CN113625360B - Microcrack formation yield prediction method, microcrack formation yield prediction system, electronic equipment and medium - Google Patents
Microcrack formation yield prediction method, microcrack formation yield prediction system, electronic equipment and medium Download PDFInfo
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Abstract
A method, system, electronic device and medium for predicting the yield of microcrack stratum are disclosed. The method may include: calibrating the sandstone microcrack stratum logging response characteristics, and determining a microcrack stratum section of a test well; determining the unimpeded flow rate and the thickness of the layer section of the microcrack layer section, and further calculating the microcrack gas-containing coefficient of the test well; calculating the resistivity difference according to the deep resistivity and the flushing zone resistivity of the test well; calculating the gas index of the effective seepage space according to the deep resistivity and the resistivity difference; establishing a gas-containing coefficient fitting formula, determining fitting coefficients, and further determining the fitting formula; substituting the logging data of the microcrack interval of the untested well into a fitting formula, calculating the microcrack gas-containing coefficient of the untested well, and further calculating the microcrack stratum yield of the untested well. The method provided by the invention can be used for identifying the microcracks through the conventional logging curves, determining the gas content coefficient of the microcracks of the tight sandstone reservoir, providing more accurate basis for the selection of reservoir engineering process, and being easy to implement and strong in operability.
Description
Technical Field
The invention relates to the technical field of oil and gas reservoir development, in particular to a method, a system, electronic equipment and a medium for predicting the yield of a microcrack stratum.
Background
For the identification study of the cracks, the logging technology generally utilizes formation microresistivity scanning logging and downhole acoustic television imaging logging to identify the cracks according to the characteristics of the cracks on acoustic and electric imaging diagrams, and calculates a series of parameters of the cracks. Meanwhile, according to the cracks identified on the acoustic and electric imaging diagrams, the logging response characteristics of the cracks on the conventional logging curves are researched, and then the cracks are identified by using the conventional logging curves. However, some cracks are very tiny, are not obvious in reflection on acoustic and electric imaging images, but the true volume of the cracks can be peeped from the thin sheet, and at the moment, the micro crack calibration and identification can not be carried out on conventional logging data by using imaging data.
At present, many studies on cracks are carried out, including well logging response characteristics of the cracks, characterization of the cracks, parameter calculation and identification method research and the like, but the studies on micro-cracks are relatively few.
Some researches on microcracks are necessary to identify microcracks of 7-section shale reservoirs of the lower temple oil-gas field by means of specific experimental measurement parameters, specific logging curves or specific programs, such as selecting acoustic wave time differences, natural gamma and compensated neutron curves with higher sensitivity to the reservoir microcracks and using artificial neural networks and wavelet transformation; the shale gas crack development index FI is obtained by calculating the difference between the actual measurement stratum rock volume compression coefficient Clog and the theoretical volume compression coefficient Cth calculated based on shale mineral components and the difference between the undisturbed stratum resistivity Rt and the stratum flushing zone resistivity Rxo, so that the identification of a crack development section is realized; and configuring pore volume percentages of a plurality of groups of soft and hard holes according to the established double-hole DEM analytical model, and comparing the volume modulus and the shear modulus of each saturated rock obtained by applying Gassmann equation and calculating according to actual logging data to obtain the optimal soft hole pore volume percentage so as to identify reservoir microcracks. The method mainly uses quantitative calculation, can not intuitively and conveniently identify and research microcracks by using conventional logging curves, and has limited application range.
Researchers have also studied microcracks using conventional logging curves, such as analyzing the formation characteristics of microcrack development from core and cast sheets, where the logging flag is that the microsphere resistivity measurements are significantly lower than the dual lateral resistivity measurements, i.e., the radial resistivity ratio RMT (the ratio of microsphere to deep lateral resistivity) is much less than 1, and the relationship of unimpeded flow to (hxΦxsg/RMT) was studied; and for example, based on sheet observation, the comprehensive application of the full-diameter core, mercury-pressing curve and logging data is used for identifying the microcrack of the volcanic rock reservoir in the z group of the Y gas field, and the microcrack development reservoir is slightly increased in sound wave and neutron on the logging curve, and slightly reduced in density, but obviously reduced in bilateral resistivity. At present, the research on micro-cracks by using conventional logging curves mainly aims at qualitative identification, and the lithology of the micro-crack stratum is different, and the logging response characteristics are also different.
The conventional logging curve is directly used for quantitative research on the gas-containing property and the productivity of the microcrack reservoir, and although the unobstructed flow calculation method is researched in the prior art, the porosity and the gas-containing saturation of parameters still need to be calculated by means of logging, the gas-containing coefficient and the yield of the microcrack cannot be estimated directly according to logging data, and the field engineering application is limited.
The microcrack in the compact sandstone reservoir is closely related to the perforation and fracturing transformation effects of the reservoir, whether the microcrack is developed or not is closely related to whether the reservoir has economic energy or not, and the distribution relation of the microcrack and the sweet spots of the reservoir is freshly discussed in patents and documents, so that deep research is necessary. Accordingly, there is a need for development of a microcrack formation production prediction method, system, electronic device and medium.
The information disclosed in the background section of the invention is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
The invention provides a method, a system, electronic equipment and a medium for predicting the formation yield of microcracks, which can be used for identifying microcracks by combining the core sheet scale logging curve technology with testing, determining the gas content coefficient of microcracks of a tight sandstone reservoir, providing more accurate basis for the selection of reservoir engineering technology, and being easy to implement and strong in operability.
In a first aspect, embodiments of the present disclosure provide a method for predicting the production of a microcracked formation, comprising:
calibrating the sandstone microcrack stratum logging response characteristics, and determining a microcrack stratum section of a test well;
determining the unimpeded flow rate and the thickness of the layer section of the microcrack layer section, and further calculating the microcrack gas-containing coefficient of the test well;
calculating the resistivity difference according to the deep resistivity of the test well and the resistivity of the flushing zone;
calculating the gas index of the effective seepage space according to the difference between the deep resistivity and the resistivity;
establishing a gas-containing coefficient fitting formula, determining fitting coefficients, and further determining the fitting formula;
substituting the logging data of the microcrack interval of the untested well into the fitting formula, calculating the microcrack gas-containing coefficient of the untested well, and further calculating the microcrack stratum yield of the untested well.
Preferably, the microcrack gas coefficient of the test well is calculated by formula (1):
FD＝OFC/H (1)
wherein FD is the gas coefficient of the microcrack of the test well, OFC is the unimpeded flow rate of the microcrack interval of the test well, and H is the thickness of the microcrack interval of the test well.
Preferably, the resistivity differentiation is calculated by equation (2):
RD＝log(RT/RXO) (2)
where RD is the resistivity differential, RT is the deep resistivity, RXO is the flushing band resistivity.
Preferably, the effective percolation space gas index is calculated by equation (3):
FSGI＝RT*RD (3)
wherein FSGI is the effective percolation space gas index.
Preferably, the gas coefficient fitting formula is:
FD＝a*RD+b*FSGI+c*AC+d*RT+e (4)
wherein a, b, c, d, e is the fitting coefficient.
Preferably, the microcrack formation yield of the untested well is calculated by equation (5):
OFC’＝FD’*H’ (5)
wherein OFC ' is the microcrack formation yield of the untested well, FD ' is the microcrack gas coefficient of the untested well, and H ' is the microcrack interval thickness of the untested well.
As a specific implementation of an embodiment of the present disclosure,
in a second aspect, embodiments of the present disclosure also provide a microcrack formation production prediction system, comprising: the microcrack layer section determining module is used for calibrating the logging response characteristics of the sandstone microcrack stratum and determining the microcrack layer section of the test well;
the gas-containing coefficient calculation module is used for determining the unimpeded flow rate and the thickness of the layer section of the microcrack layer section, and further calculating the microcrack gas-containing coefficient of the test well;
the resistivity difference degree calculation module is used for calculating the resistivity difference degree according to the deep resistivity and the flushing zone resistivity of the test well;
the gas-containing index calculation module is used for calculating the gas-containing index of the effective seepage space according to the deep resistivity and the resistivity difference degree;
the fitting module establishes a gas-containing coefficient fitting formula, determines fitting coefficients and further determines the fitting formula;
and the yield calculation module is used for substituting the logging data of the microcrack interval of the untested well into the fitting formula, calculating the microcrack gas-containing coefficient of the untested well, and further calculating the microcrack stratum yield of the untested well.
Preferably, the microcrack gas coefficient of the test well is calculated by formula (1):
FD＝OFC/H (1)
wherein FD is the gas coefficient of the microcrack of the test well, OFC is the unimpeded flow rate of the microcrack interval of the test well, and H is the thickness of the microcrack interval of the test well.
Preferably, the resistivity differentiation is calculated by equation (2):
RD＝log(RT/RXO) (2)
where RD is the resistivity differential, RT is the deep resistivity, RXO is the flushing band resistivity.
Preferably, the effective percolation space gas index is calculated by equation (3):
FSGI＝RT*RD (3)
wherein FSGI is the effective percolation space gas index.
Preferably, the gas coefficient fitting formula is:
FD＝a*RD+b*FSGI+c*AC+d*RT+e (4)
wherein a, b, c, d, e is the fitting coefficient.
Preferably, the microcrack formation yield of the untested well is calculated by equation (5):
OFC’＝FD’*H’ (5)
wherein OFC ' is the microcrack formation yield of the untested well, FD ' is the microcrack gas coefficient of the untested well, and H ' is the microcrack interval thickness of the untested well.
In a third aspect, embodiments of the present disclosure further provide an electronic device, including:
a memory storing executable instructions;
and a processor executing the executable instructions in the memory to implement the microcrack formation yield prediction method.
In a fourth aspect, embodiments of the present disclosure also provide a computer readable storage medium storing a computer program which, when executed by a processor, implements the microcrack formation production prediction method.
The methods and systems of the present invention have other features and advantages that will be apparent from or are set forth in detail in the accompanying drawings and the following detailed description, which, together, serve to explain certain principles of the invention.
Drawings
The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular descriptions of exemplary embodiments of the invention as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts throughout the exemplary embodiments of the invention.
FIG. 1 shows a flow chart of the steps of a method for microcrack formation production prediction in accordance with one embodiment of the present invention.
FIG. 2 shows a schematic diagram of a DXX well sheet and log profile according to one embodiment of the invention.
FIG. 3 shows a plot of the intersection of microcrack gas coefficient and resistivity variability in accordance with one embodiment of the present invention.
FIG. 4 shows a plot of the intersection of microcrack gas coefficient and effective percolation space gas index, according to one embodiment of the present invention.
FIG. 5 shows a plot of the intersection of microcrack gas coefficient with acoustic waves in accordance with one embodiment of the present invention.
FIG. 6 shows a plot of the intersection of microcrack gas coefficient and deep resistivity in accordance with one embodiment of the present invention.
FIG. 7 illustrates a block diagram of a microcrack formation production prediction system, according to one embodiment of the invention.
Reference numerals illustrate:
201. a microcrack interval determination module; 202. the gas-containing coefficient calculation module; 203. a resistivity difference calculation module; 204. a gas index calculation module; 205. fitting a module; 206. and a yield calculation module.
Detailed Description
Preferred embodiments of the present invention will be described in more detail below. While the preferred embodiments of the present invention are described below, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein.
The invention provides a method for predicting the yield of a microcrack stratum, which comprises the following steps:
calibrating sandstone microcrack stratum logging response characteristics by using casting body sheet data, and determining microcrack intervals of a test well; and determining the stratum with the microcracks according to the sheet data, analyzing the well diameter, porosity and resistivity curve logging response characteristics of the microcrack stratum, and establishing a well diameter, density and flushing band resistivity microcrack response characteristic identification mode.
Splitting the yield according to the test data of the micro-fracture stratum in the single-layer test interval, determining the unimpeded flow rate and the thickness of the interval of the micro-fracture interval, and further calculating the micro-fracture gas-containing coefficient of the test well; in one example, the microcrack gas coefficient of the test well is calculated by equation (1):
FD＝OFC/H (1)
wherein FD is the gas coefficient of the microcrack of the test well, OFC is the unimpeded flow rate of the microcrack interval of the test well, and H is the thickness of the microcrack interval of the test well.
Calculating the resistivity difference according to the deep resistivity and the flushing zone resistivity of the test well; in one example, the resistivity differential is calculated by equation (2):
RD＝log(RT/RXO) (2)
where RD is the resistivity differential, RT is the deep resistivity, RXO is the flushing band resistivity.
Calculating the gas index of the effective seepage space according to the deep resistivity and the resistivity difference; in one example, the effective percolation space gas index is calculated by equation (3):
FSGI＝RT*RD (3)
wherein FSGI is the effective percolation space gas index.
Establishing a gas-containing coefficient fitting formula, substituting values of a microcrack gas-containing coefficient, deep resistivity, resistivity difference, effective seepage space gas-containing index and sound waves of a test well corresponding to a plurality of depth points into the fitting formula, and calculating and determining the fitting coefficient by using a simultaneous equation to further determine the fitting formula; in one example, the gas coefficient fitting formula is:
FD＝a*RD+b*FSGI+c*AC+d*RT+e (4)
wherein a, b, c, d, e is the fitting coefficient.
Substituting the logging data of the microcrack interval of the untested well into a fitting formula, calculating the microcrack gas-containing coefficient of the untested well, and further calculating the microcrack stratum yield of the untested well. In one example, the microcrack formation yield for an untested well is calculated by equation (5):
OFC’＝FD’*H’ (5)
wherein OFC ' is the microcrack formation yield of the untested well, FD ' is the microcrack gas coefficient of the untested well, and H ' is the microcrack interval thickness of the untested well.
The invention also provides a microcrack formation yield prediction system, which comprises:
the microcrack layer section determining module is used for determining a microcrack layer section of a test well by utilizing casting body sheet data to scale sandstone microcrack stratum logging response characteristics; and determining the stratum with the microcracks according to the sheet data, analyzing the well diameter, porosity and resistivity curve logging response characteristics of the microcrack stratum, and establishing a well diameter, density and flushing band resistivity microcrack response characteristic identification mode.
The gas-containing coefficient calculation module is used for splitting the yield according to the test data of the microcrack stratum in the single-layer test interval, determining the unimpeded flow rate and the thickness of the microcrack interval, and further calculating the microcrack gas-containing coefficient of the test well; in one example, the microcrack gas coefficient of the test well is calculated by equation (1):
FD＝OFC/H (1)
wherein FD is the gas coefficient of the microcrack of the test well, OFC is the unimpeded flow rate of the microcrack interval of the test well, and H is the thickness of the microcrack interval of the test well.
The resistivity difference degree calculation module is used for calculating the resistivity difference degree according to the deep resistivity and the flushing zone resistivity of the test well; in one example, the resistivity differential is calculated by equation (2):
RD＝log(RT/RXO) (2)
where RD is the resistivity differential, RT is the deep resistivity, RXO is the flushing band resistivity.
The gas-containing index calculation module is used for calculating the gas-containing index of the effective seepage space according to the deep resistivity and the resistivity difference; in one example, the effective percolation space gas index is calculated by equation (3):
FSGI＝RT*RD (3)
wherein FSGI is the effective percolation space gas index.
The fitting module establishes a gas-containing coefficient fitting formula, substitutes values of a microcrack gas-containing coefficient, deep resistivity, resistivity difference, effective seepage space gas-containing index and sound wave of the test well corresponding to a plurality of depth points into the fitting formula, and calculates and determines the fitting coefficient by simultaneous equation, thereby determining the fitting formula; in one example, the gas coefficient fitting formula is:
FD＝a*RD+b*FSGI+c*AC+d*RT+e (4)
wherein a, b, c, d, e is the fitting coefficient.
And the yield calculation module is used for substituting the logging data of the microcrack intervals of the untested wells into a fitting formula, calculating the gas content coefficients of the microcracks of the untested wells, and further calculating the yield of the microcrack stratum of the untested wells. In one example, the microcrack formation yield for an untested well is calculated by equation (5):
OFC’＝FD’*H’ (5)
wherein OFC ' is the microcrack formation yield of the untested well, FD ' is the microcrack gas coefficient of the untested well, and H ' is the microcrack interval thickness of the untested well.
The present invention also provides an electronic device including: a memory storing executable instructions; and the processor runs the executable instructions in the memory to realize the microcrack stratum yield prediction method.
The invention also provides a computer readable storage medium storing a computer program which when executed by a processor implements the microcrack formation yield prediction method described above.
In order to facilitate understanding of the solution and the effects of the embodiments of the present invention, four specific application examples are given below. It will be understood by those of ordinary skill in the art that the examples are for ease of understanding only and that any particular details thereof are not intended to limit the present invention in any way.
Example 1
FIG. 1 shows a flow chart of the steps of a method for microcrack formation production prediction in accordance with one embodiment of the present invention.
As shown in fig. 1, the method for predicting the yield of the microcrack stratum comprises the following steps: step 101, calibrating the sandstone microcrack stratum logging response characteristics, and determining a microcrack stratum section of a test well;
FIG. 2 shows a schematic of a DXX well slice and log profile, GR representing natural gamma, SP representing natural potential, CAL1 representing well diameter, RESD representing deep resistivity, RESS representing shallow resistivity, RESX representing flushing zone resistivity, AC representing acoustic waves, DEN representing density, CNL representing neutrons, according to one embodiment of the invention.
From the flakes, it is known that at 2692.05m the formation has effective microcracks. By analyzing the logging response characteristics of the microcrack stratum, the well diameter expansion of the microcrack stratum is found, and is caused by the fact that the stratum has microcracks, borehole wall rock blocks can be broken down during drilling to form an elliptic borehole, double-borehole reflection can be utilized, and the well diameter expansion generally does not occur in a long well section. By analyzing the three resistivity curve characteristics, the flushing zone resistivity curve at the microcrack stratum is found to be sharply reduced, and the amplitude difference between the deep resistivity and the flushing zone resistivity curve is large, because the microcracks increase the stratum permeability, so that the invasion depth is increased, the flushing zone resistivity is basically reflected by mud filtrate information due to the influence of expanding, and the resistivity value of the flushing zone resistivity is lower. Meanwhile, by analyzing the characteristics of the three-porosity curves, the density curve at the microcrack stratum is found to be obviously reduced, and the density value is smaller due to the combined action of invasion deepening and expanding caused by microcracks, so that more information is detected in the detection range of a density instrument from the mud filtrate in the flushing zone, and the density measurement value is obviously reduced.
According to the analysis, the identification characteristic of the microcrack stratum is that the well diameter is expanded, the resistivity curve of a flushing zone is sharply reduced, and the density curve is obviously reduced.
Step 102, determining the unimpeded flow rate and the interval thickness of a microcrack interval, and further calculating the microcrack gas-containing coefficient of a test well; and splitting the yield according to the k h value of each sandstone layer in the test interval, namely the product value of the permeability and the thickness according to the test result of the test well, determining the unimpeded flow of the microcrack interval, and further calculating the microcrack gas content coefficient of the test well through a formula (1).
Step 103, calculating the resistivity difference according to the deep resistivity and the flushing zone resistivity of the test well, wherein the resistivity difference reflects the curve amplitude difference between the deep resistivity and the flushing zone resistivity, and represents the intensity of stratum permeability.
And 104, calculating an effective seepage space gas-containing index according to the deep resistivity and the resistivity difference, wherein the effective seepage space gas-containing index reflects the gas saturation in the effective seepage space of the microcrack stratum.
And 105, establishing a gas-containing coefficient fitting formula according to the microcrack gas-containing coefficient, the deep resistivity, the resistivity difference degree, the effective seepage space gas-containing index and the sound wave of the test well.
FIG. 3 shows a plot of the intersection of microcrack gas coefficient and resistivity variability in accordance with one embodiment of the present invention.
FIG. 4 shows a plot of the intersection of microcrack gas coefficient and effective percolation space gas index, according to one embodiment of the present invention.
FIG. 5 shows a plot of the intersection of microcrack gas coefficient with acoustic waves in accordance with one embodiment of the present invention.
FIG. 6 shows a plot of the intersection of microcrack gas coefficient and deep resistivity in accordance with one embodiment of the present invention.
As can be seen from FIGS. 3-6, the gas content coefficient of the microcrack and the resistivity are positively correlated, and the effective percolation space gas content index, the acoustic wave and the deep resistivity have a certain correlation. And then determining fitting coefficients by utilizing multiple regression, and further determining a fitting formula as follows:
FD＝1.59702*RD-0.00845*FSGI+0.019*AC+0.01553*RT-5.72463 (6)；
and 106, substituting the logging data of the microcrack intervals of the untested wells into a fitting formula, calculating the gas content coefficients of the microcracks of the untested wells, and further calculating the production of the microcrack stratum of the untested wells.
Table 1 is a table of comparison of measured unobstructed flow rates and microcrack gas coefficients for microcrack formations with calculated unobstructed flow rates and microcrack gas coefficients. As can be seen from the data in the table, the calculated result of the unobstructed flow and the microcrack gas content coefficient is closer to the measured result.
TABLE 1
Example 2
FIG. 7 illustrates a block diagram of a microcrack formation production prediction system, according to one embodiment of the invention.
As shown in fig. 7, the microcrack formation production prediction system comprises:
the microcrack interval determining module 201 is used for calibrating the sandstone microcrack stratum logging response characteristics and determining microcrack intervals of a test well;
the gas-containing coefficient calculation module 202 is used for determining the unimpeded flow rate and the interval thickness of the microcrack interval so as to calculate the microcrack gas-containing coefficient of the test well;
the resistivity differential calculation module 203 calculates resistivity differential according to the deep resistivity and the flushing resistivity of the test well;
the gas-containing index calculation module 204 calculates the gas-containing index of the effective seepage space according to the deep resistivity and the resistivity difference degree;
the fitting module 205 establishes a gas coefficient fitting formula, determines fitting coefficients, and further determines the fitting formula;
the yield calculation module 206 substitutes the logging data of the microcrack intervals of the untested wells into the fitting formula to calculate the microcrack gas-containing coefficients of the untested wells, and further calculates the microcrack formation yield of the untested wells.
Alternatively, the microcrack gas coefficient of the test well is calculated by equation (1):
FD＝OFC/H (1)
wherein FD is the gas coefficient of the microcrack of the test well, OFC is the unimpeded flow rate of the microcrack interval of the test well, and H is the thickness of the microcrack interval of the test well.
Alternatively, the resistivity differentiation is calculated by equation (2):
RD＝log(RT/RXO) (2)
where RD is the resistivity differential, RT is the deep resistivity, RXO is the flushing band resistivity.
Alternatively, the effective percolation space gas index is calculated by equation (3):
FSGI＝RT*RD (3)
wherein FSGI is the effective percolation space gas index.
Alternatively, the gas-containing coefficient fitting formula is:
FD＝a*RD+b*FSGI+c*AC+d*RT+e (4)
wherein a, b, c, d, e is the fitting coefficient.
Alternatively, the microcrack formation yield for an untested well is calculated by equation (5):
OFC’＝FD’*H’ (5)
wherein OFC ' is the microcrack formation yield of the untested well, FD ' is the microcrack gas coefficient of the untested well, and H ' is the microcrack interval thickness of the untested well.
Example 3
The present disclosure provides an electronic device including: a memory storing executable instructions; and the processor runs the executable instructions in the memory to realize the microcrack stratum yield prediction method.
An electronic device according to an embodiment of the present disclosure includes a memory and a processor.
The memory is for storing non-transitory computer readable instructions. In particular, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, random Access Memory (RAM) and/or cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like.
The processor may be a Central Processing Unit (CPU) or other form of processing unit having data processing and/or instruction execution capabilities, and may control other components in the electronic device to perform the desired functions. In one embodiment of the present disclosure, the processor is configured to execute the computer readable instructions stored in the memory.
It should be understood by those skilled in the art that, in order to solve the technical problem of how to obtain a good user experience effect, the present embodiment may also include well-known structures such as a communication bus, an interface, and the like, and these well-known structures are also included in the protection scope of the present disclosure.
The detailed description of the present embodiment may refer to the corresponding description in the foregoing embodiments, and will not be repeated herein.
Example 4
Embodiments of the present disclosure provide a computer readable storage medium storing a computer program which, when executed by a processor, implements the microcrack formation yield prediction method.
A computer-readable storage medium according to an embodiment of the present disclosure has stored thereon non-transitory computer-readable instructions. When executed by a processor, perform all or part of the steps of the methods of embodiments of the present disclosure described above.
The computer-readable storage medium described above includes, but is not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or removable hard disk), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).
It will be appreciated by persons skilled in the art that the above description of embodiments of the invention has been given for the purpose of illustrating the benefits of embodiments of the invention only and is not intended to limit embodiments of the invention to any examples given.
The foregoing description of embodiments of the invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described.
Claims (7)
1. A method for predicting the production of a microcrack formation, comprising:
calibrating the sandstone microcrack stratum logging response characteristics, and determining a microcrack stratum section of a test well;
determining the unimpeded flow rate and the thickness of the layer section of the microcrack layer section, and further calculating the microcrack gas-containing coefficient of the test well;
calculating the resistivity difference according to the deep resistivity of the test well and the resistivity of the flushing zone;
calculating the gas index of the effective seepage space according to the difference between the deep resistivity and the resistivity;
establishing a gas-containing coefficient fitting formula, determining fitting coefficients, and further determining the fitting formula;
substituting the logging data of the microcrack interval of the untested well into the fitting formula, calculating the microcrack gas-containing coefficient of the untested well, and further calculating the microcrack stratum yield of the untested well;
wherein the resistivity differential is calculated by equation (2):
RD＝log(RT/RXO) (2)
wherein RD is resistivity differential, RT is deep resistivity, RXO is flushing band resistivity;
wherein, the effective percolation space gas index is calculated by the formula (3):
FSGI ＝RT*RD (3)
wherein FSGI is the effective percolation space gas index;
the fitting formula of the gas-containing coefficient is as follows:
FD ＝ a*RD+ b*FSGI+ c*AC+ d*RT+ e (4)
where AC represents the acoustic wave and a, b, c, d, e is the fitting coefficient.
2. The method of micro-fracture formation production prediction according to claim 1, wherein the micro-fracture gas coefficient of the test well is calculated by equation (1):
FD＝OFC/H (1)
wherein FD is the gas coefficient of the microcrack of the test well, OFC is the unimpeded flow rate of the microcrack interval of the test well, and H is the thickness of the microcrack interval of the test well.
3. The method of micro-fracture formation production prediction according to claim 1, wherein the micro-fracture formation production of the untested well is calculated by equation (5):
OFC ’＝ FD’ *H’ (5)
wherein OFC ' is the microcrack formation yield of the untested well, FD ' is the microcrack gas coefficient of the untested well, and H ' is the microcrack interval thickness of the untested well.
4. A microcrack formation production prediction system, comprising:
the microcrack layer section determining module is used for calibrating the logging response characteristics of the sandstone microcrack stratum and determining the microcrack layer section of the test well;
the gas-containing coefficient calculation module is used for determining the unimpeded flow rate and the thickness of the layer section of the microcrack layer section, and further calculating the microcrack gas-containing coefficient of the test well;
the resistivity difference degree calculation module is used for calculating the resistivity difference degree according to the deep resistivity and the flushing zone resistivity of the test well;
the gas-containing index calculation module is used for calculating the gas-containing index of the effective seepage space according to the deep resistivity and the resistivity difference degree;
the fitting module establishes a gas-containing coefficient fitting formula, determines fitting coefficients and further determines the fitting formula;
the yield calculation module substitutes the logging data of the microcrack interval of the untested well into the fitting formula, calculates the microcrack gas-containing coefficient of the untested well, and further calculates the microcrack stratum yield of the untested well;
wherein the resistivity differential is calculated by equation (2):
RD＝log(RT/RXO) (2)
wherein RD is resistivity differential, RT is deep resistivity, RXO is flushing band resistivity;
wherein, the effective percolation space gas index is calculated by the formula (3):
FSGI ＝RT*RD (3)
wherein FSGI is the effective percolation space gas index;
the fitting formula of the gas-containing coefficient is as follows:
FD ＝ a*RD+ b*FSGI+ c*AC+ d*RT+ e (4)
where AC represents the acoustic wave and a, b, c, d, e is the fitting coefficient.
5. The microcrack formation production prediction system of claim 4 wherein the microcrack gas coefficient of the test well is calculated by equation (1):
FD＝OFC/H (1)
wherein FD is the gas coefficient of the microcrack of the test well, OFC is the unimpeded flow rate of the microcrack interval of the test well, and H is the thickness of the microcrack interval of the test well.
6. An electronic device, the electronic device comprising:
a memory storing executable instructions;
a processor executing the executable instructions in the memory to implement the microcrack formation production prediction method of any one of claims 1-3.
7. A computer readable storage medium, characterized in that it stores a computer program which, when executed by a processor, implements the microcrack formation production prediction method of any one of claims 1-3.
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