An improved method, device and system for finely characterizing sedimentary microfacies based on seismic-geological lithology combined characteristics

Abstract

An improved method, device and system for finely characterizing sedimentary microfacies based on seismic-geological lithology combination characteristics, including: step S1, obtaining historical logging data of lithology logging, and analyzing and summarizing the response characteristics of lithology logging according to the lithology classification standard; step S2, establishing a sensitive elastic parameter model based on the response characteristics and the single-well phase constraints of lithology logging; step S3, inputting the lithology logging data to be processed in a given area into the sensitive elastic parameter model, and the sensitive elastic parameter model characterizes the distribution range of sedimentary microfacies for the given area. Among them, the sensitive elastic parameter model uses the relationship between seismic sensitive attribute characteristics and sedimentary microfacies to establish a three-dimensional sedimentary microfacies stereo model, and the three-dimensional sedimentary microfacies stereo model characterizes the distribution range of sedimentary microfacies according to the given area. The sedimentary microfacies characterization of this method is more refined, the prediction accuracy is improved, and the drilling success rate is improved.

Description

Technical Field

[0001] The invention relates to the technical field of sedimentary phase research, and in particular to an improved method, device and system for finely depicting sedimentary microphases based on seismic-geological lithology combined characteristics. Background Art

[0002] Sedimentary facies is a stratigraphic unit that reflects certain natural environmental characteristics and has certain lithology and paleontological signs. The environment and process of sedimentation can be judged from the lithology, structure, structure and paleontology of the sediments (rocks). Sedimentary environment mainly refers to the distribution of sea, land, river, lake, swamp, glacier, desert and the height of the terrain. Therefore, the study of sedimentary facies is not only an important part of geomorphological research, but also one of the important contents of oil and gas exploration and development research.

[0003] In the current existing technologies, the research ideas and technical methods for basin sedimentary filling evolution are mainly single factor analysis method and sedimentary system research method. The single factor analysis method requires a large number of samples and analytical test results, from point to surface, from local to overall, from quantitative change to qualitative change to guide the research of transportation and sedimentation process. The sedimentary system research method uses high-precision seismic data with drilling and logging data to gradually study the overall distribution pattern of basin sedimentary system from surface to point, from overall to local. There are also related studies on basin sedimentary filling based on the tectonic background of the basin or sea level changes. It is believed that the strength of tectonic activity plays a dominant role in controlling the sedimentary filling of the basin. The sedimentary filling evolution of basins with strong tectonic activity has the characteristics of strong differences in fault activity, multiple and fast-changing material sources, and narrow and incomplete development of sedimentary facies belts. The research of these ideas and technical methods has had a profound impact on sedimentology and basin filling research.

[0004] However, the above-mentioned research methods of the prior art have low accuracy in describing sedimentary phases and are unable to accurately and objectively describe the sedimentary phase properties of rock formations, thereby affecting the accuracy of oil and gas exploration. Summary of the invention

[0005] In response to the above technical problems, the present disclosure proposes an improved method and system for finely characterizing sedimentary microfacies based on seismic-geological lithology combination characteristics.

[0006] To this end, the following aspects are included:

[0007] In the first aspect, an improved method for finely characterizing sedimentary microfacies based on seismic-geological lithology combination characteristics comprises:

[0008] Step S1, obtaining historical logging data of lithology logging, and analyzing and summarizing the response characteristics of lithology logging according to the lithology classification standard;

[0009] Step S2, establishing a sensitive elastic parameter model based on the response characteristics and the single well phase constraints of lithological logging;

[0010] Step S3, inputting the lithologic logging data to be processed in a given area into the sensitive elastic parameter model, wherein the sensitive elastic parameter model describes the distribution range of sedimentary microfacies for the given area.

[0011] Furthermore, based on the response characteristics, a lithology logging response chart is established.

[0012] Furthermore, the analysis and summary of the response characteristics of lithologic logging includes: qualitatively describing the planar distribution of different sedimentary microfacies based on amplitude and morphological characteristics, and summarizing the seismic response characteristics of high-quality reservoirs.

[0013] Furthermore, the establishment of the sensitive elastic parameter model includes taking the response characteristics as training sample data, learning and training a neural network model, and constructing the sensitive elastic parameter model.

[0014] Further, the constructing of the sensitive elastic parameter model specifically includes:

[0015] Step S21, performing refined hierarchical division on the lithology logging single well;

[0016] Step S22, performing fine-scale hierarchical division on the lithologic logging wells, and performing hierarchical comparison to obtain a fine formation model;

[0017] Step S23, establishing a coarsened sedimentary facies map based on the seismic attribute characteristics in the response characteristics and the relationship between different sedimentary microfacies types and seismic response characteristics;

[0018] Step S24, on the coarsened sedimentary facies map, using the refined stratigraphic model, finely dividing the sedimentary microfacies of the single well, thereby obtaining a refined sedimentary microfacies model;

[0019] Step S25, based on the detailed sedimentary microfacies model, using seismic sensitive attributes to establish a three-dimensional sedimentary microfacies stereo model;

[0020] Step S26, based on the three-dimensional sedimentary microfacies stereo model, characterize the sedimentary microfacies distribution range according to a given area.

[0021] Further, in step S25, the use of seismic sensitive attributes to establish a three-dimensional sedimentary microfacies stereo model specifically includes:

[0022] Step S251, based on the above-mentioned fine sedimentary facies model, a three-dimensional sedimentary microfacies stereo model is established using seismic sensitive attribute data to obtain quantitative state data of rock physics under the control of sedimentary facies;

[0023] Step S252, by analyzing the quantitative state data of rock physics under the control of sedimentary facies, find the relationship between seismic sensitive attributes and sedimentary microfacies, establish a rock physics analysis version between the two, and thus establish a three-dimensional sedimentary microfacies stereo model.

[0024] Further, in step S252, the relationship between the seismic sensitive attribute and the sedimentary microfacies is found by analyzing the quantitative state data of the rock physics under the control of the sedimentary facies, and the step of establishing the rock physics analysis quantity version between the two may specifically include:

[0025] S2521, by establishing the relationship between sedimentary microfacies and geophysics, we must first distinguish between sand layers and mud layers, as well as sand areas and mud areas, so as to distinguish between the main body of the river channel and the flanks and inter-channel areas;

[0026] S2522, on the basis of distinguishing the sand layers and sand areas, further identify the sand quality in order to distinguish the main body of the river channel and the flank of the river channel;

[0027] S2523, based on the rock physics analysis plate established earlier, establish a three-dimensional model of sedimentary microfacies.

[0028] Further, in step S252, the training process of the neural network model is as follows:

[0029] S2524: constructing a training set and a validation set, wherein the training set is a natural lithology logging data set, and the validation set is sensitive elastic parameter data after data acquisition for the current project;

[0030] S2525: label synthesis and preprocessing operations are performed on the training set to obtain a preprocessed training set, and sensitive elastic parameters are annotated on the validation set after preprocessing to obtain an annotated validation set;

[0031] S2526: Using binary cross entropy loss as the target loss function, the optimizable parameters of the neural network model are continuously adjusted through the back propagation algorithm, so that the neural network model gradually learns the characteristics of the data, thereby outputting a sensitive elastic parameter model.

[0032] In the second aspect, an improved device for finely depicting sedimentary microfacies based on seismic-geological lithology combined characteristics comprises:

[0033] Data module: used to obtain historical data of lithology logging; analyze and summarize the response characteristics of lithology logging according to the lithology classification standards;

[0034] Model building module: used to build a sensitive elastic parameter model based on the response characteristics and lithology logging single well phase constraints;

[0035] Input module: used for inputting the lithology logging data to be processed in a given area into the sensitive elastic parameter model;

[0036] Characterization module: used for the sensitive elastic parameter model to characterize the distribution range of sedimentary microfacies for the given area.

[0037] In a third aspect, an improved system for finely characterizing sedimentary microfacies based on combined seismic-geological lithology characteristics is provided, the system comprising a processor and a memory, the processor executing computer instructions stored in the memory to implement any of the methods described in the first aspect.

[0038] The technical solution disclosed in this disclosure has the following beneficial effects:

[0039] Through the learning and training of the neural network model, the relationship between the characteristics of seismic sensitive attributes and the sedimentary microfacies of geological lithology is accurately established, and then the sedimentary microfacies are characterized for the given area where the sedimentary microfacies is to be characterized. From the perspective of planar distribution characteristics, the boundaries are clearer and the sedimentary microfacies are more refined; from the perspective of earthquake prediction, the mining of earthquake information, the integration of seismic geological information and drilling information improves the prediction accuracy; by improving the accuracy and precision of the atlas, the drilling success rate is improved.

[0040] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the following preferred embodiments are specifically cited and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 This is a flow chart of an improved method for finely depicting sedimentary microfacies based on seismic-geological lithology combination characteristics disclosed in the present invention;

[0043] Figure 2 This is a structural diagram of an improved device for finely depicting sedimentary microfacies based on seismic-geological lithology combined characteristics disclosed in the present invention;

[0044] Figure 3 This is an improved structural diagram of sedimentary microfacies system based on the combined characteristics of seismic-geological lithology disclosed in the present invention;

[0045] Figure 4It is a computer-readable storage medium structure diagram of an improved method for finely depicting sedimentary microfacies based on seismic-geological lithology combination characteristics disclosed in the present invention;

[0046] Figure 5(A)-5(E) It is a data analysis diagram of an embodiment of an improved method for finely characterizing sedimentary microfacies based on seismic-geological lithology combination characteristics disclosed in the present invention, wherein FIG5(A) illustrates a well-connected geological profile of W-33-3D-W-33-1D wells, FIG5(B) illustrates a post-stack seismic profile of W-33-3D-W-33-1D wells, and FIG5(C) illustrates an amplitude attribute slice, wherein the darker the color, the stronger the amplitude; FIG5(D) illustrates different lithology combination types under different sedimentary microfacies-seismic response characteristics, and FIG5(E) illustrates an analysis and summary of seismic response characteristics of a river channel belt;

[0047] Figure 6 It is an embodiment of a single well division map of an improved method for finely depicting sedimentary microfacies based on seismic-geological lithology combination characteristics disclosed in the present invention;

[0048] Figure 7 It is an embodiment of the present invention's improved fine stratigraphic model map (small layer level) of the method for finely depicting sedimentary microfacies based on seismic-geological lithology combination characteristics;

[0049] Figure 8 It is an embodiment of a calibrated coarsened sedimentary microfacies map of an improved method for finely depicting sedimentary microfacies based on seismic-geological lithology combination characteristics disclosed in the present invention;

[0050] Fig. 9 It is an embodiment of the sedimentary microfacies stereo model diagram of an improved sedimentary microfacies stereo model diagram based on the seismic-geological lithology combination characteristics disclosed in the present invention;

[0051] FIG. 10(A) illustrates an effect diagram of a sedimentary microfacies map depicted by using an improved method for finely depicting sedimentary microfacies based on seismic-geological lithology combined characteristics disclosed in the present invention;

[0052] FIG. 10(B) illustrates the deposition microphase diagram effect depicted by the prior art. DETAILED DESCRIPTION

[0053] The following describes the embodiments of the present disclosure through specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0054] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0055] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The drawings only show components related to the present disclosure rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0056] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0057] At present, in the existing technology, the research ideas and technical methods for basin sedimentary filling evolution are mainly single factor analysis method and sedimentary system research method. The single factor analysis method requires a large number of samples and analytical test results, from point to surface, from local to overall, from quantitative change to qualitative change to guide the research of transportation and sedimentation process. The sedimentary system research method uses high-precision seismic data with drilling and logging data to gradually study the overall distribution pattern of basin sedimentary system from surface to point, from overall to local. There are also related studies on basin sedimentary filling from the tectonic background or sea level changes of the basin. It is believed that the strength of tectonic activity plays a dominant control role in the sedimentary filling of the basin. The sedimentary filling evolution of basins with strong tectonic activity has the characteristics of strong differences in fault activity, multiple and fast-changing material sources, and narrow and incomplete development of sedimentary facies belts. The research of these ideas and technical methods has had a profound impact on sedimentology and basin filling research. However, the above-mentioned research methods of the prior art have low accuracy in describing sedimentary facies, and cannot accurately and objectively describe the sedimentary facies properties of rock formations, thereby affecting the accuracy of oil and gas exploration.

[0058] Figure 1 An improved method for finely depicting sedimentary microfacies based on seismic-geological lithology combined characteristics provided in an embodiment of the present disclosure includes:

[0059] Step S1: Obtain historical logging data of lithology logging, and analyze and summarize the response characteristics of lithology logging according to the lithology classification standard;

[0060] In one embodiment, various lithological logging data exist in a large amount of historical logging data, including: primary sedimentary structures inside the core block, biological fossil characteristics, grain size analysis results, phase sequence characteristics, etc.;

[0061] By comprehensively analyzing the characteristics of the above historical logging data, the characteristics of the single well sedimentary microfacies and its vertical evolution characteristics were clarified, and a single well sedimentary facies analysis bar chart was established;

[0062] Including the main channel sediments, the natural potential and resistivity curves of the initial bell-shaped (toothed bell-shaped) and box-shaped (toothed box-shaped) braided channels under the condition of stable flow dynamics and material supply. Due to the relatively slow flow velocity of braided channels, the lithology is mainly thick sandstone, the mineral composition is low in maturity, and cross-bedding and parallel bedding are developed.

[0063] The flanks of the river channel are mainly composed of interbedded sandstone or medium-thick sandstone and thick mudstone. As the distance increases, the supply material and water flow intensity gradually decrease, and bell-shaped (toothed bell-shaped) natural potential, resistivity curve morphology and other data appear;

[0064] Because different strata have different sedimentary phases, they have different lithological combinations.

[0065] Taking the Shihezi Formation as an example, it is mainly delta deposits, and the reservoirs are mainly river channel and river channel flank sand body deposits. Since the seismic reflection characteristics of sandstone are not only related to the thickness and porosity of the sand layer, but also to the surrounding rock types, different sedimentary environments and stacking methods have different wave impedance structures, etc., this requires summarizing the corresponding seismic response characteristics for different lithology combinations under different sedimentary microfacies. On this basis, multiple sets of standards were established for the division of strata and reservoirs in the study area based on multiple parameters such as sedimentary phase, diagenetic phase, logging curve, and physical properties, and data such as seismic response characteristics were further clarified.

[0066] In one embodiment, according to the lithology classification standard, the lithology is re-implemented, the logging response characteristics of different lithologies are sorted out and summarized, and a lithology logging response chart is established;

[0067] For example, the following table shows the logging parameter charts of different lithology logging response characteristics:

[0068]

[0069] For example, the following table shows a summary of the logging response characteristics of different lithologies:

[0070]

[0071] In one embodiment, in step S1, the step of analyzing and summarizing the response characteristics of lithology logging includes:

[0072] Step S11, performing single well sedimentary phase analysis;

[0073] The present invention establishes lithology and combination characteristics through detailed data description of the core, and on the basis of fine well-seismic calibration results, comprehensively analyzes the logging-seismic response characteristics corresponding to different lithology combinations, and clarifies the overall law of their planar distribution.

[0074] For example, through comprehensive analysis of biological fossil characteristics, grain size analysis results, phase sequence characteristics and other characteristics, the sedimentary microfacies characteristics of a single well and its vertical evolution characteristics were clarified, and a single well sedimentary phase analysis bar chart was established.

[0075] The present invention conducts a comprehensive analysis of the original sedimentary structure inside the core block, the characteristics of biological fossils, the results of grain size analysis, and the phase sequence characteristics, clarifies the characteristics of the single well sedimentary microfacies and its vertical evolution characteristics, and establishes a single well sedimentary phase analysis bar chart. Taking the single well sedimentary phase analysis results of the W-26 well of the Shihezi Formation as an example, it is known that the study area develops distributary channel and distributary bay sedimentary microfacies, and the channels and channels, and channels and distributary bays are all in the form of multi-stage superposition in the vertical direction. In the multi-stage superposition structure of channel and channel microfacies, the sand bodies deposited in the early stage are cut by the later sedimentation, the sedimentary cycle is incomplete, and the "binary structure" is not developed; the superposition microfacies structure of the channel and distributary bay sedimentary microfacies reflects the rapid migration and swing of the river channel in the vertical direction. Combined with the results of regional geological research, this study believes that the Shihezi Formation in a certain work area on the eastern edge of the Ordos Basin is a typical delta (plain, front) sedimentary system, with the sediments being mainly clastic deposits, and the sedimentary microfacies mainly developed in three types: main channel, channel flanks, and inter-channel.

[0076] Among them, the types of sedimentary microfacies described in the invention include: main river channel, river channel flanks, and between river channels.

[0077] In the main channel deposition, the braided channel shows the initial bell-shaped (toothed bell-shaped) and box-shaped (toothed box-shaped) natural potential and resistivity curves when the water dynamics and material supply are stable. Since the water flow velocity in the braided channel is relatively slow, the lithology is mainly thick sandstone with low maturity of mineral components, and cross-bedding and parallel bedding are developed.

[0078] The flanks of the river channel are mainly composed of interbedded sandstone or medium-thick sandstone and thick mudstone. With the increase of distance, the supply material and water flow intensity gradually decrease, and a bell-shaped (toothed bell-shaped) natural potential and resistivity curve morphology appears.

[0079] Inter-channel deposits are the low-lying part outside the river channel. The rock types formed when the river water overflows the riverbed during floods are mainly siltstone and claystone. Oblique bedding and horizontal bedding can be seen. The curve characteristics show the superposition of finger-shaped or multiple low-amplitude funnel-shaped curves. Sandstone is not developed and mud is the main component.

[0080] Step S12, re-analyzing the lithology based on the above single well sedimentary facies type analysis results to further clarify the seismic response characteristics;

[0081] Different strata have different sedimentary phases and different lithologic combinations. For example, the Shihezi Formation is dominated by delta deposits, and the reservoirs are mainly river channel and river channel flank sand deposits. Since the seismic reflection characteristics of sandstone are not only related to the thickness and porosity of the sand layer, but also to the surrounding rock types, different sedimentary environments and stacking methods have different wave impedance structures, etc., this requires summarizing the corresponding seismic response characteristics for different lithologic combinations under different sedimentary microfacies. On this basis, multiple sets of standards have been established for the division of strata and reservoirs in the study area based on multiple parameters such as sedimentary phases, diagenetic phases, logging curves, and physical properties to further clarify the seismic response characteristics. The following table represents the reservoir evaluation classification table for a block in the eastern margin of the Ordos Basin:

[0082]

[0083] The following table shows the logging response characteristics of a reservoir type in a block on the eastern edge of the Ordos Basin:

[0084]

[0085]

[0086] The following table shows the classification of the Upper Paleozoic sandstone reservoirs in a block on the eastern edge of the Ordos Basin:

[0087]

[0088] Through the above-mentioned reservoir evaluation classification table of a block in the eastern margin of the Ordos Basin, it can be seen that the porosity greater than 10% is a good reservoir, 10%-8% is a relatively good reservoir, and less than 8% is a poor reservoir or non-reservoir; the reservoir logging response characteristic table of a block in the adjacent area shows that the porosity is greater than 6% for favorable reservoirs, and less than 6% for unfavorable reservoirs or non-reservoirs; in the classification table of the Upper Paleozoic sandstone reservoirs in a block in the eastern margin of the Ordos Basin, greater than 10% is a conventional reservoir, between 10%-8% is a relatively good reservoir among tight reservoirs, and less than 8% is a tight reservoir. In summary, the current more unified understanding is to use 8% porosity as the threshold for dividing reservoirs and non-reservoirs, and 10% as the threshold for high-quality reservoirs. The present invention has carried out a re-implementation of lithology based on typical wells in the work area, sorted out and summarized the logging response characteristics of different lithologies, and established a lithology logging response chart.

[0089] According to the lithology classification standard, the lithology was re-confirmed and the lithology of the main target layers was divided. For the Box 4 section, it is mainly sandstone, silty mudstone and mudstone. The lower part is mainly thick sandstone or thick sandstone with thin mudstone, the upper part is mainly mudstone, silty mudstone or thick mudstone with thin sandstone, and the upper part is mainly mudstone, silty mudstone or thick mudstone with thin sandstone.

[0090] After re-confirming the lithology of the wells, rock physics analysis and high-quality reservoir intersection analysis were conducted on the vertical wells in the study area. The simple longitudinal wave impedance cannot distinguish sandstone and mudstone at all, but based on the intersection of longitudinal wave impedance and density, sandstone can be distinguished from mudstone and silty mudstone. Through the intersection of porosity and longitudinal wave impedance, according to the above reservoir classification standards, the sandstone reservoirs in the area are subdivided into three categories: Class I reservoirs, Class II reservoirs, and non-reservoirs. The reason for the complex rock characteristics in the study area is mainly related to its sedimentary environment. The Shihezi Formation is mainly delta deposits. The back-and-forth swing of the river channel causes the sand and mud combination inside the river channel to be complex and diverse, and the sedimentation on the wells at different locations is quite different.

[0091] Based on the comprehensive analysis of single wells, in the Shihezi Formation, sandstone with porosity greater than 10%, box-shaped and bell-shaped GR and SP curves and a certain thickness can be regarded as high-quality Class I reservoirs; porosity between 8% and 10%, GR and SP curves are mainly toothed, interbedded sandstone or medium-thick sandstone are Class II reservoirs; porosity less than 8%, GR and SP curves are toothed, mudstone, silty mudstone or mudstone with thin layers of siltstone are non-reservoirs. Through the fine quantitative analysis of reservoirs on the well, a solid foundation is laid for the subsequent fine characterization of sedimentary microfacies and the prediction of high-quality thin layers.

[0092] Step S13, dividing different well groups on the plane, summarizing and analyzing the comprehensive response characteristics with the well groups as representative units, and summarizing the logging response characteristic quantities corresponding to different lithology combinations from the horizontal and vertical perspectives in parallel.

[0093] Example 1:

[0094] For example, the Shihezi W-33 well group, as shown in Figure 5(A), takes Shihezi as an example. Through the analysis of the well line in the northwest-southeast direction of the well group, that is, the well geological profile of the well W-33-3D-W-33-W-33-1D, the well group is located in the main part of the river channel. The sand body thickness in this section is relatively large and the sand body is well developed. Through the analysis of the well geological profile, the reservoir is mainly a combination of thick sandstone and thin high-quality sandstone. Combined with the post-stack seismic profile of the W-33-3D-W-33-W-33-1D well, as shown in Figure 5(B), it can be found that the thick sand body on the well shows the characteristics of continuous strong reflection of medium and low frequencies in seismic, and the amplitude energy weakens to both sides, which is the sedimentation on the flank of the river channel. Through the combination of well and seismic data, it is believed that the main river channel is a combination of thick dense sand and thin high-quality sand, and the corresponding seismic reflection shows medium-to-low frequency strong wave peak reflection characteristics. The river channel flanks are a combination of medium-thick dense sand, and the seismic performance is medium-to-low frequency continuous strong reflection characteristics; the inter-channel is a combination of thin sand or thick mudstone, with almost no reservoir, resulting in the amplitude showing medium-to-high frequency continuous weak reflection characteristics.

[0095] Example 2:

[0096] Another specific example is the Shihezi W-58 well group, which is located in the central and western part of the work area and is also in the main part of the river channel, and the sand body is also relatively developed. As its well-connected geological profile shows, the sand body has a thinning trend compared with the W-33 well group. The well-connected line is in the southwest-northeast direction. According to the comparison and analysis of the sand layers on the wells, the sand body thickens as a whole toward the middle of the work area, and the reservoir is mainly composed of thick sandstone and thin high-quality sandstone. The corresponding seismic reflection is characterized by medium-low frequency continuous strong reflection, and transitions to weak reflection toward the flank of the river channel. The main body of the waterway is characterized by medium-thick dense sand + thin high-quality sand combination, medium-low frequency continuous strong reflection characteristics; the flank of the river channel is a combination of medium-thick dense sand or thick dense sand, with medium-low frequency continuous strong or relatively strong reflection characteristics; the river channel is a combination of thin sand or thick mudstone, with medium-high frequency continuous weak reflection characteristics.

[0097] Example 3:

[0098] Another specific example is the Shihezi W-154 well group, which is dominated by a small river channel as the main sand layer. The sand body thickness is very thin. According to the geological profile analysis of the well, the overall thickness of the sand body is about 10-15 meters. The corresponding seismic response well shows that the seismic reflection at the location of the small main river channel is characterized by continuous strong reflection at medium and low frequencies, and transitions to weak reflection or blank reflection on the flank of the river channel. The W-154 well is a combination of medium-thick high-quality sand, and the W-154-1D well is a combination of medium-thick dense sand and high-quality sand interlayers. The river channels are mainly composed of thin sand or thick mudstone, with medium-high frequency continuous weak reflection characteristics.

[0099] Based on the amplitude and morphological characteristics, the planar distribution of different sedimentary microfacies is qualitatively described, and the seismic response characteristics of high-quality reservoirs are summarized.

[0100] Sedimentary microfacies refers to the smallest unit with unique rock structure, structure, thickness, rhythmicity and other sedimentary characteristics on the profile and certain plane configuration rules within the subfacies belt. Sedimentary microfacies marks are the key to sedimentary microfacies division. These marks are mainly obtained from detailed observation of cores, including color, mineralogical characteristics, structural characteristics of sediments, structural characteristics and biological characteristics.

[0101] In this paper, we divide the sedimentary microfacies into three types: main channel, channel flank, and inter-channel:

[0102] In the main channel deposition, the braided channel shows the initial bell-shaped (toothed bell-shaped) and box-shaped (toothed box-shaped) natural potential and resistivity curves when the water dynamics and material supply are stable. Since the water flow velocity in the braided channel is relatively slow, the lithology is mainly thick sandstone with low maturity of mineral components, and cross-bedding and parallel bedding are developed.

[0103] The flanks of the river channel are mainly composed of interbedded sandstone or medium-thick sandstone and thick mudstone. With the increase of distance, the supply material and water flow intensity gradually decrease, and a bell-shaped (toothed bell-shaped) natural potential and resistivity curve morphology appears.

[0104] Inter-channel deposits are the low-lying part outside the river channel. The rock types formed when the river water overflows the riverbed during floods are mainly siltstone and claystone. Oblique bedding and horizontal bedding can be seen. The curve characteristics show the superposition of finger-shaped or multiple low-amplitude funnel-shaped curves. Sandstone is not developed and mud is the main component.

[0105] In the example of the Shihezi Group, through the analysis of the well line in the northwest-southeast direction of the sample well group, see Figure 5 (A), W-33 and W-33-1D wells are located in the main body of the river channel, and W-33-3D is located on the flank of the river channel. Through the analysis of the well geological profile, the reservoir is mainly a combination of thick sandstone and thin high-quality sandstone. Combined with the seismic profile, see Figure 5 (B) and the seismic amplitude attribute plane, see Figure 5 (C) Amplitude attribute slices can be found that the thick sand body on the well shows a medium-low frequency continuous strong reflection feature in the seismic, and the amplitude energy weakens on both sides, which is the deposition on the flank of the river channel. Through the combination of well and seismic data, it is believed that the main channel is a combination of thick dense sand + thin high-quality sand, and the corresponding seismic reflection shows the characteristics of medium-low frequency strong wave peak reflection. The flank of the channel is a combination of medium-thick dense sand, and the seismic performance is a combination of medium-low frequency continuous strong reflection. The inter-channel is a combination of thin sand or thick mudstone, with almost no reservoir, resulting in the amplitude showing medium-high frequency continuous weak reflection characteristics, as shown in Figure 5 (D), different lithology combination types under different sedimentary microfacies-seismic response characteristics. Thus, the seismic response characteristics of the river channel belt are analyzed and summarized, as shown in Figure 5 (E).

[0106] After analyzing a large number of sample well group data, it was confirmed that:

[0107] (1) The typical seismic response characteristics of thick high-quality reservoirs are medium- and low-frequency continuous strong reflections or relatively strong reflection characteristics;

[0108] (2) The thicker the reservoir, the better the physical properties and the stronger the amplitude (the thickness of the sand body is generally within the tuning range);

[0109] (3) Thin reservoirs or thick mudstones show medium-high frequency continuous weak reflection characteristics;

[0110] (4) The degree of sand body development has a high correlation with the seismic amplitude.

[0111] The improved method for finely characterizing sedimentary microfacies based on seismic-geological lithology combined characteristics of the present invention further includes: step S2, establishing a sensitive elastic parameter model based on the response characteristics and lithology logging single well phase constraints;

[0112] In one embodiment, in step S2, a sensitive elastic parameter model is established, including using the response characteristics as training sample data, learning and training a neural network model, and constructing the sensitive elastic parameter model.

[0113] Further, the constructing of the sensitive elastic parameter model specifically includes:

[0114] Step S21, performing refined hierarchical division on the lithology logging single well;

[0115] In one embodiment, the division and comparison of small layers is the basis of the research work on reservoir geological characteristics and the premise of describing the reservoir morphology and the spatial distribution characteristics of its parameters. In the division and comparison of small layers, the principle of level-by-level comparison must be followed. In the comparison process, standard layers and standard wells must be selected first, and standard well comparison sections must be established to close the sections, and then the whole area must be compared to establish a correct isochronous comparison stratigraphic framework, unify the division of the layers, and explain the spatial variation law of the reservoirs at all levels.

[0116] The sub-layer division can describe the reservoir morphology and the spatial distribution characteristics of its parameters. In the sub-layer division comparison, the principle of level-by-level subdivision is followed. In the comparison process, standard layers and standard wells must be selected first, and standard well comparison sections must be established to close the sections. Then, the whole area comparison must be carried out to establish a correct isochronous comparison stratigraphic framework, unify the division of the layers, and explain the spatial variation laws of reservoirs at all levels of layers.

[0117] After the sequence interface is interpreted reasonably, the main layers in the study area are finely divided into small layers according to the small layer comparison method. Taking the Shihezi Formation as an example, the Box 2 section is divided into 4 small layers, the Box 4 section is divided into 4 small layers, and the Box 6 section is divided into 3 small layers. The thickness of the Taier section itself is small and the small layers are not finely divided. After the small layer division and interpretation, the seismic phase interpretation plane map and sedimentary phase plane distribution map are completed for each small layer, including 12 seismic phase interpretation plane maps and 12 sedimentary phase plane distribution maps, and compiled into the final map.

[0118] Through the above comprehensive analysis of logging, lithology and seismic, the seismic reflection characteristics under different lithology combinations and logging responses were finally determined. After comparing and optimizing various attributes, including amplitude attributes, energy attributes, frequency attributes, etc., the seismic attributes that best matched the sedimentary facies on the well were finally determined.

[0119] The fine stratigraphic model itself adopts the "river channel type" small layer comparison and division technology under the control of the base level cycle change, and the comparison work is refined at the same time. This refinement also reflects the degree of understanding of geology by different technicians. Specifically, the stratigraphic model established by previous academic research is at the "segment" level. When this invention is made, the "segment" is divided more finely into the "small layer" level for the typical river channel type reservoir type in the study area.

[0120] The basis for the classification of single wells: see Figure 6 , using fine sub-layer division, the "channel-type" sub-layer comparison and division technology under the control of base level cycle changes.

[0121] Step S22, performing fine-scale hierarchical division on the lithologic logging wells, and performing hierarchical comparison to obtain a fine formation model;

[0122] In the Shihezi Formation implementation example, segment comparison and interface identification tracking (well-connected profiles) are carried out under well-seismic matching at the segment level, and then fine comparison of small layers and tracking of seismically identifiable scale interfaces are carried out under segment constraints.

[0123] Through the refined hierarchical division of single wells and well connections, a refined stratigraphic model is obtained, see Figure 7 .

[0124] Step S23, establishing a coarsened sedimentary facies map based on the seismic attribute characteristics in the response characteristics and the relationship between different sedimentary microfacies types and seismic response characteristics;

[0125] In one embodiment, based on seismic attributes and in combination with the relationship between different sedimentary microfacies types and seismic response characteristics in the early stage, the sedimentary microfacies distribution is calibrated using the seismic attributes along the layer, and a calibrated coarsened sedimentary microfacies map is established. Figure 8 .

[0126] Step S24, on the coarsened sedimentary facies map, using the refined stratigraphic model, finely dividing the sedimentary microfacies of the single well, thereby obtaining a refined sedimentary microfacies model;

[0127] In one embodiment, the grid technology is used to further divide the sedimentary microfacies. For example, in the Shihezi Formation example, a plane grid of 50m*50m is established, and a vertical grid: according to the thickness of the small layer and the sand body, the number of vertical grids in the main body of the river channel and the flank of the river channel is set to 5, and 1 grid is set for the mudstone. After the vertical grid division of the single well and the vertical grid division of the formation model, a fine sedimentary microfacies model is obtained.

[0128] Step S24 may specifically include:

[0129] Step S241: using the fine sedimentary microfacies distribution range map, establish a fine sedimentary facies plane constraint grid;

[0130] Step S242: using the fine sedimentary microfacies distribution range map, establish a fine sedimentary facies vertical-plane well constraint grid;

[0131] Step S243: Using the fine sedimentary facies plane constraint grid obtained in step S241 and the fine sedimentary facies vertical-plane well constraint grid obtained in step S242, a fine sedimentary facies model is established.

[0132] Step S25, based on the detailed sedimentary microfacies model, using seismic sensitive attributes to establish a three-dimensional sedimentary microfacies stereo model;

[0133] Through the above comprehensive analysis of logging, lithology and seismic, the seismic reflection characteristics under different lithology combinations and logging responses were finally determined. After comparing and optimizing various attributes, including amplitude attributes, energy attributes, frequency attributes, etc., the seismic attributes that best matched the sedimentary facies on the well were finally determined.

[0134] In one embodiment, the method comprises step S251, based on the above-mentioned fine sedimentary facies model, using seismic sensitive attribute data (elastic parameters such as longitudinal and transverse wave velocity ratio, wave impedance, density, and Young's modulus) to establish a three-dimensional sedimentary microfacies stereo model, and obtaining quantitative state data of rock physics under the control of sedimentary facies;

[0135] The quantitative state data of rock physics under the control of the sedimentary phase is the rock physics analysis version of the present invention.

[0136] In one embodiment, step S252 is included, by analyzing the quantitative state data of rock physics under the control of sedimentary facies, finding the relationship between seismic sensitive attributes and sedimentary microfacies, establishing a rock physics analysis version between the two, and using a neural network model to establish a three-dimensional sedimentary microfacies stereo model. Using a neural network model to implement the sensitive elastic parameter model is to determine the relationship between various logging curves and seismic information through neural network learning.

[0137] Step S252 analyzes the quantitative state data of rock physics under the control of sedimentary facies to find the relationship between seismic sensitive attributes and sedimentary microstructures, and establishes a rock physics analysis version between the two, which may specifically include:

[0138] S2521, by establishing the relationship between sedimentary microfacies and geophysics, we must first distinguish between sand layers and mud layers, as well as sand areas and mud areas, so as to distinguish between the main body of the river channel and the flanks and inter-channel areas;

[0139] S2522, on the basis of distinguishing the sand layers and sand areas, further identify the sand quality in order to distinguish the main body of the river channel and the flank of the river channel;

[0140] S2523, based on the rock physics analysis plate established in the early stage, establish a three-dimensional model of sedimentary microfacies. Fig. 9 .

[0141] In one embodiment, a machine learning method is used to obtain the neural network model, and the neural network model uses a mainstream neural network framework, such as pytorch, tensorflow, etc.

[0142] In step S252, the training process of the neural network model is as follows:

[0143] S2524: constructing a training set and a validation set, wherein the training set is a natural lithology logging data set, and the validation set is sensitive elastic parameter data after data acquisition for the current project;

[0144] S2525: label synthesis and preprocessing operations are performed on the training set to obtain a preprocessed training set, and sensitive elastic parameters are annotated on the validation set after preprocessing to obtain an annotated validation set;

[0145] S2526: Using binary cross entropy loss as the target loss function, the optimizable parameters of the neural network model are continuously adjusted through the back propagation algorithm, so that the neural network model gradually learns the characteristics of the data, thereby outputting a sensitive elastic parameter model.

[0146] The training set uses a public large-scale natural lithology logging dataset, and the validation set uses labeled small-batch project data.

[0147] During the training process, the neural network model fully adopts the GELU activation function, all weight parameters belong to the convolution layer or deconvolution layer, and the binary cross entropy loss function is used. A dynamic learning rate that decreases linearly with the increase of epochs is added. The parameters of the neural network model are continuously adjusted through the back-propagation algorithm, so that the neural network model can gradually learn the characteristics of the data;

[0148] The training process will continue for multiple times. After the first stage of training, the correctness of the neural network model is verified through the validation set. If the output accuracy requirement is met, the training is terminated. If the output accuracy requirement is not met, the second and third stages of training will be entered until the performance of the neural network model cannot be further improved or the upper limit of the training batch is reached.

[0149] Step S26, based on the three-dimensional sedimentary microfacies stereo model, characterize the sedimentary microfacies distribution range according to a given area.

[0150] In one embodiment, the relationship between sedimentary microfacies and geophysics is analyzed to establish a rock physics analysis plate;

[0151] Based on the rock physical analysis plate, a three-dimensional sedimentary microfacies stereo model is established.

[0152] Step S34: According to the refined sedimentary facies model obtained in step S33, a refined rock physical parameter model (Vp / Vs) under the control of the sedimentary facies model is obtained.

[0153] An improved method for finely characterizing sedimentary microfacies based on seismic-geological lithology combination characteristics of the present invention also includes step S3: inputting the lithology logging data to be processed in a given area into the sensitive elastic parameter model, and the sensitive elastic parameter model characterizes the distribution range of sedimentary microfacies for the given area.

[0154] In one embodiment, a finely described sedimentary microfacies is obtained through the sensitive elastic parameter model established above.

[0155] In one embodiment, the finely detailed sedimentary microfacies are presented in sections.

[0156] The present application provides an improved method for finely characterizing sedimentary microfacies based on seismic-geological-lithological combination characteristics, which uniquely adopts seismic-geological-lithological combination characteristics (that is, combination characteristics of different sedimentary microfacies), which include well logging characteristics and seismic characteristics (including rock physics intersection analysis diagrams), rather than the prior art that uses simple well logging information.

[0157] The present invention establishes the lithology and combination characteristics through detailed observation and description of the core, and based on the fine well-seismic calibration results, comprehensively analyzes the logging-seismic response characteristics corresponding to different lithology combinations, and clarifies the overall law of their planar distribution.

[0158] In addition, the prior art tends to perform analysis on attribute fusion graphs, while the present application performs comprehensive calibration analysis of single well geological information, logging information, and seismic information, and then performs analysis based on the determined seismic sensitive attributes.

[0159] The present invention first determines the different lithological combination characteristics within the same time window, clarifies such characteristics, and then distinguishes the dominant phase, because phase is a relative concept and lithological combination is an absolute concept. Sensitive attributes are optimized according to different combination characteristics. The dominant phase of a single well is relatively simple, while the combination characteristics of rocks are relatively complex and there are more types. The multi-solution of phase is relatively strong, but the multi-solution of lithological combination is weak. Therefore, the present application adopts the method of first determining different lithological combinations and then clarifying the dominant phase of the well, rather than determining the phase first. Therefore, the present application is obviously different from the prior art and has unique advantages.

[0160] Figure 10 (A) shows the effect of the sedimentary microfacies map drawn by the improved method of finely depicting sedimentary microfacies based on the combined characteristics of seismic and geological lithology of the present invention; Figure 10 (B) shows the effect of the sedimentary microfacies map drawn by conventional means. From the comparison of the two sedimentary microfacies map effect maps formed in the family, it can be seen that:

[0161] 1. From the perspective of planar distribution characteristics, the river channel boundary analyzed by the technical method of the present invention is clearer and the microfacies is more delicately portrayed;

[0162] 2. From the perspective of earthquake prediction, the technical method of the present invention mines earthquake information, integrates earthquake geological information with drilling information, and improves the prediction accuracy;

[0163] 3. The technical method of the present invention improves the drilling success rate by improving the accuracy and precision of the atlas; the conventional success rate is about 65%, while the drilling success rate of this technology is about 85%.

[0164] Figure 2 An improved device for finely depicting sedimentary microfacies based on seismic-geological lithology combined characteristics provided in an embodiment of the present disclosure includes:

[0165] Data module: used to obtain historical data of lithology logging; analyze and summarize the response characteristics of lithology logging according to the lithology classification standards;

[0166] Model building module: used to build a sensitive elastic parameter model based on the response characteristics and lithology logging single well phase constraints;

[0167] Input module: used for inputting the lithology logging data to be processed in a given area into the sensitive elastic parameter model;

[0168] Characterization module: used for the sensitive elastic parameter model to characterize the distribution range of sedimentary microfacies for the given area.

[0169] Figure 3 is a system structure diagram according to an embodiment of the present disclosure. Figure 3 The system 30 includes a processor 31 and a memory 32, and the processor executes computer instructions stored in the memory to implement all or part of the steps of the improved method for finely characterizing sedimentary microfacies based on seismic-geological lithology combination characteristics of each embodiment of the present disclosure.

[0170] Figure 4 is a schematic diagram of a computer-readable storage medium according to an embodiment of the present disclosure. Figure 4As shown, according to the computer-readable storage medium 40 of the embodiment of the present disclosure, non-transitory computer-readable instructions 41 are stored thereon. When the non-transitory computer-readable instructions 41 are executed by the processor, all or part of the steps of the improved method for finely characterizing sedimentary microfacies based on seismic-geological lithology combination characteristics of each embodiment of the present disclosure are executed.

[0171] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0172] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

Claims

1. An improved method for finely characterizing sedimentary microfacies based on seismic-geological lithology combined characteristics, comprising: Step S1, obtaining historical logging data of lithology logging, and analyzing and summarizing the response characteristics of lithology logging according to the lithology classification standard; Step S2, establishing a sensitive elastic parameter model based on the response characteristics and the single well phase constraints of lithological logging; Among them, in the step S2, constructing the sensitive elastic parameter model includes taking the response characteristics as training sample data, learning and training a neural network model, and constructing the sensitive elastic parameter model; specifically including: Step S21, performing fine-scale hierarchical division of lithology logging single well; Step S22, performing fine-scale hierarchical division on the lithologic logging wells, and performing hierarchical comparison to obtain a fine formation model; Step S23, establishing a coarsened sedimentary facies map based on the seismic attribute features in the response features and the relationship between different sedimentary microfacies types and seismic response features; Step S24, on the coarsened sedimentary facies map, using the refined stratigraphic model, finely divide the sedimentary microfacies of a single well, thereby obtaining a refined sedimentary microfacies model; wherein, Step S24 may specifically include: Step S241: using the fine sedimentary microfacies distribution range map, establish a fine sedimentary facies plane constraint grid; Step S242: using the fine sedimentary microfacies distribution range map, establish a fine sedimentary facies vertical-plane well constraint grid; Step S243: using the fine sedimentary facies plane constraint grid obtained in step S241 and the fine sedimentary facies longitudinal plane well constraint grid obtained in step S242, a fine sedimentary facies model is established; Step S25, based on the detailed sedimentary microfacies model, using seismic sensitive attributes to establish a three-dimensional sedimentary microfacies stereo model; Step S25 may specifically include: Step S251, based on the above-mentioned fine sedimentary facies model, a three-dimensional sedimentary microfacies stereo model is established using seismic sensitive attribute data to obtain quantitative state data of rock physics under the control of sedimentary facies; Step S252, by analyzing the quantitative state data of rock physics under the control of sedimentary facies, the relationship between seismic sensitive attributes and sedimentary microfacies is found, and a rock physics analysis version between the two is established, thereby using a neural network model to establish a three-dimensional sedimentary microfacies stereo model; Step S26, based on the three-dimensional sedimentary microfacies stereo model, characterize the sedimentary microfacies distribution range according to a given area; and Step S3, inputting the lithologic logging data to be processed in a given area into the sensitive elastic parameter model, wherein the sensitive elastic parameter model describes the distribution range of sedimentary microfacies for the given area.

2. The method according to claim 1 further comprises step S5, establishing a lithology logging response chart based on the response characteristics of step S1.

3. According to the method of claim 1, in step S1, analyzing and summarizing the response characteristics of lithological logging comprises: Based on the amplitude and morphological characteristics, the planar distribution of different sedimentary microfacies is qualitatively described, and the seismic response characteristics of high-quality reservoirs are summarized.

4. According to the method of claim 1, the step of finding the relationship between the seismic sensitivity attribute and the sedimentary microfacies by analyzing the quantitative state data of the rock physics under the control of the sedimentary facies in step S252 and establishing the rock physics analysis quantity version between the two may specifically include: S2521, by establishing the relationship between sedimentary microfacies and geophysics, we must first distinguish between sand layers and mud layers, as well as sand areas and mud areas, so as to distinguish between the main body of the river channel and the flanks of the river channel and the areas between the river channels; S2522, on the basis of distinguishing between sand layers and sand areas, we further identify the sand quality, so as to distinguish between the main body of the river channel and the flanks of the river channel; S2523, based on the rock physics analysis plate established in the early stage, establish a three-dimensional model of sedimentary microfacies.

5. The method according to claim 4, in step S252, the training process of the neural network model is as follows: S2524, constructing a training set and a validation set, wherein the training set is a natural lithology logging data set, and the validation set is sensitive elastic parameter data after data acquisition for the current project; S2525 , label synthesis and preprocessing operations are performed on the training set to obtain a preprocessed training set, and sensitive elastic parameters are annotated on the validation set after preprocessing to obtain an annotated validation set; S2526, using binary cross entropy loss as the target loss function, continuously adjusts the optimizable parameters of the neural network model through the back propagation algorithm, so that the neural network model gradually learns the characteristics of the data, thereby outputting a sensitive elastic parameter model.

6. An improved system for finely characterizing sedimentary microfacies based on seismic-geological lithology combination characteristics, the system comprising a processor and a memory, the processor executing computer instructions stored in the memory to implement any of the methods of claims 1-5.

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