CN110847887A - Method for identifying and evaluating cracks of fine-grain sedimentary continental facies shale - Google Patents

Method for identifying and evaluating cracks of fine-grain sedimentary continental facies shale Download PDF

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CN110847887A
CN110847887A CN201911028860.5A CN201911028860A CN110847887A CN 110847887 A CN110847887 A CN 110847887A CN 201911028860 A CN201911028860 A CN 201911028860A CN 110847887 A CN110847887 A CN 110847887A
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CN110847887B (en
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姚东华
赵贤正
周立宏
王文革
韩国猛
蒲秀刚
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Petrochina Co Ltd
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Petrochina Dagang Oilfield Co
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    • E—FIXED CONSTRUCTIONS
    • E21—EARTH DRILLING; MINING
    • E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B47/00—Survey of boreholes or wells
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH DRILLING; MINING
    • E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B43/00—Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells
    • E21B43/25—Methods for stimulating production
    • E21B43/26—Methods for stimulating production by forming crevices or fractures

Abstract

The invention provides a method for identifying and evaluating fractures of fine-grained sedimentary continental facies shale. Extracting high-frequency values and singular indexes in multi-scale wavelet transformation from a crack sensitive logging curve and predicting three attribute parameters of filtering errors in integral attribute analysis; constructing a difference ratio curve by using each logging attribute module value and the same lithology segment mean value of the same layer phase, and determining three shale fracture evaluation sub-parameters of high-frequency attribute, singular attribute and prediction error attribute; carrying out weighted average on the variation coefficient of each shale fracture evaluation sub-parameter of the four sensitive logging curves to determine shale fracture evaluation comprehensive sub-parameters with different logging attributes; and finally, calculating probability weighting coefficients according to fracture identification probabilities of the three shale fracture evaluation comprehensive sub-parameters in the drilling coring section, and constructing shale fracture evaluation comprehensive parameters, so that identification of the shale fracture section of the single well and evaluation of fracture development degree in the fracture section are realized, and a foundation is provided for identification and effective development of shale oil sweet spots.

Description

Method for identifying and evaluating cracks of fine-grain sedimentary continental facies shale
Technical Field
The invention relates to a method for identifying and evaluating fractures of fine-grained sedimentary continental facies shale, and belongs to the field of well logging for identifying fractures by using conventional well logging data.
Background
In recent years, with the continuous high yield of the fine-grained sedimentary continental shale oil well, the shale oil has become a more realistic exploration breakthrough target and resource succession field of various oil fields in China. Compared with the conventional sandstone and the compact sandstone, the shale has poor matrix pore permeability, and the oil and gas production capacity of the shale is mainly determined by the development condition of a crack. For crack development, a plurality of methods for well logging identification are available at present. Limited by data precision, the conventional well logging curves of lithology, porosity, resistivity and the like generally only utilize the response characteristics of the conventional well logging curves at the crack to qualitatively identify the crack; and the micro-resistivity scanning imaging, the acoustic wave imaging and other new technologies are used for well logging, so that the qualitative identification and the quantitative evaluation can be carried out on the cracks, and parameters such as the crack development degree and the effectiveness can be provided. For shale, overpressure micro cracks and diagenetic shrinkage cracks are developed in large quantity besides tectonic cracks, imaging and conventional logging data reflect the micro cracks to be unobvious under the background of low resistivity, and logging identification difficulty of the shale micro cracks is high.
Disclosure of Invention
In order to solve the problems that no well-recognized shale microcrack logging method exists at present in the background technology and new technology logging such as imaging is not performed in most of drilled shale oil and gas wells, the invention provides a fine particle sedimentary continental facies shale crack recognition and evaluation method on the basis of improving the longitudinal resolution of conventional logging information by utilizing a developed logging information high-resolution processing system, so that the effective recognition of a single-well shale crack section and the evaluation of the crack development degree of the crack section are realized.
The technical scheme provided by the invention is as follows: a method for identifying and evaluating fractures of fine-grained sedimentary continental facies shale comprises the following steps:
s1: extracting logging attributes:
s1.1: performing four-layer scale decomposition on the crack sensitive logging curve by adopting a db2 wavelet, and taking a modulus value of a first-layer scale detail component as a wavelet high-frequency attribute WHF;
s1.2, calculating a Lee index α by using a wavelet high-frequency attribute WHF, and taking the reciprocal of the Lee index as a wavelet singular attribute WSV;
s1.3: calculating the predicted value of each depth point by utilizing a maximum entropy spectrum analysis technology, and subtracting the predicted value from the logging curve value of the depth point to obtain a predicted filtering error attribute PFE;
s2: constructing shale fracture evaluation sub-parameters: constructing a difference ratio curve by using the logging attribute module values extracted in the step S1 and the same lithology segment mean value of the same layer phase, and determining a high-frequency attribute evaluation parameter A1Singular attribute evaluation parameter A2Prediction error attribute evaluation parameter A3The specific expression is as follows:
Figure BDA0002249511760000021
the WHF, the WSV and the PFE are respectively a high-frequency attribute module value, a singular attribute module value and a prediction error attribute module value;
Figure BDA0002249511760000024
and
Figure BDA0002249511760000025
respectively averaging all high-frequency attribute module values, singular attribute module values and prediction error attribute module values with lithology the same as that of the calculated depth point in the layer; MAX (QWHF), MAX (QWSV) and MAX (QPFE) are maximum values of difference ratio curves QWHF, QWSV and QPFE between the high-frequency attribute module value, the singular attribute module value and the prediction error attribute module value and the average value of the same lithology section of the same layer phase;
s3: constructing shale fracture evaluation comprehensive sub-parameters:
s3.1: respectively calculating high-frequency attribute evaluation parameters A of four logging curves of high-resolution acoustic waves (HAC), high-resolution density (HDEN), high-resolution uranium-free gamma (HCGR) and 1-foot-resolution 90-inch detection depth high-resolution array induction (M1R9) by using the method of the step S21,jSingular attribute evaluation parameter A2,jAnd a prediction error attribute evaluation parameter A3,jWherein j ═ 1, 2, 3, 4 represent HAC, HDEN, HCGR and M1R9, respectively;
s3.2: calculating three shale fracture evaluation sub-parameters of the four fracture sensitive logging curves, and respectively constructing a shale fracture evaluation comprehensive sub-parameter CA by adopting a coefficient of variation weighting methodiThe specific expression is as follows:
CA1＝W1，1*A1，1+W1，2*A1，2+W1，3*A1，3+W1，4*A1，4
CA2＝W2，1*A2，1+W2，2*A2，2+W2，3*A2，3+W2，4*A2，4
CA3＝W3，1*A3，1+W3，2*A3，2+W3，3*A3，3+W3，4*A3，4
wherein the weight coefficient
Figure BDA0002249511760000026
Coefficient of variationi is 1, 2 and 3 respectively represent high-frequency attributes, singular attributes and prediction error attributes, j is 1, 2, 3 and 4 respectively represent HAC, HDEN, HCGR and M1R9, and M is 1 and 2 … … N represent the number of sampling points in the interval;
s4: constructing shale fracture evaluation comprehensive parameters:
s4.1: after comparing with the drilling coring observation result, the three shale fracture evaluation comprehensive sub-parameters CA determined in the step S31、CA2、CA3Divided into four levels within a specific numerical range, and the levels are used as next calculation substitution values Vi(ii) a When V isiAbove a threshold, a crack may be present; counting the fracture identification rate n of each comprehensive sub-parameter in the drilling and coring sectioniI is 1, 2 and 3 respectively represent a high-frequency attribute, a singular attribute and a prediction error attribute;
s4.2: constructing a shale fracture evaluation comprehensive parameter CA by using three shale fracture comprehensive sub-parameters and adopting a probability weighting method; when CA is greater than the threshold, a fracture may be present, and the greater the number of envelopes of the CA curve within the fracture segment, the more developed the fracture.
Figure BDA0002249511760000031
Wherein the weight coefficient
Figure BDA0002249511760000032
i-1, 2, 3 represent the high frequency attribute, the singular attribute and the prediction error attribute, respectively.
The invention has the beneficial effects that:
(1) according to the shale fracture evaluation method, different response mechanisms of different logging curves to shale fractures are considered, logging attribute evaluation parameters of four fracture sensitive logging curves are calculated through high-resolution sound waves, high-resolution density, high-resolution uranium-free gamma rays and a 1-foot-resolution 90-inch detection depth high-resolution array, a variation index weighting method is adopted, logging attribute shale fracture evaluation comprehensive sub-parameters are established, the influences of different curve measurement scales and dimensions are eliminated, the relative change amplitude of each curve is highlighted, and the shale fracture identification coincidence rate of a single logging attribute is improved.
(2) The shale fracture evaluation comprehensive parameter is established by a probability weighting method in consideration of different well logging attribute characteristic parameters to reflect different shale fracture capacities, three well logging attributes such as a high-frequency attribute, a singular attribute and a prediction error attribute are comprehensively considered, the shale fracture evaluation comprehensive parameter is improved, the judgment rate of a single-well shale fracture section is improved, the development degree of the fracture section is effectively evaluated, and accurate technical support is provided for shale oil sweet spot optimization and fracturing construction design.
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FIG. 1 is a block diagram of the technical solution of the present invention;
FIG. 2 is a schematic view of a cang-Dong-Ching 108-8 well EK knitted by the present invention2 1、EK2 2A conventional well logging and single well logging attribute fracture identification evaluation effect graph of the segment 2941-3162 m;
FIG. 3 shows a Cantonese female temple-Guangxi regional Guangxi 108-8 well EK constructed according to the invention2 2And a section 3110 and 3140m conventional well logging and comprehensive well logging attribute fracture identification evaluation effect graph.
Detailed Description
The invention will be described in further detail below with reference to the accompanying drawings and tables, using the oil layer of shale in the second section of the hole of 108-8 of the temple in the temple-guangxi region of the cangdong depression as an example.
The method for identifying and evaluating the fracture of the fine-grained sedimentary continental facies shale comprises the following steps:
s1: extracting logging attributes: logging attributes are a metric extracted from the log data in an attempt to extract or enhance formation fracture characteristic information hidden in the log data to improve fracture identification.
S1.1: fractures can cause some logs to wobble differently, but this is often not apparent on the original log. And carrying out wavelet multiresolution decomposition on the logging curve, and reconstructing high-frequency information capable of highlighting curve fluctuation, so that the high-frequency curve can show an obvious fluctuation phenomenon in a crack development layer section. Performing four-layer scale decomposition on the fracture sensitive logging curve by adopting a db2 wavelet, and taking a modulus value of a first-layer scale detail component as a wavelet high-frequency attribute WHF to indicate a shale fracture;
s1.2, when an instrument receives a logging signal containing fracture information, the logging signal is bound to generate transient, and the fracture information is reflected by finding out the position of the transient and calculating the degree of the transient, wherein the signal transient corresponds to an extreme point and a turning point of the signal and is collectively called as a singular point;
s1.3: calculating the predicted value of each depth point by utilizing a maximum entropy spectrum analysis technology, and subtracting the predicted value from the logging curve value of the depth point to obtain a predicted filtering error attribute PFE; because the predicted value is calculated under the condition of no crack theoretically, when the stratum crack develops, the error between the predicted value and the actually measured data value becomes large;
s2: constructing shale fracture evaluation sub-parameters: constructing a difference ratio curve by using the logging attribute module values extracted in the step S1 and the same lithology segment mean value of the same layer phase, and determining a high-frequency attribute evaluation parameter A1Singular attribute evaluation parameter A2Prediction error attribute evaluation parameter A3The specific expression is as follows:
Figure BDA0002249511760000051
Figure BDA0002249511760000052
Figure BDA0002249511760000053
the WHF, the WSV and the PFE are respectively a high-frequency attribute module value, a singular attribute module value and a prediction error attribute module value;
Figure BDA0002249511760000054
and
Figure BDA0002249511760000055
respectively averaging all high-frequency attribute module values, singular attribute module values and prediction error attribute module values with lithology the same as that of the calculated depth point in the layer; MAX (QWHF), MAX (QWSV) and MAX (QPFE) are high-frequency attribute module value, singular attribute module value and prediction error attribute module value and their layer phase respectivelyMaximum values of difference ratio curves QWHF, QWSV and QPFE among the lithologic segment mean values;
s3: constructing shale fracture evaluation comprehensive sub-parameters:
s3.1: different well logs respond differently to fractures and therefore fracture identification may exhibit different effects. Respectively calculating high-frequency attribute evaluation parameters A of four logging curves of high-resolution acoustic waves (HAC), high-resolution density (HDEN), high-resolution uranium-free gamma (HCGR) and 1-foot-resolution 90-inch detection depth high-resolution array induction (M1R9) by using the method of the step S21，jSingular attribute evaluation parameter A2，jAnd a prediction error attribute evaluation parameter A3，jWherein j ═ 1, 2, 3, 4 represent HAC, HDEN, HCGR and M1R9, respectively;
s3.2: calculating three shale fracture evaluation sub-parameters of the four fracture sensitive logging curves, eliminating the influence of measurement scales and dimensions of different curves by adopting a coefficient of variation weighting method, highlighting the relative change amplitude of each curve, and respectively constructing a shale fracture evaluation comprehensive sub-parameter CAiThe specific expression is as follows:
CA1＝W1，1*A1，1+W1，2*A1，2+W1，3*A1，3+W1，4*A1，4
CA2＝W2，1*A2，1+W2，2*A2，2+W2，3*A2.3+W2，4*A2，4
CA3＝W3，1*A3，1+W3，2*A3，2+W3，3*A3，3+W3，4*A3，4
wherein the weight coefficient
Figure BDA0002249511760000056
Coefficient of variation
Figure BDA0002249511760000057
i is 1, 2, 3 respectively representing a high frequency attribute, a singular attribute and a prediction error attribute, and j is 1, 2, 3,4 respectively represents HAC, HDEN, HCGR and M1R9, and M1 and 2 … … N represent the number of sampling points in the interval;
s4: constructing shale fracture evaluation comprehensive parameters:
s4.1: after comparison with the well core observations (FIG. 2), the three shale fracture evaluation composite sub-parameters CA determined in step S31、CA2、CA3Four grades are divided in a specific numerical range (see table 1), and the grades are used as next calculation substituted values; when V isiAbove threshold 1, a crack may be present; counting the number of envelope surfaces of each comprehensive sub-parameter in the drilling coring section, and calculating the single-attribute fracture identification rate ni(see table 2), i ═ 1, 2, 3 represent the high frequency attribute, the singular attribute, and the prediction error attribute, respectively; in fig. 2: the first layer is a stratum sequence channel; the second path is a lithologic logging curve path; the third path is an electrical logging curve path; the fourth path is a porosity logging curve path; the fifth track is a depth scale track; the sixth path is the optimized processed various mineral and rock volume content path; the seventh path is a shale fracture evaluation comprehensive subparameter 1 (high-frequency attribute); the eighth path is a shale fracture evaluation comprehensive sub-parameter 2 (singular attribute) path; the ninth path is a shale fracture evaluation comprehensive subparameter 3 (prediction error attribute) path; the tenth is a fracture development section described by well drilling and coring; the scales of the seventh, eighth and ninth tracks are 5-1, and the curve wave packet taking 1 as a base line indicates that shale fractures possibly exist.
TABLE 1 shale fracture evaluation comprehensive sub-parameter substitution calculation value table
Figure BDA0002249511760000061
TABLE 2 Single Attribute crack identification case Table
Figure BDA0002249511760000062
S4.2: considering that different logging attribute characteristic parameters reflect different fractures, constructing a shale fracture evaluation comprehensive parameter CA by using three shale fracture comprehensive sub-parameters and adopting a probability weighting method; by comparison with core observations at the core of the core section of the well core, there may be fractures when the CA is greater than the threshold value of 1.4 (see fig. 3), whereas the more the number of envelopes of the CA curve within the fracture section, the more developed the fracture. Through statistics, the depth section of 2941-3162m of the 108-8 well is 104 sections of the core description fracture development section, and the 114 sections are identified by a comprehensive fracture evaluation parameter method, wherein 15 sections are wrong, 5 sections are not identified, and the fracture identification criterion rate reaches 80%.
Figure BDA0002249511760000071
Wherein the weight coefficient
Figure BDA0002249511760000072
i-1, 2, 3 represent the high frequency attribute, the singular attribute and the prediction error attribute, respectively.
The first trace in FIG. 3 is a stratigraphic sequence trace; the second path is a lithologic logging curve path; the third path is an electrical logging curve path; the fourth path is a porosity logging curve path; the fifth track is a depth scale track; the sixth path is the optimized processed various mineral and rock volume content path; the seventh path is a shale fracture evaluation comprehensive parameter path, the scale range of the seventh path is 3.4-1.4, and a curve wave packet taking 1.4 as a base line indicates that shale fractures possibly exist; the eighth is the fracture development segment described for well coring.

Claims (4)

1. A method for identifying and evaluating a fracture of a fine-grained sedimentary continental facies shale is characterized by comprising the following steps: the method comprises the following steps:
s1: extracting logging attributes including a wavelet high-frequency attribute WHF, a wavelet singular attribute WSV and a prediction filtering error attribute PFE;
s2: constructing shale fracture evaluation sub-parameters: constructing a difference ratio curve by using the logging attribute module values extracted in the step S1 and the same lithology segment mean value of the same layer phase, and determining a high-frequency attribute evaluation parameter A1Singular attribute evaluation parameter A2Prediction error attribute evaluation parameter A3The specific expression is as follows:
Figure FDA0002249511750000011
Figure FDA0002249511750000012
Figure FDA0002249511750000013
the WHF, the WSV and the PFE are respectively a high-frequency attribute module value, a singular attribute module value and a prediction error attribute module value;
Figure FDA0002249511750000014
and
Figure FDA0002249511750000015
respectively averaging all high-frequency attribute module values, singular attribute module values and prediction error attribute module values with lithology the same as that of the calculated depth point in the layer; MAX (QWHF), MAX (QWSV) and MAX (QPFE) are maximum values of difference ratio curves QWHF, QWSV and QPFE between the high-frequency attribute module value, the singular attribute module value and the prediction error attribute module value and the average value of the same lithology section of the same layer phase;
s3: constructing shale fracture evaluation comprehensive sub-parameters:
s3.1: respectively calculating high-frequency attribute evaluation parameters A of four logging curves of high-resolution acoustic waves (HAC), high-resolution density (HDEN), high-resolution uranium-free gamma (HCGR) and 1-foot-resolution 90-inch detection depth high-resolution array induction (M1R9) by using the method of the step S21，jSingular attribute evaluation parameter A2，jAnd a prediction error attribute evaluation parameter A3，jWherein j ═ 1, 2, 3, 4 represent HAC, HDEN, HCGR and M1R9, respectively;
s3.2: calculating three shale fracture evaluation sub-parameters of the four fracture sensitive logging curves, and respectively constructing a shale fracture evaluation comprehensive sub-parameter CA by adopting a coefficient of variation weighting methodiThe specific expression is as follows:
CA1＝W1，1*A1，1+W1，2*A1，2+W1，3*A1，3+W1，4*A1，4
CA2＝W2，1*A2，1+W2，2*A2，2+W2，3*A2，3+W2，4*A2，4
CA3＝W3，1*A3，1+W3，2*A3，2+W3，3*A3，3+W3，4*A3，4
wherein the weight coefficient
Figure FDA0002249511750000021
Coefficient of variation
Figure FDA0002249511750000022
i is 1, 2 and 3 respectively represent high-frequency attributes, singular attributes and prediction error attributes, j is 1, 2, 3 and 4 respectively represent HAC, HDEN, HCGR and M1R9, and M is 1 and 2 … … N represent the number of calculation points in the interval;
s4: constructing shale fracture evaluation comprehensive parameters:
s4.1: after comparing with the drilling coring observation result, the three shale fracture evaluation comprehensive sub-parameters CA determined in the step S31、CA2、CA3Divided into four levels within a specific numerical range, and the levels are used as next calculation substitution values Vi(ii) a When V isiAbove a threshold, a crack may be present; counting the fracture identification rate n of each comprehensive sub-parameter in the drilling and coring sectioniI is 1, 2 and 3 respectively represent a high-frequency attribute, a singular attribute and a prediction error attribute;
s4.2: constructing a shale fracture evaluation comprehensive parameter CA by using three shale fracture comprehensive sub-parameters and adopting a probability weighting method; when CA is larger than a threshold value, cracks may exist, and the more the number of envelope surfaces of a CA curve in a crack section is, the more the cracks develop;
Figure FDA0002249511750000023
wherein the weight coefficient
Figure FDA0002249511750000024
i-1, 2, 3 represent the high frequency attribute, the singular attribute and the prediction error attribute, respectively.
2. The method for identifying and evaluating the cracks of the fine-grained sedimentary continental facies shale according to claim 1, wherein the method comprises the following steps: the wavelet high-frequency attribute WHF is a modulus of a first-layer scale detail component when a db2 wavelet is adopted to carry out four-layer scale decomposition on a fracture sensitive logging curve.
3. The method for identifying and evaluating the shale fractures in the continental facies of fine grain deposition as claimed in claim 1, wherein the WSV is the reciprocal of the Lee's index α calculated by the WHF.
4. The method for identifying and evaluating the cracks of the fine-grained sedimentary continental facies shale according to claim 1, wherein the method comprises the following steps: the prediction filtering error attribute PFE is obtained by calculating the predicted value of each depth point by utilizing the maximum entropy spectrum analysis technology and subtracting the predicted value from the logging curve value of the depth point.
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CN113050168A (en) * 2021-03-10 2021-06-29 长江大学 Fracture effectiveness evaluation method based on array acoustic logging and acoustic remote detection logging data
CN113050168B (en) * 2021-03-10 2024-01-26 长江大学 Crack effectiveness evaluation method based on array acoustic logging and acoustic remote detection logging data
CN114002750A (en) * 2021-09-27 2022-02-01 中国石油大学(北京) Shale sequence identification method and device, electronic equipment and storage medium
CN114002750B (en) * 2021-09-27 2023-03-24 中国石油大学(北京) Shale sequence identification method and device, electronic equipment and storage medium

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