CN115875029A - Physical property evaluation method of buried hill reservoir based on logging while drilling fractal dimension - Google Patents
Physical property evaluation method of buried hill reservoir based on logging while drilling fractal dimension Download PDFInfo
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Abstract
The invention discloses a physical property evaluation method of a buried hill reservoir based on a logging while drilling fractal dimension, which is characterized in that mechanical specific energy is calculated based on logging while drilling engineering logging parameters, the mechanical specific energy data is preprocessed, and zero point and abnormal data are removed; based on a K-means clustering algorithm, clustering analysis is carried out on mechanical specific energy according to a drilling depth sequence, the clustered mechanical specific energy is sequentially divided into different data groups according to the depth, a semi-variation function is calculated based on the clustered mechanical specific energy data groups, fractal dimensions of different intervals are further calculated, and finally, the reservoir properties are identified according to the reservoir property grading standard. The method realizes the rapid and accurate evaluation of the reservoir properties in real time while drilling by utilizing engineering parameter logging, and has guiding significance for making decisions such as drilling completion, deepening drilling, midway testing and the like in advance, ensuring exploration discovery, improving the operation efficiency and reducing the operation cost.
Description
Technical Field
The invention relates to the technical field of petroleum engineering and petrophysics, in particular to a physical property evaluation method of a buried hill reservoir based on a logging while drilling fractal dimension.
Background
The enrichment of the oil gas in the buried hill depends on the development condition of the storage space of the buried hill to a great extent and is influenced by a plurality of geological factors such as rock properties, the position of the structure, the fracture activity strength, weathering and denudation degree, the development of the storage space of the buried hill has obvious heterogeneity, the difference of the physical properties of the reservoir is large, and the exploration and development progress of the oil gas storage of the buried hill is severely restricted. At present, many explorations are conducted on the evaluation of the buried hill reservoir stratum based on conventional logging information, and partial results are obtained, but the conventional logging technology has certain hysteresis in the exploration and development process, and meanwhile, the explanation difficulty of the buried hill reservoir stratum is high, and the situation that the explanation result is inconsistent with the test result often occurs. Therefore, a set of logging-data-based while-drilling evaluation method for the buried hill reservoir needs to be established, the property of the buried hill reservoir can be quickly and accurately evaluated, and the method has important guiding significance for decisions such as drilling completion in advance, deepening drilling, midway testing and the like.
Disclosure of Invention
The invention aims to design and develop a physical property evaluation method of a buried hill reservoir based on logging while drilling fractal dimension, utilize engineering parameters of comprehensive logging, quickly and accurately evaluate the physical property of the reservoir in real time while drilling, and realize the decision of completing drilling in advance, deepening drilling and testing midway.
The technical scheme provided by the invention is as follows:
a physical property evaluation method of a buried hill reservoir based on a logging while drilling fractal dimension comprises the following steps:
step one, acquiring the bit pressure, the mechanical drilling speed, the rotating speed of a rotary table, the torque and the size of a drill bit of different drilling depths according to the drilling depths;
step two, establishing a mechanical specific energy model, obtaining mechanical specific energy data of different drilling depths and preprocessing the data;
wherein the mechanical specific energy model is:
where MSE is the mechanical specific energy, WOB is the weight on bit, A b The bit area, RPM is the rotary table rotation speed, T is the torque, and ROP is the mechanical drilling speed;
step three, carrying out cluster analysis on the mechanical specific energy data to divide different data groups;
step four, calculating the half-variation function for different data groups respectively:
wherein r (h) is a semi-variogram with formation spacing h, N (h) is the number of pairs of all observation points with h as spacing, Z (x) is a function of sampling point x, Z (x + h) is a function of sampling point h from point x, and h is the lag distance;
step five, calculating fractal dimension values of different half mutation functions;
step six, distinguishing the physical properties of the subsurface hill reservoir according to the fractal dimension value:
when the fractal dimension value is 1-1.5, the buried hill reservoir is a high-quality reservoir;
when the fractal dimension value is 1.5-1.7, the buried hill reservoir is a good reservoir;
and when the fractal dimension value is 1.7-2, the buried hill reservoir is a thin difference reservoir or a non-reservoir.
Preferably, the preprocessing is to remove abnormal data values by a first-order difference method, and specifically includes:
and obtaining a new estimated value of the mechanical specific energy through two known mechanical specific energy values, comparing the estimated value with an actual value, and rejecting the new mechanical specific energy value if the error value is greater than an allowable difference limit value.
Preferably, the estimated value satisfies:
M n ＝EXP(LN(M n-1 )+(LN(M n-1 )-LN(M n-2 )))；
in the formula, M n Predicted mechanical specific energy, M, for a drilling depth of n n-1 Calculated for the mechanical specific energy for a drilling depth of n-1, M n-2 Calculating the mechanical specific energy with the drilling depth of n-2;
the allowable difference limit is 10.
Preferably, the clustering analysis is a K-means clustering algorithm, and specifically comprises:
randomly selecting K data objects from the mechanical specific energy data as initial centroids, calculating the distance between each data object and the K initial centroids, dividing all the data objects into clusters represented by the initial centroids closest to the data objects, iteratively updating the centroids until the K centroids do not change any more, outputting clustering results, and dividing the mechanical specific energy data into different data groups according to the clustered results.
Preferably, the iteratively updating the centroid satisfies:
in the formula: c i Is the centroid of the ith cluster, X is the data object, m i Representing the number of objects in the ith cluster;
clustering quality as an objective function with the sum of squared errors:
where SSE is the sum of squared errors and d is the standard Euclidean distance between two objects in space.
Preferably, the relation between the semi-variogram and the branching curve satisfies:
r(h)∝f W ；
in the formula, f is a branching curve, and W is the slope of the branching curve and the regression equation of the half variation function.
Preferably, the fractal dimension value satisfies:
in the formula, D is a fractal dimension value.
Preferably, the fractal dimension value distribution range of the buried hill reservoir is [1,2].
The invention has the following beneficial effects:
the physical property evaluation method of the buried hill reservoir based on the logging while drilling fractal dimension is designed and developed, corresponding mechanical specific energy can be calculated through logging while drilling parameters, the mechanical specific energy is subjected to clustering analysis according to the drilling depth, the fractal dimension is calculated by using the clustered mechanical specific energy, and finally, the reservoir property is quickly and accurately evaluated in real time while drilling through the specific fractal dimension, so that the method has guiding significance for decisions such as drilling completion, deepening drilling, midway testing and the like in advance, exploration discovery is ensured, the operation efficiency is improved, and the operation cost is reduced.
Drawings
FIG. 1 is a schematic flow diagram of the method for evaluating physical properties of a subsurface hill reservoir based on logging while drilling fractal dimension.
FIG. 2 is a mechanical specific energy profile based on logging data according to the present invention.
Fig. 3 is a cross-sectional view of a fractal dimension formation in accordance with the present invention.
Detailed Description
The present invention is described in further detail below to enable those skilled in the art to practice the invention with reference to the description.
As shown in FIG. 1, the method for evaluating the physical property of the buried hill reservoir based on the logging while drilling fractal dimension, provided by the invention, comprises the following steps of:
step 1: and acquiring engineering parameters at the bottom of the well, such as corresponding data parameters of bit pressure, drilling rate, torque, rotating speed and the like according to the comprehensive logging instrument, continuously increasing the acquired engineering parameters along with the increase of the drilling depth, and sequentially importing the data parameters into a database table according to the drilling depth.
And 2, step: calculating corresponding mechanical specific energy according to the engineering parameters obtained in the database table, and drawing a mechanical specific energy stratum profile map;
the drilling mechanical specific energy is the work that the drill bit breaks the rock in unit volume, the parameters integrate the parameters such as bit pressure, rotating speed, torque, drilling rate, drill bit size and the like into a comprehensive parameter, and the drilling time effect and the formation physical property can be reflected more than engineering parameters such as single drilling time and the like, and the mechanical specific energy model is as follows:
where MSE is the mechanical specific energy, WOB is the weight on bit, A b The area of the drill bit, RPM (revolution speed) of a rotary table, T of torque and ROP of mechanical drilling speed;
and step 3: preprocessing the mechanical specific energy data by adopting a first-order difference method, and removing abnormal data values;
firstly, the first two mechanical ratio energy values are used for estimating a new mechanical ratio energy value, then the estimated mechanical ratio energy value is compared with an actual calculated value, and if the result is greater than a preset allowable difference limit value, the data value is rejected;
the method is more suitable for real-time data acquisition and processing, and realizes real-time processing of abnormal values.
Wherein the estimated value satisfies:
M n ＝EXP(LN(M n-1 )+(LN(M n-1 )-LN(M n-2 )))；
in the formula, M n For a predicted value of mechanical specific energy, M, for a drilling depth of n n-1 Calculated for the mechanical specific energy for a drilling depth of n-1, M n-2 Calculating the mechanical specific energy with the drilling depth of n-2;
the allowable difference limit is 10. Error comparison discriminant:
in the formula, x n Is a calculated value of the mechanical specific energy at a depth of n meters,
the mechanical specific energy average value of the drilled well section is shown, and omega is a difference limit value;
the allowable difference limit is 10.
And 4, step 4: the method comprises the steps of carrying out clustering analysis based on mechanical specific energy data, introducing a K-means clustering algorithm, wherein the basic idea is that K data objects are randomly selected from a data set containing a large number of data objects to serve as initial centroids, calculating the distance between each data object and the K centroids, dividing all sample data into clusters represented by centroids closest to the data objects, updating the K centroids according to the mean values of all the data objects in each newly generated cluster, and outputting a clustering result until the K centroids do not change any more in an iteration process.
The algorithm applies as follows:
randomly selecting K centroids from the mechanical specific energy data set, respectively calculating the distance between each datum in the data set and the K centroids, and dividing the data objects into classes represented by the centroids closest to the data objects one by one; the mass point is updated with the mean of all data objects contained in each cluster, the centroid C of the ith cluster i The definition is as follows:
in the formula: c i Is the centroid of the ith cluster, X is the data object, m i Representing the number of objects in the ith cluster;
using the sum of squared errors as an objective function to measure cluster quality, the following equation is shown:
where SSE is the sum of the squared errors and d is the standard Euclidean distance between two objects in space.
And 5: based on the mechanical specific energy data after the cluster analysis, calculating a semi-variation function according to the obtained data grouping, wherein the formula is as follows:
where r (h) is a semi-variogram with formation spacing h, N (h) is the number of pairs of all observation points with h as spacing, Z (x) is a function of sample point x, Z (x + h) is a function of sample points h from point x, and h is the lag distance;
and 6: calculating fractal dimension values corresponding to different depth strata:
by using the obtained half-variogram data, the fractal curve f has a power relation with the half-variogram r (h), as shown in the following formula:
r(h)∝f W
drawing f and r (h) on a log-log graph, and performing linear regression to obtain a regression equation, wherein the slope of the regression equation is marked as W, the slope W is an approximation of the fractal dimension D, and the slope W and the fractal dimension D have the following relationship:
and 7: judging whether the calculated fractal dimension is reasonable:
judging whether the calculated fractal dimension is in a reasonable range, if a large error occurs, repeating the steps and performing data verification;
the size of the fractal dimension is typically between 1 and 2, with a maximum value of the fractal dimension typically being 2 and a minimum value typically being 1, and it is considered reasonable if the calculated fractal dimension lies within this range.
And step 8: drawing a fractal dimension stratigraphic section:
drawing the calculated fractal dimension into a graph according to the clustering processed groups, wherein the X axis is the fractal dimension, and the Y axis is the drilling depth, and the graph is shown in an attached figure 3;
and step 9: quantitative evaluation of formation properties:
and (3) judging the physical properties of the reservoir according to the fractal dimension, wherein for the buried hill reservoir:
the fractal dimension is 1-1.5, and the reservoir is high in quality;
fractal dimension is 1.5-1.7, good reservoir stratum;
fractal dimension greater than 1.7, poor or non-reservoir.
Examples
Knowing the stratum depth of a certain block and the corresponding mechanical specific energy value thereof, carrying out cluster analysis on the mechanical specific energy data according to the K-means clustering algorithm, wherein the clustering result is shown in the following table I: TABLE-mechanical specific energy clustering results
Selecting a group 1 to calculate a half mutation function according to the clustered data, wherein the corresponding depth is 3090-3106m, and the calculation result is shown in the following table two; the semi-variogram for other groups can be calculated according to the same procedure, which is not described herein.
Table two: group 1 half function of variation
Calculating the fractal dimension corresponding to each group according to the half mutation function calculated by each group as shown in the following table three:
table three fractal dimension values
According to the fractal dimensions calculated in the third table, the calculated fractal dimensions are known to be in a reasonable range, wherein the drilling depths are 3090-3106m, 3120-3128m, 3129-3140m, 3158-3172m, 3173-3186m, 3187-3195m, 3208-3221m, 3222-3233m, 3259-3272m, 3273-3285m and 3286-3299m which are poor or non-reservoir reservoirs, the drilling depths are 3107-3119m, 3141-3157m, 3196-3207m and 3246-3258m which are good reservoir reservoirs, and the drilling depths are 3234-3245m which are high-quality reservoir reservoirs.
The physical property evaluation method of the buried hill reservoir based on the logging while drilling fractal dimension is designed and developed, corresponding mechanical specific energy can be calculated through logging while drilling parameters, the mechanical specific energy is subjected to cluster analysis according to the drilling depth, the fractal dimension is calculated by using the clustered mechanical specific energy, and finally, the rapid and accurate evaluation of the reservoir property in real time while drilling is realized through the specific fractal dimension, so that the method has guiding significance for making decisions such as drilling completion, deepening drilling, midway testing and the like in advance, ensuring exploration discovery, improving the operation efficiency and reducing the operation cost.
While embodiments of the invention have been described above, it is not intended to be limited to the details shown, particular embodiments, but rather to those skilled in the art, and it is to be understood that the invention is capable of numerous modifications and that various changes may be made therein without departing from the spirit and scope of the invention as defined by the appended claims and their equivalents.
Claims (8)
1. A physical property evaluation method of a buried hill reservoir based on a logging while drilling fractal dimension is characterized by comprising the following steps:
step one, acquiring the bit pressure, the mechanical drilling speed, the rotating speed of a turntable, the torque and the size of a drill bit of different drilling depths according to the drilling depths;
step two, establishing a mechanical specific energy model, obtaining mechanical specific energy data of different drilling depths and preprocessing the data;
wherein the mechanical specific energy model is:
where MSE is the mechanical specific energy, WOB is the weight on bit, A b The area of the drill bit, RPM (revolution speed) of a rotary table, T of torque and ROP of mechanical drilling speed;
step three, carrying out cluster analysis on the mechanical specific energy data to divide different data groups;
step four, respectively calculating half variation functions for different data sets:
where r (h) is a semi-variogram with formation spacing h, N (h) is the number of pairs of all observation points with h as spacing, Z (x) is a function of sample point x, Z (x + h) is a function of sample points h from point x, and h is the lag distance;
step five, calculating fractal dimension values of different half mutation functions;
step six, distinguishing the physical properties of the subsurface hill reservoir according to the fractal dimension value:
when the fractal dimension value is 1-1.5, the buried hill reservoir is a high-quality reservoir;
when the fractal dimension value is 1.5-1.7, the buried hill reservoir is a good reservoir;
and when the fractal dimension value is 1.7-2, the buried hill reservoir is a thin difference reservoir or a non-reservoir.
2. The method for evaluating the physical property of the buried hill reservoir based on the logging while drilling fractal dimension as claimed in claim 1, wherein the preprocessing is to remove abnormal data values by a first-order difference method, and specifically comprises the following steps:
and obtaining a new estimated value of the mechanical specific energy through two known mechanical specific energy values, comparing the estimated value with an actual value, and rejecting the new mechanical specific energy value if the error value is greater than an allowable difference limit value.
3. The method for evaluating the physical properties of the buried hill reservoir based on the logging while drilling fractal dimension as claimed in claim 2, wherein the pre-estimated value satisfies the following conditions:
M n ＝EXP(LN(M n-1 )+(LN(M n-1 )-LN(M n-2 )))；
in the formula, M n For a predicted value of mechanical specific energy, M, for a drilling depth of n n-1 Calculated mechanical specific energy for a drilling depth of n-1, M n-2 Calculating the mechanical specific energy with the drilling depth of n-2;
the allowable difference limit is 10.
4. The method for evaluating the physical properties of the buried hill reservoir based on the logging while drilling fractal dimension as claimed in claim 3, wherein the clustering analysis is a K-means clustering algorithm, and specifically comprises the following steps:
randomly selecting K data objects from the mechanical specific energy data as initial centroids, calculating the distance between each data object and the K initial centroids, dividing all the data objects into clusters represented by the initial centroids closest to the data objects, iteratively updating the centroids until the K centroids do not change any more, outputting clustering results, and dividing the mechanical specific energy data into different data groups according to the clustered results.
5. The method for evaluating the physical properties of the buried hill reservoir based on the logging while drilling fractal dimension as recited in claim 4, wherein the iteratively updated centroid satisfies the following conditions:
in the formula: c i Is the centroid of the ith cluster, X is the data object, m i Representing the number of objects in the ith cluster;
clustering quality as an objective function with the sum of squared errors:
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 the physical properties of the buried hill reservoir based on the logging while drilling fractal dimension as claimed in claim 5, wherein the relationship between the half-variation function and the branching curve satisfies the following conditions:
r(h)∝f W ；
in the formula, f is a branching curve, and W is the slope of the branching curve and the regression equation of the half-variation function.
7. The method for evaluating the physical properties of the buried hill reservoir based on the logging while drilling fractal dimension as claimed in claim 6, wherein the fractal dimension value satisfies the following conditions:
in the formula, D is a fractal dimension value.
8. The method for evaluating the physical properties of the buried hill reservoir based on the logging while drilling fractal dimension as claimed in claim 7, wherein the fractal dimension value distribution range of the buried hill reservoir is [1,2].
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