large-scale use of PDC drill bits or gas drilling in the field of drilling exploration and development technology for oil and gas, carbonate rock cuttings form fine particles or dust, and it is no longer possible to accurately judge the lithology of carbonate rocks by human naked eye analysis. Therefore, in order to change the current situation where the naked eye cannot accurately judge the lithology of carbonate rocks, some other alternative methods have gradually emerged.

[0003] For example, the traditional carbonate lithology identification method at the current logging construction site is to manually select a certain amount of carbonate rock samples, grind them, put them in a closed container and react them with a certain amount of dilute hydrochloric acid, and determine the content of calcium carbonate and magnesium carbonate in the samples based on the generated gas pressure, and then describe the lithology of the samples. However, this carbonate rock lithology identification method is affected by the quality of manually selected samples, and the identification process is greatly affected by human factors, and the detection cycle is long, which is not conducive to the rapid and accurate identification of carbonate rock lithology. Summary of the invention

[0004] In order to solve the problems existing in the prior art, the present invention provides a carbonate rock identification method and system based on element logging analysis, establishes a carbonate rock classification and identification model through element characteristics, and then guides the on-site quantitative, rapid and accurate identification of carbonate rocks, solving the problem of on-site carbonate rock lithology identification.

[0005] To achieve the above object, the present invention provides the following technical solution: a carbonate rock identification method based on element logging analysis, the specific steps are as follows:

[0006] S1 performs elemental spectroscopy analysis on samples that have been identified as carbonate rocks to obtain the Ca and Mg element contents;

[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 by 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 according to the intersection interval of the carbonate rock classification index value and the calcium-magnesium index value.

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

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

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

[0012] Where:

[0013] TZS—carbonate classification index;

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

[0015] HZ Mg —Mg element chemical differentiation intensity index;

[0016] H Ca —Ca element percentage;

[0017] H Mg —Mg element percentage.

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

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

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

[0021] Where:

[0022] GMZS—calcium-magnesium index;

[0023] H Ca —Ca element percentage;

[0024] H Mg —Mg element percentage.

[0025] Furthermore, in S3, in the carbonate rock discrimination model, a scatter intersection diagram is established with the carbonate rock classification index as the horizontal axis and the calcium-magnesium index as the vertical axis 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 intervals represent the lithology of different carbonate rock samples, as follows:

[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, 0.25<calcium-magnesium index ≤0.4, the carbonate rock is gray dolomite;

[0029] When -5.5<carbonate rock classification index ≤4, 0.4<calcium-magnesium index ≤0.7, the carbonate rock is gray dolomite;

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

[0031] When 9<carbonate rock classification index ≤18, 1.0<calcium-magnesium index ≤1.8, the carbonate rock is dolomite-bearing limestone;

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

[0033] The present invention also provides a carbonate rock identification system based on element logging analysis, comprising:

[0034] The content detection module is used to perform elemental spectral analysis on samples that have been determined to be carbonate rocks to obtain the Ca and Mg element contents;

[0035] A 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 judges the lithology of the carbonate rock sample according to the intersection interval 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 implements the steps in the above method when executing the computer program, or implements 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 implements the steps in the above method when executed by a processor, 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] The present invention provides a carbonate rock identification method based on element logging analysis. The content of Ca and Mg in carbonate rocks is quickly and quantitatively obtained through element spectrum analysis, and a carbonate rock identification model is established to obtain a carbonate rock classification index and a calcium-magnesium index. Carbonate rock is a sedimentary rock mainly composed of carbonate minerals, among which the most common minerals are calcite (CaCO3) and dolomite (MgCO3), and the intermediate lithology is a transitional lithology between them. Due to the different chemical differentiation intensities of Ca and Mg elements, the lithology formed by the difference in the enrichment degree of Ca and Mg elements at different stages of carbonate rock formation will also be different, and the ratio of the two elements is also different. The lithology can be accurately identified by dividing different intervals. Therefore, the present invention combines the carbonate rock classification index and the calcium-magnesium index to perform fine identification of carbonate rock lithology, which has high timeliness and low cost, can quickly and accurately determine the lithology of the formation, and can provide effective guidance for the discovery and exploration of oil and gas reservoirs, thereby improving the development efficiency of oil and gas. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the present invention, the accompanying drawings required for use in the content of the invention and the description of the embodiments are briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0042] Figure 1 Schematic diagram of carbonate rock identification based on element logging analysis;

[0043] Figure 2 Flow chart of the carbonate rock identification method based on element logging analysis. DETAILED DESCRIPTION

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

[0045] The present invention provides a carbonate rock identification method based on element logging analysis, which can quickly identify carbonate rock lithology by using chemical differentiation theory and Ca / Mg content ratio quantification. The specific steps are as follows:

[0046] Step 1: Determine the sensitive elements. There are two main components of carbonate rocks, namely Mg and Ca.

[0047] Step 2: Perform elemental spectral analysis on samples that have been identified as carbonate rocks to obtain the Ca and Mg element contents.

[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 element contents obtained in step 2 into the carbonate rock discrimination model. In the carbonate rock discrimination model, the carbonate rock classification index value and the calcium and magnesium index value are obtained through the carbonate rock classification index calculation model and the calcium and magnesium index calculation model. Figure 1 As shown in the figure, the carbonate rock discrimination model determines the lithology of the carbonate rock of the sample to be tested according to the specific carbonate rock classification index and the calcium-magnesium index value, thereby realizing accurate identification of carbonate rock.

[0050] In step 2, the Ca and Mg element contents are obtained based on the element logging technology (X-ray fluorescence spectrometer). XRF equipment (X-ray fluorescence spectrometer) is an instrument that can analyze the element content by measuring the characteristic X-rays of elements in a substance. It can perform quantitative analysis quickly and non-destructively.

[0051] The following are the steps for rapid quantitative analysis of element 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 contents to calibrate the instrument, and establish a calibration curve based on the relationship between the standard samples and the instrument measurement results.

[0054] 3) Measure the sample: Place the sample in the measurement chamber of the XRF device and generate X-rays by exciting the sample. The elements in the sample absorb part of the excited X-rays and emit characteristic X-rays. The XRF device determines the element content by detecting and analyzing these characteristic X-rays.

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

[0056] XRF equipment measures characteristic X-rays in samples to quantitatively and rapidly analyze element content. It is non-destructive, efficient and fast, and is widely used in materials science, geology, environmental monitoring and other fields.

[0057] 31 national standard samples were selected to develop a standard working curve. The standard working curve is a method for establishing a relationship between the measurement signal of the XRF instrument and the chemical composition of a known sample. It is established by measuring a series of standard samples of known concentrations and recording their corresponding XRF measurement signals. These standard samples usually have known element 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 can analyze while drilling, and has the characteristics of timely, rapid, accurate, reliable and quantitative analysis, and can have strong technical support. The method adopted by the present invention has also been verified in practice, and the effect is obvious.

[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 follows:

[0060] Chemical differentiation refers to the process in which a system with a certain geochemical composition undergoes a geochemical process that causes the chemical composition of the system to communicate with the outside world, resulting in the outflow or inflow of elements, which leads to changes in the chemical composition of the elements in the system. Due to the different intensities of chemical differentiation of each element, the lithology formed by the differences in the enrichment of Ca and Mg elements at different stages of carbonate rock formation will also be different.

[0061] The specific calculation formula is as follows:

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

[0063] Where:

[0064] TZS—carbonate classification index;

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

[0066] HZ Mg —Mg element chemical differentiation intensity index;

[0067] H Ca —Ca element percentage;

[0068] H Mg—Mg element percentage.

[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 of Ca element content to the percentage of Mg element content, and then taking the logarithm of the ratio. The specific calculation formula is as follows:

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

[0072] Where:

[0073] GMZS—calcium-magnesium index;

[0074] H Ca —Ca element percentage;

[0075] H Mg —Mg element percentage.

[0076] In step 3, the carbonate rock classification index and calcium-magnesium index are used to establish a carbonate rock discrimination model. The two indexes explain the formation of carbonate rocks from different directions. The carbonate rock classification index is based on the speed of chemical differentiation, while the calcium-magnesium index is based on atomic composition. The lithology of carbonate rocks is judged by the scatter intersection diagram of the two indexes. Through a large amount of carbonate rock lithology identification data, the points are reversely projected, and both indexes get an interval, as follows:

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

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

[0079] When -5.5<TZS≤4, 0.4<GMZS≤0.7, the carbonate rock is gray dolomite;

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

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

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

[0083] The present invention also provides a carbonate rock identification system based on element logging analysis, comprising:

[0084] The content detection module is used to perform elemental spectral analysis on samples that have been determined to be carbonate rocks to obtain the Ca and Mg element contents;

[0085] A 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 judges the lithology of the carbonate rock sample according to the intersection interval of the carbonate rock classification index and the calcium-magnesium index value.

[0087] According to 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, the steps in the above method are implemented, or when the processor executes the computer program, the functions of each module in the above system are implemented;

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

[0089] The terminal device can be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server.

[0090] The terminal device may include, but is not limited to, a processor and a memory.

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

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

[0093] If the module integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0094] Based on such understanding, the present invention implements all or part of the processes in the above method, and can also be completed by instructing related hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the above method steps when executed by a processor. Among them, the above computer program includes computer program code, and the above computer program code can be in source code form, object code form, executable file or some intermediate form, etc.

[0095] The above-mentioned computer-readable medium may include: any entity or device capable of carrying the above-mentioned computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the above-mentioned computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0096] Embodiment 1:

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

[0098] (1) Equipment calibration

[0099] Optimize national standard samples; calibrate equipment and develop standard curves; conduct repeatability and stability tests.

[0100] (2) Analysis of sensitive elements

[0101] The elemental spectrum analysis of core samples 1#-32#, which have been confirmed to be carbonate rocks, was performed using a spectrometer to obtain the contents of Ca and Mg elements.

[0102] (3) Establishing a classification index

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

[0104] Table 1 Carbonate rock classification index (TZS) and calcium magnesium index (GMZS)

[0105]

[0106]

[0107] Interpret the results

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

[0109] Table 2 Lithology identification table of core samples

[0110] Slice naming Slice number Pure limestone 2.3.11.12.13.15.16.17.18.19 Dolomitic limestone 4.5.6.28 Pure Dolomite 1.7.8.9.10.14.20.21.22.23.24.25.26.27.29.30.31.32

[0111] It can be seen from Table 1 that the core lithology determined by the carbonate rock lithology identification method of the present invention is consistent with the identification result of the traditional thin section identification method, indicating that the identification method of the present invention can achieve a detailed description of the carbonate rock lithology and can automatically, quickly and accurately determine the lithology of the formation.

Claims

1. A carbonate rock identification method based on element logging analysis, characterized in that: The specific steps are as follows: S1 performs elemental spectroscopy analysis on samples that have been identified as carbonate rocks to obtain the Ca and Mg element contents; 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 by 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 according to the intersection interval of the carbonate rock classification index value and the calcium-magnesium index value.

2. The carbonate rock identification method based on element logging analysis according to claim 1 is characterized in that: In S2, a carbonate rock classification index calculation model was established using the chemical differentiation intensity index of Ca and Mg elements.

3. The carbonate rock identification method based on element logging analysis according to claim 2 is characterized in that: In S2, the carbonate rock classification index calculation model is as follows: TZS=H Ca *HZ Ca +H Mg *HZ Mg Where: TZS—carbonate classification index; HZ Ca —Ca element chemical differentiation intensity index; HZ Mg —Mg element chemical differentiation intensity index; H Ca —Ca element percentage; H Mg —Mg element percentage.

4. The carbonate rock identification method based on element logging analysis according to claim 1 is characterized in that: In S2, a calcium-magnesium index calculation model is established according to the percentage content of the Ca element and the percentage content of the Mg element.

5. The carbonate rock identification method based on element logging analysis according to claim 4 is characterized in that: In S2, the calcium-magnesium index calculation model is specifically: GMZS=lg(H Ca / H Mg ) Where: GMZS—calcium-magnesium index; H Ca —Ca element percentage; H Mg —Mg element percentage.

6. The carbonate rock identification method based on element logging analysis according to claim 1 is characterized in that: In S3, in the carbonate rock discrimination model, a scatter intersection diagram is established with the carbonate rock classification index as the horizontal axis and the calcium-magnesium index as the vertical axis 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.

7. The carbonate rock identification method based on element logging analysis according to claim 6 is characterized in that: In S3, different intersection intervals represent the lithology of different carbonate rock samples, as follows: 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, 0.25<calcium-magnesium index ≤0.4, the carbonate rock is gray dolomite; When -5.5<carbonate rock classification index ≤4, 0.4<calcium-magnesium index ≤0.7, the carbonate rock is gray dolomite; When 4<carbonate rock classification index ≤9, 0.7<calcium-magnesium index ≤1.0, the carbonate rock is dolomitic limestone; When 9<carbonate rock classification index ≤18, 1.0<calcium-magnesium index ≤1.8, the carbonate rock is dolomite-bearing limestone; When 18≤carbonate rock classification index and 1.8≤calcium-magnesium index, the carbonate rock is pure limestone.

8. A carbonate rock identification system based on element logging analysis, characterized in that: include The content detection module is used to perform elemental spectral analysis on samples that have been determined to be carbonate rocks to obtain the Ca and Mg element contents; A 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 according to the intersection interval of the carbonate rock classification index and the calcium-magnesium index value.

9. 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 implements the steps of any one of the methods of claims 1 to 7 when executing the computer program, or implements the functions of each module in the system of claim 8.

10. A computer-readable medium, characterized in that The invention comprises a computer program stored in a computer-readable storage medium, wherein when the computer program is executed by a processor, the computer program implements the steps in any one of the methods of claims 1 to 7, or implements the functions of each module in the system of claim 8.

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