Deep low exploration area volcanic reservoir prediction method, device, equipment and medium

Abstract

The present application relates to the technical field of deep gas risk exploration, and particularly relates to a deep low exploration area volcanic reservoir prediction method, device, equipment and medium. The method comprises the following steps: according to the reservoir sensitive parameters of a research area, a post-stack virtual well constrained inversion is selected to obtain a low frequency model of a full low frequency band of a virtual well; a geological model is established based on the low frequency model of the full low frequency band and seismic interface information; based on the reflection characteristics of the volcanic rock in the research area, a Laplace operator frequency division configuration attribute is used to depict the volcanic rock body; according to the geological model and the volcanic rock depiction result, a configuration attribute of a slice position of a target layer is extracted, low frequency modeling is carried out based on the configuration attribute constraint to obtain a configuration attribute volume controlled low frequency model; and inversion is carried out based on the configuration attribute volume controlled low frequency model to obtain a volcanic reservoir prediction result of the research area. The present application realizes the spatial distribution depiction of irregular geological bodies, and the depiction of special geological bodies is clearer, which has an important supporting role for risk well deployment.

Description

Technical Field

[0001] This invention relates to the field of deep gas risk exploration technology, and in particular to methods, devices, equipment and media for predicting volcanic rock reservoirs in deep low-exploration areas. Background Technology

[0002] Deep volcanic natural gas is an important alternative resource for unconventional oil and gas fields worldwide. Existing research and exploration results confirm that deep natural gas reservoirs are mainly composed of volcanic rock reservoirs. Volcanic rock lithology identification and effective reservoir prediction are bottleneck technologies in deep natural gas exploration. This is because deep strata are buried at great depths, and sedimentary compaction is strong, resulting in small differences in wave impedance between volcanic rocks and surrounding rocks, and between different lithologies within the volcanic rock itself. Furthermore, volcanic rocks have a strong shielding effect on seismic waves, leading to weak seismic reflection energy and a low signal-to-noise ratio within the deep Yingcheng Formation volcanic rocks. The fault structure is complex, with both large basement faults and normal and reverse faults, making seismic imaging difficult and obscuring the seismic reflection characteristics of the internal structure of volcanic rock reservoirs. Deep strata contain a variety of lithologies, resulting in complex reservoir rock types. Volcanic rock reservoirs, in particular, are formed by multiple eruptions from multiple volcanoes, causing significant vertical and horizontal variations in volcanic rock facies, making it difficult to identify distribution patterns. The thickness distribution of volcanic rocks varies considerably laterally, making it difficult to accurately compare and quantitatively determine their distribution range in seismic records. Reservoir pore structures are complex, with pores, cavities, and fractures coexisting, further complicating effective reservoir prediction. Lithological identification and effective reservoir prediction of deep volcanic rocks have always been key research topics in geophysics.

[0003] The existing technology of "pre-stack inversion based on volume control modeling" for predicting volcanic reservoirs is suitable for areas with high exploration levels and requires abundant drilling information. For areas with no or few wells, this method results in lower prediction accuracy due to the lack of drilling information. Summary of the Invention

[0004] This invention proposes a method, apparatus, equipment, and medium for predicting volcanic rock reservoirs in deep, low-exploration areas, in order to solve the problem that the lack of drilling information in the existing technology leads to low accuracy or no prediction of volcanic rock reservoirs.

[0005] According to one aspect of the present invention, a method for predicting volcanic reservoirs in deep, low-exploration areas is provided, comprising:

[0006] Acquire sensitive parameters and seismic interface information in the study area that reflect lithology or reservoir properties;

[0007] Based on the aforementioned sensitive parameters, a post-stack virtual well constraint inversion is selected to obtain a low-frequency model of the virtual well across the entire low-frequency band.

[0008] A geological model is established based on the low-frequency model of the entire low-frequency band and the seismic interface information.

[0009] Based on the reflection characteristics of volcanic rocks in the study area, the frequency division configuration properties of the Laplace operator are used to characterize the volcanic rock mass;

[0010] Based on the geological model and the volcanic rock characterization results, the configurational properties of the target layer slice location are extracted, and low-frequency modeling is performed based on the configurational property constraints to obtain a configurational property volume-controlled low-frequency model.

[0011] Based on the aforementioned configuration property volume-controlled low-frequency model, inversion was performed to obtain the prediction results of volcanic reservoirs in the study area.

[0012] Preferably, before obtaining the sensitive parameters that reflect the lithology or reservoir properties of the study area, the method for determining the sensitive parameters includes:

[0013] The correlations between burial depth, regional differences, temperature, pressure, pore shape, pore structure, fluid and lithological factors and the elastic parameters of volcanic reservoirs were determined.

[0014] Select the parameter with the highest correlation as the sensitive parameter.

[0015] Preferably, the sensitive parameter is lithology.

[0016] Preferably, the method for selecting post-stack virtual well constraint inversion based on the sensitive parameters to obtain a low-frequency model of the virtual well across the entire low-frequency band includes:

[0017] Based on the aforementioned sensitive parameters and earthquake velocity, a preliminary ultra-low frequency impedance low-frequency model is established;

[0018] In the ultra-low frequency impedance low frequency model, the key frequency band data of seismic inversion obtained by re-integrating the low frequency band of seismic data is added to obtain a low frequency model with a full low frequency band.

[0019] Preferably, the method for establishing a geological model based on the low-frequency model across the entire low-frequency band and seismic interface information includes:

[0020] Based on the seismic interface information, the virtual well information of the low-frequency model is assigned its own vertical components and weights according to each layer segment;

[0021] The geological model is established by reasonably interpolating the low-frequency model, vertical components, and weights.

[0022] Preferably, the method for extracting the configurational properties of the target layer slice location based on the geological model and the overflow volcanic rock characterization results includes:

[0023] Based on the geological model and the characterization results of the volcanic rock mass, the target layer is divided into several slices, and the configuration attribute planes on each slice are extracted.

[0024] Preferably, the method for performing low-frequency modeling based on the configuration attribute constraints to obtain a configuration attribute volume-controlled low-frequency model includes:

[0025] Each of the configuration attribute planes is combined to form a planar phase, and the planar phases are combined to form a volume control joint constraint. Low-frequency modeling is performed using the joint constraint to obtain the volume control low-frequency model of the configuration attribute.

[0026] According to one aspect of the present invention, a device for predicting deep, low-exploration volcanic reservoirs is provided, comprising:

[0027] The acquisition unit is used to acquire sensitive parameters that reflect lithology or reservoir properties and seismic interface information in the study area.

[0028] The virtual well establishment unit is used to select post-stack virtual well constraint inversion based on the sensitive parameters to obtain a low-frequency model of the virtual well in the full low-frequency band.

[0029] The geological model building unit is used to build a geological model based on the low-frequency model of the entire low-frequency band and seismic interface information.

[0030] The volcanic rock characterization unit is used to characterize volcanic rock bodies based on the reflection characteristics of volcanic rocks in the study area and the frequency division configuration properties of the Laplace operator.

[0031] The low-frequency model building unit is used to extract the configuration attributes of the target layer slice location based on the geological model and the volcanic rock body characterization results, and to perform low-frequency modeling based on the configuration attribute constraints to obtain a configuration attribute volume-controlled low-frequency model.

[0032] The reservoir prediction unit is used to perform inversion based on the volume-controlled low-frequency model of the configuration attributes to obtain the prediction results of volcanic reservoirs in the study area.

[0033] According to one aspect of the present invention, an electronic device is provided, comprising:

[0034] processor;

[0035] Memory used to store processor-executable instructions;

[0036] The processor is configured to execute the above-described method for predicting volcanic reservoirs in deep, low-exploration areas.

[0037] According to one aspect of the present invention, a computer-readable storage medium is provided having computer program instructions stored thereon, which, when executed by a processor, implement the above-described method for predicting volcanic rock reservoirs in deep, low-exploration areas.

[0038] The present invention has at least the following beneficial effects:

[0039] This invention proposes a method, device, equipment, and medium for predicting volcanic reservoirs in deep, low-exploration areas. By acquiring sensitive parameters of the study area, a virtual well full-low-frequency model is established, and low-frequency modeling is performed based on the extracted configuration attribute constraints, thereby obtaining accurate volcanic reservoir prediction results. This enables the spatial distribution characterization of irregular geological bodies, provides clearer characterization of special geological bodies, and plays an important supporting role in the deployment of risk wells. Attached Figure Description

[0040] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present invention and, together with the specification, serve to explain the technical solutions of the present invention.

[0041] Figure 1 A flowchart illustrating a method for predicting deep, low-exploration volcanic reservoirs according to an embodiment of the present invention is shown.

[0042] Figure 2 This diagram illustrates a multi-parameter rock physics intersection analysis according to an embodiment of the present invention.

[0043] Figure 3 A plan view of the reference wells and virtual wells according to an embodiment of the present invention is shown;

[0044] Figure 4 The diagram shows seismic and low-frequency model profiles of the connection line between the actual drilled well and the virtual well according to an embodiment of the present invention, wherein Figure A is a pure wave seismic profile and Figure B is a low-frequency model profile.

[0045] Figure 5 This diagram shows a profile of pure wave seismic activity, configuration properties, and configuration frequency division properties according to an embodiment of the present invention.

[0046] Figure 6 A planar diagram showing the arc length, energy, configuration, root mean square, and entropy properties of a city formation according to an embodiment of the present invention is provided.

[0047] Figure 7 This diagram shows a comparison between seismic slices and configuration property profiles according to an embodiment of the present invention.

[0048] Figure 8 This diagram illustrates the configurational properties of different slices according to embodiments of the present invention.

[0049] Figure 9This diagram illustrates the implementation process of attribute control according to an embodiment of the present invention.

[0050] Figure 10 The diagram shows a comparison between conventional modeling and configuration attribute volume control modeling according to an embodiment of the present invention, wherein Figure A is a low-frequency model established by conventional methods, and Figure B is a low-frequency model established by configuration attribute volume control.

[0051] Figure 11 The diagram shows a comparison profile of conventional inversion and configuration property-constrained impedance inversion according to an embodiment of the present invention, wherein Figure A is a conventional inversion profile and Figure B is a configuration property-constrained impedance inversion profile.

[0052] Figure 12 A thickness distribution map of the Yingcheng Formation volcanic rocks according to an embodiment of the present invention is shown. Detailed Implementation

[0053] Various exemplary embodiments, features, and aspects of the present invention will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0054] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0055] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A exists alone, A and B exist simultaneously, and B exists alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0056] Furthermore, to better illustrate the present invention, numerous specific details are set forth in the following detailed embodiments. Those skilled in the art will understand that the present invention can be practiced without certain specific details. In some instances, methods, means, elements, and circuits well known to those skilled in the art have not been described in detail in order to highlight the spirit of the invention.

[0057] Figure 1 A flowchart illustrating a method for predicting deep, low-exploration volcanic reservoirs according to an embodiment of the present invention is shown. Figure 2 This diagram illustrates a multi-parameter rock physics intersection analysis according to an embodiment of the present invention. Figure 3 A plan view of the reference wells and virtual wells according to an embodiment of the present invention is shown; Figure 4 The diagram shows a seismic and low-frequency model profile of the connection line between the actual drilled well and the virtual well according to an embodiment of the present invention; Figure 5 This diagram shows a profile of pure wave seismic activity, configuration properties, and configuration frequency division properties according to an embodiment of the present invention. Figure 6 A planar diagram showing the arc length, energy, configuration, root mean square, and entropy properties of a city formation according to an embodiment of the present invention is provided. Figure 7 This diagram shows a comparison between seismic slices and configuration property profiles according to an embodiment of the present invention. Figure 8 This diagram illustrates the configurational properties of different slices according to embodiments of the present invention. Figure 9 This diagram illustrates the implementation process of attribute control according to an embodiment of the present invention. Figure 10 This diagram shows a comparison between conventional modeling and configuration attribute volume control modeling according to an embodiment of the present invention. Figure 11 This illustrates a comparison profile of conventional inversion and configuration property-constrained impedance inversion according to an embodiment of the present invention; Figure 12 A thickness distribution map of the Yingcheng Formation volcanic rocks according to an embodiment of the present invention is shown. Figure 1-12 As shown, a method for predicting volcanic reservoirs in deep, low-exploration areas includes: Step S01: Obtaining sensitive parameters reflecting lithology or reservoir properties and seismic interface information in the study area; Step S02: Selecting post-stack virtual well constraint inversion based on the sensitive parameters to obtain a low-frequency model of the virtual well's full low-frequency band; Step S03: Establishing a geological model based on the low-frequency model of the full low-frequency band and seismic interface information; Step S04: Characterizing overflow volcanic rocks based on the reflection characteristics of volcanic rocks in the study area using the Laplace operator's frequency-division configuration attributes; Step S05: Extracting configuration attributes of the target layer slice location based on the geological model and the overflow volcanic rock characterization results, and performing low-frequency modeling based on the configuration attribute constraints to obtain a configuration attribute volume-controlled low-frequency model; Step S06: Performing inversion based on the configuration attribute volume-controlled low-frequency model to obtain the predicted volcanic reservoir results in the study area.

[0058] The method for predicting deep, low-exploration volcanic reservoirs provided in this embodiment of the invention specifically includes the following steps:

[0059] Step S01: Obtain sensitive parameters that reflect lithology or reservoir properties and seismic interface information for the study area.

[0060] In this invention, before obtaining the sensitive parameters that can reflect the lithology or reservoir properties of the study area, the sensitive parameters are determined by means of: determining the correlation between the burial depth, regional differences, temperature, pressure, pore shape, pore structure, fluid and lithological influencing factors on the elastic parameters of volcanic rock reservoirs; and selecting the parameter with the highest correlation as the sensitive parameter.

[0061] In this invention, the sensitive parameter is: lithology.

[0062] In this embodiment of the invention, rock physical analysis is the foundation of seismic reservoir prediction. By analyzing the relationship between rock elastic parameters and reservoir physical parameters, sensitive parameters that can better reflect lithology or reservoir physical properties are identified. Different volcanic rocks exhibit significant differences in the threshold values ​​for reservoir elastic parameters. The selection of sensitive reservoir parameters determines the formulation of the inversion scheme and the quantitative analysis of the inversion results.

[0063] The analysis of reservoir logging response characteristics (elastic parameters), especially reservoir velocity characteristics, is one of the important tasks before conducting detailed reservoir inversion. Whether the velocities of reservoirs and non-reservoirs can be distinguished on the curve is the basis for whether reservoir prediction can be made using post-stack inversion.

[0064] The influence of factors such as burial depth, regional differences, temperature, pressure, pore shape, pore structure, fluid, and lithology on the elastic parameters of volcanic reservoirs was analyzed. Specifically, correlation analysis was performed on the correlation coefficients R between burial depth, regional differences, temperature, pressure, pore shape, pore structure, fluid, and lithology and the elastic parameters of volcanic reservoirs. 2 The results show that lithology has the greatest impact on the elastic parameters of volcanic rock reservoirs among the reservoir physical properties.

[0065] Step S02: Based on the sensitive parameters, select post-stack virtual well constraint inversion to obtain the low-frequency model of the virtual well in the full low-frequency band.

[0066] In this invention, the method for selecting post-stack virtual well constraint inversion based on the sensitive parameters to obtain a low-frequency model of the virtual well in the full low-frequency band includes: establishing a preliminary ultra-low frequency impedance low-frequency model based on the sensitive parameters and seismic velocity; and adding seismic inversion key frequency band data obtained by re-integrating the low-frequency band of seismic data to the ultra-low frequency impedance low-frequency model to obtain a low-frequency model in the full low-frequency band.

[0067] In this embodiment of the invention, the low-frequency model is usually established using the tomography velocity constrained by virtual wells. However, the frequency band of this data is usually around 0-3Hz. Based on the multiple integration of seismic data, the frequency band required for inversion of 3-10Hz can be obtained. Therefore, by using the tomography velocity constrained by virtual wells and the seismic multiple integration for joint modeling, the low-frequency model required for pre-stack / post-stack inversion can be established.

[0068] The exploration level in the study area is low and no wells have been revealed. Based on the P-wave impedance (different P-wave impedance ranges reflect different lithologies) sensitive parameters obtained from adjacent well logging obtained in step S01, post-stack virtual well constrained inversion is selected.

[0069] The specific process is as follows: Seven to eight virtual wells are set up in the study area. Based on rock physics analysis, it is known that the elastic parameters of P-wave impedance can reflect different volcanic rock lithologies. A model is established based on P-wave velocity and seismic velocity. A preliminary 0-3Hz ultra-low frequency impedance low-frequency model is obtained based on seismic velocity. The seismic data is re-integrated to obtain the 3-10Hz seismic re-integration low-frequency band. The 3-10Hz seismic re-integration low-frequency band is added to the 0-3Hz ultra-low frequency impedance low-frequency model to obtain the 3-10Hz seismic inversion key frequency band. The velocity low-frequency wave impedance model is fused with the re-integrated relative wave impedance to obtain a low-frequency model covering the entire low-frequency band.

[0070] Step S03: Based on the low-frequency model of the entire low-frequency band and the seismic interface information, establish a geological model.

[0071] In this invention, the method for establishing a geological model based on the low-frequency model of the entire low-frequency band and seismic interface information includes: allocating the virtual well information of the low-frequency model to each layer segment according to the seismic interface information, and establishing the geological model by reasonably interpolating the low-frequency model and the vertical components and weights.

[0072] In this embodiment of the invention, the establishment of the initial virtual well model comprehensively considers various basic geological characteristics such as sedimentary patterns and tectonic morphology, and establishes layers, intralayer structures, and their interrelationships. The establishment process essentially involves optimizing the horizontally varying seismic interface information and virtual well data—that is, the low-frequency virtual well model established in step S02—by allocating vertical components and their weights to each layer according to geological theory (seismic data). Then, through reasonable interpolation of these model parameters—the low-frequency virtual well model data, the vertical components of the allocated layers, and their weights—a geological model reflecting the geological structure is established.

[0073] The specific process is as follows: Based on the established low-frequency model covering the entire low-frequency band, virtual wells are extracted for different sedimentary patterns and structural forms. Then, the horizontally continuously changing seismic interface information and the vertical information reflected by the virtual wells are combined and the model parameters are reasonably interpolated to establish a geological model that reflects the geological structure. This geological model is a three-dimensional geological model.

[0074] Step S04: Based on the reflection characteristics of volcanic rocks in the study area, the volcanic rock mass is characterized using the frequency division configuration property of the Laplace operator.

[0075] In this embodiment of the invention, based on the reflection characteristics of volcanic rocks in the seismic data of the study area, the Laplace operator is selected to perform frequency division configuration attribute calculation on the original seismic data volume, highlighting special anomalous geological bodies and completing the characterization of volcanic rock bodies.

[0076] The method of performing frequency division configuration attribute calculations on the original seismic data volume using the Laplace operator is an algorithm for enhancing contrast at energy abrupt changes. It is more effective than conventional profiles for identifying volcanic rocks, especially crater areas with chaotic reflections. Using the seismic facies identification results of the Laplace volume, i.e. the volcanic rock characterization results, as constraints for reservoir prediction can improve the accuracy of volcanic rock prediction.

[0077] Step S05: Based on the geological model and the characterization results of the overflow volcanic rocks, extract the configuration attributes of the target layer slice location, perform low-frequency modeling based on the configuration attribute constraints, and obtain a configuration attribute volume-controlled low-frequency model.

[0078] In this invention, the method for extracting the configurational attributes of the target layer slice location based on the geological model and the volcanic rock mass characterization results includes: dividing the target layer into several slices based on the geological model and the overflow volcanic rock characterization results, and extracting the configurational attribute plane on each slice.

[0079] In this invention, the method for performing low-frequency modeling based on the configuration attribute constraints to obtain a configuration attribute volume-controlled low-frequency model includes: combining each configuration attribute plane to form a planar phase, combining the planar phases to form a volume-controlled joint constraint, and using the joint constraint to perform low-frequency modeling to obtain the configuration attribute volume-controlled low-frequency model.

[0080] In this embodiment of the invention, the commonly used modeling method is as follows: within a fine stratigraphic framework, sequence units are defined, and then contact relationships between each sequence are established (currently including: top parallel, bottom parallel, top-bottom parallel, pinch-out, etc.), resulting in a sequence volume that can accurately depict the real stratigraphic characteristics. Then, using calibrated well curves under the control of the sequence volume, attribute interpolation is performed to obtain an initial model. Commonly used attribute interpolation methods include Kriging interpolation, inverse distance weighted interpolation, trigonometric function interpolation, and radial basis function interpolation. All of these methods utilize mathematical means for interpolation, and the interpolation results are influenced by adjacent wells and lack geological guidance. Therefore, it is difficult to establish an initial model that conforms to sedimentary patterns.

[0081] Conventional initial model building methods can only guarantee the accuracy of the model at the well point, but cannot guarantee the rationality of the initial model's variations between wells. A high-precision initial model should not only guarantee the accuracy of the model at the well point, but also make the variations between wells as consistent as possible with geological understanding, such as including the impedance background, direction, and boundary information of geological bodies.

[0082] To address this issue, this invention employs a phase-controlled constraint modeling method, incorporating information about geological bodies contained in sedimentary facies (configurational attributes) into the initial model (virtual well model), thereby improving the accuracy and rationality of the initial model and ultimately enhancing the inversion quality.

[0083] The specific process is as follows: Based on the geological model obtained in step S03 and the volcanic rock mass characterization results obtained in step S04, the target layer is extracted into 10 slices according to the geological model, and the configuration attribute planes at each slice location are extracted; then, each type of plane attribute is combined to form a planar phase; multiple planar phases are combined into a volume-controlled joint constraint, and low-frequency modeling is performed using this volume-controlled joint constraint. The low-frequency modeling established by the configuration attribute volume control is consistent with the geological anomaly morphology of the volcanic rock.

[0084] Based on the different sedimentary facies parameters (configurational properties) used during modeling, facies-controlled modeling can be divided into three application scenarios, including:

[0085] If there are significant differences in background values ​​between facies on a slice, i.e., typical bullseye features appear on the slice, then the extracted configuration attribute is the facies background value. This configuration attribute can correct the background value of well curves, achieving similar background values ​​between the same facies and different background values ​​between different facies;

[0086] If the depositional orientation is clear and consistent across the slice, such as in rivers, then the extracted configurational attribute is the depositional orientation. This configurational attribute can be used to control the well interpolation direction using directional kriging, thus incorporating the depositional orientation into the initial model setup.

[0087] If special geological body facies boundaries exist on the slice, such as gravity flows, lacustrine fans, or volcanic rocks, the extracted configuration attribute is the facies boundary. This configuration attribute enables the use of facies boundaries to control well interpolation boundaries, ensuring that well interpolation within the same facies type is used only, without being affected by wells outside the facies.

[0088] Well-free facies control modeling utilizes the high-frequency sequence body on slices to cut sensitive geological anomaly properties (configurational properties) and characterizes multiple planar facies. Each planar facies is constrained to form volume control. This method can finely depict the morphology of non-parallel geological bodies.

[0089] Step S06: Based on the configuration property volume-controlled low-frequency model, perform inversion to obtain the prediction results of volcanic reservoirs in the study area.

[0090] In this embodiment of the invention, an inversion is performed based on the configuration property volume-controlled low-frequency model obtained in step S05 to obtain the prediction result of volcanic rock reservoirs. This inversion result is faithful to seismic data and clearly depicts the volcanic rocks.

[0091] In this embodiment of the invention, the implementation process of the method of the present invention is described in detail, taking the prediction of volcanic rock reservoirs in the Caoyingcheng Formation of the Puxiwa in the Gulong fault depression of the Songliao Basin in a certain study area as an example.

[0092] The overall exploration level of the Gulong fault depression is relatively low. Currently, only 10 wells have been drilled to the protrusion. The Puxi depression is located in the southern part of the Gulong fault depression, and there are no wells drilled in the work area.

[0093] There were no drilled wells within the study area, but data from six drilled wells in neighboring areas were collected. Figure 2 The image shows a cross-sectional view of gamma, P-wave impedance, resistivity, and neutron porosity from six wells in the Yingcheng Formation in the adjacent area. Figure 2 As can be seen from the above, the impedance can generally distinguish volcanic rocks well, with a threshold value of 14200 g / cc*m / s.

[0094] Low-frequency models are typically established using virtual well-constrained tomography velocities, but the frequency band of this data is usually around 0-3Hz. However, the frequency band of 3-10Hz necessary for inversion can be obtained based on seismic data multiple integration. Therefore, by using virtual well-constrained tomography velocities and seismic multiple integration for joint modeling, the low-frequency model necessary for pre-stack / post-stack inversion can be established.

[0095] The initial model of the virtual well needs to comprehensively consider various basic geological characteristics such as sedimentary patterns and tectonic morphology, and establish the layers, intra-layer structures, and their interrelationships. In practice, this involves optimizing the horizontally varying seismic interface information and virtual well data according to geological theory, allocating the virtual well information to the vertical components and weights of each layer, and then using reasonable interpolation of these model parameters to establish a geological model reflecting the geological structure. The relative relationships between the six adjacent wells referenced in this study and the study area are as follows: Figure 3 As shown, the study area is 300 km². 2 Located in the northern part of the adjacent area, three low-frequency model profiles were established through six wells to roughly represent the characteristics of the work area. Seven virtual wells were extracted from the profiles, namely the six dots in the box in the figure. Figure 4 To pass through the second section (i.e. Figure 3 Figure 2 shows the pure wave seismic profile and the low-frequency model profile of Figure 2 (No. 2). Figure A shows the pure wave seismic profile, and Figure B shows the low-frequency model profile.

[0096] Based on the reflection characteristics of volcanic rocks in the study area, the Laplace operator frequency division configuration attribute was selected to characterize the volcanic rock body. This method is an algorithm for enhancing contrast at energy abrupt changes, and it is more effective than conventional profiles in identifying volcanic rocks, especially crater areas with chaotic reflections. The seismic facies identification results of the Laplace body are used as constraints for reservoir prediction, thereby improving the accuracy of volcanic rock prediction.

[0097] like Figure 5 The images show the seismic pure wave profile, configuration attribute profile, and configuration + frequency division attribute profile. By comparison, it can be seen that the configuration attribute can better characterize the external shape and internal structure of the volcano, with clear features, enhanced energy, and weakened energy of the surrounding rock.

[0098] The arc length, energy, configuration, root mean square, and entropy attributes of the Yingcheng Formation were extracted respectively, such as... Figure 6 The diagram shows the planar distribution characteristics of different properties. By comparison, it can be seen that configurational properties can better characterize the planar morphology of volcanic rocks.

[0099] After selecting the configuration attributes, divide the city building group into ten equal parts, numbered from bottom to top as slice 1, 2, 3, 4...10, as follows. Figure 7 The image shows a comparison between the pure wave profile at position 8 of the connecting line slice and the configuration frequency division attribute plane profile. It can be seen that the configuration attribute can well characterize the shape and internal structure of the volcano, with clear features, laying a good foundation for the next step of phased modeling.

[0100] There are no wells in this study area, and the phase control modeling process is divided into two steps: 1. Divide the target layer into 10 slices and extract the configuration attribute planes at each slice location, such as... Figure 8 As shown; 2. The attribute volume control implementation process involves forming a planar phase from each planar attribute, and combining multiple planar phases to form a volume control, as shown. Figure 9 As shown.

[0101] By utilizing volume control constraints and virtual well modeling, a low-frequency model was obtained, such as... Figure 10 As shown, Figure A is a low-frequency model established by conventional modeling methods, and Figure B is a low-frequency model established by configuration attribute volume control. By comparing with conventional modeling methods, it can be seen that configuration attribute volume control modeling is consistent with the geological anomaly morphology of volcanic rocks.

[0102] Based on the aforementioned phase control modeling of the well-free region, an inversion was performed, yielding the following inversion results: Figure 11 As shown, Figure A is a conventional inversion profile, and Figure B is a profile obtained by configuration attribute-constrained impedance inversion. By comparison, it can be seen that the configuration attribute-constrained impedance inversion profile provides a clearer depiction of the shape and contour of the volcanic rock.

[0103] Based on the rock physics analysis threshold value, the thickness map of the Yingcheng Formation volcanic rocks was extracted, as follows: Figure 12 The diagram shows the planar thickness of the Yingcheng Formation volcanic rocks. Five volcanic rocks are developed in the Yingcheng Formation, with volcanic rock #4 having the largest area, covering 63.2 km² within the work area. 2 The thickness is relatively small, with a maximum thickness of 60 meters; the No. 1 volcanic rock has the second largest area, at 43.1 km². 2 Among the five identified volcanic rock bodies, the thickest is No. 2, reaching a maximum thickness of 300 meters. No. 2 is relatively thin, with an area of ​​26.5 km². 2 The five volcanic rock masses have a cumulative area of ​​132.8 km². 2 This provides strong support for the optimal selection of well locations.

[0104] It is understood that the various method embodiments mentioned above in this invention can be combined with each other to form combined embodiments without violating the principle and logic. Due to space limitations, this invention will not elaborate further.

[0105] The execution entity for the deep, low-exploration-area volcanic reservoir prediction method can be a deep, low-exploration-area volcanic reservoir prediction device. For example, the method can be executed by a terminal device, server, or other processing device. The terminal device can be a user equipment (UE), mobile device, user terminal, terminal, cellular phone, cordless phone, personal digital assistant (PDA), handheld device, computing device, vehicle-mounted device, wearable device, etc. In some possible implementations, the deep, low-exploration-area volcanic reservoir prediction method can be implemented by a processor calling computer-readable instructions stored in memory.

[0106] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0107] This invention also provides a device for predicting volcanic reservoirs in deep, low-exploration areas, comprising: an acquisition unit for acquiring sensitive parameters reflecting lithology or reservoir properties and seismic interface information of the study area; a virtual well establishment unit for selecting post-stack virtual well constraint inversion based on the sensitive parameters to obtain a low-frequency model of the virtual well's full low-frequency band; a geological model establishment unit for establishing a geological model based on the low-frequency model of the full low-frequency band and the seismic interface information; a volcanic rock characterization unit for characterizing volcanic rock bodies based on the reflection characteristics of volcanic rocks in the study area and using the Laplace operator's frequency-division configuration attributes; a low-frequency model establishment unit for extracting configuration attributes of the target layer slice location based on the geological model and the overflow volcanic rock characterization results, performing low-frequency modeling based on the configuration attribute constraints to obtain a configuration attribute volume-controlled low-frequency model; and a reservoir prediction unit for performing inversion based on the configuration attribute volume-controlled low-frequency model to obtain the predicted volcanic reservoir of the study area.

[0108] In some embodiments, the functions or modules and units included in the apparatus provided by the present invention can be used to execute the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0109] This invention also proposes a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the aforementioned method for predicting volcanic reservoirs in deep, low-exploration areas. The computer-readable storage medium can be a non-volatile computer-readable storage medium.

[0110] This invention also proposes an electronic device, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to execute the aforementioned method for predicting volcanic reservoirs in deep, low-exploration areas. The electronic device may be provided as a terminal, a server, or other form of device.

[0111] Over the past decade, with advancements in seismic processing and imaging technology, the quality of deep seismic data has significantly improved. Through continuous exploration and research, a reservoir prediction technology system centered on "volume-controlled modeling and pre-stack inversion" has been gradually formed for areas with high exploration levels, providing technical support for the effective development of volcanic gas reservoirs. Since 2019, the focus of volcanic exploration has shifted to "four new areas," namely, risk exploration of new fault depressions and new strata of large volcanic gas reservoirs. These areas have relatively low exploration levels, and there is an urgent need to address the challenges of predicting deep, unexplored volcanic reservoirs and the difficulty in predicting reservoirs with few well-revealed drilling findings.

[0112] This invention, based on rock physical analysis, utilizes configurational attribute constraints and 2D / 3D joint modeling to extract virtual wells for phasing control modeling, thereby achieving spatial distribution characterization of irregular geological bodies and volcanic rocks. Its key technologies include three points: ① how to extract virtual wells for the local area using neighboring wells; ② how to extract attributes sensitive to volcanic rocks for characterizing volcanic rock bodies; and ③ how to establish attribute-controlled low-frequency models using virtual wells and sensitive attribute volumes.

[0113] This invention considers the influence of the entire low-frequency band and incorporates the low-frequency band of seismic multiple integrals to obtain the 3-10Hz key frequency band for seismic inversion. It integrates the low-frequency wave impedance model of velocity with the relative wave impedance of multiple integrals, and uses configuration attribute constraints and 2D / 3D joint modeling to extract virtual wells for phase control modeling to realize the spatial distribution characterization of irregular geological bodies. The low-frequency model is faithful to the seismic data, and the characterization of special geological bodies is clearer, which plays an important supporting role in the deployment of risk wells in oil fields.

[0114] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for predicting volcanic rock reservoirs in deep, low-exploration areas, characterized in that, include: Acquire sensitive parameters and seismic interface information in the study area that reflect lithology or reservoir properties; Based on the aforementioned sensitive parameters, a post-stack virtual well constraint inversion is selected to obtain a low-frequency model of the virtual well across the entire low-frequency band. A geological model is established based on the low-frequency model of the entire low-frequency band and the seismic interface information. Based on the reflection characteristics of volcanic rocks in the study area, the frequency division configuration properties of the Laplace operator are used to characterize the volcanic rock mass; Based on the geological model and the volcanic rock characterization results, the configurational properties of the target layer slice location are extracted, and low-frequency modeling is performed based on the configurational property constraints to obtain a configurational property volume-controlled low-frequency model. Based on the aforementioned configuration property volume-controlled low-frequency model, inversion was performed to obtain the prediction results of volcanic reservoirs in the study area.

2. The method for predicting deep, low-exploration volcanic reservoirs according to claim 1, characterized in that, Before acquiring sensitive parameters that reflect lithology or reservoir properties of the study area, the method for determining the sensitive parameters includes: The correlations between burial depth, regional differences, temperature, pressure, pore shape, pore structure, fluid and lithological factors and the elastic parameters of volcanic reservoirs were determined. Select the parameter with the highest correlation as the sensitive parameter.

3. The method for predicting deep, low-exploration volcanic reservoirs according to claim 2, characterized in that, The sensitive parameter is: lithology.

4. The method for predicting deep, low-exploration volcanic reservoirs according to claim 3, characterized in that, The method for selecting post-stack virtual well constraint inversion based on the sensitive parameters to obtain a low-frequency model of the virtual well across the entire low-frequency band includes: Based on the aforementioned sensitive parameters and earthquake velocity, a preliminary ultra-low frequency impedance low-frequency model is established; In the ultra-low frequency impedance low frequency model, the key frequency band data of seismic inversion obtained by re-integrating the low frequency band of seismic data is added to obtain a low frequency model with a full low frequency band.

5. The method for predicting volcanic rock reservoirs in deep, low-exploration areas according to claim 1, characterized in that, The method for establishing a geological model based on the low-frequency model across the entire low-frequency band and seismic interface information includes: Based on the seismic interface information, the virtual well information of the low-frequency model is assigned its own vertical components and weights according to each layer segment; The geological model is established by reasonably interpolating the low-frequency model, vertical components, and weights.

6. The method for predicting volcanic reservoirs in deep, low-exploration areas according to claim 1, characterized in that, The method for extracting the configurational attributes of the target layer slice location based on the geological model and the volcanic rock mass characterization results includes: Based on the geological model and the characterization results of the volcanic rock mass, the target layer is divided into several slices, and the configuration attribute planes on each slice are extracted.

7. The method for predicting volcanic reservoirs in deep, low-exploration areas according to claim 6, characterized in that, The method for performing low-frequency modeling based on the configuration attribute constraints to obtain a configuration attribute volume-controlled low-frequency model includes: Each of the configuration attribute planes is combined to form a planar phase, and the planar phases are combined to form a volume control joint constraint. Low-frequency modeling is performed using the joint constraint to obtain the volume control low-frequency model of the configuration attribute.

8. A device for predicting volcanic rock reservoirs in deep, low-exploration areas, characterized in that, include: The acquisition unit is used to acquire sensitive parameters that reflect lithology or reservoir properties and seismic interface information in the study area. The virtual well establishment unit is used to select post-stack virtual well constraint inversion based on the sensitive parameters to obtain a low-frequency model of the virtual well in the full low-frequency band. The geological model building unit is used to build a geological model based on the low-frequency model of the entire low-frequency band and seismic interface information. The volcanic rock characterization unit is used to characterize volcanic rock bodies based on the reflection characteristics of volcanic rocks in the study area and the frequency division configuration properties of the Laplace operator. The low-frequency model building unit is used to extract the configuration attributes of the target layer slice location based on the geological model and the volcanic rock mass characterization results, and to perform low-frequency modeling based on the configuration attribute constraints to obtain a configuration attribute volume-controlled low-frequency model. The reservoir prediction unit is used to perform inversion based on the volume-controlled low-frequency model of the configuration attributes to obtain the prediction results of volcanic reservoirs in the study area.

9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 7.

Images