A method and system for predicting the breakthrough pressure of shale caprock for CO2 geological storage

Abstract

The present invention relates to the technical field of CO2 geological storage, and specifically to a method and system for predicting the breakthrough pressure of shale caprocks for CO2 geological storage. The prediction method comprises the following steps: obtaining a cumulative mercury injection curve of a shale sample; using the cumulative mercury injection curve to obtain the relationship between the cumulative mercury injection amount and the pore size; obtaining a pore size distribution curve based on the relationship; obtaining a predicted value of the breakthrough pressure through the pore size distribution curve; and realizing the prediction of the breakthrough pressure of the shale caprock for CO2 geological storage based on the predicted value. The present invention defines the critical pore size of the non-wetting phase breaking through the shale sample and predicts the breakthrough pressure of the shale based on the consideration of different pore size distribution curves of different shale samples, thereby overcoming the limitations of the existing in-situ breakthrough pressure test of the core and solving the problem of low accuracy of the existing prediction method.

Description

Technical Field

[0001] The present invention relates to the technical field of CO2 geological storage, and in particular to a method and system for predicting the breakthrough pressure of a shale cap rock for CO2 geological storage. Background Art

[0002] CO2 geological storage is one of the key technologies for addressing global climate change and reducing greenhouse gas emissions. Shale caprock plays a key role in CO2 geological storage due to its good sealing properties. Its geological characteristics are mainly as follows: First, due to the low porosity and permeability of shale, it is conducive to preventing CO2 from migrating upward to the surface; second, shale contains clay minerals with high swelling and sealing properties (such as montmorillonite and illite), which is conducive to further improving the sealing effect of the caprock; third, due to the influence of burial depth, pressure and temperature on the sealing property of shale, it is conducive to enhancing the sealing property of shale. Therefore, shale caprock can ensure the safety of CO2 in the long-term geological storage process.

[0003] In the prior art, the breakthrough pressure is mainly determined by in-situ core testing and indirect testing. The in-situ core testing adopts a step-by-step pressurization method, which utilizes the fact that the state of the core and the non-wetting phase permeation process are very similar to the gas migration through the cap rock under in-situ conditions. The non-wetting phase pressure is increased in a step-by-step manner at the inlet end and maintained for a corresponding time until the non-wetting phase outflow is detected at the outlet section, thereby defining the inlet and outlet pressure difference as the breakthrough pressure. Due to the low permeability of shale and the long test cycle, the measurement accuracy of this method is difficult to guarantee. The indirect test uses the pressure corresponding to the mercury saturation of 10% in the mercury injection method as the breakthrough pressure. For rocks with different pore characteristics, different fluids need to overcome different capillary pressures, and the pressure value corresponding to the mercury saturation of 10% is uniformly used to characterize the breakthrough pressure. The pressure value obtained by this method has a large deviation from the actual value.

[0004] At present, there is not much research on shale caprocks for CO2 geological storage, and there is no specific method for predicting the breakthrough pressure of shale caprocks for CO2 geological storage. Summary of the invention

[0005] In view of the defects in the prior art, the present invention provides a method and system for predicting the breakthrough pressure of shale caprock for CO2 geological storage.

[0006] On the one hand, the prediction method includes the following steps: obtaining a mercury injection cumulative curve of a shale sample; using the mercury injection cumulative curve, obtaining a relationship between a cumulative mercury injection amount and a pore size; obtaining a pore size distribution curve based on the relationship; and obtaining a predicted value of a breakthrough pressure through the pore size distribution curve, wherein the predicted value satisfies the following expression:

[0007] ,

[0008] in, is the mercury injection pressure, is the interfacial tension, is the contact angle, is the critical pore size; according to the predicted value, the breakthrough pressure of the shale cap rock for CO2 geological storage is predicted. The present invention defines the critical pore size of the non-wetting phase breakthrough shale sample on the basis of considering the different pore size distribution curves of different shale samples, predicts the breakthrough pressure of shale, overcomes the limitations of the existing core in-situ breakthrough pressure test and solves the problem of low accuracy of the existing prediction method, and has the advantages of short time consumption, high accuracy and low cost.

[0009] Optionally, the use of the cumulative mercury injection curve to obtain the relationship between the cumulative mercury injection amount and the pore size includes: using the cumulative mercury injection curve to obtain the relationship between the mercury injection pressure and the cumulative mercury injection amount; substituting the mercury injection pressure into the Laplace equation to obtain the relationship between the cumulative mercury injection amount and the pore size. The present invention obtains the relationship between the cumulative mercury injection amount and the pore size through the cumulative mercury injection curve and the Laplace equation, and achieves the following effects: first, it is of great significance for studying the pore structure characteristics of shale materials, such as pore size, pore connectivity, etc.; second, it is conducive to further exploring the influence of pore size on shale properties, such as permeability, adsorption, mechanical strength, etc., thereby promoting the development of the field of geological storage; third, the use of the Laplace equation for pore size calculation can improve the accuracy and scientificity of the analysis.

[0010] Optionally, the step of substituting the mercury injection pressure into the Laplace equation to obtain the relationship between the cumulative mercury injection amount and the pore size includes: substituting the mercury injection pressure into the Laplace equation to obtain the relationship between the mercury injection pressure and the pore size; and obtaining the relationship between the cumulative mercury injection amount and the pore size according to the relationship between the mercury injection pressure and the pore size. The present invention elaborates on the process of substituting the mercury injection pressure into the Laplace equation to derive the relationship between the cumulative mercury injection amount and the pore size. The following effects are achieved: First, by substituting the mercury injection pressure into the Laplace equation, the pore size corresponding to each mercury injection pressure value can be directly calculated, thereby establishing a clear mathematical relationship between the pore size and the mercury injection pressure, which provides an accurate numerical basis for understanding the pore structure of shale; second, based on the relationship between the mercury injection pressure and the pore size, the relationship between the cumulative mercury injection amount and the pore size is further derived, revealing the contribution of different pore size ranges in shale to the cumulative mercury injection amount, which helps to fully grasp the pore structure characteristics of shale, including the connectivity and distribution density of the pores; third, the relationship data between the cumulative mercury injection amount and the pore size obtained provides an important basis for in-depth research on the permeability, adsorption and other properties of shale.

[0011] Optionally, obtaining the pore size distribution curve according to the relationship includes: obtaining a relationship curve between the cumulative mercury injection amount and the pore size according to the relationship; and obtaining the pore size distribution curve by differentiating the relationship curve. The present invention describes a process of deriving the pore size distribution curve from the relationship between the cumulative mercury injection amount and the pore size. The following effects have been achieved: First, through the relationship curve between the cumulative mercury injection amount and the pore size, the contribution of different pore sizes to the cumulative mercury injection amount can be intuitively observed, which is conducive to quickly grasping the pore structure characteristics of the shale, especially the distribution of the pore size; second, based on the mathematical principle of calculus, the pore volume characteristics within each pore size range can be accurately calculated, thereby obtaining an accurate description of the pore size distribution, which is conducive to accurately understanding the pore structure of the shale; third, the obtained pore size distribution curve not only provides detailed information about the pore structure of the shale, but also provides strong support for its further analysis and optimization.

[0012] Optionally, the derivation of the relationship curve to obtain the pore size distribution curve includes: derivation of the relationship curve to obtain the rate of change of the mercury injection amount with the pore size; and obtaining the pore size distribution curve according to the rate of change. The present invention achieves the following effects by derivation of the relationship curve between the cumulative mercury injection amount and the pore size: first, the obtained rate of change reflects the ease of mercury entry at different pore sizes, thereby indirectly reflecting the distribution of pores; second, the obtained pore size distribution curve intuitively shows the distribution of different pore sizes in shale materials, which is an important basis for evaluating the pore structure characteristics of shale materials; third, it is conducive to understanding the detailed characteristics of pore size, such as pore connectivity, pore size distribution law, etc.

[0013] Optionally, obtaining the predicted value of breakthrough pressure through the pore size distribution curve includes: obtaining the critical pore size through the pore size distribution curve; substituting the critical pore size into the Laplace equation to obtain the predicted value of breakthrough pressure. The present invention uses the pore size distribution curve to obtain the predicted value of breakthrough pressure, and achieves the following effects: first, it is based on physical principles and has high scientificity and accuracy; second, it is conducive to understanding the breakthrough pressure under different geological conditions and further optimizing resource allocation; third, by predicting the breakthrough pressure, it provides an important reference for subsequent geological storage engineering design, engineering implementation or policy formulation.

[0014] Optionally, substituting the critical pore size into the Laplace equation to obtain the predicted value of the breakthrough pressure includes: substituting the critical pore size into the Laplace equation to obtain a prediction model for the breakthrough pressure; and obtaining the predicted value of the breakthrough pressure according to the prediction model. The present invention refines the process of predicting the breakthrough pressure by using the pore size distribution curve and the Laplace equation, and clarifies the role of the prediction model therein. The following effects have been achieved: first, it not only improves the accuracy and repeatability of the prediction, but also makes the prediction process more systematic and scientific; second, it can more accurately reflect the actual situation of mercury flow in the pore structure of shale materials; third, it is not only suitable for specific geological conditions, but can also be adjusted and optimized as needed to adapt to different application scenarios.

[0015] Optionally, the prediction of the breakthrough pressure of the shale caprock for geological storage of CO2 according to the predicted value includes: obtaining a mean square error value and a root mean square error value according to the predicted value and the measured value; determining an optimal predicted value according to the mean square error value and the root mean square error value; and predicting the breakthrough pressure of the shale caprock for geological storage of CO2 according to the optimal predicted value. The present invention refines and strengthens the verification and application process of the breakthrough pressure prediction model, and has the following effects: first, by calculating the mean square error and the root mean square error between the predicted value and the measured value, the accuracy of the prediction model can be quantitatively evaluated; second, by verifying the model and calculating the mean square error and the root mean square error value, the reliability of the prediction of the breakthrough pressure of the shale caprock for geological storage of CO2 can be further enhanced; third, by continuously verifying and improving the prediction model, the continuous advancement of CO2 geological storage technology can be promoted.

[0016] In the second aspect, the prediction system includes an input device, a processor, an output device and a memory, wherein the input device, the processor, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, the processor is configured to call the program instructions, and the system uses the method for predicting the breakthrough pressure of shale caprock for CO2 geological storage. The CO2 geological storage shale caprock breakthrough pressure prediction system provided by the present invention has high integration, smooth information transmission between various components, and has the characteristics of scientificity, accuracy, practicality, flexibility and efficiency. It plays an important role in CO2 geological storage projects and provides strong support for project decision-making, implementation and risk assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flow chart of a method for predicting the breakthrough pressure of a shale cap rock for CO2 geological storage according to an embodiment of the present invention;

[0018] Figure 2 A shale pore size distribution curve diagram of an embodiment of the present invention;

[0019] Figure 3 A comparison diagram of different prediction models in an embodiment of the present invention;

[0020] Figure 4 Schematic diagram of the structure of a system for predicting the breakthrough pressure of shale caprock for CO2 geological storage according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are only for illustration and are not intended to limit the present invention. In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is obvious to those of ordinary skill in the art that these specific details do not need to be adopted to implement the present invention. In other examples, in order to avoid confusing the present invention, known circuits, software or methods are not specifically described.

[0022] Throughout the specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment of the present invention. Therefore, the phrases "in one embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily all refer to the same embodiment or example. In addition, particular features, structures, or characteristics may be combined in one or more embodiments or examples in any suitable combination and / or subcombination. In addition, it should be understood by those of ordinary skill in the art that the figures provided herein are for illustrative purposes and that the figures are not necessarily drawn to scale.

[0023] See also Figure 1 The embodiment of the present invention provides a method for predicting the breakthrough pressure of shale caprock for CO2 geological storage, and the prediction method comprises the following steps:

[0024] S1. Obtain the mercury injection accumulation curve of the shale sample.

[0025] In one embodiment, the mercury injection cumulative curve of the shale sample is obtained by mercury injection method, which specifically includes the following steps:

[0026] S11. Obtain shale samples.

[0027] Specifically, representative shale samples are selected, which can reflect the overall characteristics of shale in the study area, such as pore structure, mineral composition, etc.

[0028] Furthermore, the shale sample is pretreated to remove adsorbed substances in the sample, such as water and organic matter, to reduce their influence on the pore structure measurement. The pretreatment includes grinding, drying, cutting, etc.

[0029] S12. Perform instrument calibration.

[0030] Specifically, the mercury injection instrument is calibrated before the experiment to ensure the accuracy of the experimental data. The calibration includes checking whether the various functions of the instrument are normal and adjusting the instrument parameters to meet the experimental requirements.

[0031] S13. Perform a mercury injection test.

[0032] Specifically, the processed shale sample is placed in the sample chamber of the mercury intrusion instrument, and care is taken to ensure that the sample is stably positioned in the sample chamber to avoid movement or breakage during the experiment.

[0033] Next, the mercury injection instrument is started and pressure is gradually applied. Under the action of pressure, mercury will begin to enter the pores of the shale sample. The mercury injection instrument will record the pressure required to inject mercury and the volume change of mercury.

[0034] Furthermore, the volume of injected mercury and the corresponding pressure value are recorded in real time. These data are important basis for subsequent analysis of shale pore structure.

[0035] S14. Draw a curve.

[0036] Specifically, the data recorded during the test are plotted to obtain a mercury injection accumulation curve of the shale sample.

[0037] The implementation features of this embodiment are its detailed, accurate and efficient data analysis capabilities. Through the mercury injection test, the changes in the pore structure of shale under different pressures can be accurately determined, and the cumulative curve intuitively shows the changing trend of the pore volume with the increase of mercury injection pressure. This process not only requires precise experimental equipment and strict operating procedures, but also relies on advanced data processing software to ensure the accuracy and reliability of the curve. The mercury injection cumulative curve can comprehensively reflect the microscopic pore characteristics of shale, provide an important basis for evaluating key parameters such as breakthrough pressure and permeability of shale caprock, and thus guide the safe design and implementation of CO2 geological storage projects.

[0038] S2. Using the mercury injection cumulative curve, the relationship between the cumulative mercury injection amount and the pore size is obtained.

[0039] Wherein, step S2 further comprises the following steps:

[0040] S21. Using the mercury injection cumulative curve, obtain the relationship between the mercury injection pressure and the cumulative mercury injection amount.

[0041] In one embodiment, data of the mercury injection pressure and the corresponding cumulative mercury injection amount on the mercury injection cumulative curve are extracted;

[0042] Furthermore, the data of the mercury injection pressure and the corresponding cumulative mercury injection amount are tabulated using Excel software to form a corresponding table No. 1. The data in the table No. 1 reflects the relationship between the mercury injection pressure and the cumulative mercury injection amount.

[0043] The implementation characteristics of this embodiment are directness, comprehensiveness and high resolution. By accurately reading the data points on the curve, the corresponding relationship between the mercury injection pressure and the cumulative mercury injection amount can be directly established without additional calculations or assumptions. This method comprehensively covers the entire injection process from low pressure to high pressure, and can reveal in detail the response characteristics of shale pore structure under different pressures. At the same time, the high-resolution curve data ensures the accuracy of the relationship, providing a solid foundation for in-depth analysis of shale storage and permeability performance and prediction of CO2 storage behavior.

[0044] S22. Substitute the mercury injection pressure into the Laplace equation to obtain the relationship between the mercury injection pressure and the pore size.

[0045] In one embodiment, the relationship between mercury injection pressure and pore diameter is obtained by Laplace equation.

[0046] Specifically, the Laplace equation is as follows:

[0047] ,

[0048] in, is the mercury injection pressure, is the interfacial tension, is the contact angle, In the Laplace equation, there is an obvious corresponding relationship between the mercury injection pressure and the pore diameter.

[0049] Furthermore, the mercury injection pressure data in Table 1 are respectively substituted into the Laplace equation to obtain corresponding pore size data.

[0050] Furthermore, the data of the mercury injection pressure and the corresponding pore diameter are tabulated using Excel software to form a No. 2 table with a corresponding relationship. The data in the No. 2 table reflects the relationship between the mercury injection pressure and the corresponding pore diameter.

[0051] This embodiment has the following implementation features: First, directness. This method directly measures the mercury injection pressure and uses the Laplace equation to calculate the pore size without complex model assumptions or data processing steps. Second, accuracy. and contact angle Under normal circumstances, this method can estimate the pore size relatively accurately. However, it should be noted that the surface tension and contact angle may vary depending on the experimental conditions, so appropriate corrections are required in practical applications. The third is data dependence. The accuracy of the pore size obtained by this method is highly dependent on the accurate measurement of the mercury injection pressure.

[0052] S23. According to the relationship between the mercury injection pressure and the pore diameter, obtain the relationship between the cumulative mercury injection amount and the pore diameter.

[0053] In one embodiment, the data of the accumulated mercury injection amount in Table No. 1 and the data of the pore diameter in Table No. 2 under the same mercury injection pressure are extracted.

[0054] Furthermore, the accumulated mercury inflow data extracted from Table 1 and the pore size data extracted from Table 2 are tabulated using Excel software to form a corresponding Table 3. The data in Table 3 reflects the relationship between the accumulated mercury inflow and the corresponding pore size.

[0055] In this embodiment, based on the relationship between the mercury injection pressure and the pore size, the relationship between the cumulative mercury injection amount and the pore size is further derived. The implementation characteristics are its comprehensiveness and indirectness. First, this derivation process requires the comprehensive application of the Laplace equation and the mercury injection experimental data, and the direct relationship between the mercury injection pressure and the pore size is converted into an indirect relationship between the cumulative mercury injection amount and the pore size through mathematical transformation. Secondly, due to the non-uniformity of the pore size distribution, this derivation often involves the integration or accumulation of multiple pore size intervals to reflect the contribution of different pore sizes to the cumulative mercury injection amount. In addition, the implementation process also needs to consider the impact of factors such as the connectivity of the pore network and the non-ideality of the pore shape on the results to ensure the accuracy and reliability of the derivation results. This process reflects the complexity and systematicity of pore structure analysis.

[0056] S3. Based on the relationship, obtain a pore size distribution curve.

[0057] Wherein, step S3 further comprises the following steps:

[0058] S31. Based on the relationship, a relationship curve between the cumulative amount of mercury and the pore size is obtained;

[0059] In one embodiment, the data in Table 3 are plotted using Origin software to obtain a relationship curve between the cumulative mercury injection amount and the pore size.

[0060] This embodiment derives the relationship curve between the cumulative mercury injection amount and the pore size based on the relationship between the mercury injection pressure and the pore size. The implementation characteristics of this embodiment are its detailed analysis and intuitive display. First, through precise mathematical models and algorithms, the complex pore structure information is converted into an intuitive curve graph. This process requires a high degree of data processing capabilities and calculation accuracy. Secondly, the curve graph can clearly show the changing trend of the cumulative mercury injection amount in different pore size ranges, providing an intuitive basis for pore structure analysis. In addition, the implementation process also needs to consider the influence of factors such as the connectivity of the pore network and the non-uniformity of the pore shape on the curve morphology to ensure the accuracy and representativeness of the curve. Finally, through this curve graph, the characteristics of the shale pore structure can be more deeply understood.

[0061] S32. Deriving the relationship curve, obtaining the rate of change of the mercury injection amount with the pore diameter.

[0062] In one embodiment, the rate of change of the mercury injection amount with the pore diameter during the pressure increase process is obtained by deriving the relationship curve between the cumulative mercury injection amount and the corresponding pore diameter.

[0063] The implementation characteristics of this embodiment are its mathematical rigor and direct physical meaning. First, the derivation process requires the use of calculus, which is conducive to ensuring the accuracy and precision of the calculation. Secondly, the change rate curve directly reflects the rate of increase of mercury injection under different pore sizes, providing more detailed information for pore structure analysis. Finally, through the change rate curve, the influence of pore size on mercury injection can be more intuitively understood, which provides strong support for the prediction of shale caprock breakthrough pressure.

[0064] S33. Obtain a pore size distribution curve based on the change rate.

[0065] In one embodiment, a pore size distribution curve is plotted based on the rate of change of mercury inlet with pore size, such as Figure 2 As shown. The horizontal axis is the corresponding pore size, and the vertical axis is the rate of change of the mercury injection amount with the pore size. The mercury injection amount reflects the volume of the pore.

[0066] The implementation feature of this embodiment lies in the complexity of its reverse deduction and data integration. On the one hand, the change rate reflects the contribution of different pore sizes to the amount of mercury inflow. By reversely analyzing this change rate, the distribution of pore sizes can be indirectly inferred. On the other hand, the acquisition of the pore size distribution curve provides an important basis for the detailed characterization of the pore structure.

[0067] S4. Obtain a predicted value of breakthrough pressure through the pore size distribution curve.

[0068] Wherein, step S4 further comprises the following steps:

[0069] S41. Obtaining the critical pore size through the pore size distribution curve;

[0070] In one embodiment, the critical pore size is determined using the pore size distribution characteristics of the pore size distribution curve.

[0071] Specifically, in the pore size distribution curve, when the rate of change of mercury injection with pore size is the maximum, the corresponding abscissa represents the pore size corresponding to the maximum pore volume in the shale sample, and the corresponding pore size is defined as the critical pore size.

[0072] The implementation feature of this embodiment is that it is based on the combination of statistical analysis and physical meaning. First, the critical pore size is usually defined as the pore size threshold that has a significant impact on the fluid flow in the pore system, corresponding to the characteristic point on the pore size distribution curve. Secondly, the process of determining the critical pore size needs to comprehensively consider the complexity of the pore structure, the fluid properties and the flow conditions, and is achieved by statistically analyzing the characteristic parameters of the pore size distribution curve. Finally, this process does not rely on the segmented processing of the pore size range, but is based on the continuity and integrity of the overall pore size distribution curve, ensuring the accuracy and scientificity of the critical pore size determination.

[0073] S42. Substitute the critical pore diameter into the Laplace equation to obtain a prediction model for breakthrough pressure.

[0074] In one embodiment, the interfacial tension and contact angle of the corresponding test system are selected, and the critical pore size is substituted into the Laplace equation to construct a prediction model for the breakthrough pressure.

[0075] Specifically, the prediction model satisfies the following expression:

[0076] ,

[0077] in, is the mercury injection pressure, is the interfacial tension, is the contact angle, is the critical aperture.

[0078] This embodiment introduces the critical pore size parameter into the Laplace equation to construct a breakthrough pressure prediction model. The key is to accurately describe the minimum pressure difference required for the fluid to pass through the porous medium. This model combines the basic principles of fluid mechanics with the microstructural characteristics of the material, and can predict the breakthrough behavior of fluids under different pore size distributions. The implementation features are: First, it is highly customized, and the critical pore size value is accurately set for the pore structure of a specific material to improve the prediction accuracy; second, theory and practice are closely combined, and the model parameters are verified through laboratory data to ensure the model prediction ability; third, it is widely used, not only in the field of CO2 geological storage technology, but also in many fields such as oil and gas exploration and water treatment.

[0079] S43. Obtain a predicted value of breakthrough pressure according to the prediction model.

[0080] Wherein, step S43 further includes the following steps:

[0081] S431. Perform model training on the prediction model.

[0082] In one embodiment, historical data such as mercury injection pressure, interfacial tension, contact angle and pore size are used to train the prediction model to obtain a trained prediction model, and the prediction value corresponding to the trained prediction model has a low discrete degree.

[0083] The implementation features of this embodiment are: first, emphasizing data quality, collecting a large amount of high-precision, multi-source test data, and ensuring the diversity and representativeness of training samples; second, adopting advanced algorithm optimization techniques, such as gradient descent method, genetic algorithm, etc., to improve the learning efficiency and convergence speed of model parameters; at the same time, focusing on the training of model generalization ability, reducing overfitting through methods such as cross-validation, and ensuring the prediction accuracy of the model under different conditions; finally, continuous iterative optimization, constantly adjusting model parameters and structure according to newly acquired test data or actual application feedback, and improving the adaptability and accuracy of the model.

[0084] S432. Perform model verification on the prediction model.

[0085] In one embodiment, the trained model is verified using the test set data, and the prediction performance of the prediction model of the present invention is evaluated by judging the difference between the predicted value and the measured value and combining with other prediction models.

[0086] Specifically, see Figure 3 , Figure 3 It is a comparison chart of different prediction models of the embodiments of the present invention. Among them, the horizontal axis represents the predicted value, the vertical axis represents the measured value, prediction model 1 refers to the prediction model of the present invention, prediction model 2 refers to the prediction model with a mercury saturation of 10%, and prediction model 3 refers to the prediction model of Zhou (2022). The prediction method of the prediction model 2 is to characterize the breakthrough pressure by the pressure value corresponding to the mercury saturation of 10%. The prediction method of the prediction model 3 is to establish the relationship between the breakthrough pressure and porosity of different shale samples, and then calculate the breakthrough pressure through the relationship fitting formula. Figure 3 The prediction model analysis is as follows:

[0087] (1) The data points of prediction model 1 are evenly distributed near the 1:1 line, and the difference between the predicted values ​​and the measured values ​​is small.

[0088] (2) The predicted values ​​of prediction model 2 are generally small and have a large degree of dispersion, resulting in large prediction deviations.

[0089] (3) The predicted values ​​of prediction model 3 are concentrated around 4 MPa, which has a small scope of application and is difficult to accurately predict the breakthrough pressure of different shales.

[0090] The implementation features of this embodiment are: first, ensuring the independence and representativeness of the verification data, using a data set that has not participated in the model training for verification to objectively evaluate the model performance; second, implementing comprehensive evaluation indicators, including prediction accuracy, error range, stability, etc., to comprehensively examine the prediction effect of the model; finally, conducting a sensitivity analysis to explore the impact of different input parameters on the prediction results and verify the robustness of the model.

[0091] S433. Obtain a predicted value of breakthrough pressure using the prediction model with the best prediction performance.

[0092] In one embodiment, actual data such as interfacial tension, contact angle and pore size are substituted into the prediction model with the best prediction performance to obtain a predicted value of the breakthrough pressure.

[0093] The implementation features of this embodiment are: first, based on a strict verification process, the model with excellent performance in various evaluation indicators is screened out to ensure the accuracy of the prediction results; second, the learning results of the model on the training data are fully utilized, combined with the specific conditions of the actual application scenario, and the corresponding parameter values ​​are input for prediction calculation; finally, the interpretability and comprehensibility of the prediction results are emphasized.

[0094] S5. Based on the predicted value, the breakthrough pressure of the shale caprock for CO2 geological storage is predicted.

[0095] Wherein, step S5 further comprises the following steps:

[0096] S51. Obtain a mean square error value and a root mean square error value based on the predicted value and the measured value.

[0097] In one embodiment, a series of predicted values ​​and measured values ​​of breakthrough pressure are substituted into expressions of mean square error and root mean square error to calculate and obtain mean square error value and root mean square error value.

[0098] Specifically, the expression of the mean square error is as follows:

[0099] ,

[0100] The expression of the root mean square error is as follows:

[0101] ,

[0102] in, represents the mean square error, represents the root mean square error, represents the predicted value of breakthrough pressure, Indicates the actual value of the breakthrough pressure, n Indicates the number of data.

[0103] In this embodiment, different prediction methods are given. MSE Value and RMSE Values, as described in Table 1.

[0104] Table 1. Different prediction methods MSE Value and RMSE value

[0105]

[0106] As can be seen from Table 1, the prediction method of the present invention MSE Value and RMSE The values ​​are 21.26 and 4.61 respectively. MSE Value and RMSE The values ​​are the smallest among all related prediction methods, indicating that the prediction method of the present invention has the highest accuracy.

[0107] The implementation characteristics of this embodiment are: first, ensuring the one-to-one correspondence between the predicted value and the measured value in time and space to accurately measure the prediction accuracy; second, using mathematical formulas to directly calculate MSE and RMSE , these two indicators can quantify the magnitude of the prediction deviation, MSE reflects the forecast bias, while RMSE is MSE The square root of the prediction deviation is more intuitive. Finally, the calculation process is automated, and programming tools or statistical software are used to quickly obtain results, thereby improving calculation efficiency.

[0108] S52. Based on the mean square error value and the root mean square error value, the breakthrough pressure of the shale cap rock for CO2 geological storage is predicted.

[0109] In one embodiment, the degree of closeness between the predicted value and the measured value is determined by judging the size of the mean square error value and the root mean square error value, so as to further predict the breakthrough pressure of the shale cap rock for CO2 geological storage. Specifically, the following steps are included:

[0110] S521. Determine an optimal prediction value according to the mean square error value and the root mean square error value.

[0111] Specifically, for the same shale sample, the prediction method of the present invention obtains MSE and RMSE The smaller the value of is, the closer the predicted value is to the measured value, which means the higher the prediction accuracy is. Then, the predicted value at this time is determined as the optimal predicted value.

[0112] S522. Based on the optimal prediction value, the breakthrough pressure of the shale cap rock for CO2 geological storage is predicted.

[0113] Specifically, the optimal predicted value of breakthrough pressure is regarded as an estimate that is highly close to the actual value, and this predicted value is used to accurately predict the breakthrough pressure of shale caprock during CO2 geological storage. This prediction process integrates multidisciplinary knowledge and technology, including but not limited to geology, rock mechanics, fluid mechanics and advanced computational simulation methods.

[0114] This embodiment is based on MSE and RMSE The method uses the historical monitoring data to analyze the breakthrough pressure of shale caprock in CO2 geological storage. MSE It measures the difference between the predicted value and the true value. RMSE The differences are further standardized to directly reflect the prediction accuracy. The implementation features are that it is both scientific and practical, which not only improves the prediction accuracy, but also enhances the adaptability of the model to complex geological conditions, providing reliable technical support for the safe storage of CO2.

[0115] See also Figure 4 , Figure 4 The schematic diagram of the structure of the prediction system for the breakthrough pressure of shale caprock for CO2 geological storage in the embodiment of the present invention is shown. The system includes an input device, a processor, an output device and a memory, wherein the input device, the processor, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, the processor is configured to call the program instructions, and the prediction system uses the prediction method for the breakthrough pressure of shale caprock for CO2 geological storage.

[0116] In this embodiment, the input device uses a touch screen, and the touch screen is mainly used to input data information such as the actual measured value of breakthrough pressure, mercury injection pressure, cumulative mercury injection amount, interfacial tension, contact angle and pore size.

[0117] The processor serves as the computing core of the system and has multiple core algorithm modules built in, including a mathematical derivation algorithm, a prediction model algorithm, a mean square error algorithm, and a root mean square error algorithm. The algorithms are used to solve and analyze the breakthrough pressure prediction value.

[0118] The output device uses a display screen, which is mainly used to display the results of solving and analyzing the breakthrough pressure prediction value, including the breakthrough pressure prediction value, MSE value, RMSE Value and analysis reports.

[0119] The memory adopts a high-speed solid-state hard disk, which has the characteristics of fast reading and writing speed, large capacity and high reliability. It is used to store the original data input by the input device and the result data processed by the processor, and can meet the needs of large data storage.

[0120] In summary, the present invention predicts the shale breakthrough pressure by defining the critical pore size of the non-wetting phase breaking through the shale sample based on the consideration of different pore size distribution curves of different shale samples, which effectively overcomes the limitations of the existing in-situ breakthrough pressure test of cores and solves the problem of low accuracy of the existing prediction methods. It has the advantages of short time consumption, high accuracy and low cost, and can better predict the breakthrough pressure of many shale samples.

[0121] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A method for predicting the breakthrough pressure of shale caprock for CO2 geological storage, characterized in that: The prediction method comprises the following steps: Obtain the mercury injection accumulation curve of shale samples; Using the mercury injection cumulative curve, the relationship between the mercury injection pressure and the cumulative mercury injection amount is obtained; Substituting the mercury injection pressure into the Laplace equation, the relationship between the mercury injection pressure and the pore size is obtained; According to the relationship between the mercury injection pressure and the pore diameter, the relationship between the cumulative mercury injection amount and the pore diameter is obtained; According to the relationship between the cumulative mercury inflow amount and the pore size, a pore size distribution curve is obtained; The predicted value of breakthrough pressure is obtained through the pore size distribution curve, and the predicted value satisfies the following expression: in, is the mercury injection pressure, is the interfacial tension, is the contact angle, is the critical aperture; According to the predicted value, the breakthrough pressure of the shale cap rock for CO2 geological storage is predicted.

2. The method for predicting the breakthrough pressure of shale caprock for CO2 geological storage according to claim 1, characterized in that: The obtaining of the pore size distribution curve according to the relationship between the cumulative mercury injection amount and the pore size comprises: According to the relationship between the cumulative mercury injection amount and the pore diameter, a relationship curve between the cumulative mercury injection amount and the pore diameter is obtained; The relationship curve is derived to obtain a pore size distribution curve.

3. The method for predicting the breakthrough pressure of shale caprock for CO2 geological storage according to claim 2, characterized in that: The derivation of the relationship curve to obtain the pore size distribution curve comprises: The relationship curve is derived to obtain the rate of change of the mercury inlet amount with the pore diameter; According to the change rate, a pore size distribution curve is obtained.

4. The method for predicting the breakthrough pressure of shale caprock for CO2 geological storage according to claim 1, characterized in that: The method of obtaining a predicted value of breakthrough pressure through the pore size distribution curve includes: Obtaining the critical pore size through the pore size distribution curve; Substituting the critical pore diameter into the Laplace equation, the predicted value of the breakthrough pressure is obtained.

5. The method for predicting the breakthrough pressure of shale caprock for CO2 geological storage according to claim 4, characterized in that: Substituting the critical pore diameter into the Laplace equation to obtain a predicted value of the breakthrough pressure includes: Substituting the critical pore diameter into the Laplace equation to obtain a prediction model for breakthrough pressure; According to the prediction model, a predicted value of breakthrough pressure is obtained.

6. The method for predicting the breakthrough pressure of shale caprock for CO2 geological storage according to claim 1, characterized in that: The prediction of the breakthrough pressure of the shale caprock for CO2 geological storage based on the predicted value includes: According to the predicted value and the measured value, a mean square error value and a root mean square error value are obtained; Determining an optimal prediction value according to the mean square error value and the root mean square error value; According to the optimal prediction value, the breakthrough pressure of the shale cap rock for CO2 geological storage is predicted.

7. A system for predicting the breakthrough pressure of shale caprock for CO2 geological storage, the system using the method for predicting the breakthrough pressure of shale caprock for CO2 geological storage according to any one of claims 1 to 6, characterized in that: The system includes an input device, a processor, an output device and a memory, wherein the input device, the processor, the output device and the memory are connected to each other, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions.

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