Stratum resistivity calculation method and device, computer equipment and storage medium

Abstract

The invention provides a formation resistivity calculation method and device, computer equipment and a storage medium. The method comprises the steps that types and resistivity of at least two elements of a drilling well are obtained; calculating the correlation between each element and the resistivity to obtain a first correlation coefficient of each element; selecting at least one element of which the first correlation coefficient is greater than a correlation coefficient threshold value by adopting a preset mathematical method, and determining as a sensitive element; and establishing a resistivity calculation model based on the sensitive elements and the first correlation coefficients corresponding to the sensitive elements. Based on the elements and the correlation coefficients of the elements, a resistivity calculation model capable of accurately calculating the resistivity is established, and real-time resistivity calculation in the geosteering process can be achieved only by measuring logging input of the elements in the drilling process and importing the logging input into the model through the resistivity calculation model. Resistivity information is provided in time in the drilling process, accurate judgment of the position of a drill bit is enhanced, drilling of a target window is avoided, and the drilling rate of a high-quality reservoir is increased.

Description

Technical Field

[0001] The invention relates to the technical field of logging resistivity calculation, and in particular to a formation resistivity calculation method, device, computer equipment and storage medium. Background Art

[0002] The resistivity and resistivity variation range of different lithologies, dense layers, permeable layers and oil, gas and water layers are different. When there is multiple solutions in the interpretation of gamma-ray formations, timely provision of resistivity information during the drilling process is of great significance for guiding on-site drilling and judging whether the target layer is encountered.

[0003] Due to instrument limitations and high costs, only a small number of wells conduct resistivity measurements while drilling. Element logging has the characteristics of high detection accuracy and strong economy, and has become a must-log item for shale oil and gas wells. In order to timely understand the resistivity changes of logging, it is necessary to establish a resistivity calculation model so that the resistivity can be quickly calculated for each logging to improve drilling efficiency and drilling accuracy. Summary of the invention

[0004] Based on this, it is necessary to provide a formation resistivity calculation method, device, computer equipment and storage medium to address the above technical problems.

[0005] A formation resistivity calculation method, comprising:

[0006] Obtaining the type and resistivity of at least two elements of the wellbore;

[0007] Calculating the correlation between each of the elements and the resistivity to obtain a first correlation coefficient of each of the elements;

[0008] Using a preset mathematical method, select at least one element whose first correlation coefficient is greater than a correlation coefficient threshold value and determine it as a sensitive element;

[0009] A resistivity calculation model is established based on the sensitive element and the first correlation coefficient corresponding to the sensitive element.

[0010] In one embodiment, the step of using a preset mathematical method to select at least one element whose first correlation coefficient is greater than a correlation coefficient threshold and determining it as a sensitive element comprises:

[0011] Extracting element data of drilling using oil-based drilling fluid and extracting element data of drilling using water-based drilling fluid from the horizontal well and the pilot well of the same well to obtain first element data and second element data; or extracting element data of drilling using oil-based drilling fluid and extracting element data of drilling using water-based drilling fluid from two adjacent wells to obtain first element data and second element data;

[0012] Comparing the first element data with the second element data to obtain the oil-based influence difference value of each element;

[0013] At least one of the elements corresponding to the oil-based impact difference value that is less than a preset difference threshold is selected, and at least one of the elements whose first correlation coefficient is greater than the correlation coefficient threshold is selected using a preset mathematical method to be determined as the sensitive element.

[0014] In one embodiment, the step of using a preset mathematical method to select at least one element whose first correlation coefficient is greater than a correlation coefficient threshold and determining it as a sensitive element comprises:

[0015] Detecting the correlation between the elements to obtain a plurality of second correlation coefficients;

[0016] Extracting the second correlation coefficient greater than a preset collinearity threshold as a collinearity coefficient;

[0017] An element is extracted from each of the elements corresponding to each of the collinearity coefficients, and a preset mathematical method is used to select at least one element whose first correlation coefficient is greater than a correlation coefficient threshold value and determine it as the sensitive element.

[0018] In one embodiment, the step of using a preset mathematical method to select at least one element whose first correlation coefficient is greater than a correlation coefficient threshold and determining it as a sensitive element comprises:

[0019] Eliminate elements with atomic numbers less than a preset number and trace elements from the elements in the wellbore;

[0020] Using a preset mathematical method, at least one element having a first correlation coefficient greater than a correlation coefficient threshold is selected from the eliminated elements to be determined as a sensitive element;

[0021] In one embodiment, the step of using a preset mathematical method to select at least one element whose first correlation coefficient is greater than a correlation coefficient threshold and determining it as a sensitive element comprises:

[0022] By adopting the intersection diagram method and / or the correlation heat map method, at least one element whose first correlation coefficient is greater than the correlation coefficient threshold is selected and determined as the sensitive element.

[0023] In one embodiment, the step of using a preset mathematical method to select at least one element whose first correlation coefficient is greater than a correlation coefficient threshold and determining it as a sensitive element comprises:

[0024] Obtaining an absolute value of each of the first correlation coefficients;

[0025] By using a preset mathematical method, at least one element whose absolute value of the first correlation coefficient is greater than the correlation coefficient threshold is selected and determined as a sensitive element.

[0026] In one embodiment, the step of establishing a resistivity calculation model based on the sensitive element and the first correlation coefficient corresponding to the sensitive element includes:

[0027] Based on the sensitive elements and the first correlation coefficients corresponding to the sensitive elements, a resistivity calculation model is established by adopting a multivariate regression algorithm.

[0028] A formation resistivity calculation device, comprising:

[0029] A data acquisition module, used to acquire the type and resistivity of at least two elements of the drilling;

[0030] A first correlation coefficient calculation module, used for calculating the correlation between each of the elements and the resistivity, and obtaining a first correlation coefficient between each of the elements and the resistivity;

[0031] A sensitive element selection module, used to select at least one element whose first correlation coefficient is greater than a correlation coefficient threshold by using a preset mathematical method, and determine it as a sensitive element;

[0032] The model building module is used to build a resistivity calculation model based on the sensitive element and the first correlation coefficient corresponding to the sensitive element.

[0033] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and wherein the processor implements the steps of the formation resistivity calculation method described in any of the above embodiments when executing the computer program.

[0034] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the formation resistivity calculation method described in any of the above embodiments.

[0035] The above-mentioned formation resistivity calculation method, device, computer equipment and storage medium can determine the element with a higher degree of correlation with the resistivity by comparing the first correlation coefficient with the correlation coefficient threshold, and then establish a resistivity calculation model that can accurately calculate the resistivity based on the element and the correlation coefficient of the element. Using this resistivity calculation model, it is only necessary to measure the logging input of the element during the drilling process and import it into the model, that is, it can realize real-time resistivity calculation in the geological guidance process, provide resistivity information in time during the drilling process, enhance the accurate judgment of the drill bit position, avoid drilling out of the target window, and improve the drilling rate of high-quality reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1is a schematic diagram of a flow chart of a formation resistivity calculation method in one embodiment;

[0037] Figure 2 is a structural block diagram of a formation resistivity calculation device in one embodiment;

[0038] Figure 3 is an internal structure diagram of a computer device in one embodiment;

[0039] Figure 4 It is a thermal diagram of the correlation between elements and resistivity in a certain work area in one embodiment;

[0040] Figure 5A This is a schematic diagram of the measurement results of water-based drilling fluid elements in a certain work area in an embodiment;

[0041] Figure 5B This is a schematic diagram of the measurement results of oil-based drilling fluid elements in a certain work area in an embodiment;

[0042] Figure 6 The figure is a comparison diagram of the resistivity calculated according to the embodiment of the present application and the actually measured resistivity logging curve in one embodiment. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0044] Embodiment 1

[0045] In this embodiment, Figure 1 As shown, a formation resistivity calculation method is provided, which includes:

[0046] Step 110, obtaining the type and resistivity of at least two elements of the wellbore.

[0047] Specifically, the drilling can be a single well or multiple wells, wherein the single well or multiple wells can be a pilot well or a horizontal well. In this embodiment, the element logging data and resistivity data of multiple depth points of the single well or multiple wells are first obtained to obtain the type and resistivity of the elements of the drilling at different depths.

[0048] Step 120: Calculate the correlation between each of the elements and the resistivity to obtain a first correlation coefficient of each of the elements.

[0049] In this embodiment, the first correlation coefficient is used to reflect the correlation between the element and the resistivity, that is, the degree of correlation. The value range of the first correlation coefficient is -1 to 1. The larger the absolute value of the first correlation coefficient is and the closer it is to 1, the higher the degree of correlation between the element and the resistivity is. Figure 4 As shown, the correlation between each element and resistivity is shown. Figure 4 In the embodiment, both the horizontal and vertical axes have resistivity and the names of the elements, RT is resistivity, K is potassium, and Al is aluminum. Since the symbols of the elements are known to those skilled in the art, they are not listed one by one in this embodiment. Figure 4 It can be seen that when the first correlation coefficient is 1, the two related elements are the same element, or have the same resistivity, indicating that the element has a complete influence on itself and the resistivity has a complete influence on itself, and is completely correlated.

[0050] In this embodiment, the correlation calculation process between the element and the resistivity may be obtained by calculating the ratio of the content of the element at a coordinate or a position in the well to the resistivity, or by using other existing correlation functions.

[0051] Step 130: Using a preset mathematical method, select at least one element whose first correlation coefficient is greater than a correlation coefficient threshold and determine it as a sensitive element.

[0052] In this embodiment, the first correlation coefficient greater than the correlation coefficient threshold value indicates that the correlation between the element and the resistivity is close. In this embodiment, the element whose first correlation coefficient is greater than the correlation coefficient threshold value is selected, that is, the element with a higher correlation degree is selected and determined as the sensitive element. The sensitive element has a higher correlation degree with the resistivity and has a greater impact on the resistivity value.

[0053] Step 140: establishing a resistivity calculation model based on the sensitive element and the first correlation coefficient corresponding to the sensitive element.

[0054] In this embodiment, a neural network or a mathematical algorithm is used to establish a resistivity calculation model based on sensitive elements and first correlation coefficients of the sensitive elements.

[0055] In one embodiment, step 140 further includes obtaining logging data of the element of the drilling, importing the logging data of the element into the resistivity calculation model, and using the resistivity calculation model to calculate the resistivity of the drilling according to the logging data of the element.

[0056] like Figure 6 As shown in FIG. 1 , the resistivity calculation model is used to input the measured element data into the model, and the results calculated by the model are compared with the measured resistivity logging curve. Figure 6 It can be seen that the overall consistency is high, and the model has high calculation accuracy and efficiency.

[0057] In this embodiment, after the resistivity calculation model is established, during the drilling process, the data of the elements of the drilling are measured in real time, that is, the logging data of the elements are measured, and the logging data are imported into the resistivity calculation model, so that the resistivity of the well is calculated using the resistivity calculation model, thereby accurately obtaining the resistivity of the drilling without the need for high-precision resistance measuring instruments.

[0058] In one embodiment, the step of using a preset mathematical method to select at least one element whose first correlation coefficient is greater than a correlation coefficient threshold and determine it as a sensitive element includes: extracting element data of drilling using oil-based drilling fluid and extracting element data of drilling using water-based drilling fluid based on a horizontal well and a pilot well of the same well to obtain first element data and second element data; or extracting element data of drilling using oil-based drilling fluid and extracting element data of drilling using water-based drilling fluid based on two adjacent wells to obtain first element data and second element data; comparing the first element data and the second element data to obtain the oil-based impact difference value of each element; selecting at least one element corresponding to the oil-based impact difference value less than a preset difference threshold, and using a preset mathematical method to select at least one element whose first correlation coefficient is greater than the correlation coefficient threshold and determine it as the sensitive element.

[0059] In this embodiment, in order to extract elemental data of drilling using oil-based drilling fluid and water-based drilling fluid in the same layer or a similar layer, in one embodiment, elemental data of drilling using oil-based drilling fluid and elemental data of drilling using water-based drilling fluid are extracted from the horizontal well and the pilot well of the same well to obtain first elemental data and second elemental data, wherein the horizontal well uses oil-based drilling fluid and the pilot well uses water-based drilling fluid, elemental data is extracted from the horizontal well to obtain first elemental data, and elemental data is extracted from the pilot well to obtain second elemental data. In another embodiment, elemental data of drilling using oil-based drilling fluid and elemental data of drilling using water-based drilling fluid are extracted from the horizontal well and the pilot well of the same well to obtain first elemental data and second elemental data, wherein the horizontal well uses water-based drilling fluid and the pilot well uses oil-based drilling fluid, elemental data is extracted from the horizontal well to obtain first elemental data, and elemental data is extracted from the pilot well to obtain second elemental data.

[0060] In one embodiment, first element data and second element data are obtained based on element data of drilling using oil-based drilling fluid and element data of drilling using water-based drilling fluid extracted from two adjacent wells, wherein one well uses oil-based drilling fluid and the other adjacent well uses water-based drilling fluid, element data is extracted from one well to obtain first element data, and element data is extracted from the other adjacent well to obtain second element data.

[0061] It should be understood that oil-based drilling fluids will affect some elements, thereby affecting the correlation between elements and resistivity, such as Figure 5A As shown in the figure, the measurement results of water-based drilling fluid elements in a certain work area are as follows: Figure 5B As shown in the figure, the measurement results of oil-based drilling fluid elements in a certain work area are Figure 5A and Figure 5B Comparison of the results shows that the measurement results of some elements are greatly affected by the oil-based drilling fluid.

[0062] In order to eliminate the influence of oil-based drilling fluid on the correlation measurement results, it is first necessary to eliminate the influence of oil-based drilling fluid on the elements or reduce the influence of oil-based drilling fluid on the elements. Therefore, it is necessary to select elements that are less affected by oil-based drilling fluid from among the elements. In this embodiment, element measurement is performed on drilling using oil-based drilling fluid and drilling using water-based drilling fluid, respectively, and first element data and second element data are obtained respectively. The first element data and the second element data are compared, and the same element is compared one by one to obtain the difference between each element in drilling using oil-based drilling fluid and drilling using water-based drilling fluid. The difference is the oil-based influence difference value, which reflects the difference between the same element in drilling using water-based drilling fluid and drilling using oil-based drilling fluid, and reflects the degree to which the element is affected by the oil-based drilling fluid. The larger the oil-based influence difference value, the greater the degree to which the element is affected by the oil-based drilling fluid, and the smaller the oil-based influence difference value, the smaller the degree to which the element is affected by the oil-based drilling fluid. In this embodiment, the oil-based influence difference value is less than the preset difference threshold, indicating that there is no order of magnitude difference between the first element data and the second element data, and the degree to which the element is affected by the oil-based drilling fluid is small. Therefore, the corresponding element is selected and determined as the sensitive element. The sensitive elements selected in this way are little affected by the oil-based drilling fluid or are not affected by the oil-based drilling fluid, thereby effectively improving the accuracy of the resistivity calculation model and improving the accuracy of the resistivity measurement.

[0063] In this embodiment, sensitive elements are selected by screening in two ways. One is to select by the oil-based impact difference value less than the preset difference threshold, and the other is to select by the first correlation coefficient greater than the correlation coefficient threshold. The two methods can be performed successively or simultaneously.

[0064] In one embodiment, at least one element corresponding to the oil-based influence difference value less than a preset difference threshold is first selected to obtain an extraction result, and a preset mathematical method is used to select at least one element whose first correlation coefficient is greater than the correlation coefficient threshold from the extraction result to determine it as the sensitive element. In this embodiment, selection is first performed by using a first correlation coefficient greater than the correlation coefficient threshold, and then selection is performed again from the selection result by using an oil-based influence difference value less than the preset difference threshold.

[0065] In one embodiment, a preset mathematical method is first used to select at least one element whose first correlation coefficient is greater than a correlation coefficient threshold value to obtain an extraction result, and then at least one element corresponding to the oil-based impact difference value less than a preset difference threshold value is selected from the extraction result to be determined as the sensitive element. In this embodiment, the selection is first performed by the oil-based impact difference value less than the preset difference threshold value, and then the selection is performed again from the selection result by the first correlation coefficient greater than the correlation coefficient threshold value.

[0066] In one embodiment, the step of using a preset mathematical method to select at least one element whose first correlation coefficient is greater than a correlation coefficient threshold and determine it as a sensitive element includes: detecting the correlation between the elements to obtain multiple second correlation coefficients; extracting the second correlation coefficients greater than a preset collinearity threshold as collinearity coefficients; extracting an element from each element corresponding to each collinearity coefficient, and using a preset mathematical method to select at least one element whose first correlation coefficient is greater than the correlation coefficient threshold and determine it as the sensitive element.

[0067] In this embodiment, the second correlation coefficient is used to reflect the degree of correlation between two or more elements. When the second correlation coefficient between the elements is greater than the preset collinearity threshold, it indicates that the correlation between the two or more elements is high, indicating that there is multicollinearity between these elements. When the correlation between multiple elements and resistivity is good (high correlation), and the correlation between multiple elements is also high, at this time, only one of the elements needs to be selected as a sensitive element to participate in resistivity modeling. In this way, the model architecture can be simplified and the workload of measurement and calculation processing can be reduced.

[0068] It is worth mentioning that in the process of selecting elements in this embodiment, one element can be firstly extracted from each element corresponding to each collinearity coefficient, and at least one element whose first correlation coefficient is greater than the correlation coefficient threshold is selected from the selected result to be determined as the sensitive element. Alternatively, at least one element whose first correlation coefficient is greater than the correlation coefficient threshold can be firstly selected, and one element can be extracted from each element corresponding to each collinearity coefficient from the selected result to be determined as the sensitive element. Furthermore, the step of detecting the correlation between the elements to obtain a plurality of second correlation coefficients and extracting the second correlation coefficient greater than the preset collinearity threshold as the collinearity coefficient can also be performed before step 130 or step 120.

[0069] In one embodiment, first, one element is extracted from each element corresponding to each collinearity coefficient to obtain an extraction result, and a preset mathematical method is used to select at least one element whose first correlation coefficient is greater than a correlation coefficient threshold from the extraction result to determine it as the sensitive element.

[0070] In one embodiment, a preset mathematical method is first used to select at least one element whose first correlation coefficient is greater than a correlation coefficient threshold to obtain an extraction result. Subsequently, an element is extracted from each element corresponding to each collinearity coefficient in the extraction result and determined as the sensitive element.

[0071] In one of the embodiments, the step of using a preset mathematical method to select at least one element whose first correlation coefficient is greater than a correlation coefficient threshold and determining it as a sensitive element includes: eliminating elements whose atomic numbers are less than a preset number and eliminating trace elements from the elements in the drilling; using a preset mathematical method to select at least one element whose first correlation coefficient is greater than a correlation coefficient threshold from the eliminated elements and determine it as a sensitive element.

[0072] It is worth mentioning that, considering the logging accuracy of different types of instruments, elements with smaller atomic numbers and trace elements are more difficult to measure. Therefore, elements with atomic numbers less than the preset numbers and trace elements are eliminated, thereby reducing the measurement accuracy requirements for drilling elements, thereby improving the accuracy of element measurement results and improving model accuracy. It is worth mentioning that the step of eliminating elements and the step of selecting elements according to the first correlation coefficient can be performed successively or simultaneously, and the order of the two is not limited.

[0073] In one embodiment, first, elements with atomic numbers less than a preset number and trace elements are eliminated from each element to obtain an extraction result, and a preset mathematical method is used to select at least one element with a first correlation coefficient greater than a correlation coefficient threshold from the extraction result and determine it as the sensitive element.

[0074] In one embodiment, a preset mathematical method is first used to select at least one element whose first correlation coefficient is greater than a correlation coefficient threshold to obtain an extraction result. Subsequently, elements whose atomic numbers are less than a preset number and trace elements are eliminated from the extraction result, and the elements obtained after elimination are determined as the sensitive elements.

[0075] In one of the embodiments, the step of using a preset mathematical method to select at least one element whose first correlation coefficient is greater than a correlation coefficient threshold and determining it as a sensitive element includes: selecting elements according to geological significance to obtain selection results; using a preset mathematical method to select at least one element whose first correlation coefficient is greater than the correlation coefficient threshold from the selection results and determine it as a sensitive element.

[0076] In this embodiment, geological significance refers to the geological significance of the element. Only when the element has geological significance can the model have geological significance. In this embodiment, the elements are selected in combination with the geological significance, so that the sensitive elements obtained have more geological significance.

[0077] It is worth mentioning that in the above embodiments, in each step of extracting elements to obtain sensitive elements, the order of extraction is not limited. For example, in one embodiment, a preset mathematical method is first used to select at least one element whose first correlation coefficient is greater than the correlation coefficient threshold to obtain an extraction result. Subsequently, elements whose atomic numbers are less than the preset numbers and trace elements are removed from the extraction results, and at least one element corresponding to the oil-based influence difference value less than the preset difference threshold is selected from the extraction results. One element is extracted from each element corresponding to each collinearity coefficient in the extraction results, and elements are selected from the extraction results according to geological significance to obtain sensitive elements. In this embodiment, after selecting elements according to the first correlation coefficient, the elements are respectively eliminated by atomic number, and extracted according to the oil-based influence difference value and the geological significance, thereby improving the effectiveness of the elements and improving the accuracy of the resistivity calculation model.

[0078] In another embodiment, firstly, elements with atomic numbers less than a preset number and trace elements are eliminated, and at least one element corresponding to the oil-based influence difference value less than a preset difference threshold is selected, and one element is extracted from each element corresponding to each collinearity coefficient, and elements are selected according to geological significance to obtain extraction results, and at least one element with a first correlation coefficient greater than the correlation coefficient threshold is selected from the extraction results using a preset mathematical method to obtain sensitive elements. In this embodiment, elements may also be eliminated, screened, and extracted first, and then elements are extracted according to the correlation with resistivity.

[0079] In one embodiment, the step of using a preset mathematical method to select at least one element whose first correlation coefficient is greater than the correlation coefficient threshold and determining it as a sensitive element includes: using a cross-plot method and / or a correlation heat map method to select at least one element whose first correlation coefficient is greater than the correlation coefficient threshold and determine it as the sensitive element. In one embodiment, using a cross-plot method, at least one element whose first correlation coefficient is greater than the correlation coefficient threshold is selected and determined as the sensitive element. In one embodiment, using a cross-plot method and a correlation heat map method, at least one element whose first correlation coefficient is greater than the correlation coefficient threshold is selected and determined as the sensitive element.

[0080] In one embodiment, a correlation heat map method is used to select at least one element whose first correlation coefficient is greater than a correlation coefficient threshold and determine it as the sensitive element.

[0081] In this embodiment, Figure 4 As shown in the figure, it is a heat map of the correlation between elements and resistivity in a certain work area, showing the correlation between each element and resistivity. Figure 4In the figure, both the horizontal and vertical axes have resistivity and the names of the elements, RT is resistivity, K is potassium, and Al is aluminum. Figure 4 It can be seen that when the first correlation coefficient is 1, the two related elements are the same element, or have the same resistivity, indicating that the element has a complete influence on itself and the resistivity has a complete correlation with itself. By selecting the element on the horizontal axis and combining the resistivity RT on the vertical axis, the first correlation coefficient between the element and the resistivity can be obtained; in addition, according to the correlation heat map, not only the correlation between each element and the resistivity RT can be obtained, but also the correlation between two elements can be obtained. For example, if an element is selected on the horizontal axis and an element is selected on the vertical axis, the intersection of the horizontal axis and the vertical axis is the second correlation coefficient between the two elements.

[0082] In one embodiment, the step of using a preset mathematical method to select at least one element whose first correlation coefficient is greater than a correlation coefficient threshold and determining it as a sensitive element includes: obtaining an absolute value of each of the first correlation coefficients; using a preset mathematical method to select at least one element whose absolute value of the first correlation coefficient is greater than the correlation coefficient threshold and determining it as a sensitive element.

[0083] In this embodiment, Figure 4 As shown, when the first correlation coefficient is a negative number, the correlation between the element and the resistivity is negatively correlated. It should be understood that the smaller the value of the negative correlation is, the higher the degree of negative correlation is. Therefore, in this embodiment, the absolute value of the first correlation coefficient is calculated. When the absolute value obtained is larger, the positive correlation or negative correlation between the element and the resistivity is greater. Therefore, based on this, the sensitive element can be selected and determined more accurately.

[0084] In one embodiment, the step of establishing a resistivity calculation model based on the sensitive elements and the first correlation coefficients corresponding to the sensitive elements includes: establishing a resistivity calculation model based on the sensitive elements and the first correlation coefficients corresponding to the sensitive elements using a multivariate regression algorithm.

[0085] In this embodiment, the correlation coefficient threshold is 0.44. According to the correlation heat map, K (correlation coefficient -0.52), Fe (correlation coefficient -0.45), Si (correlation coefficient 0.48), Ca (correlation coefficient 0.45), P (correlation coefficient 0.44), and Mn (correlation coefficient -0.44) are all elements with high correlation, but the correlation coefficient between Ca and Si is as high as 0.87, but the correlation coefficient between Ca and K is as high as -0.65, and the correlation between Fe and P is -0.58. The influence of multicollinearity is eliminated, and finally Ca, P, and Mn are selected for modeling;

[0086] Using the multivariate regression method, the resistivity calculation formula (resistivity calculation model) is obtained:

[0087] Rt=-62.2803+3.2046×Ca+30.3107×P+198.1420×Mn

[0088] Embodiment 2

[0089] The present invention provides a method for calculating formation resistivity based on elements. Compared with the prior art, the present invention realizes quantitative calculation of resistivity based on element logging for the first time. The present invention comprises the following steps:

[0090] (1) Obtain element logging data and resistivity data at multiple depth points of a single well or multiple wells;

[0091] The single or multiple wells may be pilot wells or horizontal wells;

[0092] (2) Use mathematical methods to analyze the correlation between different elements and resistivity, and select sensitive elements with high correlation;

[0093] Among them, the mathematical methods for selecting sensitive elements include but are not limited to the intersection diagram method, the correlation heat map method, etc.;

[0094] The higher the correlation coefficient between the sensitive element and the resistivity, the better, and the minimum should not be lower than a certain threshold;

[0095] (3) further performing quality control on the elements selected in step (2);

[0096] Quality control includes: ① selecting elements that are not affected by oil-based drilling fluid; ② not considering elements with low content and poor precision; ③ reducing multicollinearity between sensitive elements; ④ sensitive elements must have geological significance;

[0097] Among them, ① the factors not affected by oil-based drilling fluid are mainly as follows: compare the element measurement results of horizontal wells and pilot wells or adjacent wells using oil-based drilling fluid and water-based drilling fluid respectively, and the same small interval. The comparison principle is that there is no difference in the order of magnitude between the two.

[0098] ② Even if the correlation coefficient between an element and resistivity is greater than the set threshold, elements with low atomic numbers (≤11) and trace elements (considering the logging accuracy of different types of instruments) cannot be used as sensitive elements for modeling;

[0099] ③Multicollinearity means that the correlation between several elements and resistivity is good, but the correlation between several elements is also high. In this case, only one element needs to be selected as a sensitive element to participate in resistivity modeling;

[0100] (4) Based on the sensitive elements selected in step (3), a resistivity calculation model is established using a mathematical method;

[0101] Among them, mathematical methods include but are not limited to intelligent algorithms such as multivariate regression and neural networks.

[0102] (5) For the pilot well being drilled, the resistivity of the well can be obtained by applying the calculation model of step (4) based on the element measurement results.

[0103] Embodiment 3

[0104] (1) Select the element measurement results and resistivity data corresponding to 457 depth points of a well in a certain area;

[0105] (2) Select elements that are not affected by oil-based drilling fluids;

[0106] like Figure 5A and 5B As shown in the figure, the element measurement results of the pilot well with water-based drilling fluid and the horizontal well with oil-based drilling fluid in the same lithology (sandstone) and the same formation section are selected for comparison. The Ba, V, and Cl content of the horizontal well with oil-based drilling fluid is significantly higher (order of magnitude) than that of the same layer with water-based drilling fluid, and the Ni content is significantly lower. Therefore, Ba, V, Cl, and Ni are all factors that are greatly affected by oil-based drilling fluid and are not used as sensitive elements for resistivity modeling.

[0107] (3) Figure 4 As shown, a thermal diagram of the correlation between the content of 13 elements and resistivity in element logging was established, and the correlation between different elements and resistivity was analyzed;

[0108] In this embodiment, the correlation coefficient threshold is 0.44. According to the correlation heat map, K (correlation coefficient -0.52), Fe (correlation coefficient -0.45), Si (correlation coefficient 0.48), Ca (correlation coefficient 0.45), P (correlation coefficient 0.44), and Mn (correlation coefficient -0.44) are all elements with high correlation, but the correlation coefficient between Ca and Si is as high as 0.87.

[0109] However, the correlation coefficient between Ca and K elements is as high as -0.65, and the correlation between Fe and P is -0.58. After eliminating the influence of multicollinearity, Ca, P, and Mn were finally selected for modeling;

[0110] (4) Using the multivariate regression method, the resistivity calculation formula is obtained:

[0111] Rt=-62.2803+3.2046×Ca+30.3107×P+198.1420×Mn

[0112] (5) The comparison between the resistivity modeling calculation results of the element logging of a well in the work area and the measured resistivity logging curve is shown in the figure below. Figure 6As shown, the overall consistency is high, and the model has high calculation accuracy and efficiency.

[0113] In this embodiment, a method for calculating formation resistivity based on elements is provided, and the method includes: obtaining element logging data and resistivity data of multiple depth points of a single well or multiple wells; using mathematical methods to analyze the correlation between different elements and resistivity; selecting elements that have a high degree of correlation with resistivity, are not affected by oil-based drilling fluid, have high content and accuracy, have geological significance, and do not have multicollinearity with each other as sensitive elements; using mathematical methods to establish a resistivity calculation model based on element logging data; for a guide well being drilled, using the established model to calculate its resistivity based on the element logging results.

[0114] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0115] Embodiment 4

[0116] In this embodiment, Figure 2 As shown, a formation resistivity calculation device is provided, comprising:

[0117] The data acquisition module 210 is used to obtain the type and resistivity of at least two elements of the drilling;

[0118] A first correlation coefficient calculation module 220, used to calculate the correlation between each of the elements and the resistivity, and obtain a first correlation coefficient between each of the elements and the resistivity;

[0119] The sensitive element determination module 230 is used to select at least one element whose first correlation coefficient is greater than a correlation coefficient threshold value by using a preset mathematical method and determine it as a sensitive element;

[0120] The model building module 240 is used to build a resistivity calculation model based on the sensitive element and the first correlation coefficient corresponding to the sensitive element.

[0121] In one embodiment, the sensitive element determination module includes:

[0122] A first element extraction unit, used to extract element data of drilling using oil-based drilling fluid to obtain first element data;

[0123] A second element extraction unit, used to extract element data of drilling using water-based drilling fluid to obtain second element data;

[0124] A comparison unit, used for comparing the first element data and the second element data to obtain the oil base influence difference value of each element;

[0125] The first sensitive element determination unit is used to select at least one of the elements corresponding to the oil-based impact difference value that is less than a preset difference threshold, and use a preset mathematical method to select at least one of the elements whose first correlation coefficient is greater than the correlation coefficient threshold to determine it as the sensitive element.

[0126] In one embodiment, the sensitive element determination module includes:

[0127] A second correlation coefficient obtaining unit, used for detecting the correlation between the elements and obtaining a plurality of second correlation coefficients;

[0128] A collinearity coefficient extraction unit, used to extract the second correlation coefficient greater than a preset collinearity threshold as a collinearity coefficient;

[0129] The second sensitive element determination unit is used to extract an element from the elements corresponding to each collinearity coefficient, and adopt a preset mathematical method to select at least one element whose first correlation coefficient is greater than a correlation coefficient threshold value to determine it as the sensitive element.

[0130] In one embodiment, the sensitive element determination module includes:

[0131] An element removal unit, used to remove elements with atomic numbers less than a preset number and trace elements from the elements in the drilling;

[0132] The third sensitive element determination unit is used to select at least one element whose first correlation coefficient is greater than a correlation coefficient threshold from the eliminated elements by using a preset mathematical method, and determine it as a sensitive element.

[0133] In one embodiment, the sensitive element determination module is used to select at least one element whose first correlation coefficient is greater than a correlation coefficient threshold by using a cross-plot method and / or a correlation heat map method, and determine it as the sensitive element.

[0134] In one embodiment, the sensitive element determination module includes:

[0135] an absolute value obtaining unit, used for obtaining an absolute value of each of the first correlation coefficients;

[0136] The fourth sensitive element determination unit is used to select at least one element whose absolute value of the first correlation coefficient is greater than the correlation coefficient threshold by using a preset mathematical method, and determine it as a sensitive element.

[0137] In one embodiment, the model building module is further used to establish a resistivity calculation model using a multivariate regression algorithm based on the sensitive elements and the first correlation coefficients corresponding to the sensitive elements.

[0138] The specific definition of the formation resistivity calculation device can be found in the definition of the formation resistivity calculation method above, which will not be repeated here. Each unit in the above formation resistivity calculation device can be implemented in whole or in part by software, hardware and a combination thereof. The above units 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 of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above units.

[0139] Embodiment 5

[0140] In this embodiment, a computer device is provided. Its internal structure diagram can be shown as follows: Figure 3 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program, and the non-volatile storage medium is deployed with a database, which is used for the resistivity data of the logging data of the element. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with other computer devices deployed with application software. When the computer program is executed by the processor, a formation resistivity calculation method is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0141] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0142] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the formation resistivity calculation method described in any of the above embodiments when executing the computer program.

[0143] Embodiment 6

[0144] In this embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the formation resistivity calculation method described in any of the above embodiments are implemented.

[0145] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0146] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0147] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A formation resistivity calculation method, characterized in that: include: Obtaining the type and resistivity of at least two elements of the wellbore; Calculating the correlation between each of the elements and the resistivity to obtain a first correlation coefficient of each of the elements; Using a preset mathematical method, select at least one element whose first correlation coefficient is greater than a correlation coefficient threshold value and determine it as a sensitive element; A resistivity calculation model is established based on the sensitive element and the first correlation coefficient corresponding to the sensitive element.

2. The method according to claim 1, characterized in that The step of using a preset mathematical method to select at least one element whose first correlation coefficient is greater than a correlation coefficient threshold and determining it as a sensitive element comprises: Extracting element data of drilling using oil-based drilling fluid and extracting element data of drilling using water-based drilling fluid from the horizontal well and the pilot well of the same well to obtain first element data and second element data; or extracting element data of drilling using oil-based drilling fluid and extracting element data of drilling using water-based drilling fluid from two adjacent wells to obtain first element data and second element data; Comparing the first element data with the second element data to obtain the oil-based influence difference value of each element; At least one of the elements corresponding to the oil-based impact difference value that is less than a preset difference threshold is selected, and at least one of the elements whose first correlation coefficient is greater than the correlation coefficient threshold is selected using a preset mathematical method to be determined as the sensitive element.

3. The method according to claim 1, characterized in that: The step of using a preset mathematical method to select at least one element whose first correlation coefficient is greater than a correlation coefficient threshold and determining it as a sensitive element comprises: Detecting the correlation between the elements to obtain a plurality of second correlation coefficients; Extracting the second correlation coefficient greater than a preset collinearity threshold as a collinearity coefficient; An element is extracted from each of the elements corresponding to each of the collinearity coefficients, and a preset mathematical method is used to select at least one element whose first correlation coefficient is greater than a correlation coefficient threshold value and determine it as the sensitive element.

4. The method according to claim 1, characterized in that: The step of using a preset mathematical method to select at least one element whose first correlation coefficient is greater than a correlation coefficient threshold and determining it as a sensitive element comprises: Eliminate elements with atomic numbers less than a preset number and trace elements from the elements in the wellbore; By using a preset mathematical method, at least one element having a first correlation coefficient greater than a correlation coefficient threshold is selected from the eliminated elements and determined as a sensitive element.

5. The method according to any one of claims 1 to 4, characterized in that: The step of using a preset mathematical method to select at least one element whose first correlation coefficient is greater than a correlation coefficient threshold and determining it as a sensitive element comprises: By adopting the intersection diagram method and / or the correlation heat map method, at least one element whose first correlation coefficient is greater than the correlation coefficient threshold is selected and determined as the sensitive element.

6. The method according to any one of claims 1 to 4, characterized in that: The step of using a preset mathematical method to select at least one element whose first correlation coefficient is greater than a correlation coefficient threshold and determining it as a sensitive element comprises: Obtaining an absolute value of each of the first correlation coefficients; By using a preset mathematical method, at least one element whose absolute value of the first correlation coefficient is greater than the correlation coefficient threshold is selected and determined as a sensitive element.

7. The method according to any one of claims 1 to 4, characterized in that The step of establishing a resistivity calculation model based on the sensitive element and the first correlation coefficient corresponding to the sensitive element comprises: Based on the sensitive elements and the first correlation coefficients corresponding to the sensitive elements, a resistivity calculation model is established by adopting a multivariate regression algorithm.

8. A formation resistivity calculation device, characterized in that: include: A data acquisition module, used to acquire the type and resistivity of at least two elements of the drilling; A first correlation coefficient calculation module, used for calculating the correlation between each of the elements and the resistivity, and obtaining a first correlation coefficient between each of the elements and the resistivity; A sensitive element selection module, used to select at least one element whose first correlation coefficient is greater than a correlation coefficient threshold by using a preset mathematical method, and determine it as a sensitive element; The model building module is used to build a resistivity calculation model based on the sensitive element and the first correlation coefficient corresponding to the sensitive element.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, 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.

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