Method, device, equipment and storage medium for identifying shale oil sweet spot

Abstract

The embodiment of the present application discloses a method, device, equipment and storage medium for identifying shale oil sweet spots, which belongs to the field of oil and gas exploration. The method includes: determining the sweet spot level of each layer segment according to the oil and gas production of shale oil in each layer segment in the oil well in the target area; constructing an oil content curve of the oil well, the oil content curve is used to represent the oil content of each layer segment, and establishing a correlation relationship between the oil content curve and the first seismic data body corresponding to the target area; creating a stratigraphic framework model according to the first seismic data body corresponding to the target area, using the oil content curve as a characteristic curve for seismic reservoir inversion, setting key inversion parameters according to the geological characteristics of the oil well, and using waveform indication simulation or waveform difference simulation methods to perform reservoir inversion on the first seismic data body, so that the inverted second seismic data body is used to indicate the distribution law of shale oil sweet spots. The embodiment of the present application improves the drilling rate of sweet spots and reduces exploration risks.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of oil and gas exploration technology, and in particular to a method, device, equipment and storage medium for identifying shale oil sweet spots. Background Art

[0002] With the continuous improvement of conventional oil and gas exploration, the increasing difficulty and risk of exploration, the search for new replacement oil and gas resources has become one of the hot issues in oil and gas exploration today. Shale oil is an unconventional oil and gas resource with the characteristics of rich oil and gas resources, great exploration potential and low exploration degree, and has become an important replacement oil and gas resource.

[0003] The local enrichment of shale oil forms sweet spots, which are typically hidden and have complex distribution patterns. Finding shale oil sweet spots is the key to unconventional oil and gas exploration and development. Therefore, it is urgent to provide a method for identifying shale oil sweet spots. Summary of the invention

[0004] The embodiment of the present application provides a method, device, equipment and storage medium for identifying shale oil sweet spots, which can assist and expand the traditional single well prediction method, improve the drilling rate of sweet spots, reduce exploration risks, and more accurately and widely determine the distribution range of shale oil sweet spots, providing a reliable basis for the prediction of shale oil geological resources. The technical solution is as follows:

[0005] In one aspect, a method for identifying a sweet spot is provided, the method comprising:

[0006] Determine the sweet spot level of each layer section according to the oil and gas production of shale oil in each layer section in the oil wells in the target area;

[0007] Constructing an oil content curve of the oil well, wherein the oil content curve is used to represent the oil content of each layer segment, and establishing an association relationship between the oil content curve and a first seismic data volume corresponding to the target area;

[0008] A stratigraphic framework model is created based on the first seismic data body corresponding to the target area, the oil content curve is used as a characteristic curve for seismic reservoir inversion, key inversion parameters are set according to the geological characteristics of the oil well, and the first seismic data body is subjected to reservoir inversion using a waveform indication simulation or a waveform difference simulation method, so that the inverted second seismic data body is used to indicate the distribution pattern of the shale oil sweet spot segment.

[0009] In a possible implementation, determining the sweet spot level of each layer segment according to the oil and gas production of shale oil in each layer segment in the oil wells in the target area includes:

[0010] For each layer segment, perform the following steps:

[0011] If the oil and gas production of the layer segment is greater than a first threshold, determining the sweet spot segment level to which the layer segment belongs as a first level;

[0012] If the oil and gas production of the layer segment is greater than the second threshold value and not greater than the first threshold value, the sweet spot segment level to which the layer segment belongs is determined as the second level;

[0013] If the oil and gas production of the layer segment is not greater than the second threshold, determining the sweet spot segment level to which the layer segment belongs to be the third level;

[0014] The first threshold is greater than the second threshold, the first level and the second level represent sweet spot segments, and the third level represents non-sweet spot segments.

[0015] In a possible implementation, determining the sweet spot level of each layer segment according to the oil and gas production of shale oil in each layer segment in the oil wells in the target area includes:

[0016] Dividing the strata of the oil well into shale oil strata and non-shale oil strata according to different lithologies;

[0017] According to the oil and gas production of shale oil in each layer of the shale oil layer in the oil well, the shale oil layer is divided into sweet spots of different levels.

[0018] In a possible implementation, determining the sweet spot level of each layer segment according to the oil and gas production of shale oil in each layer segment in the oil wells in the target area includes:

[0019] Collecting data information of multiple oil wells in the target area, wherein the data information includes at least one of well logging curves, lithology data, oil test and production test data, or data related to oil and gas production in development layers;

[0020] Based on the data information of the plurality of oil wells, selecting a target oil well from the plurality of oil wells;

[0021] According to the oil and gas production of shale oil in each layer section in the target oil well, the sweet spot level of each layer section is determined.

[0022] In a possible implementation, selecting a target oil well from the plurality of oil wells based on the data information of the plurality of oil wells includes:

[0023] From the multiple oil wells, the oil well with the largest amount of data information, the most logging curves or the most formations encountered is selected as the target oil well.

[0024] In a possible implementation, constructing the oil content curve of the oil well includes:

[0025] The logging curve of the oil well is input into the neural network model, and the oil content curve of the oil well is obtained based on the neural network model.

[0026] In a possible implementation, inputting the logging curve of the oil well into a neural network model, and obtaining the oil content curve of the oil well based on the neural network model includes:

[0027] Acquire multiple logging curves of the oil well, wherein the multiple logging curves include logging curves with different value ranges;

[0028] Normalizing the multiple well logging curves so that the value ranges of the processed multiple well logging curves are the same;

[0029] The processed multiple logging curves are input into the neural network model, and the oil content curve of the oil well is obtained based on the neural network model.

[0030] In a possible implementation, the creating a stratigraphic framework model according to the first seismic data volume corresponding to the target area includes:

[0031] The stratigraphic framework model is created by performing linear interpolation between the top seismic volume layer and the bottom seismic volume layer of any layer segment.

[0032] In a possible implementation, performing reservoir inversion on the first seismic data volume so that the inverted second seismic data volume is used to indicate the distribution pattern of the shale oil sweet spot segment includes:

[0033] Reservoir inversion is performed on the first seismic data volume, and parameters are set for the inversion volume obtained by the inversion so that the phase axis in the inversion volume includes the characteristics of the change law of the shale oil sweet spot segment, thereby obtaining the second seismic data volume.

[0034] In a possible implementation, establishing an association relationship between the oil content curve and a first seismic data volume corresponding to the target area includes:

[0035] Acquire a first seismic data volume, layer data and velocity volume data of the target area;

[0036] According to the layer data, the top seismic layer and the bottom seismic layer of any layer segment are determined as the constrained time window for reservoir inversion, and the logging interpretation layer is matched with the seismic layer according to the velocity body data, and the correlation relationship between the oil content curve and the seismic body reflection wave phase axis is established.

[0037] In a possible implementation, before creating a stratigraphic framework model according to the first seismic data volume corresponding to the target area, the method further includes:

[0038] Creating a well-linked section of the target area, and projecting the oil well trajectory in the well-linked section to the seismic section of the first seismic data volume;

[0039] Based on the projected oil well trajectory, the seismic section is calibrated to make the seismic section in the first seismic data volume consistent with the logging section in the well-linked section.

[0040] In one possible implementation, the method further includes:

[0041] The amplitude attribute of the sub-layer segment is extracted by opening a time window for the sweet spot segment in the second seismic data volume, and the plane distribution position of the sweet spot segment of the sub-layer segment is determined according to the amplitude of the sub-layer segment.

[0042] In another aspect, a device for identifying a sweet spot segment is provided, the device comprising:

[0043] A grade determination module, used to determine the sweet spot grade of each layer section according to the oil and gas production of shale oil in each layer section in the oil wells in the target area;

[0044] A curve construction module, used for constructing an oil content curve of the oil well, wherein the oil content curve is used for indicating the oil content of each layer section;

[0045] An association establishment module, used for establishing an association relationship between the oil content curve and a first seismic data volume corresponding to the target area;

[0046] An inversion module is used to create a stratigraphic framework model based on a first seismic data body corresponding to the target area, use the oil content curve as a characteristic curve for seismic reservoir inversion, set key inversion parameters according to the geological characteristics of the oil well, and use a waveform indication simulation or a waveform difference simulation method to perform reservoir inversion on the first seismic data body, so that the inverted second seismic data body is used to indicate the distribution pattern of the shale oil sweet spot segment.

[0047] In a possible implementation, the level determination module is used to:

[0048] For each layer segment, perform the following steps:

[0049] If the oil and gas production of the layer segment is greater than a first threshold, determining the sweet spot segment level to which the layer segment belongs as a first level;

[0050] If the oil and gas production of the layer segment is greater than the second threshold value and not greater than the first threshold value, the sweet spot segment level to which the layer segment belongs is determined as the second level;

[0051] If the oil and gas production of the layer segment is not greater than the second threshold, determining the sweet spot segment level to which the layer segment belongs to be the third level;

[0052] The first threshold is greater than the second threshold, the first level and the second level represent sweet spot segments, and the third level represents non-sweet spot segments.

[0053] In a possible implementation, the level determination module includes:

[0054] A stratum division unit, used to divide the strata of the oil well into shale oil strata and non-shale oil strata according to different lithologies;

[0055] The sweet spot segment division unit is used to divide the shale oil layer system into sweet spots of different levels according to the oil and gas production of shale oil in each layer section of the shale oil layer system in the oil well.

[0056] In a possible implementation, the level determination module includes:

[0057] An information collection unit, used to collect data information of multiple oil wells in the target area, wherein the data information includes at least one of well logging curves, lithology data, oil and gas test data, or data related to oil and gas production in development layers;

[0058] A selection unit, configured to select a target oil well from the plurality of oil wells based on the data information of the plurality of oil wells;

[0059] The grade determination unit is used to determine the sweet spot grade of each layer section according to the oil and gas production of shale oil in each layer section in the target oil well.

[0060] In a possible implementation, the selection unit is used to select, from the multiple oil wells, an oil well with the largest amount of data information, the most logging curves, or the most formations encountered as the target oil well.

[0061] In a possible implementation, the curve construction module includes:

[0062] The prediction unit is used to input the logging curve of the oil well into the neural network model, and obtain the oil content curve of the oil well based on the neural network model.

[0063] In a possible implementation, the prediction unit includes:

[0064] A curve acquisition subunit, used for acquiring a plurality of logging curves of the oil well, wherein the plurality of logging curves include logging curves with different value ranges;

[0065] A normalization subunit, used for normalizing the plurality of logging curves so that the value ranges of the plurality of logging curves after normalization are the same;

[0066] The prediction subunit is used to input the processed multiple logging curves into the neural network model, and obtain the oil content curve of the oil well based on the neural network model.

[0067] In a possible implementation, the inversion module includes:

[0068] The interpolation unit is used to perform linear interpolation between the top seismic volume layer and the bottom seismic volume layer of any layer segment to create the stratigraphic framework model.

[0069] In a possible implementation, the inversion module includes:

[0070] A parameter setting unit is used to perform reservoir inversion on the first seismic data volume, and set parameters on the inversion volume obtained by inversion so that the phase axis in the inversion volume includes the characteristics of the change law of the shale oil sweet spot segment, thereby obtaining the second seismic data volume.

[0071] In a possible implementation, the association establishing module includes:

[0072] A seismic data acquisition unit, used for acquiring a first seismic data volume, layer data and velocity volume data of the target area;

[0073] The association establishing unit is used to determine the top seismic body layer and the bottom seismic body layer of any layer segment as the constrained time window for reservoir inversion according to the hierarchical data, correspond the logging interpretation layer with the seismic layer according to the velocity body data, and establish the association relationship between the oil content curve and the seismic body reflection wave phase axis.

[0074] In a possible implementation, the device further includes:

[0075] A profile creation module, used for creating a well-linked profile of the target area, and projecting the oil well trajectory in the well-linked profile to the seismic profile of the first seismic data volume;

[0076] The calibration module is used to calibrate the layer segments of the seismic section based on the projected oil well trajectory, so that the seismic layer segments in the first seismic data volume are consistent with the logging layer segments in the well-linked section.

[0077] In a possible implementation, the device further includes:

[0078] The attribute extraction module is used to extract the amplitude attributes of the sub-layer segment of the sweet spot segment in the second seismic data volume by opening a time window, and determine the planar distribution position of the sweet spot segment of the sub-layer segment according to the amplitude of the sub-layer segment.

[0079] On the other hand, a computer device is provided, comprising one or more processors and one or more memories, wherein at least one computer program is stored in the one or more memories, and the computer program is loaded and executed by the one or more processors to implement the method for identifying a sweet spot segment as described in any of the above items.

[0080] On the other hand, a computer-readable storage medium is provided, wherein at least one computer program is stored in the computer-readable storage medium, and the computer program is loaded and executed by a processor to implement the method for identifying a sweet spot segment as described in any one of the above.

[0081] The beneficial effects brought by the technical solution provided by the embodiment of the present application include at least:

[0082] The embodiment of the present application provides a prediction scheme for shale oil sweet spots, which can assist and expand the traditional single-well prediction method, improve the drilling rate of sweet spots, reduce exploration risks, and determine the distribution range of shale oil sweet spots more accurately and on a larger scale, providing a reliable basis for the prediction of shale oil geological resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0084] Figure 1 is a flow chart of a method for identifying a sweet spot segment provided by an embodiment of the present application;

[0085] Figure 2 is a flow chart of another method for identifying a sweet spot segment provided by an embodiment of the present application;

[0086] Figure 3 It is a schematic diagram of a process for constructing an oil content curve provided in an embodiment of the present application;

[0087] Figure 4 It is a comprehensive bar chart of desserts provided in an embodiment of the present application;

[0088] Figure 5is a schematic diagram of a target area and a target oil well in the target area provided in an embodiment of the present application;

[0089] Figure 6 is a schematic diagram of another target area and a target oil well in the target area provided in an embodiment of the present application;

[0090] Figure 7 It is a cross-sectional diagram of an inversion result provided in an embodiment of the present application;

[0091] Figure 8 is a root mean square amplitude attribute graph provided in an embodiment of the present application;

[0092] Fig. 9 is a structural schematic diagram of a device for identifying a sweet spot segment provided in an embodiment of the present application;

[0093] Fig.10 is a schematic diagram of the structure of a terminal provided in an embodiment of the present application;

[0094] Fig.11 It is a structural diagram of a server provided in an embodiment of the present application. DETAILED DESCRIPTION

[0095] In order to make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the implementation methods of the present application will be further described in detail below in conjunction with the accompanying drawings.

[0096] The terms "first", "second", "third", etc. used in this application may be used herein to describe various concepts, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of this application, a first seismic data volume may be referred to as a second seismic data volume, and similarly, a second seismic data volume may be referred to as a first seismic data volume.

[0097] The terms "multiple" and "each" used in this application include two or more than two, and each refers to each of the corresponding multiple. For example, the multiple well logging curves include 7 well logging curves, and each refers to each well logging curve of the 7 well logging curves.

[0098] Figure 1 is a flowchart of a method for identifying a sweet spot segment provided in an embodiment of the present application, see Figure 1 , the method comprising:

[0099] 101. According to the oil and gas production of shale oil in each layer section of the oil wells in the target area, determine the sweet spot level of each layer section.

[0100] 102. Construct an oil content curve of the oil well, the oil content curve is used to represent the oil content of each layer segment, and establish an association relationship between the oil content curve and the first seismic data volume corresponding to the target area.

[0101] 103. Create a stratigraphic framework model based on the first seismic data body corresponding to the target area, use the oil content curve as the characteristic curve of seismic reservoir inversion, set key inversion parameters according to the geological characteristics of the oil well, and use waveform indication simulation or waveform difference simulation methods to perform reservoir inversion on the first seismic data body, so that the inverted second seismic data body can be used to indicate the distribution pattern of shale oil sweet spots.

[0102] The embodiment of the present application provides a method for predicting shale oil sweet spots, which can assist and expand the traditional single-well prediction method, improve the drilling rate of sweet spots, reduce exploration risks, and determine the distribution range of shale oil sweet spots more accurately and on a larger scale, providing a reliable basis for the prediction of shale oil geological resources.

[0103] In a possible implementation, determining the sweet spot level of each layer segment according to the oil and gas production of shale oil in each layer segment in the oil wells in the target area includes:

[0104] For each layer segment, perform the following steps:

[0105] If the oil and gas production of the layer segment is greater than a first threshold, determining the sweet spot segment level to which the layer segment belongs as a first level;

[0106] If the oil and gas production of the layer segment is greater than the second threshold value and not greater than the first threshold value, the sweet spot segment level to which the layer segment belongs is determined as the second level;

[0107] If the oil and gas production of the layer segment is not greater than the second threshold, determining the sweet spot segment level to which the layer segment belongs to be the third level;

[0108] The first threshold is greater than the second threshold, the first level and the second level represent sweet spot segments, and the third level represents non-sweet spot segments.

[0109] In a possible implementation, determining the sweet spot level of each layer segment according to the oil and gas production of shale oil in each layer segment in the oil wells in the target area includes:

[0110] Dividing the strata of the oil well into shale oil strata and non-shale oil strata according to different lithologies;

[0111] According to the oil and gas production of shale oil in each layer of the shale oil layer in the oil well, the shale oil layer is divided into sweet spots of different levels.

[0112] In a possible implementation, determining the sweet spot level of each layer segment according to the oil and gas production of shale oil in each layer segment in the oil wells in the target area includes:

[0113] Collecting data information of multiple oil wells in the target area, wherein the data information includes at least one of well logging curves, lithology data, oil test and production test data, or data related to oil and gas production in development layers;

[0114] Based on the data information of the plurality of oil wells, selecting a target oil well from the plurality of oil wells;

[0115] According to the oil and gas production of shale oil in each layer section in the target oil well, the sweet spot level of each layer section is determined.

[0116] In a possible implementation, selecting a target oil well from the plurality of oil wells based on the data information of the plurality of oil wells includes:

[0117] From the multiple oil wells, the oil well with the largest amount of data information, the most logging curves or the most formations encountered is selected as the target oil well.

[0118] In a possible implementation, constructing the oil content curve of the oil well includes:

[0119] The logging curve of the oil well is input into the neural network model, and the oil content curve of the oil well is obtained based on the neural network model.

[0120] In a possible implementation, inputting the logging curve of the oil well into a neural network model, and obtaining the oil content curve of the oil well based on the neural network model includes:

[0121] Acquire multiple logging curves of the oil well, wherein the multiple logging curves include logging curves with different value ranges;

[0122] Normalizing the multiple well logging curves so that the value ranges of the processed multiple well logging curves are the same;

[0123] The processed multiple logging curves are input into the neural network model, and the oil content curve of the oil well is obtained based on the neural network model.

[0124] In a possible implementation, the creating a stratigraphic framework model according to the first seismic data volume corresponding to the target area includes:

[0125] The stratigraphic framework model is created by performing linear interpolation between the top seismic volume layer and the bottom seismic volume layer of any layer segment.

[0126] In a possible implementation, performing reservoir inversion on the first seismic data volume so that the inverted second seismic data volume is used to indicate the distribution pattern of the shale oil sweet spot segment includes:

[0127] Reservoir inversion is performed on the first seismic data volume, and parameters are set for the inversion volume obtained by the inversion so that the phase axis in the inversion volume includes the characteristics of the change law of the shale oil sweet spot segment, thereby obtaining the second seismic data volume.

[0128] In a possible implementation, establishing an association relationship between the oil content curve and a first seismic data volume corresponding to the target area includes:

[0129] Acquire a first seismic data volume, layer data and velocity volume data of the target area;

[0130] According to the layer data, the top seismic layer and the bottom seismic layer of any layer segment are determined as the constrained time window for reservoir inversion, and the logging interpretation layer is matched with the seismic layer according to the velocity body data, and the correlation relationship between the oil content curve and the seismic body reflection wave phase axis is established.

[0131] In a possible implementation, before creating a stratigraphic framework model according to the first seismic data volume corresponding to the target area, the method further includes:

[0132] Creating a well-linked section of the target area, and projecting the oil well trajectory in the well-linked section to the seismic section of the first seismic data volume;

[0133] Based on the projected oil well trajectory, the seismic section is calibrated to make the seismic section in the first seismic data volume consistent with the logging section in the well-linked section.

[0134] In one possible implementation, the method further includes:

[0135] The amplitude attribute of the sub-layer segment is extracted by opening a time window for the sweet spot segment in the second seismic data volume, and the plane distribution position of the sweet spot segment of the sub-layer segment is determined according to the amplitude of the sub-layer segment.

[0136] Figure 2 is a flowchart of a method for identifying a sweet spot segment provided by an embodiment of the present application, the method is applied in a computer device, such as Figure 2 As shown, the method includes:

[0137] 201. Classify the shale oil sweet spots in a single well according to production rules.

[0138] In an embodiment of the present application, in order to identify the shale oil sweet spot segment in the oil well, the shale oil sweet spot segment must first be classified. Taking the target area as an example, the target area can be a research area for shale oil sweet spot segments, and can be any area containing oil wells. The target area includes one or more oil wells. For example, the Ek2 section of the Cangdong Sag in the Huanghua Depression is the main stratum of shale oil, and the core and logging show that the stratum is mainly composed of lake-facies dark gray and gray-black shale strata. In recent years, many wells in the area have obtained industrial oil flows in shale strata, becoming the main battlefield for future shale oil exploration. Therefore, the Ek2 section of the Cangdong Sag can be used as the target area.

[0139] For the oil wells in the target area, the oil and gas production of shale oil in each layer of the oil well can be obtained. The oil and gas production of shale oil in each layer can reflect the production law of shale oil. Therefore, the sweet spot level of each layer is determined according to the oil and gas production of shale oil in each layer.

[0140] The specific classification method may include at least one of the following:

[0141] The first method is to perform the following steps for each layer segment: if the oil and gas production of the layer segment is greater than the first threshold, the sweet spot segment level to which the layer segment belongs is determined to be the first level; if the oil and gas production of the layer segment is greater than the second threshold but not greater than the first threshold, the sweet spot segment level to which the layer segment belongs is determined to be the second level; if the oil and gas production of the layer segment is not greater than the second threshold, the sweet spot segment level to which the layer segment belongs is determined to be the third level;

[0142] The first threshold is greater than the second threshold, the first level and the second level represent sweet spot segments, and the third level represents non-sweet spot segments.

[0143] By adopting this classification method, the layers in the oil well can be divided into three levels according to the different ranges of oil and gas production.

[0144] The second method is to divide the strata of the oil well into shale oil layers and non-shale oil layers according to different lithologies; thereby finding the shale oil layers in the oil well, and dividing the shale oil layers into sweet spots of different levels according to the oil and gas production of shale oil in each layer of the shale oil layers in the oil well.

[0145] Using this classification method, shale oil layers are first distinguished from non-shale oil layers, and then the shale oil is classified according to the oil and gas production of each layer in the shale oil layer.

[0146] The method used to classify the oil and gas production of shale oil in each layer in the shale oil stratum is similar to the first method mentioned above. That is, for each layer in the shale oil stratum, the following steps are performed: if the oil and gas production of the layer is greater than the first threshold, the sweet spot level to which the layer belongs is determined to be the first level; if the oil and gas production of the layer is greater than the second threshold and not greater than the first threshold, the sweet spot level to which the layer belongs is determined to be the second level.

[0147] It should be noted that, when the target area includes multiple oil wells, the sweet spots may be identified for each of these oil wells, or the sweet spots may be identified only for certain oil wells that meet the conditions.

[0148] In one possible implementation, the sweet spot level of each layer segment is determined based on the oil and gas production of shale oil in each layer segment in the oil wells in the target area, including: collecting data information of multiple oil wells in the target area, the data information including at least one of logging curves, lithological data, oil test and production test data, or data related to oil and gas production in the development layer segment; selecting a target oil well from the multiple oil wells based on the data information of the multiple oil wells; and determining the sweet spot level of each layer segment according to the oil and gas production of shale oil in each layer segment in the target oil well.

[0149] In one possible implementation, based on data information of multiple oil wells, a target oil well is selected from the multiple oil wells, including: from the multiple oil wells, an oil well with the largest amount of data information, the most logging curves, or the most formations encountered is selected as the target oil well.

[0150] It should be noted that the target oil well selected is at least one, and the more target oil wells there are, the more favorable the final prediction result will be. Therefore, it is necessary to try to select target oil wells, and the more the number of selected wells, the better.

[0151] For example, in the classification process, the well logging curves, lithology data, oil test and production test data, and oil and gas production related data of all oil wells in the target area are collected and counted. The oil wells with complete geological data and the most complete formations are selected as key wells, that is, target oil wells, to participate in subsequent research work. For the target oil well, the target layer to be studied is selected from the target oil well, and according to the lithology data, the target oil well is divided into shale oil formations and non-shale oil formations according to different lithologies. The oil and gas production of the shale oil layer section in the target oil well is counted (including the oil test and production stage and the development stage), and the shale layer section is divided into sweet spots of different levels according to the oil and gas production.

[0152] By adopting the above classification method, the oil wells can be divided into multiple levels such as the first sweet spot segment, the second sweet spot segment, the third sweet spot segment, and the non-sweet spot segment according to the level.

[0153] As shown in Table 1 below, taking Well X1, a key oil well in the target area, as an example, the core data of the well reveal that the Ek2 section is mainly divided into shale oil layers and non-shale oil layers. The non-shale oil layers are mainly tight sandstones, and the shale oil layers are mainly gray-black and dark gray mudstones, intercalated with a small amount of thin gray-brown and brown dolomite, and sandy bands and sandy lumps are enriched in some areas. The oil and gas production data of the Ek2 shale layers of all oil wells in the target area during the oil testing and trial production stages and the development stages were collected. The shale oil layers with a daily oil and gas production of >20 tons in the oil testing and trial production stages and >2 tons in the development and stable production stages were defined as Class I sweet spots, the shale oil layers with a daily oil and gas production of between 5-20 tons in the oil testing and trial production stages and >0.5 tons in the development and stable production stages were defined as Class II sweet spots, the shale oil layers with a daily oil and gas production of between 1-5 tons in the oil testing and trial production stages and >0.1 ton in the development and stable production stages were defined as Class III sweet spots, and the layers with a daily oil and gas production of less than 1 ton in the oil testing and trial production stages and <0.1 ton in the development and stable production stages were defined as non-sweet spots.

[0154] Table 1

[0155]

[0156] 202. Construct an oil content curve.

[0157] Among them, the oil content curve is used to indicate the oil content of each layer, which can reflect the production law of the oil well to a certain extent.

[0158] In one possible implementation, the logging curve of the oil well is input into the neural network model, and the oil content curve of the oil well is obtained based on the neural network model. The neural network model is used to predict the oil content curve of any oil well, and can be a convolutional neural network model or other types of neural network models.

[0159] In one possible implementation, the logging curve of the oil well is input into a neural network model, and the oil content curve of the oil well is obtained based on the neural network model, including: obtaining multiple logging curves of the oil well, the multiple logging curves include logging curves with different value ranges; normalizing the multiple logging curves so that the value ranges of the processed multiple logging curves are the same; inputting the processed multiple logging curves into the neural network model, and obtaining the oil content curve of the oil well based on the neural network model.

[0160] Among them, the multiple logging curves may include natural gamma, resistivity, acoustic time difference, density and other logging curves. The multiple logging curves have different value ranges, so the multiple logging curves are normalized and the value range is set so that the normalized multiple logging curves all belong to the value range, and then the processed multiple logging curves are input into the neural network model, and the oil content curve of the oil well is obtained based on the neural network model. For example, the value range can be 0-100, or it can also be other value ranges.

[0161] Through research, it was found that natural gamma, resistivity, acoustic time difference, density and other logging curves are correlated with lithology, physical properties, oil content and other indicators of shale oil. By fitting the above logging curves, an oil content curve is obtained, which can characterize the level of the sweet spot of shale oil.

[0162] In addition, the method also includes: comparing and analyzing the oil content of each layer segment in the oil content curve with the sweet spot segment grade to which each layer segment belongs, and determining the oil content dividing value between the sweet spot segment and the non-sweet spot segment. It can be understood that the layer segment with an oil content greater than the oil content dividing value should be the sweet spot segment, and the layer segment with an oil content not greater than the oil content dividing value should be the non-sweet spot segment.

[0163] Figure 3 is a schematic diagram of a process for constructing an oil content curve provided in an embodiment of the present application, Figure 4 is a comprehensive bar chart of the sweet spot of the Ek2 segment of the X1 well provided in the embodiment of the present application, Figure 4 Taking Well X1 as an example, with the help of the software platform, the GR (natural gamma), Rt (resistivity), AC (acoustic time difference), CNL (neutron), DEN (density) and other logging curves of the Ek2 section of all oil wells are intercepted and standardized. Figure 3 The neural network model shown above constructs an "oil content" curve, and other oil wells are processed in this way. The oil content curves of all key oil wells in the target area are normalized, and the value range is set to 0-100. The values ​​of the oil content curves are intersected and analyzed with the sweet spot interpretation results of the shale formation of the second section of the hole at the corresponding depth, and the boundary value between the sweet spot and the non-sweet spot is determined to be 50.

[0164] 203. Establish the correlation between the oil content curve and the seismic data body.

[0165] Taking the seismic data body of the target area as the first seismic data body as an example, in a possible implementation, the first seismic data body is the post-stack seismic body of the target area, and the computer equipment obtains the first seismic data body, layer data and velocity body data of the target area; according to the layer data, the top seismic layer and the bottom seismic layer of any layer segment are determined as the constrained time window of reservoir inversion, and the logging interpretation layer is corresponded to the seismic layer according to the velocity body data, and the correlation relationship between the oil content curve and the seismic body reflection wave phase axis is established.

[0166] In addition, if there is no velocity volume data in the target area, synthetic seismic records can be used to achieve layer calibration.

[0167] 204. A waveform indication simulation or waveform difference simulation technology is used to obtain a sweet spot segment reflecting the sweet spot law of shale oil.

[0168] In an embodiment of the present application, a stratigraphic framework model is created based on a first seismic data body corresponding to a target area, the oil content curve is used as a characteristic curve for seismic reservoir inversion, key inversion parameters are set according to the geological characteristics of the oil well, and a waveform indication simulation or a waveform difference simulation method is used to perform reservoir inversion on the first seismic data body, so that the inverted second seismic data body is used to indicate the distribution pattern of shale oil sweet spots.

[0169] When creating a stratigraphic framework model, linear interpolation can be performed between the top seismic volume layer and the bottom seismic volume layer of any layer segment to create the stratigraphic framework model. The linear interpolation process can be implemented by software running on a computer device, or by other methods.

[0170] In addition, for oil wells that are relatively close, layer segment calibration can be performed before creating the stratigraphic framework model. For example, a well-linked section of the target area is created, and the oil well trajectory in the well-linked section is projected onto the seismic section of the first seismic data body. Based on the projected well trajectory, the seismic section is calibrated to ensure that the seismic layer segments in the first seismic data body are consistent with the logging layer segments in the well-linked section.

[0171] After creating a stratigraphic framework model, taking the oil content curve as the characteristic curve of seismic reservoir inversion, and setting key inversion parameters according to the geological characteristics of the oil well, the first seismic data body is subjected to reservoir inversion using waveform indication simulation or waveform difference simulation methods to obtain an inversion body, and then parameters of the inversion body are set so that the phase axis in the inversion body includes the characteristics of the change law of the shale oil sweet spot segment. The seismic data body obtained at this time can be called the second seismic data body, which is used to indicate the distribution law of the shale oil sweet spot segment and can be used as a prediction basis for finding the shale oil sweet spot segment.

[0172] Among them, when performing reservoir inversion, waveform indication simulation or waveform difference simulation can be selected according to the number of target oil wells in the target area. For example, when the number of target oil wells in the target area is large, the waveform indication simulation method is selected for reservoir inversion.

[0173] in, Figure 5 and Figure 6 The black box in the figure represents the target area, and the black dots represent the target oil wells. Figure 5 The standard for a large number of target oil wells is that the target oil wells are distributed in the entire target area, and the number of target oil wells is large enough to cover the entire target area. For example, if the number of target oil wells in the entire target area is greater than a certain number set in advance, it can be considered that the number of target oil wells is large enough. Otherwise, see Figure 6 ,If the above criteria are not met, it can be considered that the number of target oil wells is not large enough, and the waveform difference simulation method is selected for reservoir inversion.

[0174] When performing reservoir inversion, the lateral smoothing radius can be set to 2-3, the number of effective samples to 4-6, the time window depth is generally 30ms, the high-pass frequency is between 100Hz-300Hz, the high-pass cutoff frequency is between 150Hz-350Hz, and the sampling rate is 0.1-0.3ms.

[0175] When setting parameters, the inversion volume color scale range is set to 0-100, corresponding to the value range of the oil content curve. The boundary value between the sweet spot segment and the non-sweet spot segment is set in the color table, and the boundary value corresponds to the oil content boundary value between the sweet spot segment and the non-sweet spot segment determined in the above step 202.

[0176] For example, if the demarcation value between the sweet spot segment and the non-sweet spot segment determined in step 202 is 50, the inversion seismic body color table range is set to 0-100, and the intermediate color is set to 50 in the color table as the demarcation value between the sweet spot segment and the non-sweet spot segment. The logging interpretation sweet spot segment is compared with the inversion body profile through the well to check whether the position law of the phase axis in the inversion body is consistent with the position law of the logging interpretation sweet spot segment. By appropriately adjusting the color table to achieve the optimal coincidence rate, the phase axis in the inversion body is given the geological significance of the change law of the shale oil sweet spot body, and finally becomes a sweet spot body that can reflect the law of the shale oil sweet spot, which serves as a prediction basis for finding the shale oil sweet spot.

[0177] 205. Extract the plane attributes of the sweet spot segment and determine the distribution pattern of the sweet spot plane.

[0178] In a possible implementation, the amplitude attributes of the sub-layer segments are extracted by opening a time window for the sweet spot segments in the second seismic data volume, and the planar distribution position of the sweet spot segments of the sub-layer segments is determined according to the amplitude of the sub-layer segments.

[0179] In an embodiment of the present application, the sweet spot segment in the second seismic data body can be divided into specific sub-layer segments. For a specific sub-layer segment, the amplitude attribute can be extracted by opening a time window to obtain the amplitude value of the sub-layer segment, so that the plane distribution position of the sweet spot segment of the sub-layer segment can be viewed, thereby understanding the distribution pattern of the sweet spot plane in the sweet spot segment.

[0180] See also Figure 7 and Figure 8 , Figure 7 is a cross-sectional view of the inversion result of the Ek2 segment provided in the embodiment of the present application, Figure 8 is the root mean square amplitude attribute diagram of the Ek2 segment provided in the embodiment of the present application. Figure 7 and Figure 8 Different colors represent different amplitudes, the darker the color, the larger the amplitude.

[0181] The embodiment of the present application provides a method for predicting shale oil sweet spots, which can assist and expand the traditional single-well prediction method, improve the drilling rate of sweet spots, reduce exploration risks, and determine the distribution range of shale oil sweet spots more accurately and on a larger scale, providing a reliable basis for the prediction of shale oil geological resources. The location predicted by the sweet spot inversion can be used to design and deploy well trajectories, predict the range of shale oil favorable areas, and calculate shale oil geological reserves.

[0182] Fig. 9 is a schematic diagram of a structure of a device for identifying a sweet spot segment provided in an embodiment of the present application, see Fig. 9 , the device comprises:

[0183] The grade determination module 901 is used to determine the sweet spot grade of each layer segment according to the oil and gas production of shale oil in each layer segment in the oil wells in the target area;

[0184] A curve construction module 902 is used to construct an oil content curve of an oil well, where the oil content curve is used to represent the oil content of each layer section;

[0185] The association establishment module 903 is used to establish an association relationship between the oil content curve and the first seismic data volume corresponding to the target area;

[0186] Inversion module 904 is used to create a stratigraphic framework model based on the first seismic data body corresponding to the target area, use the oil content curve as the characteristic curve of seismic reservoir inversion, set key inversion parameters according to the geological characteristics of the oil well, and use waveform indication simulation or waveform difference simulation methods to perform reservoir inversion on the first seismic data body, so that the inverted second seismic data body is used to indicate the distribution pattern of shale oil sweet spots.

[0187] In a possible implementation, the level determination module 901 is configured to:

[0188] For each layer segment, perform the following steps:

[0189] If the oil and gas production of the layer segment is greater than the first threshold, the sweet spot segment level to which the layer segment belongs is determined as the first level;

[0190] If the oil and gas production of the layer segment is greater than the second threshold value and not greater than the first threshold value, the sweet spot segment level to which the layer segment belongs is determined as the second level;

[0191] If the oil and gas production of the layer segment is not greater than the second threshold, the sweet spot segment level to which the layer segment belongs is determined as the third level;

[0192] The first threshold is greater than the second threshold, the first level and the second level represent sweet spot segments, and the third level represents non-sweet spot segments.

[0193] In a possible implementation, the level determination module 901 includes:

[0194] The stratum division unit is used to divide the strata of the oil well into shale oil strata and non-shale oil strata according to different lithologies;

[0195] The sweet spot segment division unit is used to divide the shale oil formation into sweet spots of different levels according to the oil and gas production of shale oil in each layer of the shale oil formation in the oil well.

[0196] In a possible implementation, the level determination module 901 includes:

[0197] An information collection unit, used to collect data information of multiple oil wells in the target area, the data information including at least one of logging curves, lithology data, oil test and production test data, or data related to oil and gas production in the development layer;

[0198] A selection unit, used for selecting a target oil well from a plurality of oil wells based on data information of the plurality of oil wells;

[0199] The grade determination unit is used to determine the sweet spot grade of each layer section according to the oil and gas production of shale oil in each layer section in the target oil well.

[0200] In a possible implementation, the selection unit is used to select, from among multiple oil wells, an oil well with the largest amount of data information, the largest number of well logging curves, or the largest number of formations encountered as a target oil well.

[0201] In a possible implementation, the curve construction module 902 includes:

[0202] The prediction unit is used to input the logging curve of the oil well into the neural network model, and obtain the oil content curve of the oil well based on the neural network model.

[0203] In a possible implementation, the prediction unit includes:

[0204] The curve acquisition subunit is used to acquire a variety of logging curves of the oil well, wherein the multiple logging curves include logging curves with different value ranges;

[0205] A normalization subunit is used to perform normalization processing on a plurality of logging curves so that the value ranges of the processed plurality of logging curves are the same;

[0206] The prediction subunit is used to input the processed multiple logging curves into the neural network model, and obtain the oil content curve of the oil well based on the neural network model.

[0207] In one possible implementation, the inversion module 904 includes:

[0208] The interpolation unit is used to perform linear interpolation between the top seismic volume layer and the bottom seismic volume layer in any layer segment to create a stratigraphic framework model.

[0209] In one possible implementation, the inversion module 904 includes:

[0210] The parameter setting unit is used to perform reservoir inversion on the first seismic data body, and set parameters on the inversion body obtained by inversion so that the phase axis in the inversion body includes the characteristics of the change law of the shale oil sweet spot segment, thereby obtaining the second seismic data body.

[0211] In a possible implementation, the association establishing module 903 includes:

[0212] A seismic data acquisition unit, used to acquire a first seismic data volume, layer data and velocity volume data of a target area;

[0213] The association establishment unit is used to determine the top seismic body layer and the bottom seismic body layer of any layer segment as the constrained time window for reservoir inversion based on the hierarchical data, correspond the logging interpretation layer with the seismic layer based on the velocity body data, and establish the association relationship between the oil content curve and the seismic body reflection wave phase axis.

[0214] In a possible implementation, the device further includes:

[0215] A profile creation module, used to create a well-joined profile of a target area, and project the oil well trajectory in the well-joined profile to the seismic profile of the first seismic data volume;

[0216] The calibration module is used to calibrate the seismic section based on the projected oil well trajectory, so that the seismic section in the first seismic data volume is consistent with the logging section in the well-joint section.

[0217] In a possible implementation, the device further includes:

[0218] The attribute extraction module is used to extract the amplitude attributes of the sub-layer segment by opening a time window for the sweet spot segment in the second seismic data volume, and determine the plane distribution position of the sweet spot segment of the sub-layer segment according to the amplitude of the sub-layer segment.

[0219] An embodiment of the present application also provides a computer device, which includes one or more processors and one or more memories, wherein at least one computer program is stored in the one or more memories, and the computer program is loaded and executed by the one or more processors to implement the method for identifying sweet spot segments in the above embodiment.

[0220] Optionally, the computer device is provided as a terminal. Fig.10 1 is a schematic diagram of the structure of a terminal provided in an embodiment of the present application. The terminal 1000 may be a portable mobile terminal, such as a smart phone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 player (Moving Picture Experts Group Audio Layer IV), a laptop computer or a desktop computer. The terminal 1000 may also be referred to as a user device, a portable terminal, a laptop terminal, a desktop terminal or other names.

[0221] The terminal 1000 includes: a processor 1001 and a memory 1002 .

[0222] The processor 1001 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 1001 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 1001 may also include a main processor and a coprocessor. The main processor is a processor for processing data in an awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 1001 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 1001 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0223] The memory 1002 may include one or more computer-readable storage media, which may be non-transitory. The memory 1002 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 1002 is used to store at least one computer program, which is used to be executed by the processor 1001 to implement the method for identifying the sweet spot segment provided in the method embodiment of the present application.

[0224] In some embodiments, the terminal 1000 may also optionally include: a peripheral device interface 1003 and at least one peripheral device. The processor 1001, the memory 1002 and the peripheral device interface 1003 may be connected via a bus or a signal line. Each peripheral device may be connected to the peripheral device interface 1003 via a bus, a signal line or a circuit board. Specifically, the peripheral device includes: at least one of a radio frequency circuit 1004, a display screen 1005, a camera assembly 1006, an audio circuit 1007, a positioning assembly 1008 and a power supply 1009.

[0225] The peripheral device interface 1003 may be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 1001 and the memory 1002. In some embodiments, the processor 1001, the memory 1002, and the peripheral device interface 1003 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 1001, the memory 1002, and the peripheral device interface 1003 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.

[0226] The radio frequency circuit 1004 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 1004 communicates with the communication network and other communication devices through electromagnetic signals. The radio frequency circuit 1004 converts the electrical signal into an electromagnetic signal for transmission, or converts the received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuit 1004 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, etc. The radio frequency circuit 1004 can communicate with other terminals through at least one wireless communication protocol. The wireless communication protocol includes but is not limited to: the World Wide Web, a metropolitan area network, an intranet, various generations of mobile communication networks (2G, 3G, 4G and 5G), a wireless local area network and / or a WiFi (Wireless Fidelity) network. In some embodiments, the radio frequency circuit 1004 may also include circuits related to NFC (Near Field Communication), which is not limited in this application.

[0227] The display screen 1005 is used to display a UI (User Interface). The UI may include graphics, text, icons, videos, and any combination thereof. When the display screen 1005 is a touch display screen, the display screen 1005 also has the ability to collect touch signals on the surface or above the surface of the display screen 1005. The touch signal can be input to the processor 1001 as a control signal for processing. At this time, the display screen 1005 can also be used to provide virtual buttons and / or virtual keyboards, also known as soft buttons and / or soft keyboards. In some embodiments, the display screen 1005 can be one, arranged on the front panel of the terminal 1000; in other embodiments, the display screen 1005 can be at least two, respectively arranged on different surfaces of the terminal 1000 or in a folding design; in other embodiments, the display screen 1005 can be a flexible display screen, arranged on a curved surface or a folding surface of the terminal 1000. Even, the display screen 1005 can also be arranged as a non-rectangular irregular figure, that is, a special-shaped screen. The display screen 1005 can be made of materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0228] The camera assembly 1006 is used to capture images or videos. Optionally, the camera assembly 1006 includes a front camera and a rear camera. The front camera is arranged on the front panel of the terminal, and the rear camera is arranged on the back of the terminal. In some embodiments, there are at least two rear cameras, which are any one of a main camera, a depth of field camera, a wide-angle camera, and a telephoto camera, so as to realize the fusion of the main camera and the depth of field camera to realize the background blur function, the fusion of the main camera and the wide-angle camera to realize panoramic shooting and VR (Virtual Reality) shooting function or other fusion shooting functions. In some embodiments, the camera assembly 1006 may also include a flash. The flash can be a monochrome temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm light flash and a cold light flash, which can be used for light compensation at different color temperatures.

[0229] The audio circuit 1007 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, and convert the sound waves into electrical signals and input them into the processor 1001 for processing, or input them into the radio frequency circuit 1004 to achieve voice communication. For the purpose of stereo acquisition or noise reduction, there may be multiple microphones, which are respectively arranged at different parts of the terminal 1000. The microphone may also be an array microphone or an omnidirectional acquisition microphone. The speaker is used to convert the electrical signal from the processor 1001 or the radio frequency circuit 1004 into sound waves. The speaker may be a traditional film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert the electrical signal into sound waves audible to humans, but also convert the electrical signal into sound waves inaudible to humans for purposes such as ranging. In some embodiments, the audio circuit 1007 may also include a headphone jack.

[0230] The positioning component 1008 is used to locate the current geographical location of the terminal 1000 to implement navigation or LBS (Location Based Service). The positioning component 1008 can be a positioning component based on the US GPS (Global Positioning System), China's Beidou system or Russia's Galileo system.

[0231] The power supply 1009 is used to power various components in the terminal 1000. The power supply 1009 can be an alternating current, a direct current, a disposable battery, or a rechargeable battery. When the power supply 1009 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery charged through a wired line, and a wireless rechargeable battery is a battery charged through a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0232] In some embodiments, the terminal 1000 further includes one or more sensors 1010 , including but not limited to: an acceleration sensor 1011 , a gyroscope sensor 1012 , a pressure sensor 1013 , a fingerprint sensor 1014 , an optical sensor 1015 , and a proximity sensor 1016 .

[0233] The acceleration sensor 1011 can detect the magnitude of acceleration on the three coordinate axes of the coordinate system established by the terminal 1000. For example, the acceleration sensor 1011 can be used to detect the components of gravity acceleration on the three coordinate axes. The processor 1001 can control the display screen 1005 to display the user interface in a horizontal view or a vertical view according to the gravity acceleration signal collected by the acceleration sensor 1011. The acceleration sensor 1011 can also be used to collect motion data of games or users.

[0234] The gyro sensor 1012 can detect the body direction and rotation angle of the terminal 1000, and the gyro sensor 1012 can cooperate with the acceleration sensor 1011 to collect the user's 3D actions on the terminal 1000. The processor 1001 can implement the following functions based on the data collected by the gyro sensor 1012: motion sensing (such as changing the UI according to the user's tilt operation), image stabilization during shooting, game control, and inertial navigation.

[0235] The pressure sensor 1013 can be set on the side frame of the terminal 1000 and / or the lower layer of the display screen 1005. When the pressure sensor 1013 is set on the side frame of the terminal 1000, it can detect the user's holding signal of the terminal 1000, and the processor 1001 performs left and right hand recognition or shortcut operation according to the holding signal collected by the pressure sensor 1013. When the pressure sensor 1013 is set on the lower layer of the display screen 1005, the processor 1001 controls the operability controls on the UI interface according to the user's pressure operation on the display screen 1005. The operability controls include at least one of a button control, a scroll bar control, an icon control, and a menu control.

[0236] The fingerprint sensor 1014 is used to collect the user's fingerprint, and the processor 1001 identifies the user's identity based on the fingerprint collected by the fingerprint sensor 1014, or the fingerprint sensor 1014 identifies the user's identity based on the collected fingerprint. When the user's identity is identified as a trusted identity, the processor 1001 authorizes the user to perform relevant sensitive operations, including unlocking the screen, viewing encrypted information, downloading software, paying, and changing settings. The fingerprint sensor 1014 can be set on the front, back, or side of the terminal 1000. When a physical button or a manufacturer logo is set on the terminal 1000, the fingerprint sensor 1014 can be integrated with the physical button or the manufacturer logo.

[0237] The optical sensor 1015 is used to collect the ambient light intensity. In one embodiment, the processor 1001 can control the display brightness of the display screen 1005 according to the ambient light intensity collected by the optical sensor 1015. Specifically, when the ambient light intensity is high, the display brightness of the display screen 1005 is increased; when the ambient light intensity is low, the display brightness of the display screen 1005 is reduced. In another embodiment, the processor 1001 can also dynamically adjust the shooting parameters of the camera assembly 1006 according to the ambient light intensity collected by the optical sensor 1015.

[0238] The proximity sensor 1016, also called a distance sensor, is disposed on the front panel of the terminal 1000. The proximity sensor 1016 is used to collect the distance between the user and the front of the terminal 1000. In one embodiment, when the proximity sensor 1016 detects that the distance between the user and the front of the terminal 1000 is gradually decreasing, the processor 1001 controls the display screen 1005 to switch from the screen-on state to the screen-off state; when the proximity sensor 1016 detects that the distance between the user and the front of the terminal 1000 is gradually increasing, the processor 1001 controls the display screen 1005 to switch from the screen-off state to the screen-on state.

[0239] Those skilled in the art will understand that Fig.10 The structure shown in the figure does not constitute a limitation on the terminal 1000, and the terminal 1000 may include more or less components than those shown in the figure, or combine some components, or adopt a different component arrangement.

[0240] Optionally, the computer device is provided as a server. Fig.11 This is a schematic diagram of the structure of a server provided in an embodiment of the present application. The server 1100 may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) 1101 and one or more memories 1102, wherein at least one computer program is stored in the memory 1102, and at least one computer program is loaded and executed by the processor 1101 to implement the methods provided in the above-mentioned various method embodiments. Of course, the server may also have components such as a wired or wireless network interface, a keyboard, and an input and output interface for input and output, and the server may also include other components for implementing device functions, which will not be described in detail here.

[0241] An embodiment of the present application further provides a computer-readable storage medium, in which at least one computer program is stored. The at least one computer program is loaded and executed by a processor to implement the method for identifying a sweet spot segment in the above embodiment.

[0242] The present application also provides a computer program product or a computer program, which includes a computer program code stored in a computer-readable storage medium. A processor of a computer device reads the computer program code from the computer-readable storage medium, and the processor executes the computer program code, so that the computer device implements the method for identifying a sweet spot segment as described in the above embodiment.

[0243] A person of ordinary skill in the art will appreciate that all or part of the steps to implement the above embodiments may be accomplished by hardware, or by a program to instruct the relevant hardware, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk, or an optical disk, etc.

[0244] The above description is only an optional embodiment of the embodiments of the present application and is not intended to limit the embodiments of the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the protection scope of the present application.

Claims

1. A method for identifying a sweet spot segment, characterized in that: The method comprises: Determine the sweet spot level of each layer section according to the oil and gas production of shale oil in each layer section in the oil wells in the target area; The logging curve of the oil well is input into the neural network model, and the oil content curve of the oil well is obtained based on the neural network model, wherein the oil content curve is used to represent the oil content of each layer segment, and the first seismic data body, layer data and velocity body data corresponding to the target area are obtained; according to the layer data, the top seismic layer and the bottom seismic layer of any layer segment are determined as the constrained time window of reservoir inversion, and the logging interpretation layer is corresponded to the seismic layer according to the velocity body data, and the correlation relationship between the oil content curve and the seismic body reflection wave event axis is established; A stratigraphic framework model is created according to the first seismic data body corresponding to the target area, the oil content curve is used as a characteristic curve of seismic reservoir inversion, key inversion parameters are set according to the geological characteristics of the oil well, and a waveform indication simulation or a waveform difference simulation method is used to perform reservoir inversion on the first seismic data body, and parameters are set on the inversion body obtained by inversion so that the phase axis in the inversion body includes the characteristics of the change law of the shale oil sweet spot segment, and a second seismic data body is obtained, so that the second seismic data body after inversion is used to indicate the distribution law of the shale oil sweet spot segment; Extracting the amplitude attribute of the sub-layer segment of the sweet spot segment in the second seismic data volume by opening a time window, and determining the plane distribution position of the sweet spot segment of the sub-layer segment according to the amplitude of the sub-layer segment; Before creating a stratigraphic framework model according to the first seismic data volume corresponding to the target area, the method further includes: Creating a well-linked section of the target area, and projecting the oil well trajectory in the well-linked section to the seismic section of the first seismic data volume; Based on the projected oil well trajectory, the seismic section is calibrated to make the seismic section in the first seismic data volume consistent with the logging section in the well-linked section.

2. The method according to claim 1, characterized in that The step of determining the sweet spot level of each layer segment according to the oil and gas production of shale oil in each layer segment in the oil wells in the target area includes: Dividing the strata of the oil well into shale oil strata and non-shale oil strata according to different lithologies; According to the oil and gas production of shale oil in each layer of the shale oil layer in the oil well, the shale oil layer is divided into sweet spots of different levels.

3. The method according to claim 1, characterized in that The step of determining the sweet spot level of each layer segment according to the oil and gas production of shale oil in each layer segment in the oil wells in the target area includes: Collecting data information of multiple oil wells in the target area, wherein the data information includes at least one of well logging curves, lithology data, oil test and production test data, or data related to oil and gas production in development layers; Based on the data information of the plurality of oil wells, selecting a target oil well from the plurality of oil wells; According to the oil and gas production of shale oil in each layer section in the target oil well, the sweet spot level of each layer section is determined.

4. The method according to claim 3, characterized in that The step of selecting a target oil well from the plurality of oil wells based on the data information of the plurality of oil wells comprises: From the multiple oil wells, the oil well with the largest amount of data information, the most logging curves or the most formations encountered is selected as the target oil well.

5. The method according to claim 1, characterized in that The step of inputting the logging curve of the oil well into the neural network model and obtaining the oil content curve of the oil well based on the neural network model comprises: Acquire multiple logging curves of the oil well, wherein the multiple logging curves include logging curves with different value ranges; Normalizing the multiple well logging curves so that the value ranges of the processed multiple well logging curves are the same; The processed multiple logging curves are input into the neural network model, and the oil content curve of the oil well is obtained based on the neural network model.

6. The method according to claim 1, characterized in that The step of creating a stratigraphic framework model according to the first seismic data volume corresponding to the target area includes: The stratigraphic framework model is created by performing linear interpolation between the top seismic volume layer and the bottom seismic volume layer of any layer segment.

7. A device for identifying a sweet spot segment, characterized in that: The device comprises: A grade determination module, used to determine the sweet spot grade of each layer section according to the oil and gas production of shale oil in each layer section in the oil wells in the target area; A curve construction module, used for inputting the logging curve of the oil well into the neural network model, and obtaining the oil content curve of the oil well based on the neural network model, wherein the oil content curve is used for indicating the oil content of each layer section; The association establishment module is used to obtain the first seismic data body, layer data and velocity body data corresponding to the target area; according to the layer data, the top seismic layer and the bottom seismic layer of any layer segment are determined as the constrained time window of reservoir inversion, and according to the velocity body data, the logging interpretation layer is matched with the seismic layer, and the association relationship between the oil content curve and the seismic body reflection wave event axis is established; an inversion module, for creating a stratigraphic framework model according to a first seismic data body corresponding to the target area, using the oil content curve as a characteristic curve for seismic reservoir inversion, setting key inversion parameters according to the geological characteristics of the oil well, performing reservoir inversion on the first seismic data body by waveform indication simulation or waveform difference simulation, setting parameters on the inversion body obtained by inversion so that the phase axis in the inversion body includes the characteristics of the variation law of the shale oil sweet spot segment, and obtaining a second seismic data body, so that the second seismic data body after inversion is used to indicate the distribution law of the shale oil sweet spot segment; An attribute extraction module is used to extract the amplitude attribute of the sub-layer segment by opening a time window for the sweet spot segment in the second seismic data volume, and determine the plane distribution position of the sweet spot segment of the sub-layer segment according to the amplitude of the sub-layer segment; The device also includes: A profile creation module, used for creating a well-linked profile of the target area, and projecting the oil well trajectory in the well-linked profile to the seismic profile of the first seismic data volume; The calibration module is used to calibrate the layer segments of the seismic section based on the projected oil well trajectory, so that the seismic layer segments in the first seismic data volume are consistent with the logging layer segments in the well-linked section.

8. A computer device, characterized in that: The computer device includes one or more processors and one or more memories, wherein at least one computer program is stored in the one or more memories, and the computer program is loaded and executed by the one or more processors to implement the method for identifying a sweet spot segment as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: At least one computer program is stored in the computer-readable storage medium, and the computer program is loaded and executed by the processor to implement the method for identifying a sweet spot segment according to any one of claims 1 to 6.

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