CN111401669A - Shale oil pressure post-flowback rate prediction method based on wavelet neural network - Google Patents
Shale oil pressure post-flowback rate prediction method based on wavelet neural network Download PDFInfo
- Publication number
- CN111401669A CN111401669A CN202010504401.6A CN202010504401A CN111401669A CN 111401669 A CN111401669 A CN 111401669A CN 202010504401 A CN202010504401 A CN 202010504401A CN 111401669 A CN111401669 A CN 111401669A
- Authority
- CN
- China
- Prior art keywords
- neural network
- parameters
- wavelet neural
- network
- output
- 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.)
- Pending
Links
- 238000013528 artificial neural network Methods 0.000 title claims abstract description 38
- 238000000034 method Methods 0.000 title claims abstract description 31
- 239000003079 shale oil Substances 0.000 title claims abstract description 31
- 238000010606 normalization Methods 0.000 claims abstract description 22
- 238000012360 testing method Methods 0.000 claims abstract description 13
- 238000012549 training Methods 0.000 claims abstract description 8
- 238000012545 processing Methods 0.000 claims abstract description 4
- 239000000126 substance Substances 0.000 claims description 13
- 238000004364 calculation method Methods 0.000 claims description 10
- 238000010276 construction Methods 0.000 claims description 9
- 238000012937 correction Methods 0.000 claims description 5
- 229910052500 inorganic mineral Inorganic materials 0.000 claims description 3
- 239000011707 mineral Substances 0.000 claims description 3
- 230000035699 permeability Effects 0.000 claims description 3
- 239000011148 porous material Substances 0.000 claims description 3
- 239000004576 sand Substances 0.000 claims description 3
- 238000002791 soaking Methods 0.000 claims description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 3
- 230000006870 function Effects 0.000 abstract description 11
- 238000011161 development Methods 0.000 abstract description 7
- 239000003921 oil Substances 0.000 description 14
- 239000007789 gas Substances 0.000 description 10
- 229930195733 hydrocarbon Natural products 0.000 description 3
- 150000002430 hydrocarbons Chemical class 0.000 description 3
- 238000013519 translation Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 201000004569 Blindness Diseases 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000003245 coal Substances 0.000 description 1
- 239000010779 crude oil Substances 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000003208 petroleum Substances 0.000 description 1
- 238000003825 pressing Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Forestry; Mining
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- Biophysics (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Development Economics (AREA)
- Molecular Biology (AREA)
- Game Theory and Decision Science (AREA)
- Data Mining & Analysis (AREA)
- Computational Linguistics (AREA)
- Entrepreneurship & Innovation (AREA)
- Mathematical Physics (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Biomedical Technology (AREA)
- Artificial Intelligence (AREA)
- Agronomy & Crop Science (AREA)
- Animal Husbandry (AREA)
- Marine Sciences & Fisheries (AREA)
- Mining & Mineral Resources (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a shale oil pressure post-flowback rate prediction method based on a wavelet neural network, which comprises the following steps of: dividing the sample into a training sample and a test sample; determining input parameters and output parameters, and carrying out normalization processing on the input parameters; calculating the error between the predicted network output and the expected output; correcting the network weight and the wavelet function parameters, and continuing to calculate and predict the network output until the error meets the requirement; testing the network prediction precision by using a test sample; and predicting the flow-back rate by using the trained wavelet neural network. The prediction method has stronger learning ability, faster convergence speed and higher precision; meanwhile, the invention takes geological parameters, engineering parameters and flowback system into consideration, and better accords with the characteristics of shale oil reservoir development.
Description
Technical Field
The invention relates to the technical field of oil and gas field development, in particular to a shale oil pressure back flow rate prediction method based on a wavelet neural network.
Background
At present, the development of conventional oil and gas reservoirs is almost carried out, the center of gravity of oil and gas exploration in the world is changed from conventional oil and gas to unconventional oil and gas, and the key point of the global petroleum industry is also changed to unconventional oil and gas. Unconventional hydrocarbons include shale hydrocarbons, coal bed gases, and tight hydrocarbons. In China, the shale oil reserves are rich, and the recoverable resource amount is about 160 hundred million tons, so that the development of shale oil resources has great significance for guaranteeing the energy supply safety of China and optimizing the energy consumption structure of China.
Different from the conventional oil and gas resource development mode, the technology for efficiently developing the shale oil mainly comprises horizontal well subsection clustering fracturing, namely, 'smashing' a reservoir body as far as possible to form a complex fracture network, so that the flow resistance of crude oil entering a well is reduced. After shale oil is fractured, the shale oil is generally stewed for a period of time and then is flowback, the shale oil well is found to have the optimal flowback rate in the field production process, and the optimal flowback rate is achieved, so that the oil yield of the shale oil well can be maximized. Therefore, it is very important to predict the flowback rate after shale oil pressure.
The current prediction methods of the flow-back rate after pressing mainly comprise a numerical simulation method and a machine learning method. The numerical simulation method calculates through a discrete seepage differential equation, can take geological factors into account well, but the efficiency is low. The machine learning method has the characteristic of high efficiency, but mainly aims at conventional oil gas, and even aims at shale oil gas, the consideration factor is incomplete, so that the problem of large error of a prediction result is caused.
Therefore, a method specially aiming at predicting the shale oil pressure after-flow rate is needed, the method should be matched with shale oil geological features and development features as much as possible, and the method should be efficient in the prediction process.
Disclosure of Invention
The invention aims to provide a shale oil pressure post-flowback rate prediction method based on a wavelet neural network aiming at the defects in the prior art, and the method is used for solving the problems of low efficiency of a prediction process and large error of a prediction result in the conventional shale oil pressure post-flowback rate method.
The technical scheme adopted by the invention for solving the technical problems comprises the following contents:
a shale oil pressure post-flowback rate prediction method based on a wavelet neural network comprises the following steps:
A. determining a sample, and dividing the sample into a training sample and a test sample;
B. taking geological parameters, engineering parameters and a flowback system as input parameters of a wavelet neural network, taking a flowback rate as an output parameter of the wavelet neural network, and carrying out normalization processing on the input parameters;
C. inputting the input parameters after the training samples are normalized into a network, calculating and predicting network output, and further calculating the error between the predicted network output and expected output;
D. according to the error correction network weight and the wavelet function parameters, continuously calculating and predicting network output until the error meets the requirement;
E. testing the network prediction precision by using a test sample;
F. and predicting the flow-back rate by using the trained wavelet neural network.
Further, the geological parameters include permeability, porosity, oil saturation, elastic modulus, poisson's ratio, pore pressure, reservoir thickness, water sensitive mineral content, natural fracture density, natural fracture openness.
Further, the engineering parameters include construction scale, construction displacement, construction sand ratio, crack length and horizontal segment length.
Further, the back-flow system comprises the soaking time and the size of a back-flow oil nozzle.
Further, the normalization method is standard deviation normalization, and the calculation formula is as follows:
wherein the content of the first and second substances,
for the input parameters after the normalization, the parameters are,x 0 in order to be the input parameters before normalization,
is the average of the input parameters before normalization,
is the standard deviation of the input parameter before normalization.
Further, the wavelet neural network comprises an input layer, a hidden layer and an output layer, wherein the number of nodes of the input layer and the output layer is respectively the number of the input parameters and the output parameters of the samples, and the calculation formula of the number of nodes of the hidden layer is as follows:
wherein the content of the first and second substances,Nin order to imply the number of nodes in the layer,nthe number of the nodes of the input layer is,mthe number of the output layer nodes is,c、dthe values are 1.6799 and 0.9298 respectively for empirical constants.
Further, the wavelet basis function in the wavelet neural network is a Marr function, and specifically includes:
wherein the content of the first and second substances,
in order to be a basis function of the wavelet,xis an independent variable.
The invention has the beneficial effects that:
the shale oil pressure post-flowback rate prediction method is based on wavelet neural network prediction, and the wavelet neural network is determined according to wavelet analysis theory, so that blindness in structural design of other neural networks can be effectively avoided; and secondly, the wavelet neural network has stronger learning ability, faster convergence speed and higher precision. Meanwhile, the invention takes geological parameters, engineering parameters and flowback system into consideration, and better accords with the characteristics of shale oil reservoir development. In conclusion, the shale oil pressure back-flow rate prediction method based on the wavelet neural network has the characteristics of high efficiency and accuracy.
Drawings
FIG. 1 is a schematic overall flow diagram of the present invention.
Fig. 2 is a schematic structural diagram of a wavelet neural network in the present invention.
Detailed Description
The present invention will be described in detail with reference to the following examples, which should be construed as merely illustrative and explanatory of the present invention and not restrictive thereof.
A shale oil pressure post-flowback rate prediction method based on a wavelet neural network comprises the following steps:
A. determining a sample, and dividing the sample into a training sample and a test sample;
B. taking geological parameters, engineering parameters and a flowback system as input parameters of a wavelet neural network, taking a flowback rate as an output parameter of the wavelet neural network, and carrying out normalization processing on the input parameters;
C. inputting the input parameters after the training samples are normalized into a network, calculating and predicting network output, and further calculating the error between the predicted network output and expected output;
D. according to the error correction network weight and the wavelet function parameters, continuously calculating and predicting network output until the error meets the requirement;
E. testing the network prediction precision by using a test sample;
F. and predicting the flow-back rate by using the trained wavelet neural network.
The geological parameters include permeability, porosity, oil saturation, elastic modulus, poisson's ratio, pore pressure, reservoir thickness, water sensitive mineral content, natural fracture density, natural fracture openness.
The engineering parameters comprise construction scale, construction discharge capacity, construction sand ratio, crack length and horizontal section length.
The back drainage system comprises the soaking time and the size of a back drainage oil nozzle.
The overall process is shown in figure 1, and the specific wavelet neural network structure is shown in figure 2.
The normalization method is standard deviation normalization, and the calculation formula is as follows:
wherein the content of the first and second substances,
for the input parameters after the normalization, the parameters are,x 0 in order to be the input parameters before normalization,
is the average of the input parameters before normalization,
is the standard deviation of the input parameter before normalization.
The wavelet neural network comprises an input layer, a hidden layer and an output layer, wherein the number of nodes of the input layer and the output layer is respectively the number of sample input parameters and output parameters, and the number calculation formula of the nodes of the hidden layer is as follows:
wherein the content of the first and second substances,Nin order to imply the number of nodes in the layer,nthe number of the nodes of the input layer is,mthe number of the output layer nodes is,c、dthe values are 1.6799 and 0.9298 respectively for empirical constants.
The wavelet basis function in the wavelet neural network is a Marr function, and specifically comprises the following steps:
wherein the content of the first and second substances,
in order to be a basis function of the wavelet,xis an independent variable.
The hidden layer output calculation formula is:
wherein the content of the first and second substances,
as a hidden layerjAn individual node output value;
is a wavelet basis function;
the weights from the input layer to the hidden layer are obtained;
as wavelet basis functions
A translation factor of (d);
as wavelet basis functions
The scaling factor of (c).
The wavelet neural network output layer has a calculation formula of
Wherein the content of the first and second substances,
in order to predict the output of the network,
the weight from the hidden layer to the output layer;
as a hidden layerjAn individual node output value;Nthe number of nodes of the hidden layer is shown;mthe number of output layer nodes.
The network prediction error calculation formula is as follows:
wherein the content of the first and second substances,ein order to predict the error for the network,
is the desired output;
to predict the network output.
The calculation formula of the correction network weight and the wavelet function parameter is as follows:
wherein the content of the first and second substances,
、
、
、
respectively obtaining corrected weights from the input layer to the hidden layer, weights from the hidden layer to the output layer, a scaling factor and a translation factor;
、
、
、
respectively obtaining weights from an input layer to a hidden layer, weights from the hidden layer to an output layer, a scaling factor and a translation factor before correction;
is a momentum factor;
is the learning rate;Eis the mean square error of the sample.
The method is characterized in that a 16-opening shale oil well of an oil field in China is used as a sample, wherein W1-W12 is used as a training sample, W13-W16 is used as a test sample, the geological parameters are shown in a table 1, and the engineering parameters, the flow-back system and the flow-back rate are shown in a table 2.
TABLE 1 geological parameter Table
TABLE 2 engineering parameters, flowback system and flowback rate
The comparison result of the measured flow back rate and the predicted flow back rate is shown in Table 3.
TABLE 3 comparison of measured flowback rate and predicted flowback rate
The comparison result shows that the average relative error absolute value between the actually measured flow-back rate and the predicted flow-back rate is controlled within 5 percent, which shows that the flow-back rate prediction result of the prediction method provided by the invention is more accurate and the prediction process is more efficient.
Although the present invention has been described with reference to a preferred embodiment, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (7)
1. A shale oil pressure post-flowback rate prediction method based on a wavelet neural network is characterized by comprising the following steps:
A. determining a sample, and dividing the sample into a training sample and a test sample;
B. taking geological parameters, engineering parameters and a flowback system as input parameters of a wavelet neural network, taking a flowback rate as an output parameter of the wavelet neural network, and carrying out normalization processing on the input parameters;
C. inputting the input parameters after the training samples are normalized into a network, calculating and predicting network output, and further calculating the error between the predicted network output and expected output;
D. according to the error correction network weight and the wavelet function parameters, continuously calculating and predicting network output until the error meets the requirement;
E. testing the network prediction precision by using a test sample;
F. and predicting the flow-back rate by using the trained wavelet neural network.
2. The shale oil pressure post-flowback rate prediction method based on the wavelet neural network as claimed in claim 1, characterized in that: the geological parameters comprise permeability, porosity, oil saturation, elastic modulus, Poisson's ratio, pore pressure, reservoir thickness, water sensitive mineral content, natural fracture density and natural fracture openness.
3. The shale oil pressure post-flowback rate prediction method based on the wavelet neural network as claimed in claim 1, characterized in that: the engineering parameters comprise construction scale, construction discharge capacity, construction sand ratio, crack length and horizontal section length.
4. The shale oil pressure post-flowback rate prediction method based on the wavelet neural network as claimed in claim 1, characterized in that: the flow-back system comprises the soaking time and the size of a flow-back oil nozzle.
5. The shale oil pressure post-flowback rate prediction method based on the wavelet neural network as claimed in claim 1, characterized in that: the normalization method is standard deviation normalization, and the calculation formula is as follows:
wherein the content of the first and second substances,
for the input parameters after the normalization, the parameters are,x 0 in order to be the input parameters before normalization,
is the average of the input parameters before normalization,
is the standard deviation of the input parameter before normalization.
6. The shale oil pressure post-flowback rate prediction method based on the wavelet neural network as claimed in claim 1, characterized in that: the wavelet neural network comprises an input layer, a hidden layer and an output layer, wherein the number of nodes of the input layer and the output layer is respectively the number of sample input parameters and output parameters, and the number calculation formula of the nodes of the hidden layer is as follows:
wherein the content of the first and second substances,Nin order to imply the number of nodes in the layer,nthe number of the nodes of the input layer is,mthe number of the output layer nodes is,c、dthe values are 1.6799 and 0.9298 respectively for empirical constants.
7. The shale oil pressure post-flowback rate prediction method based on the wavelet neural network as claimed in claim 1, characterized in that: the wavelet basis function in the wavelet neural network is a Marr function, and specifically comprises the following steps:
wherein the content of the first and second substances,
in order to be a basis function of the wavelet,xis an independent variable.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010504401.6A CN111401669A (en) | 2020-06-05 | 2020-06-05 | Shale oil pressure post-flowback rate prediction method based on wavelet neural network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010504401.6A CN111401669A (en) | 2020-06-05 | 2020-06-05 | Shale oil pressure post-flowback rate prediction method based on wavelet neural network |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111401669A true CN111401669A (en) | 2020-07-10 |
Family
ID=71433687
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010504401.6A Pending CN111401669A (en) | 2020-06-05 | 2020-06-05 | Shale oil pressure post-flowback rate prediction method based on wavelet neural network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111401669A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112253087A (en) * | 2020-10-20 | 2021-01-22 | 河南理工大学 | Biological disturbance reservoir physical property calculation method based on multi-source logging data |
| CN112282721A (en) * | 2020-12-25 | 2021-01-29 | 成都创源油气技术开发有限公司 | Optimal design method for fracturing fluid flowback system after fracturing of shale gas horizontal well |
| CN113006774A (en) * | 2021-03-16 | 2021-06-22 | 西南石油大学 | Online graph neural network prediction method for oil pressure peak in fracturing construction |
| CN114136862A (en) * | 2021-11-29 | 2022-03-04 | 西南石油大学 | Liquid apparent permeability calculation method of double-wettability shale |
| CN115271261A (en) * | 2022-09-26 | 2022-11-01 | 中国石油天然气股份有限公司 | Shale gas well flowback rate prediction model establishment method and device and flowback rate prediction method and device |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108446797A (en) * | 2018-03-06 | 2018-08-24 | 西南石油大学 | A kind of compact oil reservoir horizontal well volume fracturing initial productivity prediction technique |
| CN108645740A (en) * | 2018-05-30 | 2018-10-12 | 延长油田股份有限公司 | The measurement method and device that the row of returning leads after a kind of rock core self-priming fracturing fluid |
| CN110145291A (en) * | 2019-05-17 | 2019-08-20 | 中国石油大学(北京) | A kind of volume fracturing dynamic row's of returning simulator and analogy method |
-
2020
- 2020-06-05 CN CN202010504401.6A patent/CN111401669A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108446797A (en) * | 2018-03-06 | 2018-08-24 | 西南石油大学 | A kind of compact oil reservoir horizontal well volume fracturing initial productivity prediction technique |
| CN108645740A (en) * | 2018-05-30 | 2018-10-12 | 延长油田股份有限公司 | The measurement method and device that the row of returning leads after a kind of rock core self-priming fracturing fluid |
| CN110145291A (en) * | 2019-05-17 | 2019-08-20 | 中国石油大学(北京) | A kind of volume fracturing dynamic row's of returning simulator and analogy method |
Non-Patent Citations (3)
| Title |
|---|
| 冯再勇: "小波神经网络与BP网络的比较研究及应用", 《中国优秀硕士学位论文全文数据库 基础科技辑》 * |
| 李媛 等: "Marr小波离散化方法及其应用", 《微计算机信息》 * |
| 郭建成 等: "四川盆地龙马溪组页岩压后返排率及产能影响因素分析", 《石油科学通报》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112253087A (en) * | 2020-10-20 | 2021-01-22 | 河南理工大学 | Biological disturbance reservoir physical property calculation method based on multi-source logging data |
| CN112282721A (en) * | 2020-12-25 | 2021-01-29 | 成都创源油气技术开发有限公司 | Optimal design method for fracturing fluid flowback system after fracturing of shale gas horizontal well |
| CN113006774A (en) * | 2021-03-16 | 2021-06-22 | 西南石油大学 | Online graph neural network prediction method for oil pressure peak in fracturing construction |
| CN114136862A (en) * | 2021-11-29 | 2022-03-04 | 西南石油大学 | Liquid apparent permeability calculation method of double-wettability shale |
| CN114136862B (en) * | 2021-11-29 | 2024-04-09 | 西南石油大学 | Liquid apparent permeability calculation method of double-wettability shale |
| CN115271261A (en) * | 2022-09-26 | 2022-11-01 | 中国石油天然气股份有限公司 | Shale gas well flowback rate prediction model establishment method and device and flowback rate prediction method and device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111401669A (en) | Shale oil pressure post-flowback rate prediction method based on wavelet neural network | |
| CN102041995B (en) | System for monitoring complicated oil deposit flooding conditions | |
| CN109441422B (en) | Shale gas well spacing optimization mining method | |
| CN105243182B (en) | Fine and close oil pressure splits the dynamic holdup computational methods of horizontal well | |
| CN104018829B (en) | A kind of method that utilization coal bed gas well creation data measures air water phase percolation curve | |
| RU2480584C1 (en) | Method for online forecasting of main parameters of oil deposits development | |
| CN108894777B (en) | Method for determining physical properties and fracture characteristic parameters of reservoir of split-layer fractured multi-layer commingled production hydrocarbon reservoir | |
| CN104750896B (en) | A kind of fractured-cavernous carbonate reservoir method for numerical simulation | |
| CN106150477A (en) | A kind of method determining single well controlled reserves | |
| CN111794740B (en) | Method suitable for calculating dynamic reserves of fracture-cave carbonate reservoir | |
| CN114427432B (en) | Method for determining development potential of residual gas in gas reservoir | |
| CN107676085A (en) | A kind of marine facies shale gas horizontal wellbore logging PRODUCTION FORECASTING METHODS | |
| CN111456709B (en) | Horizontal well multistage fracturing segmentation clustering method based on logging curve | |
| CN113792479B (en) | Coal bed methane reservoir fracturing effect evaluation method based on physical constraint | |
| CN109063403B (en) | Optimal design method for slickwater fracturing | |
| CN112814669A (en) | Shale oil reservoir full life cycle recovery rate prediction method and system | |
| CN112049629A (en) | Fracture-cavity type oil reservoir recovery ratio prediction method based on A-type water drive characteristic curve | |
| CN106481315A (en) | Land sandstone oil reservoir individual well recoverable reserves quickly determines model and method for building up | |
| CN107355200A (en) | One kind receives micron particles dispersion improving ecology well choosing method | |
| CN104407397A (en) | Method for determining lower limit of physical property of compact oil and gas reservoir | |
| CN116950654A (en) | Low-permeability compact sandstone gas reservoir development effect evaluation method | |
| CN116384085A (en) | Compact oil and gas reservoir capacity splitting method | |
| CN111188613B (en) | Method and system for determining well control radius of tight gas reservoir gas well | |
| CN113008753A (en) | Method for optimizing gas-water phase permeability curve of gas reservoir by applying two-dimensional experiment | |
| CN115062552B (en) | Method and system for predicting nitrogen huff and puff effect of fracture-cavity oil reservoir |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20200710 |
|
| WD01 | Invention patent application deemed withdrawn after publication |