CN117219180A - Method and system for monitoring, evaluating and dynamically regulating and enhancing mineralization effect of carbon dioxide - Google Patents
Method and system for monitoring, evaluating and dynamically regulating and enhancing mineralization effect of carbon dioxide Download PDFInfo
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- 230000033558 biomineral tissue development Effects 0.000 title claims abstract description 316
- 238000012544 monitoring process Methods 0.000 title claims abstract description 169
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 title claims abstract description 168
- 230000000694 effects Effects 0.000 title claims abstract description 141
- 238000000034 method Methods 0.000 title claims abstract description 108
- 229910002092 carbon dioxide Inorganic materials 0.000 title claims abstract description 84
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- 230000002708 enhancing effect Effects 0.000 title claims abstract description 33
- 230000001105 regulatory effect Effects 0.000 title claims abstract description 27
- 238000002347 injection Methods 0.000 claims abstract description 86
- 239000007924 injection Substances 0.000 claims abstract description 86
- 238000011156 evaluation Methods 0.000 claims abstract description 73
- 230000008569 process Effects 0.000 claims abstract description 66
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- VTVVPPOHYJJIJR-UHFFFAOYSA-N carbon dioxide;hydrate Chemical compound O.O=C=O VTVVPPOHYJJIJR-UHFFFAOYSA-N 0.000 claims description 18
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- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 8
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- OYPRJOBELJOOCE-UHFFFAOYSA-N Calcium Chemical compound [Ca] OYPRJOBELJOOCE-UHFFFAOYSA-N 0.000 description 1
- FYYHWMGAXLPEAU-UHFFFAOYSA-N Magnesium Chemical compound [Mg] FYYHWMGAXLPEAU-UHFFFAOYSA-N 0.000 description 1
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Abstract
The invention discloses a method and a system for monitoring, evaluating and dynamically regulating and enhancing the mineralization effect of carbon dioxide, wherein the enhancing method comprises the following steps: carrying out a carbon dioxide mineralization process monitoring experiment by using a sealing and storing simulation experiment system, and sampling and analyzing a mineralized product to obtain a mineralization effect; extracting basic monitoring parameters as comprehensive monitoring indexes by utilizing principal component analysis; sampling and analyzing mineralized products, and selecting mineralized quality characteristic parameters to carry out mineralization effect evaluation grade division; based on the relation between the change of the comprehensive monitoring index and the mineralization quality characteristic parameter in the mineralization process, a mineralization effect monitoring and evaluating model is established by utilizing a neural network, and the mineralization effect is evaluated in real time; and (3) establishing a mineralization enhancement regulation model to obtain the relation between the mineralization effect evaluation grade and the controllable injection parameter, and constructing a dynamic regulation mode of monitoring-evaluation-regulation-monitoring-reevaluation until reaching a target or mineralization reaction limit to realize the optimization of mineralization reaction effect.
Description
Technical Field
The invention relates to the field of carbon dioxide sequestration effect evaluation, in particular to a method and a system for monitoring and evaluating the mineralization effect of carbon dioxide and enhancing dynamic regulation and control.
Background
The traditional CCS technology mostly adopts CO 2 High-pressure injection into the sealing geologic body such as waste oil and gas reservoirs, coal beds, deep salty water layers and the like for sealing so as to realize CO 2 Emission reduction, but the technology faces the problems of high sealing and storing cost, high re-leakage risk and even engineering disaster induction. Recent studies have found a more stable CO 2 The new underground mineralization sealing method makes carbon dioxide react with basic or super basic rock (i.e. basalt or olivine rock rich in magnesium or calcium) to produce solid carbonate mineral to realize permanent sealing of carbon dioxide. Meanwhile, the basalt layer has wide distribution range, compared with the conventional sealing method, the method has the advantages of lower cost, far higher carbon fixing effect than sealing of sealing geologic bodies, and the like, and gives CO 2 Mineralization seals great potential.
The evaluation result of the carbon dioxide blocking effect is an important basis for setting the carbon dioxide injection condition, and also determines the mineralization degree and the reaction result. In general, the method for evaluating the mineralization and sequestration effects of carbon dioxide adopts chemical analysis of the product obtained after the extraction and mineralization reaction to obtain characteristic substance parameters, thereby evaluating mineralization quality, and is general in engineeringAnd roughly estimating mineralization reaction effect according to the carbon dioxide concentration and the like measured by the monitoring well. Currently, monitoring carbon dioxide sequestration and sequestration assessment has proposed a number of methods, such as patent CN112924648A analysis of CO using a variety of tests based on a characteristic core 2 The mineralization process comprises lithofacies change, carbon dioxide mineralization rate and sealing quantity, the patent CN115818099A realizes the evaluation of the sealing effect of the carbon dioxide on the coal seam roof by simulating different types of real coal seams and coal seam roof, the patent CN116256282A simulates the gas component change and the crude oil carbon component change in the carbon dioxide migration and diffusion process under the real oil reservoir environment, the carbon dioxide migration law and the sealing potential are obtained, and the patent CN116226975A evaluates the sealing safety of the carbon dioxide on the salty water layer based on dimensionless numbers Ca, gr and Bo. However, these techniques do not allow for monitoring of the mineralization process, nor do they suggest a solution for enhancing the mineralization reaction.
Disclosure of Invention
Aiming at the problems and the demands, the invention aims at providing a method for monitoring, evaluating and dynamically regulating and enhancing the mineralization effect of carbon dioxide, which comprises the following steps:
s10: the method comprises the steps of utilizing a sealing simulation experiment system to develop a carbon dioxide mineralization process monitoring experiment, injecting a carbon dioxide hydrate into a mineralization sealing layer, controlling the temperature, the flow rate and the concentration of carbon dioxide injection during injection, utilizing an ultrasonic phased array and acoustic emission to monitor the mineralization sealing process, monitoring the concentration, the flow rate and the flow rate of the mineralized carbon dioxide by a monitoring well, and taking a complete mineralization reaction as a basic end when the concentration and the flow rate of the carbon dioxide are low, and sampling and analyzing a mineralized product to obtain a mineralization effect;
s20: removing environmental noise from the carbon dioxide injection temperature, flow rate and concentration controllable injection parameters, ultrasonic phased array related parameters, acoustic emission related parameters and mineralized carbon dioxide concentration, flow rate and flow basic monitoring parameters, extracting all basic monitoring parameters into comprehensive monitoring indexes by utilizing principal component analysis so as to remove unimportant characteristics, reducing analysis dimension and facilitating establishment of an evaluation model;
s30: sampling the mineralized product, obtaining analysis parameters of acidity, salinity, carbon fixation amount, characteristic product content and characteristic element content by using an acidity tester, a salinity detector, an XRD tester and a Raman spectrometer, and selecting mineralization quality characteristic parameters from the analysis parameters to carry out mineralization effect evaluation grade division;
s40: based on the relation between the change of the comprehensive monitoring index and the mineralization quality characteristic parameter in the mineralization process, a mineralization effect monitoring and evaluating model is established by utilizing the BP neural network, and the basic monitoring parameter in the monitoring process is associated with the mineralization effect evaluating grade, so that the mineralization effect is evaluated in real time by the basic monitoring parameter in the mineralization process;
s50: the method comprises the steps of establishing a mineralization enhancement regulation model, obtaining the relation between a mineralization effect evaluation grade and controllable injection parameters, setting a target mineralization effect evaluation grade to obtain an adjustable controllable injection parameter value, obtaining mineralization effect evaluation through process monitoring, simultaneously providing an enhancement regulation scheme, constructing a dynamic regulation mode of monitoring-evaluation-regulation-monitoring-re-evaluation, and continuously providing an injection optimal solution until reaching a target or mineralization reaction limit to realize the optimization of mineralization reaction effect.
In addition, the method for monitoring, evaluating and dynamically regulating and enhancing the mineralization effect of the carbon dioxide can also have the following technical characteristics:
in one example of the present invention, in the step S10, the monitoring the mineralization sealing process using ultrasonic phased array and acoustic emission includes:
the method is characterized in that the injection of the carbon dioxide hydrate is stopped during ultrasonic phased array monitoring, a speed field formed by the flow of the carbon dioxide hydrate in the mineralization process is periodically and actively monitored, an acoustic emission signal released by the generation of cracks in the whole mineralization reaction process of the sealed rock stratum is passively monitored by acoustic emission, and the ultrasonic phased array periodic monitoring is supplemented, so that the active and passive integrated mineralization whole process monitoring is realized.
In one example of the present invention, in the step S20, extracting all the basic monitoring parameters into the integrated monitoring index includes the steps of:
setting basic monitoring parameters D v =(X、Y i 、Z j K): controllable injection parameters of injection pressure, flow rate and the like of carbon dioxide hydrate, namely X= (X) 1 ，x 2 ，x 3 ，x 4 ): carbon dioxide injection concentration x 1 Temperature, x 2 Flow velocity x 3 Pressure x 4 Several other basic monitoring parameters: ultrasonic phase control related parameter Y i = (i=1, 2,3 … m), acoustic emission related parameter Z J = (j=1, 2,3 … n), mineralization process carbon dioxide concentration, flow rate and flow parameter k= (K) 1 ,K 2 ，k 3 ) Each parameter contains q samples, and,
wherein,
then D v =(X、Y i 、Z j Linear combination of K) is:
F V ＝a 1v x 1 +a 2v x 2 +a 3v x 3 +a 4v x 4 +a 5v k 1 ++a 6v k 2 +a 7v k 3 +a 8v Y i +a 9v Z j +…+a m+n+7，v D v (v＝1，2，3...m+n+7)
wherein, the main component corresponding to the characteristic root with the accumulated contribution rate reaching 85% -95% is selected as the comprehensive monitoring index F= (F) 1 ，f 2 ，f 3 ，...，f 1 )。
In one example of the present invention, in the step S30, the mineralization effect evaluation ranking includes: the mineralization effect evaluation is divided into five grades A, B, C, D, E by utilizing mineralization quality characteristic parameters, wherein the five grades A, B, C, D, E respectively represent complete mineralization, incomplete mineralization, weak mineralization and unmineralization; wherein each class should be classified in view of both the main characteristics of the mineralization quality and the other classes.
In one example of the present invention, in the step S40, the step of creating the mineralization effect monitoring and evaluating model using the BP neural network includes the steps of:
s401: the data is normalized according to the following formula:
wherein x' is a normalized variable; x is an actual value; x is x max And x min Respectively the maximum value and the minimum value of the variable x;
s402: according to the comprehensive monitoring index F in the complete mineralization process s ＝(f 1 ，f 2 ，f 3 ，...，f 1 ) Is changed into input data, mineralization quality characteristic parameter P g ’＝(p’ 1 ，p’ 2 ，p’ 3 ，…，p’ k ) For expected output data, determining the number of neurons (nodes) of network input layer, hidden layer and output layer, and initializing the connection weight alpha between the neurons of each layer st 、β tg Initializing an implicit layer threshold value zeta, outputting a layer threshold value zeta, and setting a learning rate and a neuron transfer function;
s403: according to the input vector F, the implicit interlayer connection weight alpha of the input layer st And an hidden layer threshold ζ, calculating a hidden layer output;
wherein/is the number of hidden layer nodes, alpha s0 ＝-1，f 0 ＝α t F (·) is the implicit layer transfer function, with the transfer function f (x) = (1+e) -x ) -1 ；
S404: according to hidden layer output H, connect weight beta tg And a threshold value xi, calculating the actual output P of the BP neural network;
s405: according to the actual output P= (P) 1 ，p 2 ，p 3 ，...，p k ) And the desired output P '= (P' 1 ，p’ 2 ，p’ 3 ，...，p’ k ) Calculating the overall error 0 of the network, and updating the network connection weight alpha according to the overall error st 、β tg ；
α st ＝α st +Δα st ，
β tg ＝β tg +Δβ tg ，
Wherein,η is the learning rate;
s406: whether the error meets the requirement is judged, if not, the step S403 is not returned, otherwise, the operation is ended.
It should be noted that, through carrying out the experiment of different operating modes, obtain training set, test set, reinforcing monitoring evaluation model's suitability and practicality.
In one example of the present invention, in the step S50, establishing the mineralization-enhancing regulation model includes:
and training the BP neural network by taking the mineralization effect evaluation grade, the mineralization quality characteristic parameter and the comprehensive index monitoring parameter as input data and taking the controllable injection parameters of the injection concentration, the temperature, the flow rate and the pressure of the carbon dioxide hydrate as output data to obtain the relation between the mineralization effect evaluation grade and the controllable injection parameter X.
In one example of the present invention, in step S50, the scheme for enhancing regulation includes:
if the mineralization is weak, setting a target mineralization effect evaluation grade; if the mineralization is more complete, a target controllable injection parameter X result is obtained through a mineralization enhancement regulation model, the mineralization level is improved through changing the injection parameter, and simultaneously, the parameter variable with the biggest mineralization effect influence can be obtained through sensitivity analysis for selective regulation, or the fracture network structural surface is enlarged by utilizing the fracture network reconstruction equipment for multiple hydraulic fracturing;
if the mineralization is more complete, the mineralization reaction is carried out by continuously maintaining the injection condition, or the mineralization is enhanced by utilizing a mineralization enhancing and controlling model to control the injection parameters.
In one example of the present invention, the obtaining the parameter variable with the greatest influence on the mineralization effect through the sensitivity analysis for selective regulation includes the following steps:
and establishing a BP neural network model of the mineralization effect evaluation level with respect to the mineralization quality index P, the comprehensive index monitoring parameter F and the controllable injection parameter X, and selecting a certain parameter in the controllable injection parameter X to be up-regulated or down-regulated in a certain range to obtain a parameter with larger change of the mineralization effect evaluation level.
In one example of the present invention, in step S50, constructing a dynamic regulation pattern of monitor-evaluation-regulation-monitor-re-evaluation includes:
the mineralization effect evaluation grade is obtained in real time according to basic monitoring parameters in the monitoring process, a mineralization enhancement scheme is provided, the mineralization process is continuously monitored, the mineralization effect re-evaluation is completed, the whole process is circularly carried out, and the mineralization reaction level is continuously improved.
Another object of the present invention is to provide a system for monitoring, evaluating and dynamically controlling and enhancing mineralization effect of carbon dioxide, comprising:
the data acquisition unit is configured to be used for carrying out a carbon dioxide mineralization process monitoring experiment by using a sealing simulation experiment system, injecting the carbon dioxide hydrate into the mineralization sealing layer, controlling the temperature, the flow rate and the concentration of carbon dioxide injection during injection, monitoring the mineralization sealing process by using an ultrasonic phased array and acoustic emission, monitoring the concentration, the flow rate and the flow rate of the mineralized carbon dioxide by using a monitoring well, and considering that a complete mineralization reaction is basically finished when the concentration and the flow rate of the mineralized carbon dioxide are lower, and sampling and analyzing a mineralized product to obtain a mineralization effect;
the data processing unit is configured to remove environmental noise for the temperature, flow rate and concentration controllable injection parameters, ultrasonic phased array related parameters, acoustic emission related parameters and mineralized carbon dioxide concentration, flow rate and flow rate basic monitoring parameters of carbon dioxide injection, and then extract all basic monitoring parameters into comprehensive monitoring indexes by utilizing principal component analysis so as to remove unimportant characteristics, reduce analysis dimension and facilitate establishment of an evaluation model;
the grading unit is configured to sample the mineralized product, obtain analysis parameters of acidity, salinity, carbon fixation, characteristic product content and characteristic element content by using an acidity tester, a salinity detector, an XRD tester and a Raman spectrometer, and select mineralization quality characteristic parameters from the analysis parameters to evaluate and grade mineralization effects;
the mineralization evaluation unit is configured to establish a model between the change of the comprehensive monitoring index and the mineralization quality characteristic parameter in the mineralization process by utilizing the BP neural network, and the mineralization effect evaluation grade is divided by the mineralization quality characteristic parameter, so that the basic monitoring parameter in the monitoring process is related to the mineralization effect evaluation grade by the evaluation model, the basic monitoring parameter in the mineralization process is realized, and the mineralization effect is evaluated in real time;
the mineralization optimizing unit is configured to be used for establishing a mineralization enhancement regulation model, obtaining the relation between a mineralization effect evaluation grade and controllable injection parameters, realizing setting a target mineralization effect evaluation grade to obtain an adjustable controllable injection parameter value, obtaining mineralization effect evaluation through process monitoring, simultaneously giving a scheme of enhancement regulation, constructing a dynamic regulation mode of monitoring-evaluation-regulation-monitoring-re-evaluation, and continuously giving an injection optimal solution until reaching a target or mineralization reaction limit to realize the optimization of mineralization reaction effect.
Preferred embodiments for carrying out the present invention will be described in more detail below with reference to the attached drawings so that the features and advantages of the present invention can be easily understood.
Drawings
In order to more clearly illustrate the technical solution of the embodiments of the present invention, the following description will briefly explain the drawings of the embodiments of the present invention. Wherein the showings are for the purpose of illustrating some embodiments of the invention only and not for the purpose of limiting the same.
FIG. 1 is a flow chart of a method for monitoring, evaluating and dynamically regulating and enhancing the mineralization effect of carbon dioxide according to an embodiment of the invention;
FIG. 2 is a graph of evaluation parameters in a method for monitoring and evaluating the mineralization effect of carbon dioxide and enhancing dynamic regulation according to an embodiment of the invention;
fig. 3 is a flowchart of a BP neural network in a method for monitoring and evaluating a mineralization effect of carbon dioxide and enhancing dynamic regulation according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the technical solutions of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of specific embodiments of the present invention. Like reference numerals in the drawings denote like parts. It should be noted that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be made by a person skilled in the art without creative efforts, based on the described embodiments of the present invention fall within the protection scope of the present invention.
Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this invention belongs. The terms "first," "second," and the like in the description and in the claims, are not used for any order, quantity, or importance, but are used for distinguishing between different elements. Likewise, the terms "a" or "an" and the like do not necessarily denote a limitation of quantity. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.
According to the first aspect of the invention, a method for monitoring, evaluating and dynamically regulating and enhancing the mineralization effect of carbon dioxide, as shown in fig. 1, comprises the following steps:
s10: the method comprises the steps of utilizing a sealing simulation experiment system to develop a carbon dioxide mineralization process monitoring experiment, injecting a carbon dioxide hydrate into a mineralization sealing layer, controlling the temperature, the flow rate and the concentration of carbon dioxide injection during injection, utilizing an ultrasonic phased array and acoustic emission to monitor the mineralization sealing process, monitoring the concentration, the flow rate and the flow rate of the mineralized carbon dioxide by a monitoring well, and taking a complete mineralization reaction as a basic end when the concentration and the flow rate of the carbon dioxide are low, and sampling and analyzing a mineralized product to obtain a mineralization effect;
s20: removing environmental noise from the carbon dioxide injection temperature, flow rate and concentration controllable injection parameters, ultrasonic phased array related parameters, acoustic emission related parameters and mineralized carbon dioxide concentration, flow rate and flow basic monitoring parameters, extracting all basic monitoring parameters into comprehensive monitoring indexes by utilizing principal component analysis so as to remove unimportant characteristics, reducing analysis dimension and facilitating establishment of an evaluation model;
s30: sampling the mineralized product, obtaining analysis parameters of acidity, salinity, carbon fixation amount, characteristic product content and characteristic element content by using an acidity tester, a salinity detector, an XRD tester and a Raman spectrometer, and selecting mineralization quality characteristic parameters from the analysis parameters to carry out mineralization effect evaluation grade division;
s40: based on the relation between the change of the comprehensive monitoring index and the mineralization quality characteristic parameter in the mineralization process, a mineralization effect monitoring and evaluating model is established by utilizing the BP neural network, and the basic monitoring parameter in the monitoring process is associated with the mineralization effect evaluating grade, so that the mineralization effect is evaluated in real time by the basic monitoring parameter in the mineralization process;
s50: the method comprises the steps of establishing a mineralization enhancement regulation model, obtaining the relation between a mineralization effect evaluation grade and controllable injection parameters, setting a target mineralization effect evaluation grade to obtain an adjustable controllable injection parameter value, obtaining mineralization effect evaluation through process monitoring, simultaneously providing an enhancement regulation scheme, constructing a dynamic regulation mode of monitoring-evaluation-regulation-monitoring-re-evaluation, and continuously providing an injection optimal solution until reaching a target or mineralization reaction limit to realize the optimization of mineralization reaction effect.
In one example of the present invention, in the step S10, the monitoring the mineralization sealing process using ultrasonic phased array and acoustic emission includes:
the method is characterized in that the injection of the carbon dioxide hydrate is stopped during ultrasonic phased array monitoring, a speed field formed by the flow of the carbon dioxide hydrate in the mineralization process is periodically and actively monitored, an acoustic emission signal released by the generation of cracks in the whole mineralization reaction process of the sealed rock stratum is passively monitored by acoustic emission, and the ultrasonic phased array periodic monitoring is supplemented, so that the active and passive integrated mineralization whole process monitoring is realized.
In one example of the present invention, in step S20, the removing the environmental noise includes:
the method comprises the steps of extracting monitoring environmental noise signals before mineralization, filtering the environmental noise of the real-time monitoring signals in the mineralization process by utilizing the environmental noise signals before mineralization, so as to reduce the deviation value of the signals and ensure the accuracy of the signals.
In one example of the present invention, in the step S20, as shown in fig. 2, extracting all the basic monitoring parameters into the integrated monitoring index includes the steps of:
setting basic monitoring parameters D v ＝(X、Y i 、Z j K): controllable injection parameters of injection pressure, flow rate and the like of carbon dioxide hydrate, namely X= (X) 1 ,x 2 ,x 3 ,x 4 ): carbon dioxide injection concentration x 1 Temperature, x 2 Flow velocity x 3 Pressure x 4 Several other basic monitoring parameters: ultrasonic phase control related parameter Y i = (i=1, 2,3 … m), acoustic emission related parameter Z j = (j=1, 2,3 … n), mineralization process carbon dioxide concentration, flow rate and flow parameter k= (K) 1 ,K 2 ，k 3 ) Each parameter contains q samples, and,
wherein,
then D v =(X、Y i 、Z j Linear combination of K) is:
F V ＝a 1v x 1 +a 2v x 2 +a 3v x 3 +a 4v x 4 +a 5v k 1 ++a 6v k 2 +a 7v k 3 +a 8v Y i +a 9v Z j +…+a m+n+7.v D v (v＝1，2，3...m+n+7)
wherein, the main component corresponding to the characteristic root with the accumulated contribution rate reaching 85% -95% is selected as the comprehensive monitoring index F= (F) 1 ，f 2 ，f 3 ，...，f 1 )。
In one example of the present invention, in the step S30, the mineralization effect evaluation ranking includes: using mineralization quality characteristic parameter p= (P) 1 ，p 2 ，p 3 ，...，p k ) The mineralization effect evaluation is divided into five grades A, B, C, D, E, which respectively represent complete mineralization, more complete mineralization, incomplete mineralization, weak mineralization and unmineralization; wherein each class should be classified in view of both the main characteristics of the mineralization quality and the other classes.
In one example of the present invention, in the step S40, as shown in fig. 3, the step of creating a mineralization effect monitoring and evaluating model using a BP neural network includes the steps of:
s401: in order to eliminate the order-of-magnitude difference between data and improve the learning effect of the BP neural network, the data are normalized, and the formula is as follows:
wherein x' is a normalized variable; x is an actual value; x is x max And x min Respectively the maximum value and the minimum value of the variable x;
s402: according to the comprehensive monitoring index F in the complete mineralization process s ＝(f 1 ，f 2 ，f 3 ，...，f 1 ) Is changed into input data, mineralization quality characteristic parameter P g ’＝(p’ 1 ，p’ 2 ，p’ 3 ，...，p’ k ) For expected output data, determining the number of neurons (nodes) of network input layer, hidden layer and output layer, and initializing the connection weight alpha between the neurons of each layer st 、β tg Initializing an implicit layer threshold value zeta, outputting a layer threshold value zeta, and setting a learning rate and a neuron transfer function;
s403: implicit inter-layer connection weight alpha of input layer according to input vector A st And an hidden layer threshold ζ, calculating a hidden layer output;
wherein/is the number of hidden layer nodes, alpha s0 ＝-1，f 0 ＝α t F (·) is the implicit layer transfer function, with the transfer function f (x) = (1+e) -x ) -1 ；
S404: according to hidden layer output H, connect weight beta tg And a threshold value xi, calculating the actual output P of the BP neural network;
s405: according to the actual output P= (P) 1 ，p 2 ，p 3 ，...，p k ) And the desired output P '= (P' 1 ，p’ 2 ，p’ 3 ，...，p’ k ) Calculating the overall error 0 of the network, and updating the network connection weight alpha according to the overall error st 、β tg ；
α st ＝α st +Δα st ，
β tg ＝β tg +Δβ tg ，
Wherein,η is the learning rate;
s406: whether the error meets the requirement is judged, if not, the step S403 is not returned, otherwise, the operation is ended.
In one example of the present invention, in the step S50, establishing the mineralization-enhancing regulation model includes: evaluation of the grade and quality of mineralization by mineralization effect, characteristic parameter P= (P) 1 ，p 2 ，p 3 ，...，p k ) Comprehensive index monitoring parameter f= (F) 1 ，f 2 ，f 3 ，...，f 1 ) For inputting data, the injection concentration x of the carbon dioxide hydrate is used for 1 Temperature, x 2 Flow velocity x 3 Pressure x 4 Controllable injection parameter x= (X) 1 ，x 2 ，x 3 ，x 4 ) In order to output data, the BP neural network is trained to obtain the relation between the mineralization effect evaluation grade and the controllable injection parameter X, the model construction is not developed in detail, and the model is built by using the BP neural network.
In one example of the present invention, in step S50, the scheme for enhancing regulation includes:
if the mineralization is weak, setting a target mineralization effect evaluation grade; if the mineralization is more complete, a target controllable injection parameter X result is obtained through a mineralization enhancement regulation model, the mineralization level is improved through changing the injection parameter, and simultaneously, the parameter variable with the biggest mineralization effect influence can be obtained through sensitivity analysis for selective regulation, or the fracture network structural surface is enlarged by utilizing the fracture network reconstruction equipment for multiple hydraulic fracturing;
if the mineralization is more complete, the mineralization reaction is carried out by continuously maintaining the injection condition, or the mineralization is enhanced by utilizing a mineralization enhancing and controlling model to control the injection parameters.
The method comprises the steps of obtaining parameter variables with the largest mineralization effect influence through sensitivity analysis, selectively regulating and controlling the parameter variables, establishing a BP neural network model of mineralization effect evaluation level with respect to mineralization quality index P, comprehensive index monitoring parameter F and controllable injection parameter X, and selecting a certain parameter in the controllable injection parameter X to be up-regulated or down-regulated for a certain range to obtain a parameter with larger mineralization effect evaluation level change, namely a regulating and controlling sensitivity parameter.
In one example of the present invention, in step S50, constructing a dynamic regulation pattern of monitor-evaluation-regulation-monitor-re-evaluation includes:
the mineralization effect evaluation grade is obtained in real time according to basic monitoring parameters in the monitoring process, a mineralization enhancement scheme is provided, the mineralization process is continuously monitored, the mineralization effect re-evaluation is completed, the whole process is circularly carried out, and the mineralization reaction level is continuously improved.
The dynamic regulation and enhancement method has the following beneficial effects:
(1) And carrying out a carbon dioxide mineralization process monitoring experiment by using a sealing and storing simulation experiment system, establishing a mineralization effect evaluation model, and carrying out mineralization effect evaluation in real time through the rock stratum change in the mineralization process.
(2) Due to the complex variability of the mineralization process and the monitoring environment, the data is cleaned by using the environmental noise removal and principal component analysis method, so that the accuracy of the monitoring evaluation model and the reliability of the mineralization effect evaluation are improved.
(3) By establishing a connection between carbon dioxide injection and mineralization effect evaluation, the method provides a regulation and control scheme for enhancing mineralization effect to promote mineralization reaction while obtaining effect evaluation in real time.
(4) The mineralization effect evaluation is obtained in real time by mineralization process monitoring, and a mineralization enhancing regulation scheme is provided, and the monitoring and reevaluation are carried out by changing mineralization conditions, so that the dynamic regulation mode of monitoring, evaluating, regulating, monitoring and reevaluation continuously enhances mineralization, and the effect optimization is realized.
According to the second invention, a carbon dioxide mineralization effect monitoring evaluation and dynamic regulation enhancement system comprises:
the data acquisition unit is configured to be used for carrying out a carbon dioxide mineralization process monitoring experiment by using a sealing simulation experiment system, injecting the carbon dioxide hydrate into the mineralization sealing layer, controlling the temperature, the flow rate and the concentration of carbon dioxide injection during injection, monitoring the mineralization sealing process by using an ultrasonic phased array and acoustic emission, monitoring the concentration, the flow rate and the flow rate of the mineralized carbon dioxide by using a monitoring well, and considering that a complete mineralization reaction is basically finished when the concentration and the flow rate of the mineralized carbon dioxide are lower, and sampling and analyzing a mineralized product to obtain a mineralization effect;
the data processing unit is configured to remove environmental noise for the temperature, flow rate and concentration controllable injection parameters, ultrasonic phased array related parameters, acoustic emission related parameters and mineralized carbon dioxide concentration, flow rate and flow rate basic monitoring parameters of carbon dioxide injection, and then extract all basic monitoring parameters into comprehensive monitoring indexes by utilizing principal component analysis so as to remove unimportant characteristics, reduce analysis dimension and facilitate establishment of an evaluation model;
the grading unit is configured to sample the mineralized product, obtain analysis parameters of acidity, salinity, carbon fixation, characteristic product content and characteristic element content by using an acidity tester, a salinity detector, an XRD tester and a Raman spectrometer, and select mineralization quality characteristic parameters from the analysis parameters to evaluate and grade mineralization effects;
the mineralization evaluation unit is configured to establish a mineralization effect monitoring and evaluating model by utilizing the BP neural network based on the relation between the change of the comprehensive monitoring index and the mineralization quality characteristic parameter in the mineralization process, and associate the basic monitoring parameter in the monitoring process with the mineralization effect evaluation grade so as to realize real-time evaluation of the mineralization effect by the basic monitoring parameter in the mineralization process;
the mineralization optimizing unit is configured to be used for establishing a mineralization enhancement regulation model, obtaining the relation between a mineralization effect evaluation grade and controllable injection parameters, realizing setting a target mineralization effect evaluation grade to obtain an adjustable controllable injection parameter value, obtaining mineralization effect evaluation through process monitoring, simultaneously giving a scheme of enhancement regulation, constructing a dynamic regulation mode of monitoring-evaluation-regulation-monitoring-re-evaluation, and continuously giving an injection optimal solution until reaching a target or mineralization reaction limit to realize the optimization of mineralization reaction effect.
In one example of the invention, a data processing unit includes: the noise reduction module is configured to extract the monitoring environmental noise signals before mineralization, and the environmental noise signals before mineralization are utilized to filter the environmental noise of the real-time monitoring signals in the mineralization process so as to reduce the deviation value of the signals and ensure the accuracy of the signals.
In one example of the present invention, a mineralization evaluation unit includes:
the normalization processing module is configured to normalize the data, and the formula is as follows:
wherein x' is a normalized variable; x is an actual value; x is x max And x min Respectively the maximum value and the minimum value of the variable x;
an initialization module configured to monitor the index F according to the total mineralization process s ＝(f 1 ，f 2 ，f 3 ，...，f 1 ) Is changed into input data, mineralization quality characteristic parameter P g ’＝(p’ 1 ，p’ 2 ，p’ 3 ，...，p’ k ) For expected output data, determining the number of neurons (nodes) of network input layer, hidden layer and output layer, and initializing the connection weight alpha between the neurons of each layer st 、β tg Initializing an implicit layer threshold value zeta, outputting a layer threshold value zeta, and setting a learning rate and a neuron transfer function;
an implicit layer calculation module configured to input an implicit interlayer connection weight alpha according to the input vector F st And an hidden layer threshold ζ, calculating a hidden layer output;
wherein/is the number of hidden layer nodes, alpha s0 ＝-1，f 0 ＝α t F (·) is the implicit layer transfer function, with the transfer function f (x) = (1+e) -x ) -1 ；
An actual output module configured to output H according to the hidden layer and connect the weight beta tg And a threshold value xi, calculating the actual output P of the BP neural network;
an error calculation module configured to output p= (P) according to the network reality 1 ，p 2 ，p 3 ，...，p k ) And the desired output P '= (P' 1 ，p’ 2 ，p’ 3 ，...，p’ k ) Calculating the overall error 0 of the network, and updating the network connection weight alpha according to the overall error st 、β tg ；
α st ＝α st +Δα st ，
β tg ＝β tg +Δβ tg ，
Wherein,η is the learning rate;
the error judging module is configured to judge whether the error meets the requirement, if not, the hidden layer calculating module is returned, otherwise, the operation is ended.
The dynamic regulation and control enhancement system has the following beneficial effects:
(1) And carrying out a carbon dioxide mineralization process monitoring experiment by using a sealing and storing simulation experiment system, establishing a mineralization effect evaluation model, and carrying out mineralization effect evaluation in real time through the rock stratum change in the mineralization process.
(2) Due to the complex variability of the mineralization process and the monitoring environment, the data is cleaned by using the environmental noise removal and principal component analysis method, so that the accuracy of the monitoring evaluation model and the reliability of the mineralization effect evaluation are improved.
(3) By establishing a connection between carbon dioxide injection and mineralization effect evaluation, the method provides a regulation and control scheme for enhancing mineralization effect to promote mineralization reaction while obtaining effect evaluation in real time.
(4) The mineralization effect evaluation is obtained in real time by mineralization process monitoring, and a mineralization enhancing regulation scheme is provided, and the monitoring and reevaluation are carried out by changing mineralization conditions, so that the dynamic regulation mode of monitoring, evaluating, regulating, monitoring and reevaluation continuously enhances mineralization, and the effect optimization is realized.
While exemplary embodiments of the method and system for monitoring and evaluating the mineralization effect of carbon dioxide and enhancing the dynamic regulation and control of the carbon dioxide according to the present invention have been described in detail hereinabove with reference to preferred embodiments, it will be understood by those skilled in the art that various changes and modifications may be made to the specific embodiments described above without departing from the spirit of the invention, and various technical features and structures of the invention may be combined without departing from the scope of the invention, which is defined in the appended claims.
Claims (10)
1. The method for monitoring, evaluating and dynamically regulating and enhancing the mineralization effect of the carbon dioxide is characterized by comprising the following steps of:
s10: the method comprises the steps of utilizing a sealing simulation experiment system to develop a carbon dioxide mineralization process monitoring experiment, injecting a carbon dioxide hydrate into a mineralization sealing layer, controlling the temperature, the flow rate and the concentration of carbon dioxide injection during injection, utilizing an ultrasonic phased array and acoustic emission to monitor the mineralization sealing process, monitoring the concentration, the flow rate and the flow rate of the mineralized carbon dioxide by a monitoring well, and taking a complete mineralization reaction as a basic end when the concentration and the flow rate of the carbon dioxide are low, and sampling and analyzing a mineralized product to obtain a mineralization effect;
s20: removing environmental noise from the carbon dioxide injection temperature, flow rate and concentration controllable injection parameters, ultrasonic phased array related parameters, acoustic emission related parameters and mineralized carbon dioxide concentration, flow rate and flow basic monitoring parameters, extracting all basic monitoring parameters into comprehensive monitoring indexes by utilizing principal component analysis so as to remove unimportant characteristics, reducing analysis dimension and facilitating establishment of an evaluation model;
s30: sampling the mineralized product, obtaining analysis parameters of acidity, salinity, carbon fixation amount, characteristic product content and characteristic element content by using an acidity tester, a salinity detector, an XRD tester and a Raman spectrometer, and selecting mineralization quality characteristic parameters from the analysis parameters to carry out mineralization effect evaluation grade division;
s40: based on the relation between the change of the comprehensive monitoring index and the mineralization quality characteristic parameter in the mineralization process, a mineralization effect monitoring and evaluating model is established by utilizing the BP neural network, and the basic monitoring parameter in the monitoring process is associated with the mineralization effect evaluating grade, so that the mineralization effect is evaluated in real time by the basic monitoring parameter in the mineralization process;
s50: the method comprises the steps of establishing a mineralization enhancement regulation model, obtaining the relation between a mineralization effect evaluation grade and controllable injection parameters, setting a target mineralization effect evaluation grade to obtain an adjustable controllable injection parameter value, obtaining mineralization effect evaluation through process monitoring, simultaneously providing an enhancement regulation scheme, constructing a dynamic regulation mode of monitoring-evaluation-regulation-monitoring-re-evaluation, and continuously providing an injection optimal solution until reaching a target or mineralization reaction limit to realize the optimization of mineralization reaction effect.
2. The method for monitoring, evaluating and dynamically regulating and enhancing the mineralization effect of carbon dioxide according to claim 1, wherein,
in the step S10, the monitoring the mineralization sealing process by using ultrasonic phased array and acoustic emission includes:
the method is characterized in that the injection of the carbon dioxide hydrate is stopped during ultrasonic phased array monitoring, a speed field formed by the flow of the carbon dioxide hydrate in the mineralization process is periodically and actively monitored, an acoustic emission signal released by the generation of cracks in the whole mineralization reaction process of the sealed rock stratum is passively monitored by acoustic emission, and the ultrasonic phased array periodic monitoring is supplemented, so that the active and passive integrated mineralization whole process monitoring is realized.
3. The method for monitoring, evaluating and dynamically regulating and enhancing the mineralization effect of carbon dioxide according to claim 1, wherein,
in the step S20, extracting all the basic monitoring parameters into the integrated monitoring index includes the following steps:
setting basic monitoring parameters D v ＝(X、Y i 、Z j K): controllable injection parameters of injection pressure, flow rate and the like of carbon dioxide hydrate, namely X= (X) 1 ,x 2 ,x 3 ,x 4 ): carbon dioxide injection concentration x 1 Temperature, x 2 Flow velocity x 3 Pressure x 4 Several other basic monitoring parameters: ultrasonic phase control related parameter Y i = (i=1, 2,3 … m), acoustic emission related parameter Z j = (j=1, 2,3 … n), mineralization process carbon dioxide concentration, flow rate and flow parameter k= (K) 1 ,k 2 ,k 3 ) Each parameter contains q samples, and,
wherein,
then D v ＝(X、Y i 、Z j Linear combination of K) is:
F V ＝a 1v x 1 +a vv x t +a 3v x 3 +a 4v x 4 +a 5v k 1 ++a 6v k 2 +a 7v k 3 +a 8v Y i +a 9v Z j +…+a m+n+7,v D v (v＝1,2,3…m+n+7)
wherein, the main component corresponding to the characteristic root with the accumulated contribution rate reaching 85% -95% is selected as the comprehensive monitoring index F= (F) 1 ,f 2 ,f 3 ,…,f l )。
4. The method for monitoring, evaluating and dynamically regulating and enhancing the mineralization effect of carbon dioxide according to claim 1, wherein,
in the step S30, the mineralization effect evaluation ranking includes: the mineralization effect evaluation is divided into five grades A, B, C, D, E by utilizing mineralization quality characteristic parameters, wherein the five grades A, B, C, D, E respectively represent complete mineralization, incomplete mineralization, weak mineralization and unmineralization; wherein each class should be classified in view of both the main characteristics of the mineralization quality and the other classes.
5. The method for monitoring, evaluating and dynamically regulating and enhancing the mineralization effect of carbon dioxide according to claim 1, wherein,
in the step S40, the step of establishing a mineralization effect monitoring and evaluating model by using the BP neural network includes the following steps:
s401: the data is normalized according to the following formula:
wherein x' is a normalized variable; x is an actual value; x is x max And x min Respectively the maximum value and the minimum value of the variable x;
s402: according to the comprehensive monitoring index F in the complete mineralization process s ＝(f 1 ,f 2 ,f 3 ,…,f l ) Is changed into input data, mineralization quality characteristic parameter P g ’＝(p’ 1 ,p’ 2 ,p’ 3 ,…,p’ k ) For expected output data, determining the number of neurons (nodes) of network input layer, hidden layer and output layer, and initializing the connection weight alpha between the neurons of each layer st 、β tg Initializing an implicit layer threshold value zeta, outputting a layer threshold value zeta, and setting a learning rate and a neuron transfer function;
s403: according to the input vector F, the implicit interlayer connection weight alpha of the input layer st And an hidden layer threshold ζ, calculating a hidden layer output;
wherein l is the number of hidden layer nodes, alpha s0 ＝-1，f 0 =α t F (·) is the implicit layer transfer function, with the transfer function f (x) = (1+e) -x ) -1 ；
S404: according to hidden layer output H, connect weight beta tg And a threshold value xi, calculating the actual output P of the BP neural network;
s405: according to the actual output P= (P) 1 ,p 2 ,p 3 ,…,p k ) And the desired output P '= (P' 1 ,p’ 2 ,p’ 3 ,…,p’ k ) Calculating the overall error O of the network, and updating the network connection weight alpha according to the overall error st 、β tg ；
α st ＝α st +Δα st ，
β tg ＝β tg +Δβ tg ，
Wherein,η is the learning rate;
s406: whether the error meets the requirement is judged, if not, the step S403 is not returned, otherwise, the operation is ended.
6. The method for monitoring, evaluating and dynamically regulating and enhancing the mineralization effect of carbon dioxide according to claim 1, wherein,
in the step S50, establishing the mineralization enhancement control model includes:
and training the BP neural network by taking the mineralization effect evaluation grade, the mineralization quality characteristic parameter and the comprehensive index monitoring parameter as input data and taking the controllable injection parameters of the injection concentration, the temperature, the flow rate and the pressure of the carbon dioxide hydrate as output data to obtain the relation between the mineralization effect evaluation grade and the controllable injection parameter X.
7. The method for monitoring, evaluating and dynamically regulating and enhancing the mineralization effect of carbon dioxide according to claim 1, wherein,
in step S50, the scheme for enhancing regulation includes:
if the mineralization is weak, setting a target mineralization effect evaluation grade; if the mineralization is more complete, a target controllable injection parameter X result is obtained through a mineralization enhancement regulation model, the mineralization level is improved through changing the injection parameter, and simultaneously, the parameter variable with the biggest mineralization effect influence can be obtained through sensitivity analysis for selective regulation, or the fracture network structural surface is enlarged by utilizing the fracture network reconstruction equipment for multiple hydraulic fracturing;
if the mineralization is more complete, the mineralization reaction is carried out by continuously maintaining the injection condition, or the mineralization is enhanced by utilizing a mineralization enhancing and controlling model to control the injection parameters.
8. The method for monitoring, evaluating and dynamically regulating and enhancing the mineralization effect of carbon dioxide according to claim 7,
the method for selectively regulating and controlling the parameter variable with the largest mineralization effect obtained through sensitivity analysis comprises the following steps:
and establishing a BP neural network model of the mineralization effect evaluation level with respect to the mineralization quality index P, the comprehensive index monitoring parameter F and the controllable injection parameter X, and selecting a certain parameter in the controllable injection parameter X to be up-regulated or down-regulated in a certain range to obtain a parameter with larger change of the mineralization effect evaluation level.
9. The method for monitoring, evaluating and dynamically regulating and enhancing the mineralization effect of carbon dioxide according to claim 1, wherein,
in step S50, constructing a dynamic regulation pattern of monitor-evaluation-regulation-monitor-re-evaluation includes:
the mineralization effect evaluation grade is obtained in real time according to basic monitoring parameters in the monitoring process, a mineralization enhancement scheme is provided, the mineralization process is continuously monitored, the mineralization effect re-evaluation is completed, the whole process is circularly carried out, and the mineralization reaction level is continuously improved.
10. The utility model provides a carbon dioxide mineralization effect monitoring evaluation and dynamic regulation and control reinforcing system which characterized in that includes:
the data acquisition unit is configured to be used for carrying out a carbon dioxide mineralization process monitoring experiment by using a sealing simulation experiment system, injecting the carbon dioxide hydrate into the mineralization sealing layer, controlling the temperature, the flow rate and the concentration of carbon dioxide injection during injection, monitoring the mineralization sealing process by using an ultrasonic phased array and acoustic emission, monitoring the concentration, the flow rate and the flow rate of the mineralized carbon dioxide by using a monitoring well, and considering that a complete mineralization reaction is basically finished when the concentration and the flow rate of the mineralized carbon dioxide are lower, and sampling and analyzing a mineralized product to obtain a mineralization effect;
the data processing unit is configured to remove environmental noise for the temperature, flow rate and concentration controllable injection parameters, ultrasonic phased array related parameters, acoustic emission related parameters and mineralized carbon dioxide concentration, flow rate and flow rate basic monitoring parameters of carbon dioxide injection, and then extract all basic monitoring parameters into comprehensive monitoring indexes by utilizing principal component analysis so as to remove unimportant characteristics, reduce analysis dimension and facilitate establishment of an evaluation model;
the grading unit is configured to sample the mineralized product, obtain analysis parameters of acidity, salinity, carbon fixation, characteristic product content and characteristic element content by using an acidity tester, a salinity detector, an XRD tester and a Raman spectrometer, and select mineralization quality characteristic parameters from the analysis parameters to evaluate and grade mineralization effects;
the mineralization evaluation unit is configured to establish a mineralization effect monitoring and evaluating model by utilizing the BP neural network based on the relation between the change of the comprehensive monitoring index and the mineralization quality characteristic parameter in the mineralization process, and associate the basic monitoring parameter in the monitoring process with the mineralization effect evaluation grade so as to realize real-time evaluation of the mineralization effect by the basic monitoring parameter in the mineralization process;
the mineralization optimizing unit is configured to be used for establishing a mineralization enhancement regulation model, obtaining the relation between a mineralization effect evaluation grade and controllable injection parameters, realizing setting a target mineralization effect evaluation grade to obtain an adjustable controllable injection parameter value, obtaining mineralization effect evaluation through process monitoring, simultaneously giving a scheme of enhancement regulation, constructing a dynamic regulation mode of monitoring-evaluation-regulation-monitoring-re-evaluation, and continuously giving an injection optimal solution until reaching a target or mineralization reaction limit to realize the optimization of mineralization reaction effect.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111323817A (en) * | 2020-04-15 | 2020-06-23 | 中国矿业大学(北京) | Carbon dioxide sequestration monitoring method and device based on deep learning |
| CN115653681A (en) * | 2022-10-28 | 2023-01-31 | 江苏科技大学 | System and method for sealing and storing carbon dioxide in mineral resource goaf |
| US20230152254A1 (en) * | 2021-11-16 | 2023-05-18 | Halliburton Energy Services, Inc. | Characterizing Effects Of CO2 Chemical Reaction With Rock Minerals During Carbon Capture And Sequestration |
| WO2023086396A1 (en) * | 2021-11-09 | 2023-05-19 | Schlumberger Technology Corporation | Forecasting co2 plume bodies in sequestration operations |
| CN116562126A (en) * | 2023-04-12 | 2023-08-08 | 西南石油大学 | Optimal design method and system for geological sequestration parameters of exhausted gas reservoir carbon dioxide |
-
2023
- 2023-09-19 CN CN202311206872.9A patent/CN117219180B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111323817A (en) * | 2020-04-15 | 2020-06-23 | 中国矿业大学(北京) | Carbon dioxide sequestration monitoring method and device based on deep learning |
| WO2023086396A1 (en) * | 2021-11-09 | 2023-05-19 | Schlumberger Technology Corporation | Forecasting co2 plume bodies in sequestration operations |
| US20230152254A1 (en) * | 2021-11-16 | 2023-05-18 | Halliburton Energy Services, Inc. | Characterizing Effects Of CO2 Chemical Reaction With Rock Minerals During Carbon Capture And Sequestration |
| CN115653681A (en) * | 2022-10-28 | 2023-01-31 | 江苏科技大学 | System and method for sealing and storing carbon dioxide in mineral resource goaf |
| CN116562126A (en) * | 2023-04-12 | 2023-08-08 | 西南石油大学 | Optimal design method and system for geological sequestration parameters of exhausted gas reservoir carbon dioxide |
Non-Patent Citations (2)
| Title |
|---|
| 刘廷 等: "国内外CO2 地质封存潜力评价方法研究现状", 中国地质调查, vol. 8, no. 4, 31 August 2021 (2021-08-31), pages 101 - 108 * |
| 张延旭;姜晶;王涛;王凯;: "神经网络法在油藏埋存CO_2效果预测中的应用", 精细石油化工进展, no. 02, 28 March 2018 (2018-03-28), pages 33 - 36 * |
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