CN115078362B - Drillability characterization method based on rock mesoscopic structure - Google Patents
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- 239000011435 rock Substances 0.000 title claims abstract description 101
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- 229910052500 inorganic mineral Inorganic materials 0.000 claims abstract description 36
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- 239000004568 cement Substances 0.000 claims description 3
- 239000003822 epoxy resin Substances 0.000 claims description 3
- 229920000647 polyepoxide Polymers 0.000 claims description 3
- 238000005406 washing Methods 0.000 claims description 2
- 238000007619 statistical method Methods 0.000 abstract 1
- 238000005553 drilling Methods 0.000 description 6
- 239000012634 fragment Substances 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
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Abstract
The invention discloses a drillability characterization method based on a rock mesoscopic structure, which comprises the following steps of: 1) Selecting a plurality of rock samples as research objects, and 2) manufacturing a casting body slice; 3) Shooting a microscopic image; 4) Dividing the mineral particle contour: firstly, a mineral particle contour dividing principle is formulated, preliminary division is carried out, and then fine division is carried out by matching with the formulated mineral particle dividing principle; 5) Calculating geometric parameters of mineral particles; 6) Calculating a microscopic structure quantization index; 7) Performing a rock drillability test; 8) Carrying out statistical analysis on the correlation between each mesoscopic structure quantitative index of the rock and the drillability, and screening out the mesoscopic structure quantitative index with the best correlation with the drillability of the rock; and establishing a model for predicting drillability by the rock mesoscopic structure quantitative index according to the optimal mesoscopic structure quantitative index. The method can realize the accurate division of the rock mineral particle outline, obtain the microscopic structure quantization index and reveal the rock breaking mechanism of the drill bit from the viewpoint of microscopic mechanics.
Description
Technical Field
The invention relates to the technical field of petroleum drilling, in particular to a drillability characterization method based on a rock mesoscopic structure.
Background
Rock drillability has been a parameter of major concern in oil and gas drilling projects. At present, the method for acquiring the drillability of the rock mainly comprises a micro-drilling method, a sound wave method and a rock mechanical property method. The micro-drilling method is the most commonly used method, but the problems of difficult underground coring, high drilling cost and the like exist, and the drillability of the rock in the whole well section cannot be obtained. Many models for predicting the drillability of the rock by using the acoustic method are available, but the prediction model has no popularization and cannot be popularized to other blocks and strata. The rock mechanics property method is generally poor in the effect of predicting rock drillability.
In patent CN107180302B a method for evaluating the drillability of rock by using the content of rock debris elements is proposed, which evaluates the drillability of rock by the content of rock debris elements, but does not consider the influence of the structure of mineral particles inside the rock on the drillability. In patent CN114091290B, a rock drillability evaluation method based on rock debris nanoindentation is proposed, which obtains microhardness by performing nanoindentation test on rock debris, and further characterizes rock drillability by the correlation between the microhardness and the rock drillability, and this method is firstly limited by the correlation between the microhardness and the rock drillability, and secondly does not consider the influence of the structure of mineral particles inside rock on the drillability.
In summary, a method for representing drillability based on a rock microscopic structure is needed at present, so that the drillability of rock can be accurately predicted through underground rock debris, and finally the possibility of obtaining the drillability of the rock in the whole well section through the minimum cost is realized.
Disclosure of Invention
The invention aims to provide a method for representing drillability based on a rock mesostructure, which aims to predict the drillability of rock by using underground rock debris by researching the internal relation between the rock mesostructure parameters and the drillability.
The invention provides a drillability characterization method based on a rock mesostructure, which comprises the following steps:
s1, selecting a plurality of rock samples as research objects, and respectively carrying out the following steps S2-S8.
S2, manufacturing a casting sheet: firstly, washing oil on the rock, then infusing epoxy resin into the rock in vacuum, and grinding into thin slices with the thickness not more than 0.03 mm.
S3, shooting microscopic images of the casting body slice: the magnification of the microscope was adjusted to ensure that there were no less than 200 mineral particles in the photograph.
S4, dividing the mineral particle profile: firstly, a mineral particle contour dividing principle is formulated, a celldose module in Python is used for preliminary division, and then ImageJ software is used for fine division in cooperation with the formulated mineral particle dividing principle. The mineral particle outline dividing principle is as follows: the method comprises the following steps of (1) calculating the prior large particles, (2) treating the particle fragments as one particle, (3) treating the particle fragments as two particles after the particle is divided by a crack, (4) treating the cement as one particle, (5) dividing the particle with two wide ends and narrow middle ends into two particles, and (6) dividing different mineral particles into two particles.
S5, calculating geometric parameters of the mineral particles, wherein the geometric parameters comprise the perimeter, the area and the Feret diameter of the mineral particle outline.
S6, calculating a mesoscopic structure quantization index, wherein the index comprises a mesoscopic structure coefficient TC, a minimum length-width ratio AR, a roundness SF, a square Rnd of a ratio of an object equivalent diameter to a length, a logarithm Eds of a ratio of a logarithm of an object perimeter to an object area, a ratio CPa of the object perimeter to the object area, an evaluation variance ratio t of grain sizes and a grain linkage coefficient g; and (5) calculating each mesostructure quantization index by using matlab programming.
S7, performing rock drillability test: performing a rock drillability test by using a rock drillability tester; the rock drillability value is then calculated according to the calculation method in the standard (SY/T5426-2016). The rock drillability tester adopts a test device for testing the rock drillability disclosed in patent CN 201922350632.1.
S8, statistically analyzing the correlation between each microscopic structure quantitative index of the rock and the drillability, and screening out the microscopic structure quantitative index with the best correlation with the drillability of the rock; and establishing a model for predicting drillability by the rock mesoscopic structure quantitative index according to the screened optimal mesoscopic structure quantitative index.
The specific method comprises the following steps: drawing by taking the quantitative index of each microscopic structure as an abscissa and the drillability level value of each rock sample as an ordinate to obtain a fitting curve and R of the fitting curve 2 Value, maximum R 2 The abscissa of the graph corresponding to the value is the mesoscopic structure quantitative index with the best relevance to the rock drillability; and further obtaining a functional relation between the optimal mesoscopic structure quantization index and the drillability level value, namely obtaining a model for predicting the drillability of the rock mesoscopic structure quantization index.
Compared with the prior art, the invention has the advantages that:
(1) The accurate division of rock mineral particle profile is realized, the difficult problem that the mineral particles are difficult to divide in the rock is solved, and a theoretical basis is provided for artificially and intelligently extracting a rock microscopic structure.
(2) The distribution rule of the rock microscopic structure parameters can be obtained, the microscopic structure characteristics can be quantized, and the problem that the microscopic structure is difficult to quantize is solved.
(3) The method not only obtains the relationship between the rock mesoscopic structure quantitative index and the drillability, but also establishes a model for predicting the drillability by the mesoscopic structure quantitative index, and provides possibility for predicting the underground rock drillability in a large scale and accurately at low cost.
(4) The geometric characteristics and coordinate information of the mineral particles are obtained, and scientific basis can be provided for establishing a real rock sample for numerical simulation.
(5) The invention can research the relationship between the microscopic structure quantitative index and the rock breaking of the drill bit and reveal the rock breaking mechanism of the drill bit through microscopic mechanics.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention.
Drawings
FIG. 1 is a flow chart of a method of characterizing drillability based on a rock mesostructure of the present invention.
Fig. 2 shows the flow and results of the image processing stage.
Fig. 3 is a profile of a divided mineral particle.
FIG. 4 is a graph of drillability versus CPa.
FIG. 5 is a graph of drillability versus L.
FIG. 6 is a graph relating drillability to TC.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it should be understood that they are presented herein only to illustrate and explain the present invention and not to limit the present invention.
As shown in FIGS. 1-6, the method for characterizing drillability based on rock mesostructure provided by the invention comprises the following steps:
s1, selecting a plurality of rock samples as research objects, and respectively carrying out the following steps S2-S8; in this example, 18 kinds of sandstone samples were selected as the study subjects.
S2, preparing a casting sheet (see figure 2): the rock is first washed and then cut (201), epoxy resin is then vacuum impregnated into the rock (202), pressed (203) and ground into a cast sheet (204) of 0.03mm thickness.
S3, shooting a microscopic image: pictures were taken using a professional polarizing microscope (205) and the magnification of the microscope was adjusted to ensure that no less than 200 mineral particles were present in the picture.
S4, dividing the mineral particle profile (see figure 3): firstly, a mineral particle contour dividing principle is formulated (301); then, the shot single-polarization and orthogonal polarization images are firstly subjected to primary division (302) of the mineral grain outline by using a celldump module in Python, and finally fine division (303) of the mineral grain outline is performed by using ImageJ software. The mineral particle profile dividing principle is as follows: the method comprises the following steps of (1) calculating the prior large particles, (2) treating the particle fragments as one particle, (3) treating the particle fragments as two particles after the particle is divided by a crack, (4) treating the cement as one particle, (5) dividing the particle with two wide ends and narrow middle ends into two particles, and (6) dividing different mineral particles into two particles.
S5, firstly, carrying out primary geometric parameter calculation on the divided mineral particle outline, wherein the geometric parameters comprise the perimeter, the area and the Feret diameter of the mineral particle outline.
S6, further calculating by using matlab programming according to the formula of the mesoscopic structure quantization index to obtain the final mesoscopic structure quantization index shown in the table 1. Table 1 shows the mesostructure quantitative indicators of 18 kinds of rocks.
Detailed structure quantitative index of table 1 and 18 kinds of rocks
Note: l-diameter; a-area; p-perimeter; AR — minimum aspect ratio; SF-roundness; rnd-the square of the ratio of the equivalent diameter to the length of the body; eds-logarithm of the square of the perimeter of the object to the logarithm of the area of the object; CPa-ratio of the square of the object perimeter to the object area; t-evaluation variance ratio of grain size; g-grain linkage coefficient; TC-microscopic Structure coefficient.
S7, performing rock drillability test: firstly, preparing a standard rock sample, and performing rock drillability test by using a rock drillability tester; and setting parameters of bit pressure and rotation speed according to a drillability test standard, and finally calculating the effective drilling time obtained by the test to obtain the drillability of the rock. The rock drillability level value was calculated according to the calculation method in the standard (SY/T5426-2016).
S8, statistically analyzing the correlation between each microscopic structure quantitative index of the rock and the drillability, and screening out the microscopic structure quantitative index with the best correlation with the drillability of the rock. And then establishing a model for predicting drillability by the rock mesoscopic structure quantitative index according to the screened optimal mesoscopic structure quantitative index.
The specific method comprises the following steps: drawing by taking the quantitative index of each microscopic structure as an abscissa and the drillability level value of each rock sample as an ordinate to obtain a fitting curve and R of the fitting curve 2 Value, maximum R 2 The abscissa of the graph corresponding to the value is the mesoscopic structure quantitative index with the best relevance to the rock drillability. As shown in FIGS. 4, 5 and 6, FIG. 4 is a graph of correlation between drillability and CPa, and R can be obtained by fitting a curve 2 =0.64. FIG. 5 is a graph of the correlation between drillability and L, and by fitting a curve, R can be obtained 2 =0.06. FIG. 6 is a graph relating drillability to TC. By fitting a curve, R can be obtained 2 =0.73. The total number of the microscopic structure quantization indexes is 11, the total number of the obtained related relation graphs of each microscopic structure quantization index and drillability is 11, and the graphs are more and are not listed one by one. By comparing 11 graphsR of (A) to (B) 2 Value, find the maximum R 2 And the figure corresponding to the value, wherein the microscopic structure quantitative index corresponding to the abscissa of the figure is the index with the best correlation with the drillability. In this example, R in FIG. 6 2 The largest value, TC of fig. 6, correlates best with drillability.
From figure 6 a functional relationship of TC with drillability level value is derived,
namely a model for predicting drillability of the rock mesoscopic structure quantization index TC.
Although the present invention has been described with reference to a preferred embodiment, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (6)
1. A drillability characterization method based on a rock mesostructure is characterized by comprising the following steps:
s1, selecting a plurality of rock samples as research objects, and respectively carrying out the following steps S2-S8;
s2, manufacturing a casting body slice;
s3, shooting microscopic images of the casting body slice;
s4, dividing the mineral particle contour: firstly, a mineral particle contour dividing principle is formulated, a cellpool module in Python is used for preliminary division, and then ImageJ software is used for matching with the formulated mineral particle dividing principle for fine division;
s5, calculating geometric parameters of the mineral particles, wherein the geometric parameters comprise the perimeter, the area and the Feret diameter of the contour of the mineral particles;
s6, calculating a mesoscopic structure quantization index, wherein the index comprises a mesoscopic structure coefficient TC, a minimum length-width ratio AR, a roundness SF, a square Rnd of a ratio of an object equivalent diameter to a length, a logarithm Eds of a logarithm of a square of an object perimeter to an object area, a ratio CPa of the square of the object perimeter to the object area, an evaluation variance ratio t of a grain size and a grain linkage coefficient g;
s7, performing rock drillability test;
s8, statistically analyzing the correlation between each microscopic structure quantitative index of the rock and the drillability, and screening out the microscopic structure quantitative index with the best correlation with the drillability of the rock; establishing a model for predicting drillability of the rock mesoscopic structure quantitative index according to the screened optimal mesoscopic structure quantitative index; the specific method comprises the following steps:
drawing by taking the quantitative index of each microscopic structure as an abscissa and the drillability level value of each rock sample as an ordinate to obtain a fitting curve and R of the fitting curve 2 Value, maximum R 2 The abscissa of the graph corresponding to the value is the mesoscopic structure quantitative index with the best relevance to the rock drillability; and further obtaining a functional relation between the optimal mesoscopic structure quantization index and the drillability level value, namely obtaining a model for predicting the drillability of the rock mesoscopic structure quantization index.
2. The method for characterizing drillability based on a rock microstructure according to claim 1, wherein in step S4, the mineral particle profile classification rule is: the method comprises the following steps of (1) preferentially dividing large particles with obvious boundaries, (2) treating particle scraps as one particle, (3) treating the particle as two particles after the particle is divided by a crack, (4) treating cement separated by mineral as one particle, and (5) dividing the particle with two wide ends and narrow middle into two particles.
3. The method for characterizing drillability based on rock mesostructure as claimed in claim 1, wherein in said step S6, a program is written to calculate each mesostructure quantitative index.
4. The method for characterizing the drillability of the rock mesostructure according to claim 1, wherein said step S7 comprises performing a rock drillability test using a rock drillability tester and calculating a rock drillability level value based on the test result.
5. The method for characterizing drillability based on a rock microstructure according to claim 1, wherein said step S2 is specifically: firstly, washing oil on the rock, then injecting epoxy resin into the rock in vacuum, and grinding into a thin sheet with the thickness not more than 0.03 mm.
6. The method for characterizing drillability based on a rock microstructure according to claim 1, wherein in step S3, said method for taking microscopic images is: the magnification of the microscope was adjusted to ensure that there were no less than 200 mineral particles in the photograph.
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| CN115407046B (en) * | 2022-08-05 | 2024-04-16 | 西南石油大学 | Comprehensive abrasiveness characterization method based on rock microstructure and equivalent quartz content |
| CN117911779A (en) * | 2024-01-23 | 2024-04-19 | 西南石油大学 | Particle swarm optimization neural network and mesostructure-based drillability prediction method |
| CN117935991A (en) * | 2024-01-31 | 2024-04-26 | 西南石油大学 | Sandstone drillability GBDT prediction method based on crystal structure and mineralogy characteristics |
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| CN107180302B (en) * | 2017-05-12 | 2020-03-27 | 中国石油大学(华东) | Method for evaluating drillability of rock by using element content of rock debris |
| CN110110346B (en) * | 2019-02-19 | 2022-10-25 | 成都理工大学 | Dynamic design method for personalized drill bit of complex and difficult-to-drill stratum |
| CN110069844B (en) * | 2019-04-15 | 2020-10-30 | 武汉大学 | Mesoscopic model generation method considering rock structure characteristics and mineral composition |
| CN114626014A (en) * | 2021-11-11 | 2022-06-14 | 中国石油天然气集团有限公司 | Limestone stratum drillability prediction method based on element component multivariate regression |
| CN114091290B (en) * | 2022-01-19 | 2022-04-12 | 中国石油大学(华东) | Rock drillability evaluation method based on rock debris nano indentation |
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