A method for evaluating physical properties of buried hill reservoirs based on fractal dimension of logging while drilling

Abstract

The present invention discloses a method for evaluating the physical properties of buried hill reservoirs based on fractal dimension of logging while drilling. The method calculates mechanical specific energy based on logging parameters of engineering while drilling, and pre-processes mechanical specific energy data to remove zero points and abnormal data. Based on the K-means clustering algorithm, cluster analysis is performed on mechanical specific energy according to the drilling depth sequence. The clustered mechanical specific energy is divided into different data groups according to depth. Based on the clustered mechanical specific energy data group, the semivariogram function is calculated, and then the fractal dimension of different layers is calculated. Finally, the reservoir properties are identified according to the reservoir property classification standard. The method realizes the use of engineering parameter logging to make a rapid and accurate evaluation of reservoir properties in real time while drilling, and has guiding significance for decisions such as early completion of drilling, deepening of drilling, midway testing, etc., to ensure exploration and discovery, improve operating efficiency, and reduce operating costs.

Description

Technical Field

[0001] The present invention relates to the field of petroleum engineering and rock physics technology, and more specifically to a method for evaluating physical properties of buried hill reservoirs based on fractal dimension of logging while drilling. Background Art

[0002] The enrichment of buried-hill oil and gas depends largely on the development of buried-hill reservoir space. Affected by various geological factors such as rock properties, structural location, fault activity intensity, weathering and erosion degree, the development of buried-hill reservoir space is obviously heterogeneous, and the physical properties of reservoirs vary greatly, which seriously restricts the exploration and development of buried-hill oil and gas reservoirs. At present, many explorations have been made on the evaluation of buried-hill reservoirs based on conventional logging data, and some results have been achieved. However, conventional logging technology has a certain time lag in the exploration and development process. At the same time, it is difficult to interpret buried-hill reservoirs, and the interpretation results often do not match the test results. Therefore, it is urgent to establish a set of buried-hill reservoir evaluation methods based on logging data to make a rapid and accurate evaluation of the properties of buried-hill reservoirs, which has important guiding significance for decisions such as early completion of drilling, deepening of drilling, and mid-course testing. Summary of the invention

[0003] The purpose of the present invention is to design and develop a buried hill reservoir physical property evaluation method based on the fractal dimension of logging while drilling. By using the engineering parameters of comprehensive logging, a rapid and accurate evaluation of the physical properties of the reservoir can be made in real time while drilling, thus realizing the decision-making of completing drilling in advance, deepening drilling, and mid-course testing.

[0004] The technical solution provided by the present invention is:

[0005] A method for evaluating physical properties of buried hill reservoirs based on fractal dimension of logging while drilling comprises the following steps:

[0006] Step 1: Collect drilling pressure, mechanical drilling speed, rotary table speed, torque and drill bit size at different drilling depths according to the drilling depth;

[0007] Step 2: Establish a mechanical specific energy model, obtain mechanical specific energy data of different drilling depths and perform preprocessing;

[0008] Wherein, the mechanical specific energy model is:

[0009]

[0010] Where, MSE is mechanical specific energy, WOB is bit pressure, A b is the drill bit area, RPM is the rotary table speed, T is the torque, and ROP is the mechanical drilling speed;

[0011] Step 3, performing cluster analysis on the mechanical specific energy data to divide them into different data groups;

[0012] Step 4: Calculate the semivariogram for different data groups:

[0013]

[0014] Where r(h) is the semivariogram with a stratigraphic spacing of h, N(h) is the number of pairs of all observation points with a spacing of h, Z(x) is the function of the sampling point x, Z(x+h) is the function of the sampling point h away from point x, and h is the lag distance;

[0015] Step 5: Calculate the fractal dimension values ​​of different semivariograms;

[0016] Step 6: Determine the physical properties of the buried hill reservoir based on the fractal dimension value:

[0017] When the fractal dimension value is between 1 and 1.5, the buried hill reservoir is a high-quality reservoir;

[0018] When the fractal dimension value is between 1.5 and 1.7, the buried hill reservoir is a good reservoir;

[0019] When the fractal dimension value is between 1.7 and 2, the buried hill reservoir is a thin and poor reservoir or a non-reservoir.

[0020] Preferably, the preprocessing is to remove abnormal data values ​​by a first-order difference method, which specifically includes:

[0021] The estimated value of the new mechanical specific energy is obtained through two known mechanical specific energy values, and the estimated value is compared with the actual value. If the error value is greater than the allowable difference limit, the new mechanical specific energy value is discarded.

[0022] Preferably, the estimated value satisfies:

[0023] M n =EXP(LN(M n-1 )+(LN(M n-1 )-LN(M n-2 )));

[0024] Where M n is the predicted value of mechanical specific energy at drilling depth n, M n-1 is the calculated value of mechanical specific energy at drilling depth n-1, M n-2 is the calculated value of mechanical specific energy at drilling depth n-2;

[0025] The allowable difference limit is 10.

[0026] Preferably, the cluster analysis is a K-means clustering algorithm, which specifically includes:

[0027] K data objects are randomly selected from the mechanical specific energy data as initial centroids, the distance between each data object and the K initial centroids is calculated, all data objects are divided into clusters represented by the initial centroids closest to them, the centroids are iteratively updated until the K centroids no longer change, and the clustering results are output. According to the clustering results, the mechanical specific energy data are divided into different data groups.

[0028] Preferably, the iterative updating of the centroid satisfies:

[0029]

[0030] Where: C i is the centroid of the ith cluster, X is the data object, m i represents the number of objects in the i-th cluster;

[0031] The sum of squared errors is used as the objective function to measure clustering quality:

[0032]

[0033] Where SSE is the sum of squared errors, and d is the standard Euclidean distance between two objects in space.

[0034] Preferably, the relationship between the semivariogram and the dividing line curve satisfies:

[0035] r(h)∝f W ;

[0036] Where f is the dividing line curve, and W is the slope of the regression equation of the dividing line curve and the semivariogram function.

[0037] Preferably, the fractal dimension value satisfies:

[0038]

[0039] Where D is the fractal dimension value.

[0040] Preferably, the fractal dimension value distribution range of the buried hill reservoir is [1,2].

[0041] The beneficial effects of the present invention are:

[0042] The invention designs and develops a buried hill reservoir physical property evaluation method based on the fractal dimension of logging while drilling. The method can calculate the corresponding mechanical specific energy through the logging while drilling parameters, perform cluster analysis on the mechanical specific energy according to the drilling depth, calculate the fractal dimension using the clustered mechanical specific energy, and finally realize a rapid and accurate evaluation of the reservoir properties in real time while drilling through the specific fractal dimension. The method has guiding significance for decisions such as completing drilling ahead of schedule, deepening drilling, mid-course testing, etc., ensuring exploration and discovery, improving operation efficiency, and reducing operation costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 The figure is a flow chart of the method for evaluating physical properties of buried hill reservoirs based on fractal dimension of logging while drilling according to the present invention.

[0044] Figure 2 This is the mechanical specific energy profile diagram based on logging data according to the present invention.

[0045] Figure 3 This is the fractal dimension stratigraphic cross-section diagram described in the present invention. DETAILED DESCRIPTION

[0046] The present invention is further described in detail below so that those skilled in the art can implement it according to the description.

[0047] like Figure 1 As shown, the present invention provides a buried hill reservoir physical property evaluation method based on the fractal dimension of logging while drilling, comprising the following steps:

[0048] Step 1: Obtain the bottom hole engineering parameters based on the comprehensive logging instrument, such as drilling pressure, drilling speed, torque, rotation speed and other corresponding data parameters. As the drilling depth increases, the engineering parameters obtained continue to increase. These data parameters are imported into the database table in sequence according to the drilling depth.

[0049] Step 2: Calculate the corresponding mechanical specific energy according to the engineering parameters obtained in the database table, and draw a mechanical specific energy stratigraphic profile;

[0050] Among them, drilling mechanical specific energy is the work done by the drill bit to break a unit volume of rock. This parameter integrates parameters such as drilling pressure, rotation speed, torque, mechanical drilling speed, and drill bit size into a comprehensive parameter. It can better reflect the drilling time efficiency and formation properties than single engineering parameters such as drilling time. The mechanical specific energy model is as follows:

[0051]

[0052] Where, MSE is mechanical specific energy, WOB is bit pressure, A b is the drill bit area, RPM is the rotary table speed, T is the torque, and ROP is the mechanical drilling speed;

[0053] Step 3: Use the first-order difference method to preprocess the mechanical specific energy data and eliminate abnormal data values;

[0054] First, the first two mechanical specific energy values ​​are used to estimate the new mechanical specific energy value, and then the estimated mechanical specific energy value is compared with the actual calculated value. If the result is greater than the allowable difference limit given in advance, the data value is eliminated;

[0055] This method is more suitable for real-time data collection and processing, and can realize real-time processing of outliers.

[0056] Among them, the estimated value meets:

[0057] M n =EXP(LN(M n-1 )+(LN(M n-1 )-LN(M n-2 )));

[0058] Where M n is the predicted value of mechanical specific energy at drilling depth n, M n-1 is the calculated value of mechanical specific energy at drilling depth n-1, M n-2 is the calculated value of mechanical specific energy at drilling depth n-2;

[0059] The allowable difference limit is 10. Error comparison discriminant formula:

[0060]

[0061] In the formula, x n is the calculated value of mechanical specific energy at a depth of n meters, is the average mechanical specific energy of the drilled section, and ω is the difference limit;

[0062] The allowable difference limit is 10.

[0063] Step 4: Perform cluster analysis based on mechanical specific energy data. The K-means clustering algorithm is introduced here. The basic idea is to randomly select K data objects as the initial centroids from a data set containing a large number of data objects, calculate the distance between each data object and the K centroids, and divide all sample data into clusters represented by the centroids closest to it. The K centroids are updated according to the mean of all data objects in each newly generated cluster until the K centroids no longer change during the iteration process, and then the clustering results are output.

[0064] The algorithm application process is as follows:

[0065] K centroids are randomly selected from the mechanical specific energy data set, and the distance between each data in the data set and the K centroids is calculated respectively, and the data objects are divided one by one into the class represented by the centroid closest to them; then the mass point is updated with the mean of all data objects contained in each cluster, and the centroid C of the i-th cluster is i The definition is as follows:

[0066]

[0067] Where: C i is the centroid of the ith cluster, X is the data object, m i represents the number of objects in the i-th cluster;

[0068] The sum of squared errors is used as the objective function to measure clustering quality, as shown in the following formula:

[0069]

[0070] Where SSE is the sum of squared errors, and d is the standard Euclidean distance between two objects in space.

[0071] Step 5: Based on the mechanical specific energy data after cluster analysis, the semivariogram function is calculated according to the obtained data grouping. The formula is as follows:

[0072]

[0073] Where r(h) is the semivariogram with a stratigraphic spacing of h, N(h) is the number of pairs of all observation points with a spacing of h, Z(x) is the function of the sampling point x, Z(x+h) is the function of the sampling point h away from point x, and h is the lag distance;

[0074] Step 6: Calculate the fractal dimension values ​​corresponding to different depths of strata:

[0075] Using the semivariogram data that has been obtained, since there is a power relationship between the fractal curve f and the semivariogram r(h), as shown in the following formula:

[0076] r(h)∝f W

[0077] Plot f and r(h) on a double logarithmic coordinate graph and perform linear regression to obtain a regression equation. The slope of the regression equation is recorded as W. The slope W is an approximation of the fractal dimension D. The slope W has the following relationship with the fractal dimension D:

[0078]

[0079] Step 7: Determine whether the calculated fractal dimension is reasonable:

[0080] Determine whether the calculated fractal dimension is within a reasonable range. If a large error occurs, repeat the above steps to verify the data.

[0081] The size of the fractal dimension is generally between 1 and 2, where the maximum value is generally 2 and the minimum value is generally 1. If the calculated fractal dimension is within this range, it is considered reasonable.

[0082] Step 8: Draw the fractal dimension stratigraphic section:

[0083] The calculated fractal dimension is plotted into a graph according to the groups after clustering, where the X-axis is the fractal dimension and the Y-axis is the drilling depth. Figure 3;

[0084] Step 9: Quantitative evaluation of formation properties:

[0085] Based on the fractal dimension, reservoir properties are judged. For buried hill reservoirs:

[0086] The fractal dimension is between 1 and 1.5, indicating high-quality reservoirs;

[0087] The fractal dimension is between 1.5 and 1.7, indicating a good reservoir;

[0088] Fractal dimension greater than 1.7, poor or non-reservoir.

[0089] Example

[0090] Given the formation depth of a certain block and its corresponding mechanical specific energy value, the mechanical specific energy data are clustered according to the above K-means clustering algorithm. The clustering results are shown in Table 1 below: Table 1 Mechanical specific energy clustering results

[0091]

[0092]

[0093]

[0094]

[0095] According to the data that have been clustered into groups as mentioned above, group 1 is selected to calculate the semivariogram, and the corresponding depth is 3090-3106m. The calculation results are shown in Table 2 below. The semivariograms of other groups can be calculated according to the same steps, which will not be repeated here.

[0096] Table 2: Semivariogram values ​​for group 1

[0097]

[0098] According to the semivariogram calculated for each group, the fractal dimension corresponding to each group is calculated as shown in Table 3 below:

[0099] Table Three-dimensional value

[0100]

[0101] According to the fractal dimensions calculated in Table 3, it can be seen that the calculated fractal dimensions are all within a reasonable range. Among them, the drilling depths of 3090-3106m, 3120-3128m, 3129-3140m, 3158-3172m, 3173-3186m, 3187-3195m, 3208-3221m, 3222-3233m, 3259-3272m, 3273-3285m, and 3286-3299m are all poor or non-reservoirs, the drilling depths of 3107-3119m, 3141-3157m, 3196-3207m, and 3246-3258m are all good reservoirs, and the drilling depth of 3234-3245m is a high-quality reservoir.

[0102] The invention designs and develops a buried hill reservoir physical property evaluation method based on the fractal dimension of logging while drilling. The method can calculate the corresponding mechanical specific energy through the logging while drilling parameters, perform cluster analysis on the mechanical specific energy according to the drilling depth, calculate the fractal dimension using the clustered mechanical specific energy, and finally realize a rapid and accurate evaluation of the reservoir properties in real time while drilling through the specific fractal dimension. The method has guiding significance for decisions such as completing drilling ahead of schedule, deepening drilling, mid-course testing, etc., ensuring exploration and discovery, improving operation efficiency, and reducing operation costs.

[0103] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and the implementation modes. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the embodiments shown and described herein.

Claims

1. A method for evaluating physical properties of buried hill reservoirs based on fractal dimension of logging while drilling, characterized in that: The steps include: Step 1: Collect drilling pressure, mechanical drilling speed, rotary table speed, torque and drill bit size at different drilling depths according to the drilling depth; Step 2: Establish a mechanical specific energy model, obtain mechanical specific energy data of different drilling depths and perform preprocessing; Wherein, the mechanical specific energy model is: Where, MSE is mechanical specific energy, WOB is bit pressure, A b is the drill bit area, RPM is the rotary table speed, T is the torque, and ROP is the mechanical drilling speed; Step 3, performing cluster analysis on the mechanical specific energy data to divide them into different data groups; Step 4: Calculate the semivariogram for different data groups: Where r(h) is the semivariogram with a stratigraphic spacing of h, N(h) is the number of pairs of all observation points with a spacing of h, Z(x) is the function of the sampling point x, Z(x+h) is the function of the sampling point h away from point x, and h is the lag distance; Step 5: Calculate the fractal dimension values ​​of different semivariograms; The relationship between the semivariogram function and the fractal curve satisfies: r(h)∝f W ; Where f is the fractal curve, W is the slope of the regression equation of the fractal curve and the semivariogram; The fractal dimension value satisfies: Where D is the fractal dimension value; Step 6: Determine the physical properties of the buried hill reservoir based on the fractal dimension value: When the fractal dimension value is between 1 and 1.5, the buried hill reservoir is a high-quality reservoir; When the fractal dimension value is between 1.5 and 1.7, the buried hill reservoir is a good reservoir; When the fractal dimension value is between 1.7 and 2, the buried hill reservoir is a thin and poor reservoir or a non-reservoir.

2. The method for evaluating physical properties of buried hill reservoirs based on fractal dimension of logging while drilling according to claim 1, characterized in that: The preprocessing is to remove abnormal data values ​​by first-order difference method, which specifically includes: The estimated value of the new mechanical specific energy is obtained through two known mechanical specific energy values, and the estimated value is compared with the actual value. If the error value is greater than the allowable difference limit, the new mechanical specific energy value is discarded.

3. The method for evaluating physical properties of buried hill reservoirs based on fractal dimension of logging while drilling according to claim 2, characterized in that: The estimated value satisfies: M n =EXP(LN(M n-1 )+(LN(M n-1 )-LN(M n-2 ))); Where M n is the predicted value of mechanical specific energy at drilling depth n, M n-1 is the calculated value of mechanical specific energy at drilling depth n-1, M n-2 is the calculated value of mechanical specific energy at drilling depth n-2; The allowable difference limit is 10.

4. The method for evaluating physical properties of buried hill reservoirs based on fractal dimension of logging while drilling according to claim 3, characterized in that: The cluster analysis is a K-means clustering algorithm, which specifically includes: K data objects are randomly selected from the mechanical specific energy data as initial centroids, the distance between each data object and the K initial centroids is calculated, all data objects are divided into clusters represented by the initial centroids closest to them, the centroids are iteratively updated until the K centroids no longer change, and the clustering results are output. According to the clustering results, the mechanical specific energy data are divided into different data groups.

5. The method for evaluating physical properties of buried hill reservoirs based on fractal dimension of logging while drilling according to claim 4, characterized in that: The iterative update of the centroid satisfies: Where: C i is the centroid of the ith cluster, X is the data object, m i represents the number of objects in the i-th cluster; The sum of squared errors is used as the objective function to measure clustering quality: Where SSE is the sum of squared errors, and d is the standard Euclidean distance between two objects in space.

6. The method for evaluating physical properties of buried hill reservoirs based on fractal dimension of logging while drilling according to claim 5, characterized in that: The fractal dimension value distribution range of the buried hill reservoir is [1,2].

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