Alkali ore bed grade determination method and device and storage medium

Abstract

The embodiment of the invention provides a method and system for determining the grade of an alkali ore bed and a storage medium, and belongs to the field of solid mineral resource exploration. The determination method comprises the following steps: acquiring a logging response characteristic value of a target alkaline ore bed in a to-be-measured area and each rock-forming mineral contained in the target alkaline ore bed in the to-be-measured area, and determining a logging response characteristic value corresponding to each rock-forming mineral; and inputting the two logging response characteristic values into a pre-constructed interpretation model to obtain the volume percentage content of each rock-forming mineral, and determining the ore grade according to the theoretical density and the compensation density logging value of each rock-forming mineral. Therefore, the grade of the alkali ore bed is determined according to a rock-forming mineral physical volume model pre-established based on the category of each rock-forming mineral contained in the target alkali ore bed of the to-be-measured area and an interpretation model established based on the rock-forming mineral physical model and the interpretation equation set established based on the logging response characteristic values corresponding to the rock-forming minerals. And the calculation precision and the calculation speed of the ore grade in the mineral resource general survey stage are improved.

Description

Technical Field

[0001] The invention relates to the technical field of solid mineral exploration, and in particular to a method, a device and a storage medium for determining the grade of an alkaline ore layer. Background Art

[0002] Trona is an important basic chemical raw material, widely used in chemical, light industry, metallurgy, building materials, petroleum, medicine, food and other industries. With the advancement of science and technology and the rapid development of the world economy, the demand for trona is increasing, so the exploration and development of trona is particularly important.

[0003] Normally, chemical methods are used to analyze ore grade. This method has many steps and takes a long time, and cannot meet the requirements of some mining and selection steps that require online detection of ore grade. Some scholars have developed methods such as sequential Gaussian simulation to estimate ore grade, improved BP algorithm to estimate ore grade, and grey system theoretical model to predict ore grade. The above-mentioned ore grade valuation methods are mostly geostatistical methods. However, most geostatistical methods make certain assumptions, such as stationary assumptions and intrinsic assumptions, which limit the effectiveness of the obtained valuations. Moreover, it is also required to master a wealth of geostatistical knowledge in order to produce meaningful results, which cannot meet the requirements of ore grade calculation accuracy and speed for reserve estimation in the mineral survey stage. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide a method, device and storage medium for determining the grade of an alkaline ore layer, so as to solve the technical problems existing in the above-mentioned prior art.

[0005] In order to achieve the above-mentioned purpose, an embodiment of the present invention provides a method for determining the grade of an alkali ore layer, comprising: obtaining a well logging response characteristic value of a target alkali ore layer in a test area and each rock-forming mineral contained in the target alkali ore layer in the test area, and determining the well logging response characteristic value corresponding to each rock-forming mineral;

[0006] Inputting the well logging response characteristic values ​​of the target alkali ore layer in the test area and the well logging response characteristic values ​​corresponding to each rock-forming mineral into a pre-constructed interpretation model to obtain the volume percentage of each rock-forming mineral contained in the target alkali ore layer in the test area, wherein the interpretation model is constructed by a rock-forming mineral physical volume model established based on the categories of each rock-forming mineral contained in the target alkali ore layer in the test area and an interpretation equation group established based on the rock-forming mineral physical model and the well logging response characteristic values ​​corresponding to each rock-forming mineral; and

[0007] The ore grade in the target alkaline ore layer in the area to be tested is determined based on the volume percentage, theoretical density and compensated density logging value of each rock-forming mineral.

[0008] Optionally, the rock-forming minerals contained in the target alkaline ore layer in the area to be tested include at least any one of clay, quartz, feldspar, calcite, sodium bicarbonate, salt rock, and pores.

[0009] Optionally, the logging response characteristic value corresponding to each rock-forming mineral includes at least any one of natural gamma, compensated neutron, deep resistivity, shallow resistivity, compensated density, photoelectric absorption cross-section index, natural potential and acoustic time difference.

[0010] Optionally, the construction of the explanatory equations includes:

[0011] Based on the sensitivity to the lithology of each rock-forming mineral, a target logging response characteristic value is screened out from the logging response characteristic values ​​corresponding to each rock-forming mineral;

[0012] Obtaining the well logging curve corresponding to the target well logging response characteristic value, and determining the well logging characteristic diagram of each rock-forming mineral lithology through core calibration logging;

[0013] Performing scalar identification on the logging characteristic map to classify the stratigraphic lithology of the target alkaline ore layer in the area to be tested;

[0014] The interpretation equation group is constructed according to the formation lithology and the target logging response characteristic value.

[0015] Optionally, the explanatory equations are:

[0016]

[0017] In the formula, ρ b is the theoretical logging compensation density, Δt is the theoretical logging acoustic time difference, CNL is the theoretical logging compensation neutron, ρ1...ρ m To compensate the density test value, Δt1...Δt m is the acoustic time difference test value, CNL1...CNL m To compensate for the neutron test value, V1...V m is the physical volume corresponding to each rock-forming mineral.

[0018] Optionally, before inputting the well logging response characteristic value of the target alkali ore layer in the test area and the well logging response characteristic value corresponding to each rock-forming mineral into a pre-constructed interpretation model to obtain the volume percentage of each rock-forming mineral contained in the target alkali ore layer in the test area, the method further includes:

[0019] Optimize the explanation model:

[0020] Presetting the percentage of each rock-forming mineral contained in the target alkaline ore layer in the area to be tested;

[0021] Inputting the percentage content of each rock-forming mineral and the well logging response characteristic value corresponding to each rock-forming mineral into the pre-built interpretation model, and outputting the theoretical well logging value;

[0022] If the difference between the theoretical logging value and the actual logging value is greater than a preset threshold, the percentage content of each rock-forming mineral is adjusted until the difference between the theoretical logging value and the actual logging value is less than or equal to the preset threshold, thereby obtaining an optimized interpretation model.

[0023] Optionally, the ore grade in the target alkaline ore layer in the test area is determined according to the following formula:

[0024]

[0025] In the formula, C represents the ore grade, V i Represents the volume percentage of a single rock-forming mineral, ρ i It represents the theoretical density of a single rock-forming mineral, and DEN represents the compensated density logging value.

[0026] Optionally, the ore grade M in the target alkaline ore layer within a certain thickness range in the area to be tested is determined according to the following formula:

[0027]

[0028] In the formula, h2-h1 represents the thickness range, C x Indicates the grade of each ore within this thickness range.

[0029] Optionally, the ore grade Ms in the target alkaline ore layer within a certain area of ​​the area to be tested is determined according to the following formula:

[0030]

[0031]

[0032] Where Si, Hi, and Mi represent the area, thickness, and average grade component of a unit in the effective thickness diagram, respectively; S and H represent the area and average effective thickness of a given unit, respectively.

[0033] Optionally, the step of obtaining the rock-forming minerals contained in the target alkaline ore layer in the area to be tested includes:

[0034] X-ray diffraction quantitative analysis is performed on rock samples in the target alkali ore layer in the test area to obtain various rock-forming minerals contained in the target alkali ore layer in the test area.

[0035] Accordingly, the present invention also provides a device for determining the grade of an alkaline ore layer, comprising:

[0036] An acquisition unit, used to acquire a well logging response characteristic value of a target alkaline ore layer in a test area and each rock-forming mineral contained in the target alkaline ore layer in the test area, and determine a well logging response characteristic value corresponding to each rock-forming mineral;

[0037] An input unit, used for inputting the well logging response characteristic value of the target alkali ore layer in the test area and the well logging response characteristic value corresponding to each rock-forming mineral into a pre-constructed interpretation model to obtain the volume percentage of each rock-forming mineral contained in the target alkali ore layer in the test area, wherein the interpretation model is constructed by a rock-forming mineral physical volume model established based on the category of each rock-forming mineral contained in the target alkali ore layer in the test area and an interpretation equation group established based on the rock-forming mineral physical model and the well logging response characteristic value corresponding to each rock-forming mineral; and

[0038] The determination unit is used to determine the ore grade in the target alkaline ore layer in the test area according to the volume percentage content, theoretical density and compensated density logging value of each rock-forming mineral.

[0039] Optionally, the input unit is used to:

[0040] Based on the sensitivity to the lithology of each rock-forming mineral, a target logging response characteristic value is screened out from the logging response characteristic values ​​corresponding to each rock-forming mineral;

[0041] Obtaining the well logging curve corresponding to the target well logging response characteristic value, and determining the well logging characteristic diagram of each rock-forming mineral lithology through core calibration logging;

[0042] Performing scalar identification on the logging characteristic map to classify the stratigraphic lithology of the target alkaline ore layer in the area to be tested;

[0043] The interpretation equation group is constructed according to the formation lithology and the target logging response characteristic value.

[0044] Optionally, the determining device further includes an optimization unit for optimizing the interpretation model.

[0045] Optionally, the optimization unit is used to:

[0046] Presetting the percentage of each rock-forming mineral contained in the target alkaline ore layer in the area to be tested;

[0047] Inputting the percentage content of each rock-forming mineral and the well logging response characteristic value corresponding to each rock-forming mineral into the pre-built interpretation model, and outputting the theoretical well logging value;

[0048] If the difference between the theoretical logging value and the actual logging value is greater than a preset threshold, the percentage content of each rock-forming mineral is adjusted until the difference between the theoretical logging value and the actual logging value is less than or equal to the preset threshold, thereby obtaining an optimized interpretation model.

[0049] Optionally, the determination unit is used to determine the ore grade in the target alkaline ore layer in the test area according to the following formula:

[0050]

[0051] In the formula, C represents the ore grade, V i Represents the volume percentage of a single rock-forming mineral, ρ i It represents the theoretical density of a single rock-forming mineral, and DEN represents the compensated density logging value.

[0052] Optionally, the determination unit is used to determine the ore grade M in the target alkaline ore layer within a certain thickness range in the area to be tested according to the following formula:

[0053]

[0054] In the formula, h2-h1 represents the thickness range, C x Indicates the grade of each ore within this thickness range.

[0055] Optionally, the determination unit is used to determine the ore grade Ms in the target alkaline ore layer within a certain area range of the area to be tested according to the following formula:

[0056]

[0057]

[0058] Where Si, Hi, and Mi represent the area, thickness, and average grade component of a unit in the effective thickness diagram, respectively; S and H represent the area and average effective thickness of a given unit, respectively.

[0059] Optionally, the acquisition unit is used to:

[0060] X-ray diffraction quantitative analysis is performed on rock samples in the target alkali ore layer in the test area to obtain various rock-forming minerals contained in the target alkali ore layer in the test area.

[0061] On the other hand, the present invention further provides a processor for running a program, wherein the program is used to execute the above-mentioned determination method when being run.

[0062] On the other hand, an embodiment of the present invention further provides a computer-readable storage medium, on which instructions are stored, and the instructions are used to enable a machine to execute the above-mentioned determination method.

[0063] Through the above technical scheme, the grade of the alkaline ore layer is determined according to the interpretation model constructed by the rock-forming mineral physical volume model established in advance based on the categories of each rock-forming mineral contained in the target alkaline ore layer in the test area and the interpretation equation group established based on the rock-forming mineral physical model and the logging response characteristic values ​​corresponding to each rock-forming mineral, thereby improving the calculation accuracy and speed of the ore grade in the mineral prospecting stage.

[0064] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0066] Figure 1 It is a flow chart of an implementation of a method for determining the grade of an alkaline ore layer provided by an embodiment of the present invention;

[0067] Figure 2 An X-ray diffraction quantitative analysis report of an alkaline ore layer provided in an embodiment of the present invention;

[0068] Figure 3 is a schematic diagram of a rock volume physical model provided by an embodiment of the present invention;

[0069] Figure 4 A well logging characteristic chart of various lithologies provided by an embodiment of the present invention;

[0070] Figure 5(a) , 5(b) It is a quantitative identification plate provided by an embodiment of the present invention;

[0071] Figure 6 is a flow chart of an optimized well logging process provided by an embodiment of the present invention;

[0072] Figure 7 It is a flow chart of an implementation of a method for determining the grade of an alkaline ore layer provided by another embodiment of the present invention;

[0073] Figure 8 This is an average grade result diagram of an alkaline ore layer provided by an embodiment of the present invention;

[0074] Fig. 9 It is a schematic diagram of a device for determining the grade of an alkaline ore layer provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0075] Trona resources are widely distributed on the earth, mainly in America, Asia and Africa. The total sodium carbonate (including a small amount of sodium carbonate + sodium bicarbonate) reserves of 20 major trona deposits in 11 countries are 82.9 billion tons. China's Inner Mongolia Autonomous Region and Henan Province have discovered 26 trona mines, with a proven reserve of nearly 2 billion tons of trona resources, which is a large resource scale.

[0076] Natural soda ore was discovered in the Naiman Depression (Naiman Banner, Inner Mongolia) in Kailu exploration area outside Liaohe River, among which the maximum thickness of the ore-bearing layer of the Mesozoic Jiufotang Formation in the Nai 10 well area exceeded 300 meters. According to the prediction of well-seismic data, the geological body in this area is large in scale and has industrial mining value.

[0077] During the mineral survey stage, it is necessary to preliminarily ascertain the size, quantity and overall distribution pattern of the ore body, and to ascertain the mineral composition, chemical composition, ore grade, etc. of the ore.

[0078] During the development stage of mineral deposits, with the continuous progress of ore mining, the ore depletion rate of the mine increases year by year. In the mining production of mining enterprises, ore grade detection can not only help analyze and guide mining production, but also serve as an organizational guarantee for the subsequent mineral processing (smelting) production ratio. Therefore, the demand for ore grade detection work in developed mineral deposits is increasing.

[0079] The sedimentary center of Naiman Depression is thick and buried deep (generally 2000m), which makes drilling and coring difficult and costly during exploration. In the survey stage, there is little drilling and coring data, and because the core sampling rate is less than 100%, the average grade calculated based on the core test data is only an approximation, which is difficult to meet the requirements of ore grade accuracy for reserve scale estimation. It is necessary to develop a technical method to quickly determine the ore grade of a single well.

[0080] The core of the present invention is to develop a method for determining the grade of alkali ore layers, which improves the speed and accuracy of determining ore grades. In the case of insufficient drilling and coring data in the early stage of exploration, it plays a greater supporting role in determining the scale and quantity of ore bodies, and provides accurate data support for the decision-making of subsequent exploration investment, the development of alkali ore business, and the formulation of mining plans.

[0081] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0082] See also Figure 1 As shown, it is a flow chart of an implementation method for determining the grade of an alkaline ore layer provided by an embodiment of the present invention, which includes the following execution steps:

[0083] Step 100: Obtain the well logging response characteristic value of the target alkaline ore layer in the test area and each rock-forming mineral contained in the target alkaline ore layer in the test area, and determine the well logging response characteristic value corresponding to each rock-forming mineral.

[0084] In some embodiments, the rock-forming minerals contained in the target alkaline ore layer in the test area include at least any one of clay, quartz, feldspar, calcite, sodium bicarbonate, salt rock, and pores.

[0085] In some embodiments, referring to Table 1, the logging response characteristic values ​​corresponding to each rock-forming mineral include at least any one of natural gamma, compensated neutron, deep resistivity, shallow resistivity, compensated density, photoelectric absorption cross-section index, natural potential and acoustic time difference.

[0086] Table 1 Logging response characteristic values ​​of rock-forming minerals in natural alkali deposits

[0087]

[0088]

[0089] In some embodiments, obtaining the rock-forming minerals contained in the target alkali ore layer in the test area includes: performing X-ray diffraction quantitative analysis on rock samples in the target alkali ore layer in the test area to obtain the rock-forming minerals contained in the target alkali ore layer in the test area.

[0090] In some embodiments, see Figure 2 As shown, each rock-forming mineral contained in the target alkaline ore layer in the area to be tested can be obtained from the X-ray diffraction quantitative analysis report.

[0091] Step 101: input the well logging response characteristic value of the target alkaline ore layer in the test area and the well logging response characteristic value corresponding to each rock-forming mineral into a pre-constructed interpretation model to obtain the volume percentage of each rock-forming mineral contained in the target alkaline ore layer in the test area.

[0092] Among them, the interpretation model is constructed based on a rock-forming mineral physical volume model established based on the categories of each rock-forming mineral contained in the target alkaline ore layer in the test area, and a group of interpretation equations established based on the rock-forming mineral physical model and the logging response characteristic values ​​corresponding to each rock-forming mineral.

[0093] In some embodiments, a rock volume physical model is established. The rock volume physical model is established based on the main rock-forming minerals. The rock composition in this area is divided into seven types: clay (illite), quartz, feldspar, calcite, sodium bicarbonate, salt rock, and pores. Figure 3 shown.

[0094] Step 3: Establish the well logging interpretation equation group.

[0095] In some embodiments, the construction of the interpretation equation includes the following steps:

[0096] S1: based on the sensitivity to the lithology of each rock-forming mineral, a target logging response characteristic value is screened out from the logging response characteristic values ​​corresponding to each rock-forming mineral.

[0097] In some embodiments, it can be seen from the rock-forming mineral logging response characteristic values ​​in Table 1 that the curves sensitive to lithological responses are mainly natural gamma (Gr), compensated neutron (CNL), resistivity (Rt), compensated density (Den), and photoelectric absorption cross-section index (Pe).

[0098] S2: Performing scalar identification on the logging characteristic map to classify the stratigraphic lithology of the target alkaline ore layer in the area to be tested.

[0099] In some embodiments, the logging feature plates of various lithologies are identified by core calibration logging (i.e., natural alkali logging identification). Figure 4 As shown in Figure 2, a quantitative identification chart is established on this basis, as shown in Figure 2 Figure 5(a) and 5(b) As shown, the stratigraphic lithology is divided.

[0100] According to Figure 5(a), natural alkali and salt rock can be distinguished by resistivity logging and compensated neutron logging. According to Figure 5(b), mudstone, siltstone, limy mudstone and argillaceous limestone can be distinguished by natural gamma logging and compensated neutron logging. Combined with the logging characteristic plates of various lithologies, the quantitative identification standard of lithology logging in Naiman area is established, as shown in Table 2.

[0101] Table 2 Quantitative classification standards for formation lithology logging data in Naiman area

[0102]

[0103] S3: constructing the interpretation equation group according to the formation lithology and the target logging response characteristic value.

[0104] In some embodiments, the set of explanatory equations constructed is as follows:

[0105]

[0106] In the formula, ρ b represents the theoretical density of the target alkali ore layer, ρ1…ρ m represents the theoretical density of a single mineral, △t represents the acoustic time difference of the target alkaline ore layer, △t1……△t m Indicates the acoustic time difference of a single mineral, CNL indicates the compensation neutron of the target alkaline ore layer, CNL1...CNL mIndicates the compensation neutron of a single mineral, V1…V m Indicates the volume percentage of a single mineral.

[0107] In some embodiments, before inputting the well logging response characteristic value of the target alkali ore layer in the test area and the well logging response characteristic value corresponding to each rock-forming mineral into a pre-constructed interpretation model to obtain the volume percentage of each rock-forming mineral contained in the target alkali ore layer in the test area, the method further includes:

[0108] Optimize the explanation model:

[0109] Presetting the percentage of each rock-forming mineral contained in the target alkaline ore layer in the area to be tested;

[0110] Inputting the percentage content of each rock-forming mineral and the well logging response characteristic value corresponding to each rock-forming mineral into the pre-built interpretation model, and outputting the theoretical well logging value;

[0111] If the difference between the theoretical logging value and the actual logging value is greater than a preset threshold, the percentage content of each rock-forming mineral is adjusted until the difference between the theoretical logging value and the actual logging value is less than or equal to the preset threshold, thereby obtaining an optimized interpretation model.

[0112] In some embodiments, the Figure 6 As shown in the optimized logging processing flow chart, the optimized interpretation model is obtained. First, the initial values ​​of the reservoir parameters are input into the initial interpretation model and the response equation (i.e., the interpretation equation group) to obtain the theoretical logging values. By comparing the theoretical logging values ​​with the actual logging values, it is determined whether the theoretical logging values ​​are sufficiently close to the actual logging values. If so, the optimal interpretation result (i.e., the optimized interpretation model) is output. If not, the reservoir parameter values ​​(natural gamma (Gr), compensated neutron (CNL), deep resistivity (Rt), shallow resistivity (Rs), compensated density (DEN), photoelectric absorption cross-section index (Pe), natural potential (SP), acoustic time difference (AC), and the initial value of the percentage of the selected minerals are adjusted by the optimization method, which is set to 10%. When the software calculates, the initial value is continuously adjusted in each iteration) until the theoretical logging values ​​are sufficiently close to the actual logging values, and the optimized interpretation model is obtained.

[0113] Step 102: Determine the ore grade in the target alkaline ore layer in the area to be tested according to the volume percentage content, theoretical density and compensated density logging value of each rock-forming mineral.

[0114] In some embodiments, the ore grade in the target alkaline ore layer in the test area is determined according to the following formula:

[0115]

[0116] In the formula, C represents the ore grade, V i Represents the volume percentage of a single rock-forming mineral, ρ i It represents the theoretical density of a single rock-forming mineral, and DEN represents the compensated density logging value.

[0117] In some embodiments, the ore grade M in the target alkaline ore layer within a certain thickness range in the area to be tested is determined according to the following formula:

[0118]

[0119] In the formula, h2-h1 represents the thickness range, C x Indicates the grade of each ore within this thickness range.

[0120] In some embodiments, when it is necessary to know the average grade Ms within a certain area (S) (n wells, single well thickness is hi), it is necessary to first delineate the single well effective thickness contour map within the area, use the area weighting method to determine the average effective thickness H, and then use the volume weighting method to determine the average grade. The ore grade Ms in the target alkaline ore layer within a certain area of ​​the test area is determined according to the following formula:

[0121]

[0122]

[0123] Where Si, Hi, and Mi represent the area, thickness, and average grade component of a unit in the effective thickness diagram, respectively; S and H represent the area and average effective thickness of a given unit, respectively.

[0124] In some embodiments, see Figure 7 As shown, it is a flowchart of an implementation method for determining the grade of an alkaline ore layer provided by another embodiment of the present invention, comprising the following execution steps:

[0125] S700: Rock and mineral data determine the main rock-forming minerals.

[0126] S701: Establish rock volume physical model.

[0127] S702: Establish a set of mineral logging interpretation equations.

[0128] S703: Optimize well logging interpretation.

[0129] S704: Determine the mineral content of the single layer.

[0130] S705: Determine the ore grade.

[0131] In some embodiments, the method is applied in Naiman, Inner Mongolia, and the average grade of the alkaline ore layer is calculated by weighted average method of ore body thickness, quality and area. Figure 8 shown.

[0132] Tracks 1, 5, and 6 in the figure are well logging curves, which are Track 1 (GR, SP, CAL), Track 5 (LLD, LLS, RMLL), and Track 6 (AC, DEN, CNL, PE), respectively. They are the results obtained by logging instruments. Track 2 is the depth track, and Track 3 is the lithology logging track, which is determined by observing and identifying the rock cuttings returned to the ground during the drilling process. Track 4 is the rock and mineral composition diagram interpreted by logging, and Track 7 is the calculated grade of the natural alkali ore layer.

[0133] In some embodiments, according to the preferred well logging curves sensitive to lithological reactions (GR in Track 1, LLD, LLS in Track 2, AC, DEN, CNL, PE in Track 6), the mineral composition and its percentage at each depth can be calculated by optimizing the well logging interpretation (Track 4). The volume percentage is converted into the mass percentage (Track 7) according to the following formula, which is the ore grade in the target alkaline ore layer in the area to be tested:

[0134]

[0135] In the formula, C represents the ore grade, V i Represents the volume percentage of a single rock-forming mineral, ρ i It represents the theoretical density of a single rock-forming mineral, and DEN represents the compensated density logging value.

[0136] The present invention provides a method for determining the grade of an alkaline ore layer, which determines the main rock-forming minerals according to rock and mineral data, determines the theoretical value of the logging response of the main rock-forming minerals, establishes a rock volume physical model and an optimal logging interpretation equation group, interprets the mineral content using an optimization processing method, and determines the ore grade of a single ore layer. The method requires less coring or rock cuttings data, and saves time and coring analysis costs compared to traditional methods.

[0137] On the other hand, the present invention provides a device for determining the grade of an alkaline ore layer, the schematic diagram of which is shown in FIG. Fig. 9 As shown, including:

[0138] The acquisition unit 900 is used to acquire the well logging response characteristic value of the target alkaline ore layer in the test area and each rock-forming mineral contained in the target alkaline ore layer in the test area, and determine the well logging response characteristic value corresponding to each rock-forming mineral;

[0139] An input unit 901 is used to input the well logging response characteristic value of the target alkali ore layer in the test area and the well logging response characteristic value corresponding to each rock-forming mineral into a pre-constructed interpretation model to obtain the volume percentage of each rock-forming mineral contained in the target alkali ore layer in the test area, wherein the interpretation model is constructed by a rock-forming mineral physical volume model established based on the category of each rock-forming mineral contained in the target alkali ore layer in the test area and an interpretation equation group established based on the rock-forming mineral physical model and the well logging response characteristic value corresponding to each rock-forming mineral; and

[0140] The determination unit 902 is used to determine the ore grade in the target alkaline ore layer in the test area according to the volume percentage content, theoretical density and compensated density logging value of each rock-forming mineral.

[0141] In some implementations, the input unit 901 is used to:

[0142] Based on the sensitivity to the lithology of each rock-forming mineral, a target logging response characteristic value is screened out from the logging response characteristic values ​​corresponding to each rock-forming mineral;

[0143] Obtaining the well logging curve corresponding to the target well logging response characteristic value, and determining the well logging characteristic diagram of each rock-forming mineral lithology through core calibration logging;

[0144] Performing scalar identification on the logging characteristic map to classify the stratigraphic lithology of the target alkaline ore layer in the area to be tested;

[0145] The interpretation equation group is constructed according to the formation lithology and the target logging response characteristic value.

[0146] In some implementations, the determining device further includes an optimization unit 903 for optimizing the interpretation model.

[0147] In some implementations, the optimization unit 903 is used to:

[0148] Presetting the percentage of each rock-forming mineral contained in the target alkaline ore layer in the area to be tested;

[0149] Inputting the percentage content of each rock-forming mineral and the well logging response characteristic value corresponding to each rock-forming mineral into the pre-built interpretation model, and outputting the theoretical well logging value;

[0150] If the difference between the theoretical logging value and the actual logging value is greater than a preset threshold, the percentage content of each rock-forming mineral is adjusted until the difference between the theoretical logging value and the actual logging value is less than or equal to the preset threshold, thereby obtaining an optimized interpretation model.

[0151] In some embodiments, the determining unit 902 is used to determine the ore grade in the target alkaline ore layer in the test area according to the following formula:

[0152]

[0153] In the formula, C represents the ore grade, V i Represents the volume percentage of a single rock-forming mineral, ρ i It represents the theoretical density of a single rock-forming mineral, and DEN represents the compensated density logging value.

[0154] In some embodiments, the determination unit 902 is used to determine the ore grade M in the target alkaline ore layer within a certain thickness range in the area to be tested according to the following formula:

[0155]

[0156] In the formula, h2-h1 represents the thickness range, C x Indicates the grade of each ore within this thickness range.

[0157] In some embodiments, the determination unit is used to determine the ore grade Ms in the target alkaline ore layer within a certain area of ​​the area to be tested according to the following formula:

[0158]

[0159]

[0160] Where Si, Hi, and Mi represent the area, thickness, and average grade component of a unit in the effective thickness diagram, respectively; S and H represent the area and average effective thickness of a given unit, respectively.

[0161] In some implementations, the acquisition unit 900 is used to:

[0162] X-ray diffraction quantitative analysis is performed on rock samples in the target alkali ore layer in the test area to obtain various rock-forming minerals contained in the target alkali ore layer in the test area.

[0163] On the other hand, the present invention further provides a processor for running a program, wherein the program, when being run, is used to execute the determination method described in any of the above embodiments.

[0164] On the other hand, the present invention provides a computer-readable storage medium having instructions stored thereon, the instructions being used to enable a machine to execute the determination method described in any of the above embodiments.

[0165] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0166] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0167] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0168] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0169] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0170] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0171] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0172] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0173] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for determining the grade of an alkaline ore layer, characterized in that: include: Acquire the well logging response characteristic value of the target alkaline ore layer in the test area and each rock-forming mineral contained in the target alkaline ore layer in the test area, and determine the well logging response characteristic value corresponding to each rock-forming mineral; Inputting the well logging response characteristic values ​​of the target alkali ore layer in the test area and the well logging response characteristic values ​​corresponding to each rock-forming mineral into a pre-constructed interpretation model to obtain the volume percentage of each rock-forming mineral contained in the target alkali ore layer in the test area, wherein the interpretation model is constructed by a rock-forming mineral physical volume model established based on the categories of each rock-forming mineral contained in the target alkali ore layer in the test area and an interpretation equation group established based on the rock-forming mineral physical model and the well logging response characteristic values ​​corresponding to each rock-forming mineral; and The ore grade in the target alkaline ore layer in the area to be tested is determined based on the volume percentage, theoretical density and compensated density logging value of each rock-forming mineral.

2. The determination method according to claim 1, characterized in that: The rock-forming minerals contained in the target alkaline ore layer in the test area include at least any one of clay, quartz, feldspar, calcite, sodium bicarbonate, salt rock, and pores.

3. The determination method according to claim 1, characterized in that: The logging response characteristic values ​​corresponding to the respective rock-forming minerals include at least any one of natural gamma, compensated neutron, deep resistivity, shallow resistivity, compensated density, photoelectric absorption cross-section index, natural potential and acoustic time difference.

4. The determination method according to claim 1, characterized in that: The construction of the explanatory equations includes: Based on the sensitivity to the lithology of each rock-forming mineral, a target logging response characteristic value is screened out from the logging response characteristic values ​​corresponding to each rock-forming mineral; Obtaining the well logging curve corresponding to the target well logging response characteristic value, and determining the well logging characteristic diagram of each rock-forming mineral lithology through core calibration logging; Performing scalar identification on the logging characteristic map to classify the stratigraphic lithology of the target alkaline ore layer in the area to be tested; The interpretation equation group is constructed according to the formation lithology and the target logging response characteristic value.

5. The determination method according to claim 1, characterized in that: The explanatory equations are: In the formula, ρ b is the theoretical logging compensation density, Δt is the theoretical logging acoustic time difference, CNL is the theoretical logging compensation neutron, ρ1…ρ m To compensate the density test value, Δt1…Δt m is the acoustic time difference test value, CNL1…CNL m To compensate for the neutron test value, V1…V m is the physical volume corresponding to each rock-forming mineral.

6. The determination method according to claim 1, characterized in that: Before inputting the well logging response characteristic value of the target alkali ore layer in the test area and the well logging response characteristic value corresponding to each rock-forming mineral into a pre-built interpretation model to obtain the volume percentage of each rock-forming mineral contained in the target alkali ore layer in the test area, the method further includes: Optimize the explanation model: Presetting the percentage of each rock-forming mineral contained in the target alkaline ore layer in the area to be tested; Inputting the percentage content of each rock-forming mineral and the well logging response characteristic value corresponding to each rock-forming mineral into the pre-built interpretation model, and outputting the theoretical well logging value; If the difference between the theoretical logging value and the actual logging value is greater than a preset threshold, the percentage content of each rock-forming mineral is adjusted until the difference between the theoretical logging value and the actual logging value is less than or equal to the preset threshold, thereby obtaining an optimized interpretation model.

7. The determination method according to claim 1, characterized in that: The ore grade in the target alkaline ore layer in the test area is determined according to the following formula: In the formula, C represents the ore grade, V i Represents the volume percentage of a single rock-forming mineral, ρ i It represents the theoretical density of a single rock-forming mineral, and DEN represents the compensated density logging value.

8. The determination method according to claim 1, characterized in that: The ore grade M in the target alkaline ore layer within a certain thickness range in the area to be tested is determined according to the following formula: In the formula, h2-h1 represents the thickness range, C x Indicates the grade of each ore within this thickness range.

9. The determination method according to claim 8, characterized in that: The ore grade Ms in the target alkaline ore layer within a certain area of ​​the area to be tested is determined according to the following formula: Where Si, Hi, and Mi represent the area, thickness, and average grade component of a unit in the effective thickness diagram, respectively; S and H represent the area and average effective thickness of a given unit, respectively.

10. The determination method according to claim 1, characterized in that: The step of obtaining the rock-forming minerals contained in the target alkaline ore layer in the area to be tested includes: X-ray diffraction quantitative analysis is performed on rock samples in the target alkali ore layer in the test area to obtain various rock-forming minerals contained in the target alkali ore layer in the test area.

11. A device for determining the grade of an alkaline ore layer, characterized in that: include: An acquisition unit, used to acquire a well logging response characteristic value of a target alkaline ore layer in a test area and each rock-forming mineral contained in the target alkaline ore layer in the test area, and determine a well logging response characteristic value corresponding to each rock-forming mineral; An input unit, used for inputting the well logging response characteristic value of the target alkali ore layer in the test area and the well logging response characteristic value corresponding to each rock-forming mineral into a pre-constructed interpretation model, so as to obtain the volume percentage of each rock-forming mineral contained in the target alkali ore layer in the test area, wherein the interpretation model is constructed by a rock-forming mineral physical volume model established based on the category of each rock-forming mineral contained in the target alkali ore layer in the test area and an interpretation equation group established based on the rock-forming mineral physical model and the well logging response characteristic value corresponding to each rock-forming mineral; as well as The determination unit is used to determine the ore grade in the target alkaline ore layer in the test area according to the volume percentage content, theoretical density and compensated density logging value of each rock-forming mineral.

12. The determination device according to claim 11, characterized in that: The input unit is used for: Based on the sensitivity to the lithology of each rock-forming mineral, a target logging response characteristic value is screened out from the logging response characteristic values ​​corresponding to each rock-forming mineral; Obtaining the well logging curve corresponding to the target well logging response characteristic value, and determining the well logging characteristic diagram of each rock-forming mineral lithology through core calibration logging; Performing scalar identification on the logging characteristic map to classify the stratigraphic lithology of the target alkaline ore layer in the area to be tested; The interpretation equation group is constructed according to the formation lithology and the target logging response characteristic value.

13. The determination device according to claim 11, characterized in that: The determining device further comprises an optimizing unit for optimizing the explanation model.

14. The determination device according to claim 13, characterized in that: The optimization unit is used to: Presetting the percentage of each rock-forming mineral contained in the target alkaline ore layer in the area to be tested; Inputting the percentage content of each rock-forming mineral and the well logging response characteristic value corresponding to each rock-forming mineral into the pre-built interpretation model, and outputting the theoretical well logging value; If the difference between the theoretical logging value and the actual logging value is greater than a preset threshold, the percentage content of each rock-forming mineral is adjusted until the difference between the theoretical logging value and the actual logging value is less than or equal to the preset threshold, thereby obtaining an optimized interpretation model.

15. The determination device according to claim 11, characterized in that: The determination unit is used to determine the ore grade in the target alkaline ore layer in the test area according to the following formula: In the formula, C represents the ore grade, V i Represents the volume percentage of a single rock-forming mineral, ρ i It represents the theoretical density of a single rock-forming mineral, and DEN represents the compensated density logging value.

16. The determination device according to claim 11, characterized in that: The determination unit is used to determine the ore grade M in the target alkaline ore layer within a certain thickness range in the area to be tested according to the following formula: In the formula, h2-h1 represents the thickness range, C x Indicates the grade of each ore within this thickness range.

17. The determination device according to claim 16, characterized in that: The determination unit is used to determine the ore grade Ms in the target alkaline ore layer within a certain area of ​​the area to be tested according to the following formula: Where Si, Hi, and Mi represent the area, thickness, and average grade component of a unit in the effective thickness diagram, respectively; S and H represent the area and average effective thickness of a given unit, respectively.

18. The determination device according to claim 11, characterized in that: The acquisition unit is used for: X-ray diffraction quantitative analysis is performed on rock samples in the target alkali ore layer in the test area to obtain various rock-forming minerals contained in the target alkali ore layer in the test area.

19. A processor, characterized in that: Used to run a program, wherein the program is used to execute the determination method according to any one of claims 1 to 10 when being run.

20. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, which are used to enable a machine to execute the determination method described in any one of claims 1 to 10.

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