CN115045646A - Shale gas well site optimization method - Google Patents
Shale gas well site optimization method Download PDFInfo
- Publication number
- CN115045646A CN115045646A CN202210640378.2A CN202210640378A CN115045646A CN 115045646 A CN115045646 A CN 115045646A CN 202210640378 A CN202210640378 A CN 202210640378A CN 115045646 A CN115045646 A CN 115045646A
- Authority
- CN
- China
- Prior art keywords
- shale
- area
- well
- carbon content
- training
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 30
- 238000005457 optimization Methods 0.000 title claims abstract description 21
- 238000011160 research Methods 0.000 claims abstract description 54
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims abstract description 47
- 229910052799 carbon Inorganic materials 0.000 claims abstract description 47
- 238000011161 development Methods 0.000 claims abstract description 10
- 238000005516 engineering process Methods 0.000 claims abstract description 7
- 238000012549 training Methods 0.000 claims description 63
- 238000005553 drilling Methods 0.000 claims description 44
- 238000013145 classification model Methods 0.000 claims description 18
- 230000015572 biosynthetic process Effects 0.000 claims description 15
- 230000002159 abnormal effect Effects 0.000 claims description 6
- 238000010276 construction Methods 0.000 claims description 5
- 230000009286 beneficial effect Effects 0.000 claims description 4
- 208000035126 Facies Diseases 0.000 claims description 3
- 230000008859 change Effects 0.000 claims description 3
- 239000005416 organic matter Substances 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 238000013499 data model Methods 0.000 description 2
- 230000035800 maturation Effects 0.000 description 2
- 238000013508 migration Methods 0.000 description 2
- 230000005012 migration Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 239000003550 marker Substances 0.000 description 1
- 230000008569 process Effects 0.000 description 1
Images
Classifications
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21B—EARTH OR ROCK 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
- E21B43/30—Specific pattern of wells, e.g. optimising the spacing of wells
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21B—EARTH OR ROCK 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 OR ROCK DRILLING; MINING
- E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/30—Assessment of water resources
Landscapes
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Geology (AREA)
- Mining & Mineral Resources (AREA)
- Physics & Mathematics (AREA)
- Environmental & Geological Engineering (AREA)
- Fluid Mechanics (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Geochemistry & Mineralogy (AREA)
- Geophysics (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
The invention discloses a shale gas well position optimization method, which comprises the following steps: s1, predicting a target shale layer in a research area by using a GR curve, and calculating a total organic carbon content curve of each well by using resistivity logging and acoustic logging/density logging in combination with real-time geological data; s2, performing intersection analysis on the total organic carbon content and the density log curve of each well in the research area to construct a fitting relation between the total organic carbon content and the density; and S3, performing superposition and inversion on the wave impedance of the well constrained earthquake in the research area to obtain a density data volume of the research area, and calculating the total organic carbon content data volume of the research area according to the fitting relation between the total organic carbon content and the density based on the density data volume of the research area. The invention fully utilizes the combination of real geological data and logging data (resistivity, sound wave and density) to construct a fitting curve of the effective development interval (TOC > 2.0%) of shale gas, and further combines a method for optimizing well positions by a seismic inversion technology.
Description
Technical Field
The invention relates to the technical field of shale gas well position selection, in particular to a shale gas well position optimization method.
Background
The existing popular geosteering method is mainly characterized in that a drill bit is guided to pass through a target interval according to comparison results of drilled well data and a well to be guided, geological modeling is mostly two-dimensional, the guiding method is suitable for a specific block or a specific stratum, universality is poor, copying and popularization in other work areas are not facilitated, and a geosteering method which is simple and convenient to operate and easy to popularize and apply on site is urgently researched on site in China.
CN105464592A discloses a shale gas horizontal well geosteering method, which comprises the following steps: according to the lithological and electrical characteristics of the target layer, stratigraphic division is carried out on the target layer, and a comparison mark layer is determined; based on the three-dimensional post-stack time migration and pre-stack time migration data, a Geoframe interpretation system is utilized to carry out human-machine interaction interpretation on a workstation, fine tracking comparison is carried out on a target layer, and the structural form of each layer is realized; a, target track adjustment; horizontal section geosteering: in the process of horizontal section actual drilling, the stratum inclination angle along the track direction is often changed, while-drilling data needs to be collected in time, the gas layer marker layer is compared, the current actual drilling position is confirmed, the track is monitored in real time, prediction is carried out in advance, directional construction is guided, and the drilling track is ensured to run in the designed stratum range. Although the above-mentioned technology can realize well position guidance, the optimization of the well position is difficult to realize, and the selection error of the well position is large.
Disclosure of Invention
The invention aims to provide a shale gas well position optimization method to solve the technical problem that well position selection errors are large due to the fact that well position optimization is difficult to achieve in the prior art.
In order to solve the technical problems, the invention specifically provides the following technical scheme:
a shale gas well site optimization method comprises the following steps:
s1, predicting a target shale layer in a research area by using a GR curve, and calculating a total organic carbon content curve of each well by using resistivity logging and acoustic logging/density logging in combination with real-time geological data;
step S2, performing intersection analysis on the total organic carbon content and the density log of each well in the research area, and determining the correlation between the total organic carbon content and the density to construct a fitting relation between the total organic carbon content and the density;
s3, performing superposition and inversion on wave impedance of the well constrained earthquake in the research area to obtain a density data volume of the research area, and calculating to obtain a total organic carbon content data volume of the research area according to the fitting relation between the total organic carbon content and the density based on the density data volume of the research area; (ii) a
S4, inversing the spatial change of the total organic carbon content of the target shale layer on a plane and a section by using the fitting relation between the total organic carbon content and the density and a seismic inversion technology, and determining a shale distribution range with the total organic carbon content of more than 2% on the target shale layer;
and step S5, selecting a shale gas drilling well position according to the stratum pressure characteristics of the research area in the shale distribution range.
As a preferred embodiment of the present invention, the predicting a target shale layer in a research area by using a GR curve includes:
acquiring GR values and shale thickness values of shale layers in a plurality of mined areas as training positive samples, acquiring GR values and shale thickness values of non-shale layers in a plurality of mined areas as training negative samples, and uniformly mixing the training positive samples and the training negative samples to obtain training samples;
performing model training on a training sample by using a classification model to obtain a shale layer recognition model, wherein the model expression of the shale layer recognition model is as follows:
Label＝F(GR,TH)；
wherein Label is characterized as an identifier of a shale layer category, GR is characterized as a GR value identifier, TH is characterized as a shale thickness value identifier, and F is characterized as a classification model body identifier;
inputting the GR value and the shale thickness value of each interval in the research area into a shale layer identification model to identify the shale layer type of each interval, wherein if the shale layer identification model outputs a shale layer, the corresponding interval is marked with the shale layer;
if the shale layer identification model output is a non-shale layer, marking the non-shale layer with the corresponding layer section;
carrying out regional combination on all shale layers to obtain the target shale layer;
the shale layer categories include shale layers and non-shale layers.
In a preferred embodiment of the present invention, the target shale layer is within a depth range below a lower limit of organic matter maturation.
As a preferred scheme of the present invention, when the number of wells in the study area is greater than or equal to a preset threshold, a total organic carbon content curve is compiled by using well drilling data;
and when the drilling quantity of the research area is less than a preset threshold value, determining the beneficial development area of the shale gas by utilizing the source direction and the sedimentary facies characteristics and combining seismic interpretation to compile a total organic carbon content curve.
As a preferred scheme of the invention, the inversion on the plane and the section plane comprises the step of selecting a typical section plane along the construction direction and the vertical construction direction on the selected plane area to carry out seismic inversion processing.
As a preferable scheme of the present invention, the selection rule in the planar region includes that the total organic carbon content value is high; the surface condition is relatively simple, and the fault does not develop; burying a target layer to a shallow depth; the shale area is large; there is at least one of drilling, seismic, logging, etc. data for conventional oil and gas exploration.
As a preferred scheme of the invention, the selection of the shale gas drilling well position according to the formation pressure characteristics of the research area comprises the following steps:
selecting a plurality of mined normal pressure condition areas to obtain a shale area value, a shale thickness value and a terrain height value of a shale gas drilling well position as a second training positive sample, obtaining shale area values, shale thickness values and terrain height values of the shale gas drilling well site in a plurality of mined abnormal condition areas as third training positive samples, obtaining shale area values, shale thickness values and terrain height values of the non-shale gas drilling well site in a plurality of mined normal pressure condition areas as second training negative samples, obtaining shale area values, shale thickness values and terrain height values of non-shale gas drilling well sites in a plurality of mined abnormal condition areas as third training negative samples, uniformly mixing the second training positive sample and the second training negative sample to obtain a second training sample, and uniformly mixing the third training positive sample and the third training negative sample to obtain a third training sample;
performing model training on the second training sample by using a classification model to obtain an atmospheric well position recognition model, wherein the model expression of the atmospheric well position recognition model is as follows:
Label normal ＝F(S,TH,H)；
in the formula, Label normal The well position identifier is characterized as a normal pressure condition area, S is characterized as a shale area value identifier, TH is characterized as a shale thickness value identifier, H is characterized as a terrain height value identifier, and F is characterized as a classification model body identifier;
performing model training on the third training sample by using a classification model to obtain a high-pressure well position recognition model, wherein the model expression of the high-pressure well position recognition model is as follows:
Label high ＝F(S,TH,H)；
in the formula, Label high The well position category identifier is characterized as a high-pressure condition area, the well position category identifier is characterized as a shale area value identifier, the well position category identifier is characterized as a shale thickness value identifier, the well position category identifier is characterized as a terrain height value identifier, and the well position category identifier is characterized as a classification model body identifier;
a formation pressure characteristic of the area of interest is determined, wherein,
if the formation pressure characteristic of the research area is the normal pressure condition, inputting the shale area value, the shale thickness value and the terrain height value of each interval in the research area into a normal pressure well position identification model to identify the well position category of each interval;
if the formation pressure characteristic of the research area is a high-pressure condition, inputting the shale area value, the shale thickness value and the terrain height value of each interval in the research area into a high-pressure well position identification model to identify the well position category of each interval;
the shale layer categories include shale layers and non-shale layers.
As a preferred scheme of the invention, if the normal pressure well position identification model is output as a well position area, marking the shale gas drilling well position on the corresponding layer section;
and if the shale layer identification model is output as a non-well location area, marking the corresponding interval with a non-shale gas drilling well location.
As a preferable scheme of the invention, the resistivity logging and the acoustic logging/density logging respectively obtain the resistivity, the acoustic and the density of the drilled well.
In a preferred embodiment of the present invention, the shale distribution range with the total organic carbon content > 2% is a shale gas effective development interval.
Compared with the prior art, the invention has the following beneficial effects:
according to the method, the combination of real geological data and logging data is fully utilized, a fitting curve of the shale gas effective development layer section (TOC > 2.0%) is constructed, a method for optimizing the well position by combining a seismic inversion technology is further combined, shale layer determination and well position determination are carried out on a well position identification model and a shale layer identification model, the well position selection efficiency can be effectively improved, manual experience determination can be avoided by depending on the data model for determination, and the well position selection precision is high.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary, and that other embodiments can be derived from the drawings provided by those of ordinary skill in the art without inventive effort.
FIG. 1 is a flow chart of a shale gas well site optimization method provided by an embodiment of the invention;
FIG. 2 is a graph of the total organic carbon content of a wellbore provided by an embodiment of the present invention;
FIG. 3 is a plot of the total organic carbon content of a study area as provided by an example of the present invention;
fig. 4 is a shale inversion result diagram of a shale gas well location provided by the embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
As shown in fig. 1 to 4, the present invention provides a shale gas well site optimization method, which comprises the following steps:
s1, predicting a target shale layer in a research area by using a GR curve, and calculating a total organic carbon content curve of each well by using resistivity logging and acoustic logging/density logging in combination with real-time geological data;
predicting a target shale layer in a research area by using a GR curve, comprising:
acquiring GR values and shale thickness values of shale layers in a plurality of mined areas as training positive samples, acquiring GR values and shale thickness values of non-shale layers in a plurality of mined areas as training negative samples, and uniformly mixing the training positive samples and the training negative samples to obtain training samples;
performing model training on the training sample by using the classification model to obtain a shale layer recognition model, wherein the model expression of the shale layer recognition model is as follows:
Label＝F(GR,TH)；
wherein Label is characterized as an identifier of a shale layer category, GR is characterized as a GR value identifier, TH is characterized as a shale thickness value identifier, and F is characterized as a classification model body identifier;
inputting the GR value and the shale thickness value of each interval in the research area into a shale layer identification model to identify the shale layer type of each interval, wherein if the shale layer identification model outputs a shale layer, the shale layer is marked by the corresponding interval;
if the shale layer identification model output is a non-shale layer, marking the non-shale layer with the corresponding layer section;
performing regional combination on all shale layers to obtain a target shale layer;
shale layer categories include shale layers and non-shale layers.
The common method is to determine the target shale layer according to human experience, and the model is used for determining the target shale layer in the embodiment, so that the subjectivity of the human experience is avoided, and the determination result of the target shale layer generated by the model is more reliable.
The target shale layer is within a depth range below a lower limit of organic matter maturation.
Step S2, performing intersection analysis on the total organic carbon content and the density logging curve of each well in the research area, and determining the correlation between the total organic carbon content and the density to construct a fitting relation between the total organic carbon content and the density;
when the drilling number of the research area is more than or equal to a preset threshold value, compiling a total organic carbon content curve by using drilling data;
and when the drilling quantity of the research area is less than a preset threshold value, determining the beneficial development area of the shale gas by utilizing the source direction and the sedimentary facies characteristics and combining seismic interpretation to compile a total organic carbon content curve.
S3, performing superposition and inversion on wave impedance of the well constraint earthquake of the research area to obtain a density data volume of the research area, and calculating to obtain a total organic carbon content data volume of the research area according to the fitting relation between the total organic carbon content and the density based on the density data volume of the research area; (ii) a
S4, inverting the spatial change of the total organic carbon content of the target shale layer on a plane and a section by utilizing the fitting relation between the total organic carbon content and the density and a seismic inversion technology, and determining a shale distribution range with the total organic carbon content being more than 2% on the target shale layer;
inversion on plane and section includes seismic inversion processing by selecting a typical section along the direction of the formation and perpendicular to the direction of the formation at the selected plane area.
The selection rule in the plane area comprises that the total organic carbon content value is high; the surface condition is relatively simple and the fault is not developed; burying a target layer to a shallow depth; the shale area is large; there is at least one of drilling, seismic, logging, etc. data for conventional oil and gas exploration.
And S5, selecting a shale gas drilling well position according to the formation pressure characteristics of the research area in the shale distribution range.
Selecting a shale gas drilling well position according to the formation pressure characteristics of the research area, comprising:
selecting a plurality of mined normal pressure condition areas to obtain a shale area value, a shale thickness value and a terrain height value of a shale gas drilling well position as a second training positive sample, obtaining shale area values, shale thickness values and terrain height values of the shale gas drilling well site in a plurality of mined abnormal condition areas as third training positive samples, obtaining shale area values, shale thickness values and terrain height values of the non-shale gas drilling well site in a plurality of mined normal pressure condition areas as second training negative samples, obtaining shale area values, shale thickness values and terrain height values of non-shale gas drilling well sites in a plurality of mined abnormal condition areas as third training negative samples, uniformly mixing the second training positive sample and the second training negative sample to obtain a second training sample, and uniformly mixing the third training positive sample and the third training negative sample to obtain a third training sample;
performing model training on the second training sample by using the classification model to obtain an atmospheric well position recognition model, wherein the model expression of the atmospheric well position recognition model is as follows:
Label normal ＝F(S,TH,H)；
in the formula, Label normal The well position identifier is characterized as a normal pressure condition area, S is characterized as a shale area value identifier, TH is characterized as a shale thickness value identifier, H is characterized as a terrain height value identifier, and F is characterized as a classification model body identifier;
performing model training on the third training sample by using the classification model to obtain a high-pressure well position recognition model, wherein the model expression of the high-pressure well position recognition model is as follows:
Label high ＝F(S,TH,H)；
in the formula, Label high The well position category identifier is characterized as a high-pressure condition area, the well position category identifier is characterized as a shale area value identifier, the well position category identifier is characterized as a shale thickness value identifier, the well position category identifier is characterized as a terrain height value identifier, and the well position category identifier is characterized as a classification model body identifier;
a formation pressure characteristic of the area of interest is determined, wherein,
if the stratum pressure characteristic of the research area is the normal pressure condition, inputting the shale area value, the shale thickness value and the terrain height value of each interval in the research area into a normal pressure well position recognition model to recognize the well position category of each interval;
if the formation pressure characteristic of the research area is a high-pressure condition, inputting the shale area value, the shale thickness value and the terrain height value of each interval in the research area into a high-pressure well position identification model to identify the well position category of each interval;
shale layer categories include shale layers and non-shale layers.
If the normal pressure well position identification model is output as a well position area, marking the corresponding interval with a shale gas drilling well position;
and if the shale layer identification model is output as a non-well location area, marking the corresponding interval with a non-shale gas drilling well location.
And respectively obtaining the resistivity, the sound wave and the density of the well by resistivity logging and sound wave logging/density logging.
And according to the drilled information, determining whether the stratum pressure abnormity exists in the research area. If the area of interest is in a high pressure zone of the formation, it is preferably a location with a small elevation difference and a certain depth of exploration and development as a shale gas drilling well location. If the research area is in the atmospheric region, the shale gas drilling well position with high total organic carbon content and large thickness and the upward inclined part of the shale section is preferably selected as the shale gas drilling well position, the shale gas drilling well position is selected according to human experience in a common method, and the shale gas drilling well position is selected by using the model in the embodiment, so that the subjectivity of the human experience is avoided, and the well position optimization result generated by the model is more reliable.
The shale distribution range with the total organic carbon content of more than 2 percent is the effective development layer section of the shale gas.
According to the method, the combination of real geological data and logging data is fully utilized, a fitting curve of the shale gas effective development layer section (TOC > 2.0%) is constructed, a method for optimizing the well position by combining a seismic inversion technology is further combined, shale layer determination and well position determination are carried out on a well position identification model and a shale layer identification model, the well position selection efficiency can be effectively improved, manual experience determination can be avoided by depending on the data model for determination, and the well position selection precision is high.
The above embodiments are only exemplary embodiments of the present application, and are not intended to limit the present application, and the protection scope of the present application is defined by the claims. Various modifications and equivalents may be made by those skilled in the art within the spirit and scope of the present application and such modifications and equivalents should also be considered to be within the scope of the present application.
Claims (10)
1. The shale gas well site optimization method is characterized by comprising the following steps of:
s1, predicting a target shale layer in a research area by using a GR curve, and calculating a total organic carbon content curve of each well by using resistivity logging and acoustic logging/density logging in combination with real-time geological data;
step S2, performing intersection analysis on the total organic carbon content and the density log of each well in the research area, and determining the correlation between the total organic carbon content and the density to construct a fitting relation between the total organic carbon content and the density;
s3, performing superposition and inversion on wave impedance of the well constrained earthquake in the research area to obtain a density data volume of the research area, and calculating to obtain a total organic carbon content data volume of the research area according to the fitting relation between the total organic carbon content and the density based on the density data volume of the research area;
s4, inverting the spatial change of the total organic carbon content of the target shale layer on a plane and a section by utilizing the fitting relation between the total organic carbon content and the density and a seismic inversion technology, and determining a shale distribution range with the total organic carbon content being more than 2% on the target shale layer;
and S5, selecting a shale gas drilling well position according to the formation pressure characteristics of the research area in the shale distribution range.
2. The shale gas well site optimization method as claimed in claim 1, wherein: the predicting of the target shale layer in the research area by using the GR curve comprises the following steps:
acquiring GR values and shale thickness values of shale layers in a plurality of mined areas as training positive samples, acquiring GR values and shale thickness values of non-shale layers in a plurality of mined areas as training negative samples, and uniformly mixing the training positive samples and the training negative samples to obtain training samples;
performing model training on a training sample by using a classification model to obtain a shale layer recognition model, wherein the model expression of the shale layer recognition model is as follows:
Label＝F(GR,TH)；
wherein Label is characterized as an identifier of a shale layer category, GR is characterized as a GR value identifier, TH is characterized as a shale thickness value identifier, and F is characterized as a classification model body identifier;
inputting the GR value and the shale thickness value of each interval in the research area into a shale layer identification model to identify the shale layer type of each interval, wherein if the shale layer identification model outputs a shale layer, the corresponding interval is marked with the shale layer;
if the shale layer identification model output is a non-shale layer, marking the non-shale layer with the corresponding layer section;
carrying out regional combination on all shale layers to obtain the target shale layer;
the shale layer categories include shale layers and non-shale layers.
3. The shale gas well site optimization method as claimed in claim 2, wherein: the target shale layer is within a depth range below a lower organic matter maturity limit.
4. The shale gas well site optimization method as claimed in claim 3, wherein: when the drilling number of the research area is more than or equal to a preset threshold value, compiling a total organic carbon content curve by using drilling data;
and when the drilling quantity of the research area is less than a preset threshold value, determining the beneficial development area of the shale gas by utilizing the source direction and the sedimentary facies characteristics and combining seismic interpretation to compile a total organic carbon content curve.
5. The shale gas well site optimization method as claimed in claim 4, wherein: the inversion on the plane and the section plane comprises the step of selecting a typical section plane along the construction direction and the vertical construction direction on the selected plane area to carry out seismic inversion processing.
6. The shale gas well site optimization method as claimed in claim 5, wherein: the selection rule of the planar area comprises that the total organic carbon content value is high; the surface condition is relatively simple and the fault is not developed; burying depth of a target layer; the shale area is large; there is at least one of drilling, seismic, logging, etc. data for conventional oil and gas exploration.
7. The shale gas well site optimization method as claimed in claim 6, wherein: the selection of shale gas drilling well locations according to formation pressure characteristics of a study area comprises:
selecting a plurality of mined normal pressure condition areas to obtain a shale area value, a shale thickness value and a terrain height value of a shale gas drilling well position as a second training positive sample, obtaining shale area values, shale thickness values and terrain height values of shale gas drilling well positions in a plurality of mined abnormal condition areas as third training positive samples, obtaining shale area values, shale thickness values and terrain height values of the non-shale gas drilling well site in a plurality of mined normal pressure condition areas as second training negative samples, obtaining shale area values, shale thickness values and terrain height values of non-shale gas drilling well sites in a plurality of mined abnormal condition areas as third training negative samples, uniformly mixing the second training positive sample and the second training negative sample to obtain a second training sample, and uniformly mixing the third training positive sample and the third training negative sample to obtain a third training sample;
performing model training on the second training sample by using a classification model to obtain an atmospheric well position recognition model, wherein a model expression of the atmospheric well position recognition model is as follows:
Label normal ＝F(S,TH,H)；
in the formula, Label normal The well position identifier is characterized as a normal pressure condition area, S is characterized as a shale area value identifier, TH is characterized as a shale thickness value identifier, H is characterized as a terrain height value identifier, and F is characterized as a classification model body identifier;
performing model training on the third training sample by using a classification model to obtain a high-pressure well position recognition model, wherein the model expression of the high-pressure well position recognition model is as follows:
Label high ＝F(S,TH,H)；
in the formula, Label high The well position category identifier is characterized as a high-pressure condition area, the well position category identifier is characterized as a shale area value identifier, the well position category identifier is characterized as a shale thickness value identifier, the well position category identifier is characterized as a terrain height value identifier, and the well position category identifier is characterized as a classification model body identifier;
a formation pressure characteristic of the area of interest is determined, wherein,
if the stratum pressure characteristic of the research area is the normal pressure condition, inputting the shale area value, the shale thickness value and the terrain height value of each interval in the research area into a normal pressure well position recognition model to recognize the well position category of each interval;
if the formation pressure characteristic of the research area is a high-pressure condition, inputting the shale area value, the shale thickness value and the terrain height value of each interval in the research area into a high-pressure well position identification model to identify the well position category of each interval;
the shale layer categories include shale layers and non-shale layers.
8. The shale gas well site optimization method as claimed in claim 7, wherein if the normal pressure well site recognition model is output as a well site area, the corresponding interval is marked with a shale gas drilling well site;
and if the shale layer identification model is output as a non-well location area, marking the corresponding interval with a non-shale gas drilling well location.
9. The shale gas well site optimization method as claimed in claim 8, wherein the resistivity log and sonic/density log respectively obtain the resistivity, sonic and density of the borehole.
10. The shale gas well site optimization method as claimed in claim 9, wherein the shale distribution range with the total organic carbon content > 2% is shale gas effective development interval.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210640378.2A CN115045646B (en) | 2022-06-07 | 2022-06-07 | Shale gas well site optimization method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210640378.2A CN115045646B (en) | 2022-06-07 | 2022-06-07 | Shale gas well site optimization method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115045646A true CN115045646A (en) | 2022-09-13 |
| CN115045646B CN115045646B (en) | 2023-06-06 |
Family
ID=83161090
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210640378.2A Active CN115045646B (en) | 2022-06-07 | 2022-06-07 | Shale gas well site optimization method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115045646B (en) |
Citations (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103670388A (en) * | 2013-12-12 | 2014-03-26 | 中国石油天然气股份有限公司 | Method for evaluating organic carbon content of shale |
| CN103792338A (en) * | 2014-01-28 | 2014-05-14 | 中国石油天然气股份有限公司 | Method and device for determining organic carbon content of source rock |
| CN104345345A (en) * | 2014-10-30 | 2015-02-11 | 中国石油天然气集团公司 | Method for forecasting TOC (Total Organic Carbon) content of shale reservoir stratum |
| CN105158796A (en) * | 2015-09-16 | 2015-12-16 | 中国石油天然气股份有限公司 | Method and device for determining TOC content |
| CN105277982A (en) * | 2014-07-25 | 2016-01-27 | 中国石油化工股份有限公司 | Shale total organic carbon content earthquake prediction method |
| WO2016041189A1 (en) * | 2014-09-19 | 2016-03-24 | 杨顺伟 | Method for evaluating shale gas reservoir and seeking desert area |
| CN105822301A (en) * | 2016-03-23 | 2016-08-03 | 中国石油大学(北京) | Method for establishing logging prediction mathematic model about TOC (Total Organic Carbon) |
| CN105842733A (en) * | 2016-03-17 | 2016-08-10 | 成都创源油气技术开发有限公司 | Shale reservoir earthquake identification method |
| CA2931435A1 (en) * | 2015-05-29 | 2016-11-29 | Cgg Services Sa | Method for developing a geomechanical model based on seismic data, well logs and sem analysis of horizontal and vertical drill cuttings |
| WO2017024530A1 (en) * | 2015-08-11 | 2017-02-16 | 深圳朝伟达科技有限公司 | Method for calculating content of organic carbon in hydrocarbon source rock |
| CN107817520A (en) * | 2017-09-20 | 2018-03-20 | 中国石油化工股份有限公司 | The pressure coefficient Forecasting Methodology and system of marine facies mud shale stratum |
| CN109509111A (en) * | 2017-09-15 | 2019-03-22 | 中国石油化工股份有限公司 | The prediction technique and system of prospect pit strata pressure |
| CN109507733A (en) * | 2018-12-11 | 2019-03-22 | 中国石油天然气股份有限公司大港油田分公司 | A kind of method and device for predicting organic matter abundance in hydrocarbon source rock |
| US10689954B1 (en) * | 2019-04-24 | 2020-06-23 | Dagang Oil Field Company Of Cnpc | Research method of trajectory design and on-site tracking and adjustment of shale oil horizontal well |
| US20200224522A1 (en) * | 2018-12-03 | 2020-07-16 | University Of Science And Technology, Beijing | Methods of Optimizing Well Spacing for Shale Gas Development |
| CN111608649A (en) * | 2020-05-21 | 2020-09-01 | 中国地质调查局成都地质调查中心 | Method for predicting beneficial area of exogenous supply type shale gas exploration |
| CN112394423A (en) * | 2020-11-16 | 2021-02-23 | 中国海洋石油集团有限公司 | Stratum overpressure prediction method for organic hydrocarbon generation cause |
| CN113777655A (en) * | 2021-08-24 | 2021-12-10 | 中国石油化工股份有限公司 | Method for predicting planar distribution of hydrocarbon source rocks in small layers of salt lake basin |
| US20220170366A1 (en) * | 2020-12-01 | 2022-06-02 | Southwest Petroleum University | 3d in-situ characterization method for heterogeneity in generating and reserving performances of shale |
-
2022
- 2022-06-07 CN CN202210640378.2A patent/CN115045646B/en active Active
Patent Citations (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103670388A (en) * | 2013-12-12 | 2014-03-26 | 中国石油天然气股份有限公司 | Method for evaluating organic carbon content of shale |
| CN103792338A (en) * | 2014-01-28 | 2014-05-14 | 中国石油天然气股份有限公司 | Method and device for determining organic carbon content of source rock |
| CN105277982A (en) * | 2014-07-25 | 2016-01-27 | 中国石油化工股份有限公司 | Shale total organic carbon content earthquake prediction method |
| WO2016041189A1 (en) * | 2014-09-19 | 2016-03-24 | 杨顺伟 | Method for evaluating shale gas reservoir and seeking desert area |
| CN104345345A (en) * | 2014-10-30 | 2015-02-11 | 中国石油天然气集团公司 | Method for forecasting TOC (Total Organic Carbon) content of shale reservoir stratum |
| CA2931435A1 (en) * | 2015-05-29 | 2016-11-29 | Cgg Services Sa | Method for developing a geomechanical model based on seismic data, well logs and sem analysis of horizontal and vertical drill cuttings |
| WO2017024530A1 (en) * | 2015-08-11 | 2017-02-16 | 深圳朝伟达科技有限公司 | Method for calculating content of organic carbon in hydrocarbon source rock |
| CN105158796A (en) * | 2015-09-16 | 2015-12-16 | 中国石油天然气股份有限公司 | Method and device for determining TOC content |
| CN105842733A (en) * | 2016-03-17 | 2016-08-10 | 成都创源油气技术开发有限公司 | Shale reservoir earthquake identification method |
| CN105822301A (en) * | 2016-03-23 | 2016-08-03 | 中国石油大学(北京) | Method for establishing logging prediction mathematic model about TOC (Total Organic Carbon) |
| CN109509111A (en) * | 2017-09-15 | 2019-03-22 | 中国石油化工股份有限公司 | The prediction technique and system of prospect pit strata pressure |
| CN107817520A (en) * | 2017-09-20 | 2018-03-20 | 中国石油化工股份有限公司 | The pressure coefficient Forecasting Methodology and system of marine facies mud shale stratum |
| US20200224522A1 (en) * | 2018-12-03 | 2020-07-16 | University Of Science And Technology, Beijing | Methods of Optimizing Well Spacing for Shale Gas Development |
| CN109507733A (en) * | 2018-12-11 | 2019-03-22 | 中国石油天然气股份有限公司大港油田分公司 | A kind of method and device for predicting organic matter abundance in hydrocarbon source rock |
| US10689954B1 (en) * | 2019-04-24 | 2020-06-23 | Dagang Oil Field Company Of Cnpc | Research method of trajectory design and on-site tracking and adjustment of shale oil horizontal well |
| CN111608649A (en) * | 2020-05-21 | 2020-09-01 | 中国地质调查局成都地质调查中心 | Method for predicting beneficial area of exogenous supply type shale gas exploration |
| CN112394423A (en) * | 2020-11-16 | 2021-02-23 | 中国海洋石油集团有限公司 | Stratum overpressure prediction method for organic hydrocarbon generation cause |
| US20220170366A1 (en) * | 2020-12-01 | 2022-06-02 | Southwest Petroleum University | 3d in-situ characterization method for heterogeneity in generating and reserving performances of shale |
| CN113777655A (en) * | 2021-08-24 | 2021-12-10 | 中国石油化工股份有限公司 | Method for predicting planar distribution of hydrocarbon source rocks in small layers of salt lake basin |
Non-Patent Citations (2)
| Title |
|---|
| 师敏强: "丁山龙马溪组页岩气储层"甜点"预测研究", 《中国优秀硕士学位论文全文数据库工程科技Ⅰ辑》, pages 114 - 117 * |
| 陈愿愿等: "粒子群优化支持向量机算法在页岩储层总有机碳含量预测中的应用-以渝西地区Z井区为例", 《石油物探》, vol. 60, no. 4, pages 652 - 663 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115045646B (en) | 2023-06-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20240102380A1 (en) | Well log correlation and propagation system | |
| US5838634A (en) | Method of generating 3-D geologic models incorporating geologic and geophysical constraints | |
| EP3715579A1 (en) | Wellbore trajectory system and method | |
| CN105488248A (en) | Judgment method for deep ore stratum and rock stratum | |
| Kianoush et al. | Application of pressure-volume (PV) fractal models in modeling formation pressure and drilling fluid determination in an oilfield of SW Iran | |
| CN106033127A (en) | Ground stress orientation seismic prediction method based on shear wave speed variation rate | |
| Ali et al. | Integrated fracture characterization of thamama reservoirs in Abu Dhabi oil field, United Arab Emirates | |
| CN112505754A (en) | Method for collaborative partitioning sedimentary microfacies by well-seismic based on high-precision sequence grid model | |
| Taheri et al. | Quantifying fracture density within the Asmari reservoir: an integrated analysis of borehole images, cores, and mud loss data to assess fracture-induced effects on oil production in the Southwestern Iranian Region | |
| CN116856845A (en) | Tight sandstone gas reservoir horizontal well geosteering method | |
| CN115045646B (en) | Shale gas well site optimization method | |
| Lee | Source mechanism characterization and integrated interpretation of microseismic data monitoring two hydraulic stimulations in Pouce Coupe Field, Alberta | |
| CN112377104B (en) | Method and device for dynamically adjusting actual drilling track of tight sandstone gas reservoir horizontal well | |
| CN114114411B (en) | Quantitative determination method and device for three-dimensional fault dredge | |
| Gunnarsson | 3D modeling in Petrel of geological CO2 storage site | |
| Underschultz et al. | Estimating formation water salinity from wireline pressure data: Case study in the Vulcan sub-basin | |
| Webb et al. | A time-lapse case study in West Africa: Integrating disciplines for a complete reservoir study and field management | |
| CN112528106A (en) | Volcanic lithology identification method | |
| Fretwell et al. | A new approach to 3-D geological modeling of complex sand injectite reservoirs: The Alba field, United Kingdom central North Sea | |
| Wach et al. | Well placement, cost reduction, and increased production using reservoir models based on outcrop, core, well-log, seismic data, and modern analogs: Onshore and offshore Western Trinidad | |
| CN113409460B (en) | Machine learning type three-dimensional quantitative characterization method for clastic rock reservoir interlayer | |
| CN117348063B (en) | Evaluation method for quantitatively representing width of reverse fault fracture zone | |
| CN113093275B (en) | Method and device for improving drilling success rate of curved-flow river and curved-flow river delta oilfield | |
| Tamulonis | A methodology for integrating unconventional geologic and engineering data into a geocellular model | |
| Kondratyev et al. | Submarine fan reservoir architecture and heterogeneity influence on hard-to-recover reserves. Achimov Fm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |