Reservoir prediction optimization method and system based on buried hill volcanic rock core test

Abstract

The invention relates to a reservoir prediction optimization method and system based on a buried hill volcanic rock core test. The method comprises the following steps: S1, performing ultrasonic active source detection on a volcanic rock sample through an annular observation system to obtain actually measured travel time and a ray path; and S2, theoretical travel time and a ray path are calculated based on the initial speed model, iterative updating is carried out on the initial speed model based on a preset inversion method until the theoretical travel time and the ray path are consistent with the actually measured travel time and the ray path, model iteration is completed, the model is output, and the output model is the speed structure in the volcanic rock sample. And S3, establishing a rock physical model according to the speed structure and the core pores. And S4, calculating according to the rock physical model to obtain a rock physical interpretation quantity version related to the dessert parameters, and performing engineering and geological dessert parameter prediction by using pre-stack seismic data. According to the method, the key parameters of the buried hill volcanic reservoir can be effectively predicted through accurate rock physical modeling.

Description

Technical Field

[0001] The present invention relates to the field of geophysical exploration rock physics modeling, and in particular to a reservoir prediction optimization method based on buried hill volcanic rock core testing and a system thereof. Background Art

[0002] The study and evaluation of buried-hill volcanic reservoirs are of great significance in the process of oil and gas exploration and development. Seismic reservoir parameter prediction is a key means of gaining insight into reservoir characteristics and understanding their distribution, while rock physics modeling serves as a crucial bridge connecting reservoir elastic parameters with physical properties. However, traditional rock physics modeling faces numerous challenges when applied to buried-hill volcanic reservoirs. Firstly, obtaining accurate rock physics parameters is challenging. Conventional experimental measurement methods, such as core analysis, rely on traditional methods, which only provide small, discrete parameter data and fail to capture the overall heterogeneity of the reservoir. Furthermore, the geological conditions of buried-hill volcanic reservoirs are complex, with extremely complex and diverse internal pore structures and fracture distributions. Conventional methods cannot comprehensively and accurately capture this critical information. Furthermore, the relationship between geophysical data and rock physics parameters is complex, making it difficult to accurately establish a link. While seismic and well logging data can provide macroscopic reservoir detection, the complexity of the reservoir makes it difficult to directly extract accurate rock physics parameters.

[0003] Ultrasonic tomography offers unique advantages for studying the internal structure of objects, providing information on their velocity structure, which is closely correlated with rock physical properties. However, its application in rock physical modeling of buried-hill volcanic reservoirs is still immature, lacking a systematic approach and efficient integration. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a reservoir prediction optimization method based on buried hill volcanic rock core testing and a system thereof.

[0005] The technical solution adopted by the present invention to solve the technical problem is: a reservoir prediction optimization method based on buried hill volcanic rock core testing, comprising the following steps:

[0006] S1. Conduct ultrasonic active source detection on volcanic rock samples through a ring observation system to obtain measured travel times and ray paths;

[0007] S2. Calculating theoretical travel times and ray paths based on the initial velocity model, and iteratively updating the initial velocity model based on a preset inversion method until the theoretical travel times and ray paths are consistent with the measured travel times and ray paths. The model iteration is completed, and the model is output. The output model is the velocity structure inside the volcanic rock sample.

[0008] S3. Establishing a rock physics model based on the velocity structure and core pores;

[0009] S4. Calculate rock physics interpretation quantities related to sweet spot parameters based on the rock physics model, and use pre-stack seismic data to predict engineering and geological sweet spot parameters.

[0010] Furthermore, in the reservoir prediction optimization method based on buried hill volcanic rock core testing of the present invention, step S2 includes:

[0011] Integrating the slowness s along the propagation path l from the ultrasonic excitation device to the ultrasonic receiving device in the annular observation system to obtain the travel time t;

[0012] Discretize the two-dimensional section into n grids, and the travel time t is expressed as: t ij =a ij s j ;

[0013] Among them, s j is the slowness in the jth grid, a ij is the length of the i-th ray passing through the j-th grid;

[0014] The initial velocity model is expressed in matrix form as:

[0015] t=As

[0016] Furthermore, in the reservoir prediction optimization method based on buried-hill volcanic rock core testing of the present invention, step S2 includes:

[0017] The initial velocity model is forward calculated based on the rapid marching method to obtain approximation A, and the initial velocity model is inverted based on the preset inversion method and approximation A to obtain a slowness inversion result. The model iteration is completed until the theoretical travel time and ray path are consistent with the measured travel time and ray path, and the model is output.

[0018] Furthermore, in the reservoir prediction optimization method based on buried-hill volcanic rock core testing of the present invention, step S2 includes:

[0019] The initial velocity model is forward calculated based on the rapid advance method, and its expression in two-dimensional space is:

[0020]

[0021] The corresponding discrete expression is:

[0022]

[0023] Where s(x,z) represents the inverse of velocity, and represents the backward difference and forward difference along the x-axis direction; and represents the backward and forward differences along the z-axis.

[0024] Furthermore, in the reservoir prediction optimization method based on buried hill volcanic rock core testing of the present invention, step S2 further includes:

[0025] The first-order expression and the second-order expression along the x direction are derived by using Taylor expansion of the discrete expression:

[0026]

[0027] Substituting the first-order backward difference operator along the x direction and the first-order backward difference operator along the z direction, we obtain:

[0028]

[0029] Furthermore, in the reservoir prediction optimization method based on buried-hill volcanic rock core testing of the present invention, in step S2, the iterative updating of the initial velocity model based on a preset inversion method includes:

[0030] Laplace regularization is used to constrain the preset inversion method, and the Gauss-Newton method is used to iterate the preset inversion method after the Laplace regularization is introduced to obtain a slowness inversion result.

[0031] Furthermore, in the reservoir prediction optimization method based on buried-hill volcanic rock core testing of the present invention, step S2 includes:

[0032] The least square method is used as the preset inversion method:

[0033]

[0034] Where, t obs is the measured travel time extracted from the measured data, and t(s) is the theoretical travel time obtained by forward calculation.

[0035] Furthermore, in the reservoir prediction optimization method based on buried-hill volcanic rock core testing of the present invention, step S2 includes:

[0036] The Laplace regularization is:

[0037]

[0038] Where L is the given Laplace operator The matrix approximation of ; the matrix-vector product Lm is the pixel value vector of the Laplace function of the slowness distribution Alpha is a regularization parameter, used to penalize the Laplacian of the reconstructed slowness image.

[0039] Further, in the reservoir prediction optimization method based on buried hill volcanic rock core testing provided by the application, the annular observation system is designed according to the shape of the volcanic rock sample, and the annular observation system comprises a plurality of probes arranged around the core of the volcanic rock sample, each probe sequentially excites a passive source ultrasonic signal as an ultrasonic excitation device, and each probe does not receive a signal when exciting the passive source ultrasonic signal, and the remaining probes receive signals as ultrasonic receiving devices.

[0040] In addition, the application also provides a reservoir prediction optimization system based on buried hill volcanic rock core testing, comprising:

[0041] The measured data unit is used for ultrasonic passive source detection of the volcanic rock sample by the annular observation system to obtain measured travel time and ray path;

[0042] The velocity model construction unit is used for calculating theoretical travel time and ray path based on the initial velocity model, and iteratively updating the initial velocity model based on a preset inversion method until the theoretical travel time and ray path are consistent with the measured travel time and ray path, then the model iteration is completed, and the model is output, and the output model is the velocity structure inside the volcanic rock sample;

[0043] The petrophysical model construction unit is used for establishing a petrophysical model according to the velocity structure and the core pore;

[0044] The prediction unit is used for calculating a petrophysical interpretation version related to the sweet spot parameter based on the petrophysical model, and using prestack seismic data to predict engineering and geological sweet spot parameters.

[0045] The reservoir prediction optimization method and system based on buried hill volcanic rock core testing provided by the application have the following beneficial effects: the application provides an efficient method for accurately providing parameters for buried hill volcanic reservoir petrophysical modeling based on ultrasonic tomography, can fully consider the complex characteristics of buried hill volcanic reservoirs, design an annular observation system to obtain ultrasonic information on different paths, deeply mine useful information in ultrasonic tomography data, and obtain the velocity structure inside the volcanic rock sample. The application realizes full-process automatic operation from ultrasonic data acquisition, processing to petrophysical modeling parameter extraction and application, significantly improves work efficiency and modeling accuracy, ensures the accuracy and stability of the results, realizes effective prediction of key parameters of buried hill volcanic reservoirs through accurate petrophysical modeling, provides a more solid and reliable theoretical basis and technical support for exploration and development of buried hill volcanic reservoirs, reduces exploration risks, and significantly improves oil and gas exploitation benefits. BRIEF DESCRIPTION OF DRAWINGS

[0046] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0047] Figure 1 1 is a flow chart of a reservoir prediction optimization method based on buried hill volcanic rock core testing provided by an embodiment of the present invention;

[0048] Figure 2 is a schematic structural diagram of a ring-shaped observation system in some embodiments;

[0049] Figure 3 is a CT scan image of the internal fracture structure of sandstone in a certain area after fracturing in some embodiments;

[0050] Figure 4 Schematic diagrams of two velocity models, one primary fracture and one secondary fracture, in some embodiments;

[0051] Figure 5 The inversion results of some embodiments are obtained by iterating the initial velocity model 100 times using only the Gauss-Newton method;

[0052] Figure 6 The inversion results of some embodiments after introducing Laplace regularization and using the Gauss-Newton method to iterate the initial velocity model 100 times;

[0053] Figure 7 are rock samples actually measured in some embodiments;

[0054] Figure 8 are velocity structure inversion results of actual rock samples after Laplace regularization is introduced in some embodiments;

[0055] Figure 9 It is a rock physics interpretation version applicable to buried hill volcanic reservoirs obtained by calculation based on the established rock physics model in some embodiments;

[0056] Figure 10 Some embodiments use pre-stack seismic data to perform porosity inversion on buried-hill volcanic reservoirs, and compare and analyze the inversion results of satellite wells with logging data;

[0057] Figure 11 It is a structural schematic diagram of a reservoir prediction optimization system based on buried hill volcanic rock core testing provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, the specific embodiments of the present invention are now described in detail with reference to the accompanying drawings. In the following description, it should be understood that the directions or positional relationships indicated by "front", "back", "up", "down", "left", "right", "longitudinal", "horizontal", "vertical", "horizontal", "top", "bottom", "inside", "outside", "head", "tail", etc. are based on the directions or positional relationships shown in the accompanying drawings and are constructed and operated in specific directions. They are only for the convenience of describing the technical solution and do not indicate that the devices or components referred to must have specific directions. Therefore, they should not be understood as limiting the present invention.

[0059] It should also be noted that, unless otherwise expressly specified or limited, terms such as "mounted," "connected," "connected," "fixed," and "disposed" should be understood broadly. For example, they can refer to fixed or detachable connections, or integration; mechanical or electrical connections; direct connections or indirect connections through an intermediate medium; and can refer to internal communication between two elements or interaction between two elements. When an element is referred to as being "on" or "under" another element, the element can be "directly" or "indirectly" on the other element, or one or more intervening elements may be present.

[0060] In the following description, specific details such as particular system structures and techniques are provided for purposes of illustration, not limitation, to facilitate a thorough understanding of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

[0061] like Figure 1 As shown, in a preferred embodiment, the reservoir prediction optimization method based on buried hill volcanic rock core testing of this embodiment includes the following steps:

[0062] S1. Ultrasonic active source detection is performed on volcanic rock samples using a ring-shaped observation system to obtain measured travel times and ray paths. This step uses ultrasonic tomography to scan the buried-hill volcanic reservoir, determining velocity distributions at different locations within the reservoir. This provides key and more spatially continuous parameters for rock physics modeling, helping to build more accurate rock physics models and improve the prediction of buried-hill volcanic reservoir parameters.

[0063] In some embodiments, the annular observation system is designed according to the shape of the volcanic rock sample. The annular observation system includes a plurality of probes surrounding the core of the volcanic rock sample. Each probe acts as an ultrasonic excitation device to sequentially excite an active source ultrasonic signal. Each probe does not receive a signal when stimulating an active source ultrasonic signal, and the remaining probes act as ultrasonic receiving devices to receive the signal. Figure 2 As shown, Figure 2 The schematic diagram of the annular observation system according to an embodiment of the present invention is shown. A volcanic rock sample is simulated, with each probe sequentially stimulating an active ultrasonic wave. Each probe receives no signal during the stimulation phase, while the remaining ultrasonic receivers receive the signal. If n positions are designed in the annular ring for stimulating and receiving ultrasonic signals, a total of n time information (i.e., n travel times) will be obtained. The number n can be selected based on actual experimental conditions and is not specifically limited in this invention.

[0064] S2. Calculate theoretical travel times and ray paths based on the initial velocity model, and iteratively update the initial velocity model based on a preset inversion method until the theoretical travel times and ray paths are consistent with the measured travel times and ray paths. The model iteration is then complete, and the model is output. The output model represents the velocity structure within the volcanic rock sample. It should be noted that the theoretical travel times and ray paths described in the embodiments of the present invention are considered consistent with the measured travel times and ray paths when they are within a preset allowable deviation range, or when the accuracy of the velocity model reaches a preset standard accuracy. It should also be noted that the embodiments of the present invention do not limit the specific number of iterations of the velocity model. For example, in some embodiments, the number of iterations may be 100, 150, 200, or the like.

[0065] S3. Establish a rock physics model based on the velocity structure and core pores. In some embodiments, the core pores can be derived based on the velocity structure within the volcanic rock sample. It is understood that the pores are generally filled with air, with a constant velocity, such as 340 m / s.

[0066] S4. Obtain rock physics interpretation quantities related to sweet spot parameters based on rock physics model calculations, and use pre-stack seismic data to predict engineering and geological sweet spot parameters.

[0067] This embodiment is an efficient method based on ultrasonic tomography to accurately provide parameters for rock physics modeling of buried-hill volcanic reservoirs. It fully considers the complex characteristics of buried-hill volcanic reservoirs, designs a ring-shaped observation system to obtain ultrasonic information along different paths, and deeply mines useful information from ultrasonic tomography data to obtain the velocity structure within the volcanic rock sample. It achieves full automation of the entire process, from ultrasonic data acquisition and processing to rock physics modeling parameter extraction and application, significantly improving work efficiency and modeling accuracy, ensuring the accuracy and stability of the results. Through precise rock physics modeling, it effectively predicts key parameters of buried-hill volcanic reservoirs, providing a more solid and reliable theoretical basis and technical support for the exploration and development of buried-hill volcanic reservoirs, reducing exploration risks, and significantly improving oil and gas production efficiency.

[0068] Combine Figures 2 to 10 The reservoir prediction optimization method based on buried hill volcanic rock core testing proposed by the present invention is described in detail below through a specific implementation method.

[0069] The main core contents of this invention include two aspects: First, a ring-shaped observation system and observation method are designed based on the shape of the volcanic rock sample to achieve full-scale ultrasonic detection of complex rock cores. Second, a velocity structure inversion method is designed to improve inversion accuracy, completing the precise characterization of the pores and velocity structure inside the rock core. The specific steps of the embodiment of this invention include:

[0070] (1) A circular observation system is used to analyze volcanic rock samples. Under the assumption of high-frequency approximation, the propagation path of ultrasound inside the volcanic rock sample can be approximated as a ray, and the travel time t can be expressed as the integral of the slowness s along the propagation path l from the ultrasonic excitation device to the receiving device:

[0071]

[0072] (2) Discretize the two-dimensional section into n grids, and record the slowness in the j-th grid as s j , the length of the i-th ray passing through the j-th grid is a ij , then according to, the travel time information generated by the ultrasonic wave propagating in the volcanic rock sample to be tested can be expressed as: t ij =a ij s j , the matrix form can be expressed as: t = As.

[0073] It can be understood that the matrix expression is also the initial velocity model.

[0074] (3) The linear equations can be solved by the preset inversion method to obtain the value of the slowness s, and then calculate the velocity v. However, the information provided by the equation system t = As is insufficient to uniquely determine all unknowns, and its exact solution cannot be obtained through numerical calculation. Therefore, this embodiment uses the least squares method as the preset inversion method to solve this problem:

[0075]

[0076] Where, t obs is the measured travel time extracted from the measured data, and t(s) is the theoretical travel time obtained by forward calculation.

[0077] It can be understood that in the process of ultrasonic tomography (i.e., the process of inverting the velocity structure), the forward travel time modeling is to use the discrete velocity model to calculate the theoretical travel time and ray path. obs is the travel time information extracted from the measured data, t(s) is the travel time calculated by forward simulation, and the slowness s can be obtained through the inversion method. The velocity structure inside the volcanic rock sample can be obtained by continuous iteration.

[0078] (4) The initial velocity model is forward calculated based on the rapid marching method to obtain approximate A, and the initial velocity model is inverted based on the preset inversion method and approximate A to obtain the slowness inversion result. The model iteration is completed and the model is output until the theoretical travel time and ray path are consistent with the measured travel time and ray path.

[0079] It can be understood that the grid-based ray tracing algorithm (Fast Marching Method, FMM) can be used to calculate travel times and ray paths according to the required accuracy. The FMM is a fast calculation method based on wavefront propagation, which can determine the ray path and obtain the travel time of ultrasound in volcanic rock samples. Specifically, the eikonal equation is expressed in two dimensions as follows:

[0080]

[0081] Discretize formula (3), and the corresponding discrete expression is:

[0082]

[0083] Where s(x,z) represents the inverse of velocity, and represents the backward and forward differences along the x-axis. and represents the backward and forward differences along the z-axis.

[0084] Furthermore, the discrete expression is derived using Taylor expansion to obtain the first-order expression and the second-order expression along the x direction:

[0085]

[0086] It can be understood that the same applies to the z direction.

[0087] Furthermore, by bringing in the first-order backward difference operator along the x direction and the first-order backward difference operator along the z direction, we obtain:

[0088]

[0089] Furthermore, Laplace regularization is used to constrain the preset inversion method, and the Gauss-Newton method is used to iterate the preset inversion method after introducing Laplace regularization to obtain a slowness inversion result. This embodiment further improves the inversion accuracy by introducing regularization constraints.

[0090] Understandably, the Gauss-Newton method can lead to non-smoothness in the slowness inversion results. Furthermore, ultrasonic tomography requires multiple sensors to sequentially excite and receive sound waves, obtaining travel time information at different observation angles and thus revealing the internal structure. In practice, sensors are sparsely distributed, further leading to multiple solutions to the equations. To address this issue, embodiments of the present invention employ methods such as regularization constraints to improve the accuracy and resolution of the inversion results.

[0091] Specifically, Laplace regularization is:

[0092]

[0093] Where L is the given Laplace operator The matrix approximation of . The matrix-vector product Lm is the pixel value vector of the Laplace function of the slowness distribution α is a regularization parameter used to penalize the Laplace operator of reconstructing the slowness image.

[0094] This embodiment designs an optimized annular observation system and introduces geostatistical-based regularization constraints to invert the velocity structure of volcanic rock samples. Compared to traditional volcanic rock physical acoustic experiments, this method can obtain more observation data and significantly improve inversion accuracy, enabling a more precise characterization of the internal structure of buried-hill volcanic reservoirs and providing more reliable data support for reservoir prediction.

[0095] It is understood that when solving the ultrasonic active source tomography problem, the embodiment of the present invention generally needs to give an initial velocity model, and use the rapid marching method to forward-model the approximate A based on the velocity model, perform inversion to obtain the slowness s, and then use the updated slowness to obtain A again until the iteration is completed. Ultrasonic tomography requires solving The gradient of this least squares problem can be expressed as 2J(s) T (t(s)-t obs ), J(s) is the Jacobian matrix of t(s), and the problem can be solved by the Gauss-Newton method. According to the velocity structure obtained by inverting the ultrasonic tomography data, a volcanic rock physical model suitable for the buried hill is established, and a rock physical interpretation version related to the sweet spot parameters is formed, and then the pre-stack seismic data is used to realize the prediction of engineering and geological sweet spot parameters. It should be noted that the Gauss-Newton method is an iterative method for solving nonlinear least squares problems. It finds the optimal solution by step-by-step approximation. The iterative steps of the Gauss-Newton method in the embodiment of the present invention can refer to the prior art and will not be repeated here.

[0096] Figure 3 The CT scan image shows the internal fracture structure of sandstone in a certain area after fracturing. It can be seen that the sandstone contains a wider main fracture and thinner secondary fractures.

[0097] Figure 4 Two velocity models are shown: one for a main fracture and one for a secondary fracture. When using ultrasonic active source tomography, the main consideration is whether the changes in the internal physical properties of volcanic rocks, such as fractures, can be accurately identified. In order to be closer to the actual situation, the velocity model is constructed using CT scanning data of actual volcanic rocks. The model is constructed based on the spatial distribution of CT values. The observation system is in a rectangular coordinate system. Figure 4 The model in Figure 1 is discretized into a 300 × 300 grid, and the cracks are distributed within the model according to the grid points. The black points (grid points) evenly distributed in a ring represent the locations of the ultrasonic excitation and receiving devices in the ring observation system.

[0098] Figure 5This is the inversion result after 100 iterations of the Gauss-Newton method. The inversion usually starts from an initial model. The uniform background model is used as the initial model for the inversion, and the initial velocity v0 is set to 3000m / s. When using ultrasonic tomography technology to analyze volcanic rocks, the distribution of cracks and pores inside the volcanic rocks is unknown, so the initial model selected is a uniform model. The initial model is far from the true model. At the same time, due to the sparsity of the observation system, it further leads to the multi-solution problem. In order to better highlight the contrast, the inversion results mainly show the low-speed area obtained by inversion, that is, the velocity area with a velocity less than 2985m / s, such as Figure 5 As shown, the inversion results show a high number of artifacts near the fractures, interfering with fracture identification and resulting in low inversion accuracy. However, the low-velocity region of 2500-2700 m / s is concentrated at the location of the main fracture. When there is only one main fracture, the Gauss-Newton method can accurately infer the main fracture's direction. However, when there is a main fracture and a secondary fracture, more artifacts are present in the fracture's center.

[0099] Figure 6 It is the inversion result after 100 iterations of Gauss-Newton method after introducing Laplace regularization. After the regularization term is introduced, the ultrasonic tomography problem becomes a solution. By introducing the Laplace function as a measure of image roughness and penalizing image roughness, the inversion results become smoother and artifacts are effectively suppressed. The inversion model of a single primary fracture is clearer, and the model of two fractures also reveals the cracking direction of finer secondary fractures.

[0100] Figure 7 It is the actual rock sample measured. Figure 8 It is the velocity structure inversion result of the actual rock sample after introducing Laplace regularization. Figure 9 It is a rock physics interpretation version applicable to buried hill volcanic reservoirs calculated after establishing a rock physics model based on the measured core pore structure and velocity inversion model, providing a reference for reservoir parameter analysis. Figure 10 The porosity inversion results of buried-hill volcanic reservoirs were obtained using pre-stack seismic data, and the inversion results were compared with logging data to demonstrate the accuracy of the method in reservoir prediction.

[0101] It can be understood that this embodiment can be used to conduct active source ultrasonic tomography experiments on actual volcanic rock cores in a certain work area, and further explore the feasibility of velocity structure inversion of volcanic rock samples. A ring-shaped observation system was used for this experiment to obtain relevant information about ultrasound. Taking into account the size of the core measured in the experiment and the size of the ultrasonic probe, 8 sensors were set around the core to perform ultrasonic excitation and reception in turn. The experimental core was measured by a movable ultrasonic probe, and the position setting and excitation method of the excitation probe and the receiving probe remained the same as those in the numerical simulation. Comparison Figure 7 The low speed area and Figure 6 The fracture area inside the core can be found to be relatively consistent with the positions of the two. Through the active source ultrasonic tomography technology proposed in this patent, the low-velocity area inside the core can be inverted, and the internal defects of the core can be detected. The rock physics modeling and pre-stack seismic inversion based on this invention have achieved good application results in this work area. Figure 9 Comparison of the logging data with the inversion results shows that the model established using the method of the present invention has higher prediction accuracy, which provides strong support for the exploration and development of buried hill volcanic reservoirs.

[0102] This embodiment uses an optimized annular observation system and introduces geostatistical-based regularization constraints to invert the velocity structure of volcanic rock samples. Compared with traditional volcanic rock physical acoustic experiments, more observation data can be obtained and the inversion accuracy is significantly improved. This can more accurately characterize the internal structure of the buried-hill volcanic rock reservoir and provide more reliable data support for reservoir prediction.

[0103] refer to Figure 11 In another preferred embodiment, the reservoir prediction optimization system based on buried-hill volcanic rock core testing of this embodiment includes:

[0104] The measured data unit is used to perform ultrasonic active source detection on volcanic rock samples using a ring-shaped observation system to obtain measured traveltimes and ray paths. This embodiment of the present invention fully considers the challenges encountered in actual volcanic rock testing. The ring-shaped observation system is designed to obtain ultrasonic information along different paths and introduces regularization constraints to further improve inversion accuracy. This allows the velocity structure (elasticity information) within the volcanic rock sample to be determined from ultrasonic data.

[0105] The velocity model construction unit is used to calculate the theoretical travel time and ray path based on the initial velocity model, and iteratively update the initial velocity model based on a preset inversion method until the theoretical travel time and ray path are consistent with the measured travel time and ray path. The model iteration is completed and the model is output. The output model is the velocity structure inside the volcanic rock sample.

[0106] The rock physics model building unit is used to build a rock physics model based on the velocity structure and core pores.

[0107] A prediction unit is configured to calculate a rock physical interpretation version of the sweet spot parameter according to a rock physical model, and to use pre-stack seismic data to predict engineering and geological sweet spot parameters.

[0108] The embodiment of the present application can fully consider the complex characteristics of buried hill volcanic reservoir, design a ring observation system to obtain ultrasonic information on different paths, deeply mine useful information in ultrasonic tomography data, and obtain the velocity structure inside the volcanic rock sample. The embodiment of the present application realizes full-process automation operation from ultrasonic data acquisition and processing to rock physical modeling parameter extraction and application, significantly improves work efficiency and modeling accuracy, and ensures the accuracy and stability of the results. Through accurate rock physical modeling, the key parameters of the buried hill volcanic reservoir are effectively predicted, which provides a more solid and reliable theoretical basis and technical support for exploration and development of the buried hill volcanic reservoir, reduces the exploration risk, and greatly improves the oil and gas exploitation benefit. The present application can be applied to land and offshore oil and gas seismic exploration rock physical modeling.

[0109] Those skilled in the art will further appreciate that the functions of the examples described herein, including any related steps of a method, can be implemented using electronic hardware, computer software, or any combination thereof. To clearly illustrate this interchangeability of hardware and software, various examples have been described herein generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the particular application and design constraints imposed on the overall system. Skilled artisans can implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

[0110] It can be understood that the above embodiments only express the preferred embodiments of the present application, and the description is more specific and detailed, but it should not be understood as limiting the scope of the patent of the present application; it should be pointed out that for ordinary skilled in the art, the above technical features can be freely combined without departing from the concept of the present application, and some modifications and improvements can be made, which all belong to the protection scope of the present application; therefore, any equivalent transformation and modification within the scope of the claims of the present application should belong to the scope of the claims of the present application.

Claims

1. A reservoir prediction optimization method based on buried hill volcanic rock core testing, characterized in that: The following steps are involved: S1. Conduct ultrasonic active source detection on volcanic rock samples through a ring observation system to obtain measured travel times and ray paths; S2. Calculating theoretical travel times and ray paths based on the initial velocity model, and iteratively updating the initial velocity model based on a preset inversion method until the theoretical travel times and ray paths are consistent with the measured travel times and ray paths. The model iteration is completed, and the model is output. The output model is the velocity structure inside the volcanic rock sample. S3. Establishing a rock physics model based on the velocity structure and core pores; S4. Calculate rock physics interpretation quantities related to sweet spot parameters based on the rock physics model, and use pre-stack seismic data to predict engineering and geological sweet spot parameters.

2. The reservoir prediction optimization method based on buried hill volcanic rock core testing according to claim 1, characterized in that: Step S2 includes: Integrating the slowness s along the propagation path l from the ultrasonic excitation device to the ultrasonic receiving device in the annular observation system to obtain the travel time t; Discretize the two-dimensional section into n grids, and the travel time t is expressed as: t ij =a ij s j ; Among them, s j is the slowness in the jth grid, a ij is the length of the i-th ray passing through the j-th grid; The initial velocity model is expressed in matrix form as: t=As.

3. The reservoir prediction optimization method based on buried hill volcanic rock core testing according to claim 2, characterized in that: In step S2, it includes: The initial velocity model is forward calculated based on the rapid marching method to obtain approximation A, and the initial velocity model is inverted based on the preset inversion method and approximation A to obtain a slowness inversion result. The model iteration is completed until the theoretical travel time and ray path are consistent with the measured travel time and ray path, and the model is output.

4. The reservoir prediction optimization method based on buried hill volcanic rock core testing according to claim 3 is characterized in that: In step S2, it includes: The initial velocity model is forward calculated based on the rapid advance method, and its expression in two-dimensional space is: The corresponding discrete expression is: Where s(x,z) represents the inverse of velocity, and represents the backward difference and forward difference along the x-axis direction; and represents the backward and forward differences along the z-axis.

5. The reservoir prediction optimization method based on buried hill volcanic rock core testing according to claim 4 is characterized in that: Step S2 further includes: The discrete expression is derived using Taylor expansion to obtain the first-order expression and the second-order expression along the x direction: Substituting the first-order backward difference operator along the x direction and the first-order backward difference operator along the z direction, we obtain: 。 6. The reservoir prediction optimization method based on buried hill volcanic rock core testing according to claim 3, characterized in that: In step S2, iteratively updating the initial velocity model based on a preset inversion method includes: Laplace regularization is used to constrain the preset inversion method, and the Gauss-Newton method is used to iterate the preset inversion method after the Laplace regularization is introduced to obtain a slowness inversion result.

7. The reservoir prediction optimization method based on buried hill volcanic rock core testing according to claim 6, characterized in that: In step S2, it includes: The least square method is used as the preset inversion method: Where, t obs is the measured travel time extracted from the measured data, and t(s) is the theoretical travel time obtained by forward calculation.

8. The reservoir prediction optimization method based on buried hill volcanic rock core testing according to claim 6, characterized in that: In step S2, it includes: The Laplace regularization is: Where L is the given Laplace operator The matrix approximation of ; the matrix-vector product Lm is the pixel value vector of the Laplace function of the slowness distribution α is a regularization parameter used to penalize the Laplace operator of reconstructing the slowness image.

9. The reservoir prediction optimization method based on buried hill volcanic rock core testing according to claim 1, characterized in that: The annular observation system is designed according to the shape of the volcanic rock sample. The annular observation system includes several probes surrounding the core of the volcanic rock sample. Each probe acts as an ultrasonic excitation device to sequentially excite an active source ultrasonic signal. Each probe does not receive a signal when stimulating the active source ultrasonic signal, and the remaining probes act as ultrasonic receiving devices to receive the signal.

10. A reservoir prediction optimization system based on buried hill volcanic rock core testing, characterized in that: include: The measured data unit is used to perform ultrasonic active source detection on volcanic rock samples through a ring observation system to obtain measured travel times and ray paths; a velocity model construction unit, configured to calculate theoretical travel times and ray paths based on an initial velocity model, and iteratively update the initial velocity model based on a preset inversion method until the theoretical travel times and ray paths are consistent with the measured travel times and ray paths, completing the model iteration and outputting a model representing the velocity structure inside the volcanic rock sample; a rock physics model building unit, configured to build a rock physics model based on the velocity structure and core pores; The prediction unit is used to calculate the rock physical interpretation quantity related to the sweet spot parameters based on the rock physical model, and use pre-stack seismic data to predict the engineering and geological sweet spot parameters.

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