A method for predicting drillability of limestone formation based on element component multiple regression

Abstract

The application discloses a limestone stratum drillability prediction method based on element component multivariate regression, which comprises the following steps: 1, measuring element information of a rock sample; 2, analyzing the content of various elements, and obtaining various main elements of the rock sample according to the content of various elements; 3, combining rock drillability level values to analyze the various main elements, and finding out influence elements which have greater influence on the rock drillability level values; 4, establishing a prediction model based on the influence elements and the rock drillability through multivariate regression analysis of the influence elements and the rock drillability; 5, corresponding analyzing the drillability level value interpretation result predicted based on the prediction model and a mechanical drilling speed curve of actual drilling in a mine field, and verifying the accuracy of the prediction result. The application has the characteristics of convenient operation, real-time prediction, low cost and the like, can regress a limestone stratum rock drillability prediction model according to the measured element content, and further accurately and reliably evaluates rock drillability.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas exploration and development technology, specifically relating to a method for predicting the drillability of limestone formations based on elemental composition multivariate regression. Background Technology

[0002] In the exploration and development of oil and gas resources, rock drillability is one of the most important parameters, affecting important drilling factors such as drilling rate, drill bit selection, and drilling parameters. Based on the rock's inherent resistance to drill bit breakage and in conjunction with different rock-breaking methods, rock drillability is classified in various ways.

[0003] Assessing rock drillability is a crucial step in drilling operations. Currently, there are two main methods for evaluating rock drillability: one is based on the rock's physical and mechanical properties, and the other is by determining the rock's mineral content to infer its lithology, mechanical properties, and other information to assess drillability. However, both methods require core samples, leading to high costs and the inability to assess the drillability of the encountered formation in real time.

[0004] Chinese patent document CN107180302A discloses a method for evaluating rock drillability using the elemental content of drilling cuttings. Specifically, it includes analyzing the main chemical components and contents of the rock, measuring the chemical elemental composition and contents of drilling cuttings from the area to be tested, screening 10-12 elements most relevant to lithology, plotting the elemental contents of formation layers against lithological profiles, and drawing curves showing the changes in rock drillability grades obtained from well logging data. Based on these curves, seven elements related to rock drillability are further selected from the aforementioned 10-12 elements. Regression analysis is then performed based on the elemental contents and drillability grade data from the curves to derive a calculation model for evaluating rock drillability. However, in practical applications, this method screens a large number of elements related to drillability, increasing the complexity of the analysis. Furthermore, this method only establishes a single correspondence between drillability and multiple elements. When different minerals contain the same element, such as Si, which is present in both quartz and clay minerals, but whose contribution to drillability differs greatly, this method cannot reflect this difference, thus leading to poor accuracy in its prediction results. Summary of the Invention

[0005] The purpose of this invention is to solve the above-mentioned problems in the prior art and to provide a method for predicting the drillability of limestone formations based on elemental composition multiple regression. This invention has the characteristics of convenient operation, real-time prediction, and low cost. It can generate a rock drillability prediction model of limestone formations based on the measured element content through multiple regression, thereby accurately and reliably evaluating the rock drillability.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A method for predicting the drillability of limestone formations based on elemental composition multivariate regression is characterized by the following steps:

[0008] S1. Elemental information of rock cutting samples was measured using an X-ray fluorescence rock cutting analyzer;

[0009] S2. Analyze the elemental information to obtain the content of various elements, and then determine the main elements of the rock fragment sample based on the content of various elements.

[0010] S3. Combine the rock drillability grade value with the analysis of various major elements to identify the elements that have a greater impact on the rock drillability grade value.

[0011] S4. By conducting multiple regression analysis on the influencing elements and rock drillability, a prediction model based on the influencing elements and rock drillability is established.

[0012] S5. Analyze the correspondence between the interpretation results of the drillability rating based on the prediction model and the mechanical drilling rate curve of the actual drilling in the mine to verify the accuracy of the prediction results, and predict the drillability of limestone formations based on the accurate prediction model verified by the results.

[0013] The method for establishing the prediction model in step S4 is as follows:

[0014] First, establish the following multiple linear regression model based on the influencing elements:

[0015] Y = a0 + a1X1 + a2X2 + ... + a n X n (1)

[0016] In equation (1), a0 is a constant in the multiple linear regression equation; a j (j = 1, 2, ..., n) are partial regression coefficients, X j (j = 1, 2, ..., n) represents an element or a combination of elements, and Y represents a known rock drillability grade; a j (j = 1, 2, ..., n) are partial regression coefficients, representing X when other variables in the model remain constant. j For every unit change, Y will change by an average of a. j Units;

[0017] Then, the least squares method is used to solve for the constant a0 and the partial regression coefficient a. j Then, based on the solved constant a0 and partial regression coefficient a j Develop a predictive model.

[0018] The prediction model established in step S4 is as follows:

[0019]

[0020] Among them, K d The value represents the rock drillability grade to be predicted, where Si is the silicon content, Ca is the calcium content, Al is the aluminum content, and Fe is the iron content.

[0021] The specific measurement method for step S1 is as follows:

[0022] Extract 6-7g of rock cuttings sample collected through conventional rock cuttings logging and place it in a grinding device to grind it to 150 mesh to obtain rock cuttings powder. Place the rock cuttings powder into a special mold of the X-ray fluorescence rock cuttings analyzer, pressurize it to 12MPa and then release the pressure to obtain rock powder pellets. Place the pellets into the analysis chamber for measurement to obtain the elemental information of the rock cuttings sample.

[0023] The measurement in step S1 is performed under vacuum conditions, and the analysis chamber needs to be evacuated before the measurement begins.

[0024] The rock cuttings sample in step S1 needs to be cleaned before measurement, and then dried at 50-60 degrees Celsius for 24 hours.

[0025] The X-ray fluorescence rock debris analyzer in step S1 is a WDXRF spectrometer.

[0026] The principal element in step S2 refers to the element with a mass fraction greater than 0.2%.

[0027] The influencing elements in step S3 are determined based on the strength of their correlation with the drillability grade value.

[0028] If the verification result is inaccurate in step S5, resample and repeat steps S1-S5 until the verification result is accurate.

[0029] By adopting the above technical solution, the beneficial technical effects of the present invention are:

[0030] 1. The method of this invention first determines the influencing elements for predicting rock drillability through steps S1-S3, and then establishes a prediction model based on the influencing elements and rock drillability through multiple regression analysis of the influencing elements and rock drillability to evaluate the rock drillability. Compared with the prior art, the technical innovation and advantages of this invention are mainly reflected in two aspects:

[0031] (1) Based on the mineral and elemental composition characteristics of limestone strata, the method of this invention first screens out the influencing elements that are highly correlated with rock drillability, rather than performing regression model analysis on all elements that make up limestone. This effectively reduces the workload of testing and analysis while ensuring the reliability of prediction.

[0032] (2) The method of this invention considers the correlation between the same element and different minerals, as well as the differential impact of the element on rock drillability, and takes into account the specific independent variable settings of the regression model, thereby improving the model's relevance and reliability. For example, as a component of quartz minerals, Si can increase the rock drillability grade, while as a component of clay minerals, it can decrease the drillability grade. The influence of Si in clay minerals is reflected through the correlation between Si and Al in clay.

[0033] 2. This invention features convenient operation, real-time prediction, and low cost. It can generate a rock drillability prediction model for limestone formations based on the multivariate regression of measured element content, thereby accurately and reliably evaluating rock drillability. Attached Figure Description

[0034] Figure 1 This is a flowchart of the present invention;

[0035] Figure 2 This is a scatter plot of the drillability grade of a rock sample versus its Si content.

[0036] Figure 3 This is a scatter plot of the drillability grade of a rock sample versus its Ca content.

[0037] Figure 4 This is a scatter plot of the drillability grade of a rock sample versus its Al content.

[0038] Figure 5 This is a scatter plot of the drillability grade of a rock sample versus its Fe content.

[0039] Figure 6 This is a comparison chart of the rock drillability grade predicted by this invention and the mechanical drilling rate. Detailed Implementation

[0040] Example 1

[0041] This invention discloses a method for predicting the drillability of limestone formations based on multiple regression of elemental composition. This method is characterized by its ease of operation, real-time prediction, and low cost. It can generate a rock drillability prediction model for limestone formations based on the measured elemental content through multiple regression, thereby accurately and reliably evaluating rock drillability. Figure 1 As shown, it includes the following steps:

[0042] S1. Elemental information of rock cutting samples is measured using an X-ray fluorescence (XRF) rock cutting analyzer, preferably a WDXRF spectrometer. The specific measurement method is as follows:

[0043] First, cuttings samples were collected using conventional cuttings logging. The collected cuttings samples were then cleaned and dried at 50-60 degrees Celsius for 24 hours.

[0044] Secondly, extract 6-7g of rock fragments and place them in a grinding device to pulverize them to 150 mesh to obtain rock fragment powder. Then, place the rock fragment powder into a special mold of the X-ray fluorescence rock fragment analyzer, pressurize it to 12MPa, and then release the pressure to obtain a rock powder pellet. Then, place it into the analysis chamber. Since the measurement needs to be carried out under vacuum conditions, the analysis chamber needs to be evacuated at the beginning of the measurement. After evacuation, the rock fragment sample is measured to obtain the elemental information of the rock fragment sample, which can determine which elements are present in the rock fragment sample.

[0045] S2. Analyze and detect the various elemental information obtained in step S1 to determine the content of various elements in the rock cuttings sample, and then determine the main elements of the rock cuttings sample based on the content of various elements. According to actual working conditions, this invention preferably determines elements with a mass fraction greater than 0.2% as the main elements.

[0046] S3. Analyze various major elements in conjunction with the rock drillability grade value to identify the elements that have a significant impact on the rock drillability grade value.

[0047] Specifically, the influencing elements are determined based on the strength of their correlation with the drillability grade value, including both positive and negative correlations. Taking limestone as an example, it typically contains major elements such as Si, Ca, Al, and Fe, and their correlations are as follows:

[0048] (1) The Si element content in limestone represents the sand content. The higher the Si element content, the more difficult the rock is to drill. Therefore, the Si element content is positively correlated with the drillability grade.

[0049] (2) Ca is found in carbonate rocks, and its content is related to the content of dolomite and limestone. The Ca content in limestone is negatively correlated with the drillability rating.

[0050] (3) The content of Al element is closely related to the content of clay minerals. Therefore, its element content can represent the content of mud. The more mud content in the rock, the easier the rock is to drill. The content of Al element is negatively correlated with the drillability grade value.

[0051] (4) The Fe element is positively correlated with the drillability grade value.

[0052] S4. By performing multiple regression analysis on the influencing elements and rock drillability, a prediction model based on the influencing elements and rock drillability is established.

[0053] The method for establishing the prediction model in this step is as follows:

[0054] First, establish the following multiple linear regression model based on the influencing elements:

[0055] Y = a0 + a1X1 + a2X2 + ... + an X n (1)

[0056] In equation (1), a0 is a constant in the multiple linear regression equation; a j (j = 1, 2, ..., n) are partial regression coefficients, X j (j = 1, 2, ..., n) represents an element or a combination of elements, and Y represents a known rock drillability grade; a j (j = 1, 2, ..., n) are partial regression coefficients, representing X when other variables in the model remain constant. j For every unit change, Y will change by an average of a. j Units;

[0057] Then, the least squares method is used to solve for the constant a0 and the partial regression coefficient a. j Then, based on the solved constant a0 and partial regression coefficient a j Develop a predictive model.

[0058] Furthermore, the established prediction model is as follows:

[0059]

[0060] In equation (2), K d The value represents the rock drillability grade to be predicted, where Si is the silicon content, Ca is the calcium content, Al is the aluminum content, and Fe is the iron content.

[0061] S5. Analyze the interpretation results of the drillability rating based on the prediction model against the mechanical drilling rate curves of actual drilling in the mine to verify the accuracy of the prediction results. Then, based on the accurate prediction model, predict the drillability of the limestone formation. If the verification results are inaccurate, resample and repeat steps S1-S5 until the verification results are accurate.

[0062] Example 2

[0063] This embodiment uses a limestone stratum in a certain block as an example to verify the method described in Embodiment 1, as detailed below:

[0064] S1. Elemental information of rock cutting samples was measured using an X-ray fluorescence rock cutting analyzer.

[0065] The specific steps are as follows:

[0066] (1) Collect rock fragments from the limestone strata in the study area.

[0067] (2) The collected rock fragments were cleaned and dried for 24 hours. Then, 6-7g of the sample was extracted for analysis.

[0068] (3) Place the extracted 6-7g rock fragment sample into the grinding device dedicated to the WDXRF spectrometer for grinding. The grinding time is 12 seconds to obtain a 150-mesh rock fragment powder sample.

[0069] (4) Place the ground rock fragment powder sample into the mold of the WDXRF spectrometer, pressurize it to 12 MPa, and then release the pressure to compress the powder into a powder sample for the WDXRF spectrometer. When using high pressure to compress the powder into a sample, a binder is usually added to improve sample quality before measurement and analysis. If a binder is used, it must be considered and removed during analysis, as it is not part of the sample.

[0070] (5) Before starting the analysis, turn on the WDXRF spectrometer to preheat the instrument, then calibrate the instrument in advance, and initialize parameters such as tube flow, tube pressure, sample position, sample number, and vacuum degree.

[0071] (6) Place the prepared rock fragment powder sample into the analysis chamber of the WDXRF spectrometer, start the computer program, and begin analysis. Since X-ray fluorescence analysis requires vacuum conditions, the computer controls the evacuation of the WDXRF spectrometer analysis chamber before analysis. After the instrument starts analysis, the computer collects elemental information.

[0072] S2. The collected elemental information is stored in a database according to depth, and the elemental information is analyzed by dedicated processing and interpretation software to obtain the content of various elements. Then, based on the content of various elements, the main elements of the rock fragments are obtained.

[0073] S3. Analyze various major elements in conjunction with the rock drillability grade value to identify the elements that have a significant impact on the rock drillability grade value, and conduct qualitative analysis and interpretation of them. The specific methods are as follows:

[0074] The contents of various major elements and the rock drillability grades in the rock cuttings samples measured in steps S1, S2, and S3 above are shown in Table 1 below:

[0075] Table 1

[0076]

[0077] Based on the data in Table 1, a scatter plot can be generated showing the drillability rating of the rock samples and the corresponding elemental content. Figure 2-5 As shown, the correlation between various key elements and drillability is analyzed:

[0078] (1) The Si element content in limestone represents the sand content. The higher the Si element content, the more difficult the rock is to drill. Therefore, the Si element content is positively correlated with the drillability grade.

[0079] (2) Ca is found in carbonate rocks, and its content is related to the content of dolomite and limestone. The Ca content in limestone is negatively correlated with the drillability rating.

[0080] (3) The content of Al element is closely related to the content of clay minerals. Therefore, its element content can represent the content of mud. The more mud content in the rock, the easier the rock is to drill. The content of Al element is negatively correlated with the drillability grade.

[0081] (4) The Fe element is positively correlated with the drillability grade value.

[0082] Since Si, Ca, Al, and Fe elements in rock samples show a strong correlation with drillability grade values, while Mg and K elements show a weaker correlation, the influence of Mg and K elements on drillability grade values ​​is considered. Based on the above analysis of the main elements, Si, Ca, Al, and Fe elements are selected as the elements with the greatest influence on rock drillability grade values.

[0083] S4. By performing multiple regression analysis on the influencing elements and rock drillability, a prediction model based on the influencing elements and rock drillability is established.

[0084] Specifically, first substitute the relevant data of the corresponding influencing elements in Table 1 into the following multiple linear regression model:

[0085] Y = a0 + a1X1 + a2X2 + ... + a n X n (1)

[0086] In equation (1), a0 is a constant in the multiple linear regression equation; a j (j = 1, 2, ..., n) are partial regression coefficients, X j (j = 1, 2, ..., n) represents an element or a combination of elements, and Y represents a known rock drillability grade; a j (j = 1, 2, ..., n) are partial regression coefficients, representing X when other variables in the model remain constant. j For every unit change, Y will change by an average of a. j Unit

[0087] The constant a0 and the partial regression coefficient a are obtained by solving the problem. j .

[0088] Based on the constant a0 and the partial regression coefficient a j The following prediction model can be established:

[0089]

[0090] In equation (2), K dThe value represents the rock drillability grade to be predicted, where Si is the silicon content, Ca is the calcium content, Al is the aluminum content, and Fe is the iron content.

[0091] S5. Based on the prediction model, the drillability rating of the limestone strata in the study area is predicted to be 9.20–10. The results are shown in Table 2.

[0092] Table 2

[0093]

[0094]

[0095] The interpretation results of the drillability rating based on the prediction model are compared with the mechanical drilling rate curves of actual drilling in the mine. The results of the interpretation results of the drillability rating based on the prediction model and the mechanical drilling rate curves of actual drilling in the mine are shown below. Figure 6 As shown. The rock drillability rating is an important parameter reflecting rock drillability. The higher the drillability rating, the more difficult the rock is to drill, and the lower the mechanical drilling speed in actual mining operations. Through comparison... Figure 6 The results showed that the interpretation of the drillability rating based on the prediction model had a high degree of agreement with the mechanical drilling rate curve of actual drilling in the mine, proving the accuracy of the model's prediction results.

[0096] The above description is merely a specific embodiment of the present invention. Any feature disclosed in this specification may be replaced by other equivalent or similar features unless otherwise specified. All features or steps in all methods or processes disclosed may be combined in any way except for mutually exclusive features and / or steps.

Claims

1. A method for predicting the drillability of limestone formations based on elemental composition multiple regression, wherein the method considers the same element in different minerals and the correlation between elements, characterized in that... Includes the following steps: S1. Elemental information of rock cutting samples was measured using an X-ray fluorescence rock cutting analyzer; S2. Analyze the elemental information to obtain the content of various elements, and then determine the main elements of the rock fragment sample based on the content of various elements. S3. Combine the rock drillability grade value with the analysis of various major elements to identify the elements that have a greater impact on the rock drillability grade value. S4. By conducting multiple regression analysis on the influencing elements and rock drillability, a prediction model based on the influencing elements and rock drillability is established. S5. Analyze the correspondence between the interpretation results of the drillability rating based on the prediction model and the mechanical drilling rate curve of the actual drilling in the mine to verify the accuracy of the prediction results, and predict the drillability of limestone formation based on the accurate prediction model verified by the verification results. The method for establishing the prediction model in step S4 is as follows: First, establish the following multiple linear regression model based on the influencing elements: （1） In equation (1), a0 is a constant in the multiple linear regression equation; a j (j=1,2,…,n) are partial regression coefficients, X j (j=1, 2, ..., n) represents an element or a combination of elements, and Y represents the known rock drillability grade; a j (j=1, 2, ..., n) are partial regression coefficients, representing the partial regression coefficients X when other variables in the model remain constant. j For every unit change, Y will change by an average of a. j Units; Then, the least squares method is used to solve for the constant a0 and the partial regression coefficient a. j Then, based on the solved constant a0 and partial regression coefficient a j Develop a predictive model; The prediction model established in step S4 is as follows: （2） In equation (2), K d The value represents the rock drillability grade to be predicted, where Si is the silicon content, Ca is the calcium content, Al is the aluminum content, and Fe is the iron content.

2. The method for predicting the drillability of limestone formations based on elemental composition multiple regression according to claim 1, characterized in that: The specific measurement method for step S1 is as follows: Extract 6-7g of rock cuttings sample collected through conventional rock cuttings logging and place it in a grinding device to grind it to 150 mesh to obtain rock cuttings powder. Place the rock cuttings powder into a special mold of the X-ray fluorescence rock cuttings analyzer, pressurize it to 12MPa and then release the pressure to obtain rock powder pellets. Place the pellets into the analysis chamber for measurement to obtain the elemental information of the rock cuttings sample.

3. The method for predicting the drillability of limestone formations based on elemental composition multiple regression according to claim 2, characterized in that: The measurement in step S1 is performed under vacuum conditions, and the analysis chamber needs to be evacuated before the measurement begins.

4. The method for predicting the drillability of limestone formations based on elemental composition multiple regression according to claim 1, characterized in that: The rock cuttings sample in step S1 needs to be cleaned before measurement, and then dried at 50-60 degrees Celsius for 24 hours.

5. The method for predicting the drillability of limestone formations based on elemental composition multiple regression according to claim 1, characterized in that: The X-ray fluorescence rock debris analyzer in step S1 is a WDXRF spectrometer.

6. The method for predicting the drillability of limestone formations based on elemental composition multiple regression according to claim 1, characterized in that: The principal element in step S2 refers to the element with a mass fraction greater than 0.2%.

7. The method for predicting the drillability of limestone formations based on elemental composition multiple regression according to claim 1, characterized in that: The influencing elements in step S3 are determined based on the strength of their correlation with the drillability grade value.

8. The method for predicting the drillability of limestone formations based on elemental composition multiple regression according to claim 1, characterized in that: If the verification result is inaccurate in step S5, resample and repeat steps S1-S5 until the verification result is accurate.

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