Novel polyhalite sylvite ore identification method and device and storage medium

Abstract

The invention relates to a novel polyhalite sylvite ore identification method and device and a storage medium. According to the novel polyhalite sylvite ore identification method, different feature conditions (such as a first feature condition and a second feature condition) are used for identifying an ore bed. Through combination and cross validation of various data, the accuracy of ore bed identification is effectively improved, and the possibility of misjudgment is reduced. And finally, in combination with core test data and logging data, establishing a potassium content and ore density characteristic prediction model of the potassium salt ore bed, so that the potassium content, the density and other characteristics of the ore bed can be accurately predicted under the condition that coring is not carried out. By using the model, the efficiency of an exploration process and the reliability of a result can be greatly improved. In addition, after it is confirmed that the target ore bed is the novel polyhalite sylvite ore, the economic potential of the ore bed can be rapidly evaluated by using the feature prediction model, and data support is provided for subsequent mineral development decisions.

Description

Technical Field

[0001] The present invention relates to the field of marine soluble solid potash ore layer identification, and in particular to a novel polyhalite potash ore identification method, device and storage medium. Background Art

[0002] Potash is an indispensable mineral resource for agricultural food production. Geophysical methods can be used to obtain information on deep underground structures and evaluate potential mineral resources, which is an indispensable means in mineral exploration. Optimizing geophysical detection technology is of great significance for studying the mineralization mechanism, accurate prediction, and resource assessment of "new polyhalite potash mines". Well logging data plays an important role in identifying potash strata because it has the ability to continuously record information such as lithology, electrical properties, and physical properties of the formation. At present, conventional methods and techniques for identifying conventional potash strata using well logging data have been formed. These methods and techniques can be summarized as: ① Curve overlap method; ② Intersection diagram method; ③ Curve comprehensive identification method.

[0003] However, for the "new polyhalite potash mine", there is a problem in identifying overlapping areas using conventional logging methods, and traditional methods are not accurate. Summary of the invention

[0004] Based on this, it is necessary to provide a new polyhalite potash ore identification method, device and storage medium with improved accuracy.

[0005] In order to achieve the above-mentioned object, on the one hand, the embodiment of the present application provides a novel method for identifying polyhalite potash ore, comprising the steps of:

[0006] Obtain well logging data and core test data of the target mineral layer; wherein the well logging data includes well logging potassium content data, well logging thorium data and well logging resistivity data;

[0007] Based on the well logging potassium content data and well logging resistivity data, confirm the first characteristic conditions of the target ore layer;

[0008] Based on the well logging potassium content data and well logging thorium data, confirm the second characteristic conditions of the target ore layer;

[0009] According to the first characteristic condition and / or the second characteristic condition, confirm whether the target ore layer is a new type of polyhalite potash ore;

[0010] When the target ore layer is a new type of polyhalite potash ore, a characteristic prediction model of the new type of polyhalite potash ore is established based on core test data and well logging data;

[0011] The well logging data of the mineral layer to be measured is obtained, and the characteristic data of the mineral layer to be measured is obtained according to the well logging data of the mineral layer to be measured and the characteristic prediction model.

[0012] In one embodiment, the step of confirming the first characteristic condition of the target ore layer based on the well logging potassium content data and the well logging resistivity data comprises:

[0013] Obtain the common logarithm of the well logging resistivity data and calculate the product of the common logarithm and the well logging potassium content data

[0014] When the product is greater than the first preset value, it is confirmed that the first characteristic condition meets the first standard.

[0015] In one embodiment, the step of confirming the second characteristic condition of the target ore layer based on the well logging potassium content data and the well logging thorium data comprises:

[0016] Obtain the quotient of the well logging potassium content data and the well logging thorium data;

[0017] When the value of the quotient is greater than the second preset value, the second characteristic condition is confirmed to meet the second standard.

[0018] In one embodiment, the step of confirming whether the target ore layer is a novel polyhalite potash ore comprises:

[0019] If the first characteristic condition meets the first standard, and the second characteristic condition meets the second standard, then the target ore layer is confirmed to be a new type of polyhalite potash ore; or,

[0020] If the first characteristic condition meets the first standard, the target ore layer is confirmed to be a new type of polyhalite potash ore; or,

[0021] If the second characteristic condition meets the second standard, the target ore layer is confirmed to be a new type of polyhalite potash mine.

[0022] In one embodiment, the first preset value is 20; the second preset value is 1.

[0023] In one embodiment, the first criterion is: K 测井 ×log(RD 测井 )>20; The second standard is: K 测井 / TH 测井 >1.

[0024] In one embodiment, the well logging data also includes gamma data, uranium content data and wellbore data:

[0025] The steps to confirm whether the target ore layer is a new type of polyhalite potash ore include:

[0026] A comprehensive identification model is established based on gamma data, uranium content data, wellbore data, first characteristic conditions and second characteristic conditions;

[0027] Based on the comprehensive identification model, confirm whether the target ore layer is a new type of polyhalite potash mine.

[0028] In one embodiment, the core test data includes the sample measured potassium content and the sample measured density data; the well logging data also includes well logging density data;

[0029] The steps of establishing a characteristic prediction model for a new polyhalite potash ore based on core test data and well logging data include:

[0030] Fitting the measured potassium content of the sample and the well logging potassium content data to obtain a first fitting relationship;

[0031] The measured density data of the sample and the well logging density data are fitted to obtain a second fitting relationship; wherein the first fitting relationship and the second fitting relationship constitute a characteristic prediction model.

[0032] In one embodiment, the first fitting relationship is a linear relationship for quantitative prediction of potassium content, and the equation is as follows:

[0033] K 测试 =1.0497*K 测井 +0.3534, R 2 =0.8638

[0034] The second fitting relationship is the linear relationship for quantitative prediction of ore density, and the relationship is as follows:

[0035] ρ 测试 =0.7231*ρ 测井 +0.7663, R 2 =0.7421

[0036] In the above formula, K 测试 K is the measured potassium content of the sample; 测井 is the well logging potassium content data; ρ 测试 is the measured density data of the sample; ρ 测井 Well logging density data.

[0037] On the one hand, an embodiment of the present invention provides a novel polyhalite potash ore identification device, comprising:

[0038] An acquisition module is used to acquire well logging data and core test data of the target mineral layer; wherein the well logging data includes well logging potassium content data, well logging thorium data and well logging resistivity data;

[0039] A first confirmation module is used to confirm the first characteristic condition of the target ore layer based on the well logging potassium content data and the well logging resistivity data;

[0040] A second confirmation module is used to confirm the second characteristic condition of the target ore layer based on the well logging potassium content data and the well logging thorium data;

[0041] A third confirmation module is used to confirm whether the target ore layer is a new type of polyhalite potash ore according to the first characteristic condition and / or the second characteristic condition;

[0042] A model building module is used to build a characteristic prediction model of the new polyhalite potash ore based on core test data and logging data when the target ore layer is a new polyhalite potash ore;

[0043] The output module is used to obtain the logging data of the mineral layer to be tested, and obtain the characteristic data of the mineral layer to be tested according to the logging data of the mineral layer to be tested and the characteristic prediction model.

[0044] On the one hand, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0045] On the other hand, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0046] One of the above technical solutions has the following advantages and beneficial effects:

[0047] The above method first obtains the well logging data and core test data of the target ore layer, and then confirms the first characteristic condition of the target ore layer through the well logging potassium content data and the well logging resistivity data, confirms the second characteristic condition of the target ore layer through the well logging potassium content data and the well logging thorium data, and further qualitatively identifies the target ore layer through the first characteristic condition and / or the second characteristic condition, and uses different characteristic conditions (such as the first characteristic condition and the second characteristic condition) to identify the ore layer. The combination and cross-validation of multiple data effectively improves the accuracy of ore layer identification and reduces the possibility of misjudgment. Finally, combined with the core test data and the well logging data, a characteristic prediction model of the potassium content and ore density of the potash ore layer is established, so that the potassium content and density and other characteristics of the ore layer can be accurately predicted without coring. The use of this model can greatly improve the efficiency of the exploration process and the reliability of the results. In addition, after confirming that the target ore layer is a new type of polyhalite potash ore, the use of the characteristic prediction model can quickly evaluate the economic potential of the ore layer and provide data support for subsequent mineral development decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0050] FIG. 1( a ) is a schematic flow chart of a novel polyhalite potash ore identification method in one embodiment;

[0051] FIG1( b ) is a diagram showing the effect of using the first standard to identify a new type of polyhalite potassium salt ore layer in one embodiment;

[0052] FIG1( c ) is a diagram showing the effect of using the second standard to identify a new type of polyhalite potash ore layer in one embodiment;

[0053] FIG1(d) is a diagram showing the effect of identifying a new polyhalite potassium salt layer by overlapping thorium and potassium curves in one embodiment;

[0054] FIG. 1( e ) is a diagram showing the lithology identification effect of a thorium and potassium curve intersection diagram for identifying a new type of polyhalite potash ore layer in one embodiment;

[0055] Figure 2 is a schematic flow chart of the steps of confirming the first characteristic condition of the target ore layer based on the well logging potassium content data and the well logging resistivity data in one embodiment;

[0056] Figure 3 is a schematic flow chart of the steps of confirming the second characteristic condition of the target ore layer based on the well logging potassium content data and the well logging thorium data in one embodiment;

[0057] Figure 4 A flowchart of the steps of establishing a characteristic prediction model of a new polyhalite potash ore based on core test data and well logging data in one embodiment;

[0058] Figure 5 It is a block diagram of a novel polyhalite potash ore identification device in one embodiment;

[0059] FIG6( a ) is an example 1 of applying the identification method proposed in the present invention to a new polyhalite potash mine located in a key well in Xuanhan, Sichuan, provided by an embodiment of the present invention;

[0060] FIG6( b ) is a second example of applying the identification method proposed in the present invention to a new polyhalite potash mine located in a key well in Xuanhan, Sichuan, provided by an embodiment of the present invention;

[0061] Figure 7 It is the linear relationship between the potassium content data obtained by actual measurement of the core samples in the study area provided by the embodiment of the present invention and the potassium content data interpreted by the well logging;

[0062] Figure 8 It is the linear relationship between the density data obtained by actual measurement of core samples in the study area provided by the embodiment of the present invention and the density data fitted by well logging interpretation;

[0063] Fig. 9 It is a comparative interpretation profile of a well connection in the study area provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0064] In order to facilitate understanding of the present application, the present application will be described more fully below with reference to the relevant drawings. Embodiments of the present application are provided in the drawings. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive.

[0065] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0066] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present application and have no specific meaning. Therefore, "module" and "component" can be used interchangeably.

[0067] It can be understood that the “connection” in the following embodiments should be understood as “electrical connection”, “communication connection”, etc. if the connected circuits, modules, units, etc. have electrical signals or data transmission between each other.

[0068] When used herein, the singular forms "a", "an", and "said / the" may also include plural forms, unless the context clearly indicates otherwise. It should also be understood that the terms "include / comprise" or "have" etc. specify the presence of stated features, wholes, steps, operations, components, parts or combinations thereof, but do not exclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts or combinations thereof.

[0069] At present, for potash ore layers with simple lithology, they can be accurately identified using conventional logging data. Under normal circumstances, polyhalite layers often show response characteristics of high natural gamma, high potassium, low thorium, and low uranium on conventional logging curves. Using these response characteristics, pure polyhalite layers can be intuitively and accurately identified. However, for new polyhalite potash ore layers, they are salt crystal cemented clastic particles distributed in a rock salt matrix. They are found in marine evaporite strata with a burial depth of more than 3000m, coexisting with rock salt and anhydrite, and multiple lithologies coexist, resulting in unclear logging curve response characteristics. The polyhalite identification method using conventional logging data is prone to identification overlap areas, and the discrimination accuracy is low when facing new polyhalite potash mines with complex lithology. The identification method of the present application can effectively solve the above problems.

[0070] In one embodiment, as shown in FIG. 1( a ), a novel polyhalite potash ore identification method comprises the steps of:

[0071] S110, acquiring well logging data and core test data of the target mineral layer; wherein the well logging data includes well logging potassium content data, well logging thorium data and well logging resistivity data;

[0072] Among them, the target ore layer refers to the ore layer determined during the mineral exploration process. Well logging data refers to the physical parameters related to the ore layer obtained by downhole measuring instruments, including well logging potassium content data, well logging thorium data and well logging resistivity data. Core test data refers to the experimental analysis results of rock samples taken from underground through coring operations. These data usually include mineral composition, density, porosity, etc.

[0073] Specifically, one or more specific mineral layers in mineral exploration are identified, which are called target mineral layers. Various logging data of the target mineral layer are obtained through downhole measuring instruments, including physical parameters such as logging potassium content, logging thorium content and logging resistivity. For example, the target mineral layer can be measured through conventional, natural gamma ray spectrum logging and logging instruments, and the logging data of the target mineral layer can be obtained through geological logging, rock and mineral identification analysis, and systematic sampling tests. At the same time, core sampling tests are carried out to obtain core samples of the mineral layer, and these samples are analyzed to obtain core test data.

[0074] S120, confirming the first characteristic condition of the target ore layer based on the well logging potassium content data and the well logging resistivity data;

[0075] Specifically, the target mineral layer is analyzed using the previously acquired well logging data, especially the well logging potassium content data and the well logging resistivity data. Through a specific calculation method, such as multiplying the well logging potassium content by the common logarithm of the well logging resistivity, it is determined whether the product exceeds a certain preset value, thereby confirming whether the target mineral layer meets the first characteristic condition. This condition is a key indicator for identifying the mineral layer, indicating that the mineral layer may belong to a new type of polyhalite potash mine.

[0076] S130, confirming the second characteristic condition of the target ore layer based on the well logging potassium content data and the well logging thorium data;

[0077] Specifically, in this step, the potassium content and thorium content in the logging data are further analyzed, and their ratio is calculated to confirm the second characteristic condition of the target ore layer. For example, by judging whether the ratio of the logging potassium content to the logging thorium content exceeds the set threshold, it is determined whether the ore layer has the properties that meet the second characteristic condition. This condition is another key indicator for identifying the ore layer, indicating that the ore layer may belong to a new type of polyhalite potash mine.

[0078] S140, confirming whether the target ore layer is a new type of polyhalite potash ore according to the first characteristic condition and / or the second characteristic condition;

[0079] Specifically, the step of confirming whether the target ore layer is a new type of polyhalite potash ore includes: if the first characteristic condition meets the first standard and the second characteristic condition meets the second standard, then confirming that the target ore layer is a new type of polyhalite potash ore; or, if the first characteristic condition meets the first standard, then confirming that the target ore layer is a new type of polyhalite potash ore; or, if the second characteristic condition meets the second standard, then confirming that the target ore layer is a new type of polyhalite potash ore. That is, the two characteristic conditions can be used in combination, or one of the characteristic conditions can be used alone to confirm whether the target ore layer is a new type of polyhalite potash ore. This method can quickly and effectively and accurately identify a large amount of ore layer data to accurately construct a feature prediction model. The effect diagram of identifying a new type of polyhalite potash ore layer using the first standard is shown in Figure 1 (b), the effect diagram of identifying a new type of polyhalite potash ore layer using the second standard is shown in Figure 1 (c), and conventional logging methods such as curve overlap method and intersection diagram method for identifying lithology are shown in Figure 1 (d) and Figure 1 (e), respectively.

[0080] Furthermore, the logging data may also include gamma data, uranium content data and borehole diameter data: the step of confirming whether the target ore layer is a new type of polyhalite potash ore includes: establishing a comprehensive identification model based on gamma data, uranium content data, borehole diameter data, the first characteristic condition and the second characteristic condition; based on the comprehensive identification model, confirming whether the target ore layer is a new type of polyhalite potash ore. The comprehensive identification model of the target ore layer logging of the "new type of polyhalite potash ore" based on the logging data can also be: "three highs, two lows, and one large". The specific characteristics in the study area are high gamma (97-181API), high potassium (6.736-10.911%), high resistivity (6600-13900Ω.m); low thorium (0.794-2.277ppm), low uranium (0.005-1.54ppm); large borehole diameter (i.e., obvious diameter expansion phenomenon occurs).

[0081] S150, when the target ore layer is a new type of polyhalite potash ore, establishing a characteristic prediction model of the new type of polyhalite potash ore based on core test data and well logging data;

[0082] Specifically, after confirming that the target ore layer is a new type of polyhalite potash ore, a feature prediction model is established by analyzing core test data and well logging data. This model models the relationship between ore layer characteristics such as potassium content and density and well logging data, so that these characteristics can be predicted through well logging data in the future.

[0083] S160, acquiring well logging data of the mineral layer to be measured, and obtaining characteristic data of the mineral layer to be measured based on the well logging data and the characteristic prediction model of the mineral layer to be measured.

[0084] Specifically, in this step, the well logging data of the ore layer to be tested that has not been analyzed is obtained. Using the previously established characteristic prediction model, these well logging data are input into the model to obtain the key characteristic data of the ore layer to be tested. These data may include the potassium content, ore density, etc. of the ore layer, which can provide an important reference for resource assessment. In this way, the economic value of the ore layer can be quickly evaluated without comprehensive sampling. It should be noted that before obtaining the characteristic data of the ore layer to be tested based on the well logging data and characteristic prediction model of the ore layer to be tested, the properties of the ore layer need to be tested, and the detection method can adopt the above-mentioned steps S110-S140. After the ore layer to be tested is confirmed to be a new type of polyhalite potash ore, the step of "obtaining the characteristic data of the ore layer to be tested based on the well logging data and characteristic prediction model of the ore layer to be tested" is performed.

[0085] The above method first obtains the well logging data and core test data of the target ore layer, and then confirms the first characteristic condition of the target ore layer through the well logging potassium content data and the well logging resistivity data, confirms the second characteristic condition of the target ore layer through the well logging potassium content data and the well logging thorium data, and further qualitatively identifies the target ore layer through the first characteristic condition and / or the second characteristic condition, and uses different characteristic conditions (such as the first characteristic condition and the second characteristic condition) to identify the ore layer. The combination and cross-validation of multiple data effectively improves the accuracy of ore layer identification and reduces the possibility of misjudgment. Finally, combined with the core test data and the well logging data, a characteristic prediction model of the potassium content and ore density of the potash ore layer is established, so that the potassium content and density and other characteristics of the ore layer can be accurately predicted without coring. The use of this model can greatly improve the efficiency of the exploration process and the reliability of the results. In addition, after confirming that the target ore layer is a new type of polyhalite potash ore, the use of the characteristic prediction model can quickly evaluate the economic potential of the ore layer and provide data support for subsequent mineral development decisions.

[0086] In one embodiment, Figure 2 As shown, the step of confirming the first characteristic condition of the target ore layer based on the well logging potassium content data and the well logging resistivity data includes:

[0087] S210, obtaining the common logarithm of the well logging resistivity data, and calculating the product of the common logarithm and the well logging potassium content data;

[0088] S220: When the product is greater than the first preset value, confirm that the first characteristic condition meets the first standard.

[0089] Specifically, the first standard is as follows: K 测井 ×log(RD 测井 )>first preset value, wherein the first preset value may be 20. That is, in K 测井 ×log(RD 测井 )>20, it is considered to meet the first standard and proceed to the next step of judgment. 测井 is the well logging potassium content data; RD 测井 is the logging resistivity data. Further, K 测井 ×log(RD 测井 )<20, it is considered not to meet the first standard.

[0090] In one embodiment, Figure 3 As shown, the step of confirming the second characteristic condition of the target ore layer based on the well logging potassium content data and the well logging thorium data includes:

[0091] S310, obtaining the quotient of the well logging potassium content data and the well logging thorium data;

[0092] S320: When the value of the quotient is greater than a second preset value, confirm that the second characteristic condition meets the second standard.

[0093] Specifically, the second standard is as follows: K 测井 / TH 测井 > a second preset value, where the second preset value may be 1. 测井 / TH 测井 When K > 1, it is considered to meet the second standard and proceed to the next step of judgment. 测井 is the well logging potassium content data; TH 测井 is the logging thorium data. Furthermore, K 测井 / TH 测井 If <1, it is considered not to meet the second standard.

[0094] In one embodiment, Figure 4 As shown, the core test data includes the measured potassium content and the measured density data of the sample; the well logging data also includes the well logging density data;

[0095] The steps of establishing a characteristic prediction model for a new polyhalite potash ore based on core test data and well logging data include:

[0096] S410, performing fitting processing on the actually measured potassium content of the sample and the well logging potassium content data to obtain a first fitting relationship;

[0097] S420, performing fitting processing on the sample measured density data and the well logging density data to obtain a second fitting relationship; wherein the first fitting relationship and the second fitting relationship constitute a feature prediction model.

[0098] Specifically, the first fitting relationship is a linear relationship for quantitative prediction of potassium content, and the linear relationship for quantitative prediction of potassium content is as follows:

[0099] K 测试 =1.0497*K 测井 +0.3534, R 2 =0.8638

[0100] The second fitting relationship is the linear relationship for quantitative prediction of ore density. The linear relationship for quantitative prediction of ore density is as follows:

[0101] ρ 测试 =0.7231*ρ 测井 +0.7663, R 2 =0.7421

[0102] In the above formula, K 测试 K is the measured potassium content of the sample; 测井 is the well logging potassium content data; ρ 测试 is the measured density data of the sample; ρ测井 is the well logging density data. Based on the ascending linear formula, the remaining uncored single wells K can be 测井 Data and ρ 测井 The data is recalibrated and calculated, and then the potassium content and ore density of the corresponding new polyhalite potash target layer are calculated, which is equivalent to the actual measured sample, providing a quantitative prediction basis for the resource estimation of the target ore layer. With the above linear relationship, in the exploration process, only the logging data needs to be used to calculate the K content of different layers. 测试 and ρ 测井 By using the linear relationship, we can calculate the potassium content and ore density of the target ore layer of the new polyhalite potash mine at the corresponding layer, which are equivalent to the actual measured values ​​of the sample, and use these reliable fitting values ​​to estimate the resource volume of the target ore layer.

[0103] In one embodiment, Figure 5 As shown, the embodiment of the present invention also provides a novel polyhalite potash ore identification device, comprising:

[0104] An acquisition module is used to acquire well logging data and core test data of the target mineral layer; wherein the well logging data includes well logging potassium content data, well logging thorium data and well logging resistivity data;

[0105] A first confirmation module is used to confirm the first characteristic condition of the target ore layer based on the well logging potassium content data and the well logging resistivity data;

[0106] A second confirmation module is used to confirm the second characteristic condition of the target ore layer based on the well logging potassium content data and the well logging thorium data;

[0107] A third confirmation module is used to confirm whether the target ore layer is a new type of polyhalite potash ore according to the first characteristic condition and / or the second characteristic condition;

[0108] A model building module is used to build a characteristic prediction model of the new polyhalite potash ore based on core test data and logging data when the target ore layer is a new polyhalite potash ore;

[0109] The output module is used to obtain the logging data of the mineral layer to be tested, and obtain the characteristic data of the mineral layer to be tested according to the logging data of the mineral layer to be tested and the characteristic prediction model.

[0110] For the specific limitations of the new polyhalite potash ore identification device, please refer to the limitations of the new polyhalite potash ore identification method mentioned above, which will not be repeated here. Each module in the above-mentioned new polyhalite potash ore identification device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules. It should be noted that the division of modules in the embodiment of the present application is schematic, which is only a logical function division, and there may be other division methods in actual implementation.

[0111] In one embodiment, comprehensive logging identification is carried out in the Puguang area of ​​Xuanhan County in the northeast of the Sichuan Basin. FIG6 is an application example of using the proposed new polyhalite potash ore discrimination model in one embodiment of the present invention to identify a new polyhalite potash ore layer in a key well in Xuanhan, northeast of the Sichuan Basin. FIG6 (a) explains a total of 7 layers of new polyhalite potash ore layers, with a cumulative thickness of 42.8 m, a single layer thickness of 1.3 to 14.8 m, and an average single layer thickness of 6.1 m. The logging response characteristics are: the overall natural gamma of the new polyhalite potash ore layer is medium to high, between 51.5 and 185.9 API, the uranium-free gamma is between 39.1 and 132.4 API, the resistivity is high resistance, between hundreds and tens of thousands of ohm meters, and the core density is between 2.35 and 2.81 g / cm3. Figure 6(b) shows a total of 4 new polyhalite potash layers with a cumulative thickness of 33.5 m, a single layer thickness of 2.1 to 14.9 m, and an average single layer thickness of 8.4 m. The logging response characteristics are: the natural gamma of the new polyhalite potash layer is medium to high, ranging from 39.2 to 151.6 API, the uranium-free gamma is between 39.5 and 145.5 API, the resistivity is high, ranging from 464 to 13370 Ω·m, and the core density is between 2.05 and 2.61 g / cm3. Figure 7 The linear relationship between the measured data of potassium content in the new polyhalite potash ore layer and the well logging potassium content data is fitted: K 测试 =1.0497*K 测井 +0.3534, R 2 =0.8638. Figure 8 is the linear relationship between the measured density data of the sample and the logging density data: 测试 =0.7231*ρ 测井 +0.7663, R 2 =0.7421. After verification, it is confirmed that other uncored wells have a good correlation with the existing fitting curve. The prediction model can be used to correct and calculate the data of other uncored wells to provide a quantitative prediction basis. Fig. 9It is a comparative interpretation profile of a well in the study area provided by an embodiment of the present invention, and it is judged that the new polyhalite potassium salt ore layer has a multi-layer distribution characteristic, and the lithology combination is anhydrite rock-new polyhalite potassium salt ore-rock salt rock-anhydrite rock.

[0112] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0113] Obtain well logging data and core test data of the target mineral layer; wherein the well logging data includes well logging potassium content data, well logging thorium data and well logging resistivity data;

[0114] Based on the well logging potassium content data and well logging resistivity data, confirm the first characteristic conditions of the target ore layer;

[0115] Based on the well logging potassium content data and well logging thorium data, confirm the second characteristic conditions of the target ore layer;

[0116] According to the first characteristic condition and / or the second characteristic condition, confirm whether the target ore layer is a new type of polyhalite potash ore;

[0117] When the target ore layer is a new type of polyhalite potash ore, a characteristic prediction model of the new type of polyhalite potash ore is established based on core test data and well logging data;

[0118] The well logging data of the mineral layer to be measured is obtained, and the characteristic data of the mineral layer to be measured is obtained according to the well logging data of the mineral layer to be measured and the characteristic prediction model.

[0119] In one embodiment, a computer-readable storage medium is further provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0120] Obtain well logging data and core test data of the target mineral layer; wherein the well logging data includes well logging potassium content data, well logging thorium data and well logging resistivity data;

[0121] Based on the well logging potassium content data and well logging resistivity data, confirm the first characteristic conditions of the target ore layer;

[0122] Based on the well logging potassium content data and well logging thorium data, confirm the second characteristic conditions of the target ore layer;

[0123] According to the first characteristic condition and / or the second characteristic condition, confirm whether the target ore layer is a new type of polyhalite potash ore;

[0124] When the target ore layer is a new type of polyhalite potash ore, a characteristic prediction model of the new type of polyhalite potash ore is established based on core test data and well logging data;

[0125] The well logging data of the mineral layer to be measured is obtained, and the characteristic data of the mineral layer to be measured is obtained according to the well logging data of the mineral layer to be measured and the characteristic prediction model.

[0126] When the embodiments of the present application are specifically implemented, reference may be made to the above-mentioned embodiments, which have corresponding technical effects.

[0127] It is understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit may be implemented in one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), general purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described in the present application, or a combination thereof.

[0128] For software implementation, the technology described herein can be implemented by a unit that performs the functions described herein. The software code can be stored in a memory and executed by a processor. The memory can be implemented in the processor or outside the processor.

[0129] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0130] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0131] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0132] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0133] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. If the function 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. Based on such an understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a U disk, a mobile hard disk, a ROM, a RAM, a magnetic disk or an optical disk. It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variation thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0134] The above description is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest range consistent with the principles and novel features applied for herein.

Claims

1. A new method for identifying polyhalite potash ore, It is characterized in that include: Acquire well logging data and core test data of the target mineral layer; wherein the well logging data includes well logging potassium content data, well logging thorium data and well logging resistivity data; Based on the well logging potassium content data and the well logging resistivity data, confirming the first characteristic condition of the target mineral layer; Based on the well logging potassium content data and the well logging thorium data, confirm the second characteristic condition of the target ore layer; According to the first characteristic condition and / or the second characteristic condition, confirm whether the target ore layer is a new type of polyhalite potash ore; In the case where the target ore layer is a new type of polyhalite potash ore, a characteristic prediction model of the new type of polyhalite potash ore is established based on the core test data and the well logging data; The well logging data of the mineral layer to be measured is obtained, and the characteristic data of the mineral layer to be measured is obtained according to the well logging data of the mineral layer to be measured and the characteristic prediction model.

2. The novel polyhalite potash ore identification method according to claim 1, It is characterized in that The step of confirming the first characteristic condition of the target ore layer based on the well logging potassium content data and the well logging resistivity data comprises: Obtain the common logarithm of the well logging resistivity data, and calculate the product of the common logarithm and the well logging potassium content data When the product is greater than a first preset value, it is confirmed that the first characteristic condition meets the first standard.

3. The novel polyhalite potash ore identification method according to claim 2, It is characterized in that The step of confirming the second characteristic condition of the target ore layer based on the well logging potassium content data and the well logging thorium data comprises: Obtaining a quotient of the well logging potassium content data and the well logging thorium data; When the value of the quotient is greater than a second preset value, it is confirmed that the second characteristic condition meets the second standard.

4. The novel polyhalite potash ore identification method according to claim 3, It is characterized in that The step of confirming whether the target ore layer is a novel polyhalite potash ore comprises: If the first characteristic condition meets the first standard, and the second characteristic condition meets the second standard, then the target ore layer is confirmed to be a new type of polyhalite potash ore; or, If the first characteristic condition meets the first standard, it is confirmed that the target ore layer is a new type of polyhalite potash ore; or, If the second characteristic condition meets the second standard, it is confirmed that the target ore layer is a new type of polyhalite potash ore.

5. The novel polyhalite potash salt ore identification method according to claim 3 or 4, It is characterized in that The first preset value is 20; the second preset value is 1.

6. The novel polyhalite potash ore identification method according to claim 1, It is characterized in that The logging data also includes gamma data, uranium content data and wellbore data: The step of confirming whether the target ore layer is a novel polyhalite potash ore comprises: Establishing a comprehensive identification model according to the gamma data, the uranium content data, the wellbore data, the first characteristic condition and the second characteristic condition; Based on the comprehensive identification model, it is confirmed whether the target ore layer is a new type of polyhalite potash ore.

7. The novel polyhalite potash ore identification method according to claim 1, It is characterized in that The core test data includes the sample's measured potassium content and the sample's measured density data; the well logging data also includes well logging density data; The step of establishing a characteristic prediction model of a novel polyhalite potash ore based on the core test data and the well logging data comprises: Performing fitting processing on the actually measured potassium content of the sample and the well logging potassium content data to obtain a first fitting relationship; The measured density data of the sample and the well logging density data are fitted to obtain a second fitting relationship; wherein the first fitting relationship and the second fitting relationship constitute the feature prediction model.

8. A new type of polyhalite potash ore identification device, It is characterized in that include: An acquisition module is used to acquire well logging data and core test data of the target mineral layer; wherein the well logging data includes well logging potassium content data, well logging thorium data and well logging resistivity data; A first confirmation module, used to confirm a first characteristic condition of the target ore layer based on the well logging potassium content data and the well logging resistivity data; A second confirmation module is used to confirm a second characteristic condition of the target ore layer based on the well logging potassium content data and the well logging thorium data; A third confirmation module is used to confirm whether the target ore layer is a new type of polyhalite potash ore according to the first characteristic condition and / or the second characteristic condition; A model building module, for building a characteristic prediction model of the new polyhalite potash ore based on the core test data and the logging data when the target ore layer is the new polyhalite potash ore; The output module is used to obtain the logging data of the mineral layer to be measured, and obtain the characteristic data of the mineral layer to be measured according to the logging data of the mineral layer to be measured and the characteristic prediction model.

9. A computer-readable storage medium having a computer program stored thereon, It is characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program. It is characterized in that When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

Images