Recognition and prediction method for petroliferous basin mixed rock

Abstract

The invention provides an identification and prediction method for petroliferous basin mixed rock. The identification and prediction method comprises the following steps: S1, identifying single well lithofacies; s2, the sedimentary facies type and the sedimentary microfacies type of the sand-mud mixed accumulation area are recognized and divided; s3, establishing a lithofacies combination classification scheme, and forming a matching relationship between the lithofacies combination and the sedimentary microfacies; s4, establishing a well logging database of the mixed rock stratum section of the sand-mud mixed zone; s5, establishing rock physical phase classification by using an SOM neural network clustering analysis method; s6, establishing a lithofacies combination type and sedimentary microfacies identification model; and S7, establishing a spatial distribution model of the sedimentary microfacies and lithofacies combination of the mixed rock. And the mixed rock reservoir is classified and predicted, and the prediction result has higher geological causes and rationality.

Description

Technical Field

[0001] This invention relates to the field of compound preparation, and more particularly to a method for identifying and predicting mixed sedimentary rocks in oil and gas basins. Background Technology

[0002] Mixed sedimentary rocks belong to the category of mixed sediments, broadly referring to the mixture of terrigenous clastic and carbonate rocks in sedimentation (Mout, 1984). Influenced by paleogeography, sedimentary environment, lithology, and physical properties, they exhibit complex and variable reservoir lithology and strong heterogeneity, making reservoir evaluation quite challenging. As an important exploration and development target in oilfield exploration and production, the uncertainty in reservoir evaluation further complicates exploration and production.

[0003] Mixed sedimentary rocks, as a special and important sedimentary type, have attracted increasing attention in recent years. Many geologists at home and abroad have conducted extensive research on the classification of rock types, analysis of main controlling factors, and heterogeneity of mixed sedimentary rocks using data from core samples, drilling, seismic data, and well logging. However, a unified understanding of their lithofacies classification has yet to be reached. Furthermore, previous research and exploration practices have shown that the lithofacies type of mixed sedimentary rocks has a significant impact on the physical properties of reservoirs, the prediction of sweet spots, and the deployment of exploration wells. Therefore, it is urgent to establish a scientific and highly operable method for lithofacies classification, identification, and prediction of mixed sedimentary rock reservoirs to solve the problem of difficult prediction of mixed sedimentary rock reservoirs in oilfield exploration and production.

[0004] Current research on the classification, identification, and prediction methods of mixed sedimentary rock facies includes conventional logging curve qualitative identification, lithology scanning logging, and seismic identification. Although many scholars have conducted some research on the lithology and classification of mixed sedimentary rocks, a systematic and scientific method for the classification of mixed sedimentary rock facies has not yet been formed, and many lithological classifications cannot be directly applied in exploration and production.

[0005] For example, Mao Rui et al. published a paper titled "Lithology Identification of Mixed Sediments Based on Lithology Scanning Logging" in Xinjiang Petroleum Geology in 2022. They used lithology scanning logging technology and comprehensively utilized felsic mineral content, carbonate mineral content, neutron porosity, and nuclear magnetic resonance porosity to establish a lithology identification chart for mixed sedimentary rocks. Although this method used scanning logging to determine the mineral content in different lithologies, the paper only classified mixed sedimentary rocks into three categories, which is too few classifications and the research is not comprehensive enough, making it difficult to promote and apply.

[0006] Chinese invention patent application CN106370814B discloses a "well logging identification method for lacustrine mixed sedimentary reservoirs based on composition-structure classification". This method uses thin section data combined with conventional well logging curves to qualitatively identify the rock composition and structure of mixed sedimentary reservoirs. While this method has some scientific merit, it relies heavily on thin section identification to classify mixed sedimentary rocks. This classification is not based on the framework of sedimentary environment and sedimentary facies, which can lead to a low degree of agreement and matching between the classification and well logging data. Due to the ambiguity of well logging data, when there are no sedimentary environment constraints, multiple prediction results may occur when using well logging curve data to identify rock types, ultimately resulting in a low lithofacies type identification rate.

[0007] Chinese invention patent application CN110837115A discloses a "seismic identification method and device for lithology of tight reservoirs of continental mixed sedimentary rocks". It uses seismic elastic parameters to establish a lithology identification model for mixed sedimentary rocks and identifies the lithology of mixed sedimentary rock reservoirs. This technology has certain innovations. However, this technology does not consider how to solve the problem of unclear facies classification of mixed sedimentary rocks. It only provides a method for seismic calibration of existing lithology classifications in well logging. Moreover, it only uses one method for lithology calibration and lacks supporting and limiting parameters for seismic elastic parameters. Its accuracy is questionable.

[0008] Chinese invention patent application CN106469257 discloses a "classification and naming method for mixed sedimentary rocks based on the content of three end-member minerals". It names mixed sedimentary rocks by the content of minerals in the three end-members of "exogenous detritus, endogenous particles and carbonate interstitial material" and classifies them into 10 categories. This technology only uses the single parameter of mineral content to classify mixed sedimentary rocks and does not take into account their sedimentary environment, sedimentary structure, organic matter abundance and other characteristics. This makes the classification relatively one-sided. Moreover, the technology does not provide how to identify the classified mixed sedimentary rocks. Therefore, it cannot be directly applied in exploration and production and its promotion performance is low. Summary of the Invention

[0009] In view of the above problems, the present invention is proposed to provide a method for identifying and predicting mixed sedimentary rocks in oil and gas basins that overcomes or at least partially solves the above problems.

[0010] According to one aspect of the present invention, a method for identifying and predicting mixed sedimentary rocks in oil and gas basins is provided, the method comprising:

[0011] Step S1: Identify the lithofacies of a single well;

[0012] Step S2: Identify and classify the sedimentary facies types and sedimentary microfacies types in the sand-mud mixture zone;

[0013] Step S3: Establish a classification scheme for lithofacies assemblages and form a matching relationship between lithofacies assemblages and sedimentary microfacies;

[0014] Step S4: Establish a well logging database for the mixed sedimentary rock strata of the sand-mud mixed sedimentary zone;

[0015] Step S5: Establish a rock physical facies classification using the SOM neural network clustering analysis method;

[0016] Step S6: Establish lithofacies assemblage type and sedimentary microfacies identification model;

[0017] Step S7: Establish a spatial distribution model of sedimentary microfacies and lithofacies assemblages of mixed sedimentary rocks.

[0018] Optionally, step S1: identifying the lithofacies of a single well specifically includes:

[0019] By using core observation and thin section identification, and by comparing and analyzing the differences in macroscopic and microscopic characteristics of core sampling wells, lithofacies types can be identified.

[0020] Optionally, the macroscopic features specifically include: core color, structure, and sedimentary texture.

[0021] Optionally, step S2: identifying and classifying the sedimentary facies types and sedimentary microfacies types in the sand-mud mixture zone specifically includes:

[0022] Based on core observations and previous research, this study identifies the sedimentary structures of the sandstone-mudstone mixed facies zone and the sedimentary types of sandstone and mudstone.

[0023] The sedimentary structures include: mechanically subsided sedimentary structures, turbidity current sedimentary structures, and clastic flow sedimentary structures;

[0024] The sedimentary types of the sandstone include sandy sliding-slump rocks, sandy clastic flow rocks, mixed sandstone and conglomerate deposits, and wavy bedding sandstone.

[0025] The sedimentary types of the mudstone include mechanically deposited mudstone and gravity flow-derived mudstone;

[0026] Using high-frequency sedimentary cycle analysis, based on the conclusions of sedimentary structure and sedimentary type, the sand-mud mixed sedimentary zone develops two sedimentary facies types: nearshore underwater fan and deep-water turbidite fan.

[0027] The sedimentary microfacies types include gray-gray semi-deep lakes, felsic semi-deep lakes, fan-end, fan-front, and fan-braided channel types.

[0028] Optionally, step S3: establishing a classification scheme for lithofacies assemblages and forming a matching relationship between lithofacies assemblages and sedimentary microfacies specifically includes:

[0029] Guided by the sedimentary patterns of sand-mud mixed sedimentary facies zones, we carried out facies assemblage identification work in core sampling wells and classified them into facies assemblage types;

[0030] The lithofacies assemblages include: calcareous semi-deep lacustrine-laminated felsic limestone or calcareous mixed shale facies; felsic semi-deep lacustrine-laminated felsic limestone interbedded with lacustrine felsic mixed shale facies; fan-end-laminated felsic mixed shale / limestone with minor deformed siltstone interlayers; fan-front-laminated felsic / calcareous mixed shale facies with siltstone interlayers; lacustrine felsic limestone interbedded with dolomitic fine sandstone facies; lacustrine felsic / calcareous mixed shale with lacustrine sandstone interlayers; fan-braided channel-massive felsic mixed shale and massive argillaceous / dolitic siltstone interbedded facies; lacustrine felsic dolomite and lacustrine dolomitic siltstone interbedded facies; lacustrine gravelly medium-coarse sandstone with lacustrine argillaceous siltstone interlayers; and massive argillaceous siltstone or fine sandstone facies.

[0031] Optionally, step S5: establishing a rock physical facies classification using the SOM neural network clustering analysis method specifically includes:

[0032] Based on the abundant logging data from the sand-mud mixture zone, the logging data is trained and normalized.

[0033] The SOM neural network clustering algorithm was used to perform cluster analysis on the logging data in the database, and the logging data of the mixed sedimentary zone was divided into 16 logging facies. The logging facies corresponded to the petrophysical facies, and 16 petrophysical facies of the mixed sedimentary rocks in the mixed sedimentary zone were established.

[0034] Optionally, the clustering analysis of well logging data in the database using the SOM neural network clustering algorithm specifically includes:

[0035] Sample normalization processing involves normalizing the input sample data and the corresponding weight vector matrix.

[0036] To determine the winning node, when a sample is input, the similarity between the input sample and the weight vectors corresponding to all nodes in the competition layer is compared, and the weight vector with the highest similarity is determined as the winning node.

[0037] Weight adjustment and network output: Only the winning node has the right to adjust the weight vector; other neurons do not. The winning neuron outputs 1, otherwise it outputs 0. The expression is as follows:

[0038]

[0039]

[0040] In the equation, X j (t) represents the input sample vector, wij η(t) represents the weighting coefficients of input neuron i and output neuron j over time, and η(t) represents the learning rate.

[0041] Optionally, step S6: establishing a lithofacies assemblage type and sedimentary microfacies identification model specifically includes:

[0042] Based on the classification results of mixed sedimentary rock facies assemblages in sand-mud mixed sedimentary zones, the matching relationship between sedimentary microfacies and facies assemblages, and the classification results of rock physical facies, the matching relationship between 10 types of facies assemblages and 16 types of rock physical facies, and the relationship between 5 types of sedimentary microfacies and 16 types of rock physical facies in core sampling wells were statistically analyzed. A lithofacies assemblage and sedimentary microfacies identification model based on rock physical facies classification was established.

[0043] Optionally, in the process of establishing the identification model of lithofacies assemblages and sedimentary microfacies based on lithofacies classification, there is a case where one lithofacies assemblage type corresponds to multiple lithofacies. The frequency of occurrence of lithofacies corresponding to each lithofacies assemblage type in the core sampling well is counted, and the lithofacies with the highest frequency is the dominant lithofacies category corresponding to the lithofacies assemblage type.

[0044] Optionally, step S7: establishing a spatial distribution model of sedimentary microfacies and lithofacies assemblages of mixed sedimentary rocks specifically includes:

[0045] By correlating the sand layers of the upper sub-section of the Sha-4 pure layer within the mixed sedimentary zone with the sub-layers, a stratigraphic correlation framework profile of the sand layers and sub-layers in this section is established.

[0046] Using the established model for identifying facies assemblages and sedimentary microfacies of mixed sedimentary rocks, we identified facies assemblages and sedimentary microfacies of single wells in the mixed sedimentary zone to be predicted, clarified the spatial distribution characteristics of sedimentary microfacies and facies assemblages in the study interval, and established the spatial distribution sequence of sedimentary microfacies and facies assemblages.

[0047] This invention provides a method for identifying and predicting mixed sedimentary rocks in oil and gas basins. The method includes: Step S1: Identifying the lithofacies of a single well; Step S2: Identifying and classifying the sedimentary facies types and sedimentary microfacies types in sand-mud mixed sedimentary zones; Step S3: Establishing a classification scheme for lithofacies assemblages and forming a matching relationship between lithofacies assemblages and sedimentary microfacies; Step S4: Establishing a well logging database for mixed sedimentary rock intervals in sand-mud mixed sedimentary zones; Step S5: Establishing a lithofacies physical facies classification using the SOM neural network clustering analysis method; Step S6: Establishing a lithofacies assemblage type and sedimentary microfacies identification model; Step S7: Establishing a spatial distribution model of sedimentary microfacies and lithofacies assemblages in mixed sedimentary rocks. This method classifies and predicts mixed sedimentary rock reservoirs, resulting in predictions that are more geologically accurate and reasonable.

[0048] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0049] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 A flowchart illustrating a method for identifying and predicting mixed sedimentary rocks in oil and gas basins, provided as an embodiment of the present invention;

[0051] Figure 2 This is a diagram for identifying the lithofacies type of mixed sedimentary rocks provided in an embodiment of the present invention;

[0052] Figure 3 This is a microfacies model diagram of mixed sedimentary rocks provided in an embodiment of the present invention;

[0053] Figure 4 This invention provides a classification scheme for shale lithofacies assemblages.

[0054] Figure 5 This invention provides a classification scheme for mixed sedimentary rock facies assemblages.

[0055] Figure 6 A comparison table of qualitative differences in well logging among different types of mixed sedimentary rock facies assemblages provided in this embodiment of the invention;

[0056] Figure 7 The physical facies classification model for mixed sedimentary rocks provided in this embodiment of the invention;

[0057] Figure 8 Table of lithofacies assemblage types and petrological phase relationships provided for embodiments of the present invention;

[0058] Figure 9 This is a sequence of sedimentary microfacies distribution in mixed sedimentary rocks provided in an embodiment of the present invention;

[0059] Figure 10 The distribution sequence of mixed sedimentary rock facies assemblages provided in the embodiments of the present invention. Detailed Implementation

[0060] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0061] The terms "comprising" and "having," and any variations thereof, in the specification, embodiments, claims, and drawings of this invention are intended to cover non-exclusive inclusion, such as including a series of steps or units.

[0062] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0063] Take a mixed sedimentary reservoir as an example.

[0064] like Figure 1 As shown, a method for identifying and predicting mixed sedimentary rocks in oil and gas basins specifically includes:

[0065] Step 1: For a specific mixed sedimentary reservoir, core observation and thin section identification methods are used to compare and analyze the differences in macroscopic and microscopic characteristics such as core color, structure, and sedimentary texture from core sampling wells to identify lithofacies types. Shale mainly develops eight lithofacies types, primarily including carbonate-rich, mixed mineral, and fels-rich lithofacies, while clay-rich lithofacies are not well developed; sandstone mainly develops eight lithofacies types, primarily including siltstone, fine sandstone, and medium-coarse sandstone. Figure 2 ).

[0066] Step 2: Based on the lithological types of single wells, determine the sedimentary facies types and sedimentary microfacies types of the study area. First, based on core observations and previous research, identify the sedimentary structures and sand and mud depositional types of the sand-mud mixed facies zone. The main sedimentary structure types include mechanical subsidence sedimentary structures, turbidity current sedimentary structures, and clastic flow sedimentary structures. The main sandstone depositional types are sandy sliding-slump rocks, sandy clastic flow rocks, sandstone-conglomerate mixed accumulations, and wavy bedding sandstone. The main mudstone depositional types are mechanically subsided mudstone and gravity flow mudstone. Next, using high-frequency sedimentary cycle analysis, based on the conclusions of sedimentary structures and depositional types, the sand-mud mixed facies zone mainly develops two sedimentary facies types: nearshore underwater fans and deep-water turbidite fans. The main sedimentary microfacies types are five types: gray semi-deep lacustrine, felsic semi-deep lacustrine, fan tip, fan front, and braided channel mid-fan. Figure 3 ).

[0067] Step 3: Establish a classification scheme for the facies assemblages of mixed sedimentary rocks, clarifying the correspondence between facies assemblages and sedimentary microfacies. Based on the sedimentary environment and considering the relative homogeneity of the internal structure of mixed sedimentary rocks, a classification scheme for the shale facies assemblages of the Jiyang Depression is established. Figure 4 Guided by the sedimentary patterns of sand-mud mixed sedimentary facies zones, facies assemblages were identified in core sampling wells, resulting in 10 facies assemblages. The correspondence between these assemblages and sedimentary microfacies is shown in the figure. Figure 5 ): Facies of semi-deep lacustrine to laminated felsic limestone or mixed shale; facies of semi-deep lacustrine to laminated felsic limestone interbedded with laminated felsic mixed shale; facies of fan-end to laminated felsic mixed shale / limestone with minor deformed siltstone interlayers; facies of fan-front to laminated felsic / lime mixed shale facies with siltstone interlayers; facies of laminated felsic limestone interbedded with dolomitic fine sandstone; facies of laminated felsic / lime mixed shale with layered (gravelly dolomitic medium to coarse) sandstone interlayers; facies of braided channels in the fan to interbedded massive felsic mixed shale and massive argillaceous / dolitic siltstone; facies of interbedded layered felsic dolomite and laminated dolomitic siltstone; facies of layered gravelly medium to coarse sandstone with interbedded laminated argillaceous siltstone; facies of massive argillaceous siltstone or (gravelly) fine sandstone.

[0068] Step 4: Establish a well logging database for the mixed sedimentary rocks in the sand-mud mixed sedimentary zone of the study area. The data sources include the upper pure sub-member of the Sha-4 layer from single wells throughout the entire zone.

[0069] Seven logging curve data types (GR / AC / DEN / RD / CNL / RLLD / RLLS) were used. Significant differences were observed in the combined characteristics and numerical features of the GR and AC logging curves, the RD and AC logging curves, the DEN, AC, and CNL logging curves, and the RLLD and RLLS logging curves. These differences effectively reflect the differences in logging characteristics among the ten lithofacies assemblages within the mixed sedimentary zone. Figure 6 ).

[0070] Step 5: Based on the abundant logging data of the sand-mud mixture zone, this type of data is first trained and normalized. Then, the SOM neural network clustering algorithm is used to perform cluster analysis on the logging data in the database, dividing the logging data of the mixture zone into 16 logging facies. The logging facies correspond to the petrophysical facies, thus establishing 16 petrophysical facies of the mixed sedimentary rocks in the mixture zone. Figure 7 ).

[0071] Among them, rock physical facies are genetic units formed by a variety of geological processes. They are the combined effects of sedimentary microfacies, diagenetic reservoir facies, and later tectonic alteration. They can reflect both rock physical characteristics and the specific sedimentary environment in which they were formed. To achieve quantitative and automated classification of rock physical facies, well logging data analysis is necessary. Cluster analysis of well logging data is a method with good applicability for classifying rock physical facies.

[0072] Among them, the SOM (Structured Object Model) method, a neural network clustering analysis method, is an unsupervised artificial neural network. It is a simple single-layer neural network structure with only an input layer and a competition layer. It has four advantages: it can perform clustering without knowing the number of categories; it transforms the complex nonlinear statistical relationships between high-dimensional data items into simple geometric relationships on the low-dimensional output results; it can achieve self-learning; and compared with traditional clustering methods, the output results of this algorithm can maintain the topological structure.

[0073] The algorithm flow is as follows:

[0074] (1) Sample normalization processing: Normalize the input sample data and the corresponding weight vector matrix.

[0075] (2) Determine the winning node. When a sample is input, compare the similarity between the input sample and the weight vectors corresponding to all nodes in the competition layer, and determine the weight vector with the highest similarity as the winning node.

[0076] (3) Weight Adjustment and Network Output. Only the winning node has the right to adjust the weight vector; other neurons do not. The winning neuron outputs 1, and the other outputs 0. The expression is as follows:

[0077]

[0078]

[0079] In the above equation, X j (t) represents the input sample vector, w ij η(t) represents the weight coefficients of input neuron i and output neuron j at time t, and η(t) represents the learning rate.

[0080] Step 6: Based on the classification results of the mixed sedimentary rock facies assemblage in the sand-mud mixed sedimentary zone, the matching relationship between sedimentary microfacies and facies assemblage, and the petrophysical facies classification results, statistically analyze the matching relationship between 10 types of facies assemblages and 16 types of petrophysical facies in the core sampling wells, and the relationship between 5 types of sedimentary microfacies and 16 types of petrophysical facies. Figure 8 This allows for the establishment of identification models for lithofacies assemblages and sedimentary microfacies based on rock physical facies classification.

[0081] In the process of establishing the relationship between the petrophysical facies and facies assemblage types and sedimentary micro-matching of mixed sedimentary rocks, a situation may arise where one facies assemblage type corresponds to multiple petrophysical facies (logging facies). To address this problem of multiple interpretations in logging, we statistically analyze the frequency of occurrence of petrophysical facies corresponding to each facies assemblage type in the core sampling wells. The petrophysical facies with the highest frequency is the dominant petrophysical facies category corresponding to that facies assemblage type.

[0082] Step 7: Establish the spatial distribution model of sedimentary microfacies and lithofacies assemblages in the upper pure sub-member of the Sha-4 layer within the mixed sedimentary zone. First, conduct correlation and sub-layer correlation of sandstone groups within the upper pure sub-member of the Sha-4 layer within the mixed sedimentary zone to establish a stratigraphic framework profile at the sandstone group and sub-layer levels for this section. Second, using the lithofacies assemblages and sedimentary microfacies identification model established in Step 6, identify the lithofacies assemblages and sedimentary microfacies of the single wells to be predicted within the mixed sedimentary zone. Finally, clarify the spatial distribution characteristics of sedimentary microfacies and lithofacies assemblages in the studied section, and establish a spatial distribution sequence of sedimentary microfacies and lithofacies assemblages. Figure 9 , 10 ).

[0083] Guided by the method of this invention, the target of mixed sedimentary rock reservoir in a certain sand-mud mixed sedimentary area was predicted, and good exploration results were achieved.

[0084] The method described in this invention targets mixed sedimentary reservoirs. Its core lies in utilizing "sedimentary microfacies" and "lithophysical facies," which reflect the original sedimentary environment and sedimentary processes of the reservoir, to establish a classification scheme and identification / prediction method for mixed sedimentary reservoirs. Based on the definitions of lithofacies and lithophysical facies—"lithhofacies are rocks or rock assemblages formed in a specific sedimentary environment; they are the main components of sedimentary facies"—and "lithophysical facies are genetic units formed by multiple geological processes; they are the comprehensive effects of sedimentary microfacies, diagenetic reservoir facies, and subsequent tectonic alteration; they reflect both rock physical characteristics and the specific sedimentary environment in which they were formed"—it is clear that lithofacies and sedimentary facies are subordinate to each other. Lithophysical facies are a comprehensive manifestation of sedimentary processes. Therefore, the classification of lithofacies types needs to be based on sedimentary facies for greater rationality, and the identification of lithofacies types needs to be combined with the classification of lithophysical facies. Only by combining these can the evaluation of mixed sedimentary reservoirs be truly practically meaningful. This invention clarifies the classification, identification, and prediction methods of mixed sedimentary rock facies from the perspectives of sedimentary environment and sedimentary mechanisms, ultimately enabling the classification and prediction of mixed sedimentary reservoirs.

[0085] This invention classifies the lithofacies assemblages of mixed sedimentary reservoirs and establishes methods for identifying and predicting these assemblages. It clarifies, to a certain extent, the classification criteria for lithofacies assemblages and the prediction methods for different types of lithofacies, providing significant guidance for the current exploration, development, and strategic deployment of mixed sedimentary reservoirs. This invention is closely linked to exploration deployment; applying this method to classify and predict mixed sedimentary reservoirs yields predictions that are more geologically accurate and reasonable, aligning with the actual exploration and development practices of oilfields.

[0086] Beneficial effects: It effectively solves the problem of low accuracy in predicting mixed sedimentary rock reservoirs in sand-mud mixed sedimentary areas of oil and gas basins in existing technologies, and improves the prediction effect of reservoir distribution. It has important theoretical significance and application value for accurately predicting favorable reservoirs and oil reservoirs in sand-mud mixed sedimentary areas of oil and gas basins.

[0087] The above specific embodiments further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for identifying and predicting mixed sedimentary rocks in oil and gas basins, characterized in that, The identification and prediction method includes: Step S1: Identify the lithofacies of a single well; Step S2: Identify and classify the sedimentary facies types and sedimentary microfacies types in the sand-mud mixture zone; Step S3: Establish a classification scheme for lithofacies assemblages and form a matching relationship between lithofacies assemblages and sedimentary microfacies; Step S4: Establish a well logging database for the mixed sedimentary rock strata of the sand-mud mixed sedimentary zone; Step S5: Establish a rock physical facies classification using the SOM neural network clustering analysis method; Step S6: Establish lithofacies assemblage type and sedimentary microfacies identification model; Step S7: Establish a spatial distribution model of sedimentary microfacies and lithofacies assemblages of mixed sedimentary rocks.

2. The method for identifying and predicting mixed sedimentary rocks in oil and gas basins according to claim 1, characterized in that, Step S1: Identifying the lithofacies of a single well specifically includes: By using core observation and thin section identification, and by comparing and analyzing the differences in macroscopic and microscopic characteristics of core sampling wells, lithofacies types can be identified.

3. The method for identifying and predicting mixed sedimentary rocks in oil and gas basins according to claim 2, characterized in that, The macroscopic features specifically include: core color, structure, and sedimentary texture.

4. The method for identifying and predicting mixed sedimentary rocks in oil and gas basins according to claim 1, characterized in that, Step S2: Identifying and classifying the sedimentary facies types and sedimentary microfacies types in the sand-mud mixture zone specifically includes: Based on core observations and previous research, this study identifies the sedimentary structures of the sandstone-mudstone mixed facies zone and the sedimentary types of sandstone and mudstone. The sedimentary structures include: mechanically subsided sedimentary structures, turbidity current sedimentary structures, and clastic flow sedimentary structures; The sedimentary types of the sandstone include sandy sliding-slump rocks, sandy clastic flow rocks, mixed sandstone and conglomerate deposits, and wavy bedding sandstone. The sedimentary types of the mudstone include mechanically deposited mudstone and gravity flow-derived mudstone; Using high-frequency sedimentary cycle analysis, based on the conclusions of sedimentary structure and sedimentary type, the sand-mud mixed sedimentary zone develops two sedimentary facies types: nearshore underwater fan and deep-water turbidite fan. The sedimentary microfacies types include gray-gray semi-deep lakes, felsic semi-deep lakes, fan-end, fan-front, and fan-braided channel types.

5. The method for identifying and predicting mixed sedimentary rocks in oil and gas basins according to claim 1, characterized in that, Step S3: Establishing a classification scheme for lithofacies assemblages and forming a matching relationship between lithofacies assemblages and sedimentary microfacies specifically includes: Guided by the sedimentary patterns of sand-mud mixed sedimentary facies zones, we carried out facies assemblage identification work in core sampling wells and classified them into facies assemblage types; The lithofacies assemblages include: calcareous semi-deep lacustrine-laminated felsic limestone or calcareous mixed shale facies; felsic semi-deep lacustrine-laminated felsic limestone interbedded with lacustrine felsic mixed shale facies; fan-end-laminated felsic mixed shale / limestone with minor deformed siltstone interlayers; fan-front-laminated felsic / calcareous mixed shale facies with siltstone interlayers; lacustrine felsic limestone interbedded with dolomitic fine sandstone facies; lacustrine felsic / calcareous mixed shale with lacustrine sandstone interlayers; fan-braided channel-massive felsic mixed shale and massive argillaceous / dolitic siltstone interbedded facies; lacustrine felsic dolomite and lacustrine dolomitic siltstone interbedded facies; lacustrine gravelly medium-coarse sandstone with lacustrine argillaceous siltstone interlayers; and massive argillaceous siltstone or fine sandstone facies.

6. The method for identifying and predicting mixed sedimentary rocks in oil and gas basins according to claim 1, characterized in that, Step S5: Establishing a rock physical facies classification using the SOM neural network clustering analysis method specifically includes: Based on the abundant logging data from the sand-mud mixture zone, the logging data is trained and normalized. The SOM neural network clustering algorithm was used to perform cluster analysis on the logging data in the database, and the logging data of the mixed sedimentary zone was divided into 16 logging facies. The logging facies corresponded to the petrophysical facies, and 16 petrophysical facies of the mixed sedimentary rocks in the mixed sedimentary zone were established.

7. The method for identifying and predicting mixed sedimentary rocks in oil and gas basins according to claim 6, characterized in that, The specific steps of using the SOM neural network clustering algorithm to perform cluster analysis on well logging data within the database include: Sample normalization processing involves normalizing the input sample data and the corresponding weight vector matrix. To determine the winning node, when a sample is input, the similarity between the input sample and the weight vectors corresponding to all nodes in the competition layer is compared, and the weight vector with the highest similarity is determined as the winning node. Weight adjustment and network output: Only the winning node has the right to adjust the weight vector; other neurons do not. The winning neuron outputs 1, otherwise it outputs 0. The expression is as follows: In the equation, X j (t) represents the input sample vector, w ij η(t) represents the weighting coefficients of input neuron i and output neuron j over time, and η(t) represents the learning rate.

8. The method for identifying and predicting mixed sedimentary rocks in oil and gas basins according to claim 1, characterized in that, Step S6: Establishing lithofacies assemblage type and sedimentary microfacies identification model specifically includes: Based on the classification results of mixed sedimentary rock facies assemblages in sand-mud mixed sedimentary zones, the matching relationship between sedimentary microfacies and facies assemblages, and the classification results of rock physical facies, the matching relationship between 10 types of facies assemblages and 16 types of rock physical facies, and the relationship between 5 types of sedimentary microfacies and 16 types of rock physical facies in core sampling wells were statistically analyzed. A lithofacies assemblage and sedimentary microfacies identification model based on rock physical facies classification was established.

9. The method for identifying and predicting mixed sedimentary rocks in oil and gas basins according to claim 8, characterized in that, In the process of establishing the identification model of lithofacies assemblages and sedimentary microfacies based on rock physical facies classification, there is a case where one lithofacies assemblage type corresponds to multiple rock physical facies. The frequency of occurrence of rock physical facies corresponding to each lithofacies assemblage type in the core sampling well is counted, and the rock physical facies with the highest frequency is the dominant rock physical facies category corresponding to the lithofacies assemblage type.

10. The method for identifying and predicting mixed sedimentary rocks in oil and gas basins according to claim 1, characterized in that, Step S7: Establishing a spatial distribution model of sedimentary microfacies and lithofacies assemblages in mixed sedimentary rocks specifically includes: By correlating the sand layers of the upper sub-section of the Sha-4 pure layer within the mixed sedimentary zone with the sub-layers, a stratigraphic correlation framework profile of the sand layers and sub-layers in this section is established. Using the established model for identifying facies assemblages and sedimentary microfacies of mixed sedimentary rocks, we identified facies assemblages and sedimentary microfacies of single wells in the mixed sedimentary zone to be predicted, clarified the spatial distribution characteristics of sedimentary microfacies and facies assemblages in the study interval, and established the spatial distribution sequence of sedimentary microfacies and facies assemblages.

Images