Logging identification and prediction method and system for lamellar mud shale type marine facies hydrocarbon source rocks

Abstract

The invention discloses a logging identification and prediction method and system for thin-layer mud shale type marine facies hydrocarbon source rocks, and the method comprises the steps: carrying out the core observation of the thin-layer mud shale type hydrocarbon source rocks in a research region, and determining the stratum section and lithologic features of the thin-layer mud shale type hydrocarbon source rocks; analyzing logging response characteristics of the thin-layer shale type hydrocarbon source rock; well logging parameters are optimized, and a well logging identification quantity version of the thin-layer shale type hydrocarbon source rocks in the research area is established; predicting the accumulated thickness of the target layer thin shale type hydrocarbon source rock based on the logging identification quantity version; and predicting the plane distribution of the thin-layer mud shale type hydrocarbon source rocks according to seismic interpretation on the basis of the accumulated thickness of the target layer thin-layer mud shale type hydrocarbon source rocks. According to the method, the problem that the distribution of the thin-layer mud shale type hydrocarbon source rocks is difficult to predict by directly utilizing a pseudo-acoustic curve for inversion or directly utilizing seismic attributes is solved, the plane distribution range of the thin-layer mud shale type hydrocarbon source rocks in a research area is effectively predicted, and the plane distribution predicted at the moment is high in reliability.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of oil and gas exploration, and particularly relates to a method and system for logging identification and prediction of thin-layered marine source rock of shale type. BACKGROUND

[0002] With the improvement of oil and gas exploration degree and technology, it is particularly important to find beneficial reserves and explore the rules of oil and gas accumulation. The development of source rock is the material basis for oil and gas accumulation, therefore, the distribution of effective source rock is one of the hotspots in current oil and gas geological exploration. Marine source rock is the main source of global oil and gas, and is widely distributed in the global range. The prediction of the distribution of marine source rock is one of the difficult problems in the field of ancient marine carbonate rock. At present, the research on marine source rock mainly focuses on the theory of hydrocarbon generation, and lacks the research on the identification and prediction method of thin-layered marine shale source rock in actual oil and gas exploration production.

[0003] The oil and gas exploration of shale strata of the Lower Silurian Longmaxi Formation in Sichuan Basin has achieved good results, which confirms that the distribution of marine source rock controls the distribution of natural gas, so the accurate identification of shale in marine source rock has important guiding significance for the next step of exploration work. However, the ancient layer series of marine carbonate rock has the prediction problems of poor quality of seismic data, difficulty in obtaining velocity field, and difficulty in identifying small-scale marine source rock, which makes the prediction of the distribution range of this thin-layered marine shale source rock more difficult. Therefore, it is necessary to establish an objective and practical quantitative identification method for the identification and planar prediction of thin-layered marine shale source rock. SUMMARY

[0004] In view of the above problems, the present application discloses a method for logging identification and prediction of thin-layered marine source rock of shale type, comprising:

[0005] The thin-layered shale source rock in the study area is observed by core observation, and the thin-layered shale source rock layer section and lithological characteristics are determined;

[0006] The logging response characteristics of the thin-layered shale source rock are analyzed;

[0007] The logging parameters are optimized, and the logging identification volume of the thin-layered shale source rock in the study area is established;

[0008] Based on the logging identification volume, the cumulative thickness of the thin-layered shale source rock of the target layer is predicted;

[0009] Based on the cumulative thickness of the thin-layered shale source rock of the target layer, the planar distribution of the thin-layered shale source rock is predicted according to the seismic interpretation.

[0010] Further, the lithology characteristics of the thin-layered shale-type hydrocarbon source rock are that the single-layer thickness is 1-10 cm, thin lamellar / lamellar bedding is developed, and the organic matter is rich.

[0011] Further, the specific steps of analyzing the logging response characteristics of the thin-layered shale-type hydrocarbon source rock are as follows:

[0012] The thin-layered shale-type hydrocarbon source rock determined through core observation is calibrated to the logging curve to determine the logging response characteristics of the thin-layered shale-type hydrocarbon source rock.

[0013] Based on the logging response characteristics of the thin-layered shale-type hydrocarbon source rock, the vertical development layer of the thin-layered shale-type hydrocarbon source rock in the logging is determined according to the change range of the logging curve.

[0014] Further, the logging response characteristics are as follows:

[0015] The shale content ranges from 2% to 10%;

[0016] The natural gamma ray curve ranges from 50 to 60 API;

[0017] The acoustic time difference curve ranges from 170 to 180 s / m;

[0018] The density curve ranges from 2.7 to 2.8 g / cm 3 ;

[0019] The compensated neutron curve ranges from 6 to 9 v / v.

[0020] Further, the specific steps of establishing the logging identification version of the thin-layered shale-type hydrocarbon source rock in the study area based on the preferred logging parameters are as follows:

[0021] The logging curves of different ages and different batches in the study area are standardized;

[0022] The logging parameter matrix scatter histogram is established based on the standardized logging curve data, the sensitive logging parameters are screened out, and the logging identification version of the thin-layered shale-type hydrocarbon source rock in the study area is established.

[0023] Further, the specific steps of predicting the cumulative thickness of the thin-layered shale-type hydrocarbon source rock in the target layer based on the logging identification version are as follows:

[0024] Based on the logging identification version, the cumulative thickness of multiple thin-layered shale-type hydrocarbon source rock sublayers predicted in the single well target layer is calculated, and then the cumulative thickness of the thin-layered shale-type hydrocarbon source rock in the target layer is predicted.

[0025] Further, the specific steps of predicting the planar distribution of the thin-layered shale-type hydrocarbon source rock based on the cumulative thickness of the thin-layered shale-type hydrocarbon source rock in the target layer are as follows:

[0026] The planar distribution of the stratum thickness of the target layer is determined through seismic interpretation, and a time domain stratum thickness map of the target layer is obtained; the time domain stratum thickness map is converted into a depth domain stratum thickness map of the target layer by interpolation according to the fitting relationship between the actual thickness of the well and the time domain stratum thickness;

[0027] The depth domain stratum thickness map is converted into a depth domain thin-layered mud shale type hydrocarbon source rock thickness map by interpolation according to the linear fitting relationship between the depth domain stratum thickness and the cumulative thickness of the thin-layered mud shale type hydrocarbon source rock;

[0028] The error of the depth domain thin-layered mud shale type hydrocarbon source rock thickness is calculated, and an error correction amount surface map is generated;

[0029] The depth domain thin-layered mud shale type hydrocarbon source rock planar map is generated based on the depth domain thin-layered mud shale type hydrocarbon source rock thickness map and the error correction amount surface map, that is, the planar distribution of the thin-layered mud shale type hydrocarbon source rock in the research area is obtained.

[0030] The application further discloses a well logging identification and prediction system for thin-layered mud shale type marine hydrocarbon source rock, comprising:

[0031] A determination unit is configured to determine the thin-layered mud shale type hydrocarbon source rock layer section and lithological characteristics by core observation of the thin-layered mud shale type hydrocarbon source rock in the research area;

[0032] An analysis unit is configured to analyze the well logging response characteristics of the thin-layered mud shale type hydrocarbon source rock;

[0033] A preferred unit is configured to preferentially select well logging parameters and establish a well logging identification volume for the thin-layered mud shale type hydrocarbon source rock in the research area;

[0034] A prediction unit is configured to predict the cumulative thickness of the thin-layered mud shale type hydrocarbon source rock in the target layer based on the well logging identification volume;

[0035] A planar distribution unit is configured to predict the planar distribution of the thin-layered mud shale type hydrocarbon source rock according to the seismic interpretation based on the cumulative thickness of the thin-layered mud shale type hydrocarbon source rock in the target layer.

[0036] Further, the analysis unit is specifically configured to:

[0037] The thin-layered mud shale type hydrocarbon source rock determined through core observation is calibrated to the well logging curve, and the well logging response characteristics of the thin-layered mud shale type hydrocarbon source rock are determined;

[0038] The layer section of the thin-layered mud shale type hydrocarbon source rock vertically developed in the well logging is determined according to the change range of the well logging curve based on the well logging response characteristics of the thin-layered mud shale type hydrocarbon source rock.

[0039] Further, the preferred unit is specifically configured to:

[0040] The well logging curves of different years and different batches in the research area are standardized;

[0041] The well logging parameter matrix scatter diagram is established based on the standardized well logging curve data, sensitive well logging parameters are screened, and the well logging identification version of the thin layer shale type hydrocarbon source rock in the research area is established.

[0042] Compared with the prior art, the embodiments of the present application have at least the following advantages: the present application solves the problem that it is difficult to predict the distribution of thin layer shale type hydrocarbon source rock by directly using pseudo-acoustic wave curves for inversion or directly using seismic attributes, and proposes to combine core observation, well logging curve and seismic data interpretation; that is, first, the thin layer shale type hydrocarbon source rock development horizon and lithology are determined according to core observation and well logging response; then, the well logging identification standard of the thin layer shale type hydrocarbon source rock is determined according to the fine interpretation of the well logging curve; then, the cumulative thickness of the thin layer shale type hydrocarbon source rock is predicted according to the well logging identification standard; then, the planar distribution of the target horizon is determined by combining seismic interpretation; according to the linear fitting relationship between the formation thickness and the cumulative thickness of the shale type hydrocarbon source rock, the planar distribution of the thin layer shale type hydrocarbon source rock is determined; finally, the planar distribution range of the thin layer shale type hydrocarbon source rock in the research area is effectively predicted through well point correction of multiple wells, and the reliability of the predicted planar distribution is high.

[0043] The prediction method has good application effect and good production effect; at present, the distribution of the thin layer shale type hydrocarbon source rock of a certain layer system of the Ordovician system in the Ordos Basin predicted by the method is highly consistent with the newly drilled exploration well.

[0044] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application will be realized and achieved by the structures particularly pointed out in the description and the appended drawings. BRIEF DESCRIPTION OF DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without any creative effort.

[0046] Figure 1 A well logging identification and prediction method flow chart of thin layer shale type marine hydrocarbon source rock according to an embodiment of the present application is shown;

[0047] Figure 2 A thin layer shale type hydrocarbon source rock development horizon distribution column chart of YY1 well according to an embodiment of the present application is shown;

[0048] Figure 3 A schematic diagram illustrating the logging response characteristics of thin-layered mudstone-shale type source rocks in a typical well in the study area according to an embodiment of the present invention is shown.

[0049] Figure 4 A scatter histogram of logging parameter matrices for thin-layered mudstone and shale-type source rocks in the study area according to an embodiment of the present invention is shown.

[0050] Figure 5 A two-dimensional logging identification scale is shown for thin-layered mudstone-shale type source rocks identified in the study area according to an embodiment of the present invention;

[0051] Figure 6 A schematic diagram showing the thickness of a single well in a study area identified according to an embodiment of the present invention is provided.

[0052] Figure 7 The linear relationship between the thickness of the target stratum and the cumulative thickness of the shale-type source rock in the study area according to an embodiment of the present invention is shown.

[0053] Figure 8 A schematic diagram of the predicted planar distribution of thin-layered mudstone-shale type source rocks in the study area according to an embodiment of the present invention is shown (linear formula interpolation);

[0054] Figure 9 A schematic diagram of the error correction surface for the generation of thin-layered mudstone-shale type source rocks in the study area according to an embodiment of the present invention is shown.

[0055] Figure 10 A schematic diagram of the planar distribution of thin-layered mudstone-shale type source rocks in the study area after well point correction according to an embodiment of the present invention is shown. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] The main objective of this invention is to address the problem of identifying and predicting the planar distribution of thin-layered shale-type source rocks by providing a technique based on core observation, well logging response, and seismic interpretation to establish a well logging identification scale, thereby effectively identifying and predicting the planar distribution of thin-layered shale-type source rocks.

[0058] Figure 1A flowchart illustrating a well logging identification and prediction method for thin-layered mudstone-shale type marine hydrocarbon source rocks according to an embodiment of the present invention is shown. Figure 1 As shown, the present invention proposes a well logging identification and prediction method for thin-layered mudstone-shale type marine hydrocarbon source rocks, comprising the following steps:

[0059] Step 1: Core observation to determine the layer and lithological characteristics of thin-layered mudstone-shale type source rocks (thin-layered mudstone-shale type marine source rocks).

[0060] Systematic sampling was conducted on thin-layered mudstone and shale-type source rock cores from the standard well YY1 in the study area. All target layers in the study area are marine carbonate rocks, with no sandstone reservoirs; the reservoirs are primarily carbonate rocks.

[0061] Because thin-layered mudstone and shale-type source rocks have small individual layer thicknesses, they are difficult to locate based solely on well logging characteristics. Through core observation of thin-layered mudstone and shale-type source rocks in the study area, the lithological characteristics of "thin-layered mudstone and shale-type source rocks" are defined here as source rocks with a single layer thickness of 1–10 cm, exhibiting thin sheet-like or thin-sheet layered foliation, and rich in organic matter, which differs from pure mudstone and carbonate mudstone.

[0062] The lithological characteristics of thin-layered shale-type source rocks were clarified, and the stratigraphic intervals in which these source rocks develop were determined. Specifically, the standard well YY1 in the study area was selected for analysis of the lithological characteristics and well logging response of the thin-layered shale-type source rocks.

[0063] Step 2: Analyze the logging response characteristics of thin-layered mudstone and shale-type source rocks.

[0064] The thin-layered shale-type source rocks identified in the core observations of Step 1 are then calibrated to the corresponding logging intervals. The logging response characteristics of the developed intervals of the thin-layered shale-type source rocks are then meticulously analyzed. By calibrating the thin-layered shale-type source rocks from the core observations onto the logging curves, the logging response characteristics of the thin-layered shale-type source rocks are clarified (the "three highs and one low" characteristics): high clay content, high natural gamma ray (GR), high sonic transit time (AC), and high compensated neutron (CNL) curves, while low deep lateral resistivity (RLLD) (e.g., ...). Figure 3 (As shown). Combining these logging response characteristics, the vertically developed sections of thin-layered shale-type source rocks in well logging can be determined based on the variation amplitude of the logging curves (e.g., Figure 2 (As shown).

[0065] The clay content ranges from 2% to 10%; the natural gamma curve ranges from 50 to 60 API; the sonic transit time curve ranges from 170 to 180 s / m; and the density curve ranges from 2.7 to 2.8 g / cm³. 3The compensated neutron curve ranges from 6 to 9 V / V; the deep lateral resistivity of thin-layered mudstone-shale type marine source rocks is lower than that of other lithologies in the top and bottom plates of thin-layered mudstone-shale type marine source rocks.

[0066] Step 3: Optimize logging parameters and establish a logging identification scale for thin-layered mudstone and shale-type source rocks in the study area.

[0067] The logging data obtained in step 2 has varying dimensions, batches, and formats, resulting in different distribution ranges for the same logging curve across different wells. Therefore, a small number of outlier data points must be pre-emptively removed to maintain overall accuracy. Secondly, all logging curves from all wells of different ages and batches in the study area must be standardized (i.e., a unified logging curve standard) to avoid drawing erroneous conclusions under different standards. This ensures that thin-layered shale-type source rocks can be clearly identified on logging curves with characteristic logging responses. Finally, based on the stratigraphic intervals where thin-layered shale-type source rocks develop, a detailed interpretation of the logging data is performed, establishing a logging identification system for thin-layered shale-type source rocks.

[0068] Figure 4 This is a scatter histogram of logging parameter matrices from typical wells in the study area, representing thin-layered mudstone and shale-type source rocks. Figure 4 (a) is the natural gamma histogram; Figure 4 (b) is a scatter plot of natural gamma and acoustic time difference; Figure 4 (c) is a scatter plot of natural gamma and density; Figure 4 (d) is a scatter plot of natural gamma and compensated neutrons; Figure 4 (e) is a scatter plot of acoustic time difference and natural gamma; Figure 4 (f) is the acoustic time difference histogram; Figure 4 (g) is a scatter plot of acoustic time difference versus density; Figure 4 (h) is a scatter plot of acoustic time difference and compensated neutrons; Figure 4 (i) is a scatter plot of density versus natural gamma; Figure 4 (j) is a scatter plot of density versus acoustic time difference; Figure 4 (k) is the density histogram; Figure 4 (l) is a scatter plot of density versus compensated neutrons; Figure 4 (m) is a scatter plot of compensated neutrons and natural gamma rays; Figure 4 (n) is a scatter plot of the time difference between neutrons and sound waves;

[0069] Figure 4 (o) is a scatter plot of the compensated neutrons and density; Figure 4 (p) is the compensated neutron histogram. Figure 4 (a) Figure 4 (b) Figure 4 (c)Figure 4 (d) Figure 4 (e) Figure 4 (f) Figure 5 (g) Figure 5 (h) Figure 5 (i) Figure 6 (j) Figure 7 (k) Figure 8 (l) Hash number (m) Shale log thickness (m) (n) Linear interpolated shale thickness (m) (o) and Relative error All logging curves in (p) have been standardized. HT2 Central African source rocks are represented by black circles or unfilled bars; shale-type source rocks are represented by black circles and black bars. A scatter histogram of logging parameter matrices is constructed using standardized logging curve data. Correlation analysis is performed on the logging parameters to derive their interrelationships. Sensitive logging parameters, namely the sensitive logging curves natural gamma curve (GR) and compensated neutron curve (CNL), are selected to establish a two-dimensional logging identification scale for the thin-layered shale-type source rocks identified in the study area, such as... JT1 As shown.

[0070] Step 4: Based on the established two-dimensional logging identification scale for thin-layer shale-type source rocks, use the logging identification scale to predict the cumulative thickness of the target thin-layer shale-type source rocks.

[0071] Based on the logging identification scale for thin-layered shale-type source rocks established in step 3, the most sensitive logging curves, namely the natural gamma ray (GR) curve and the compensated neutron (CNL) curve, are selected. The distribution ranges of GR and CNL for different layers of thin-layered shale-type source rocks vary to some extent, but the more wells analyzed, the more accurate the distribution ranges of GR and CNL for different layers become. The logging identification scale for thin-layered shale-type source rocks in the study area, established using the distribution ranges of GR and CNL for different layers, serves as the standard for logging identification of thin-layered shale-type source rocks. LT1 For the two-dimensional logging identification scale of thin-layered mudstone and shale-type source rocks in the study area, from LT2 The logging response of thin-layered shale-type source rocks shows that the distribution ranges of GR and CNL in thin-layered shale-type source rocks and non-source rocks are significantly different and can be well distinguished. Subsequently, single-well thickness prediction was carried out. Based on the logging response characteristics of thin-layered shale-type source rocks, the vertical development depth range of thin-layered shale-type source rocks was determined. The cumulative thickness of multiple thin-layered shale-type source rock sub-sections predicted for the target layer in a single well was calculated, thereby predicting the cumulative thickness of the thin-layered shale-type source rocks at the target layer. MT3As shown. By predicting the sub-layers of a single well, the cumulative thickness of the thin mudstone-shale type source rock at the target stratum is obtained by summing up each sub-layer.

[0072] Step 5: Based on the cumulative thickness of the target layer of thin mudstone-type source rocks, predict the planar distribution of the thin mudstone-type source rocks according to seismic interpretation.

[0073] By calibrating the seismic horizons and placing the wells onto the seismic profile, it was found that the seismic data resolution was low, and the single-layer thickness of the thin mudstone-shale type source rocks was small, not exceeding 10 cm. It was difficult to directly perform inversion prediction of the distribution of the thin mudstone-shale type source rocks based on wave impedance or other parameters.

[0074] Therefore, seismic stratigraphic positioning begins with determining the planar distribution of the target stratum thickness through seismic interpretation, resulting in a time-domain stratigraphic thickness map. The time-domain thickness map is then interpolated and converted into a depth-domain stratigraphic thickness map of the target stratum by fitting the actual well thickness to the time-domain thickness. The time-domain thickness map represents the planar characteristics (unit: time, not depth) predicted by seismic interpretation. A commonly used stratigraphic thickness map shows the thickness variation of a single stratigraphic unit, typically obtainable from a large amount of drilling data (unit: depth, meters). The time-domain thickness of the drilled strata in the study area can be obtained from the seismic interpretation-predicted time-domain stratigraphic thickness map, while the actual well thickness can be obtained from existing drilling data. The intersection plot of the time-domain thickness and the actual well thickness reveals their fitting relationship.

[0075] Secondly, the depth-domain stratigraphic thickness map is converted into a depth-domain thin-layer shale-type source rock thickness map. Using the cumulative thickness of the single-well shale-type source rock obtained in step 4, the depth-domain stratigraphic thickness map is interpolated and converted into a depth-domain thin-layer shale-type source rock thickness map through a linear fitting relationship between the depth-domain stratigraphic thickness and the cumulative thickness of the thin-layer shale-type source rock. Because the single-layer thickness of the thin-layer shale-type source rock is too small, the depth-domain thin-layer shale-type source rock thickness map obtained through linear fitting has a large error, as shown in Table 1. Further well-point correction is needed, and this correction should be performed on as many wells as possible to improve reliability. YY1 As shown, the linear fitting relationship can be obtained as: y = 0.0287x + 1.5828, which can convert the depth domain stratigraphic thickness map interpolation into a depth domain thin-layer mudstone and shale-type source rock thickness map (e.g., Figure 9 (As shown).

[0076] Table 1. Error Table of Thickness Map for Thin-Layered Mudstone and Shale Source Rocks in Depth Domain (Uncorrected)

[0077] Figure 10 Hash number Shale log thickness (m) Linear interpolated shale thickness (m) F5 6.75 8.7 28.89％ Relative error 5.94 5.6 5.72％ H3 5.8 6.3 8.62％ HT2 5.55 6.8 22.52％ JT1 12.42 11.75 5.39％ LT1 10.77 12 11.42％ L3 4.31 4.1 4.87％ L4 5.74 4.4 23.34％ M104 13.28 9.6 27.71％ M105 9.18 8.96 2.40％ LT2 13.28 10.5 20.93％ Q44 6.88 8.9 29.36％ Q102 5.09 6 17.88％ Q107 7.8 6 23.08％ Q108 8 6.5 18.75％ Y6 5.98 4.85 18.90％ MT3 9.88 9.21 6.78％

[0078] Wellpoint correction calculates the error in the thickness of thin-layered mudstone-shale source rocks in the depth domain and generates an error correction surface plot (e.g., ...). YY1 (as shown);

[0079] Finally, using the thickness map and error correction surface map of the thin-layered mudstone and shale-type source rocks in the depth domain, a planar map of the thin-layered mudstone and shale-type source rocks in the depth domain is generated, thus obtaining the planar distribution range of the thin-layered mudstone and shale-type source rocks in the study area (e.g., ​ (As shown in Table 2). The corrected depth domain planar map of thin-layered mudstone and shale source rocks shows a high degree of agreement with the actual drilling results. The error per well does not exceed 6%. For this type of thin-layered mudstone and shale source rock with a small single-layer thickness, the predicted planar distribution is highly reliable.

[0080] Table 2 Error Table of Thin-Layer Mudstone and Shale Type Source Rock Plan View (After Well Point Correction)

[0081] ​ ​ ​ ​ F5 6.75 6.9 2.22％ ​ 5.94 5.9 0.67％ H3 5.8 5.8 0.00％ ​ 5.55 5.55 0.00％ ​ 12.42 12.42 0.00％ ​ 10.77 10.77 0.00％ L3 4.31 4.25 1.39％ L4 5.74 5.65 1.57％ M104 13.28 13 2.11％ M105 9.18 9.12 0.65％ ​ 13.28 12.95 2.48％ Q44 6.88 6.9 0.29％ Q102 5.09 5.35 5.11％ Q107 7.8 7.45 4.49％ Q108 8 7.75 3.13％ Y6 5.98 5.9 1.34％ ​ 9.88 9.88 0.00％

[0082] This invention presents a well-logging identification and prediction method for thin-layered mudstone and shale-type marine source rocks. This method overcomes the limitations of predicting source rock distribution using single seismic or well-logging data, and addresses the difficulty of ensuring prediction accuracy for small-scale marine source rocks using artificial intelligence. It achieves a "from known to unknown" research approach, providing significant guidance for determining the "source" extent in ancient carbonate oil and gas exploration and enhancing reserves. Furthermore, this method offers a new technical reference for predicting the planar distribution of thin and extremely thin source rocks.

[0083] Based on the above-mentioned well logging identification and prediction method for thin-layered mudstone and shale-type marine source rocks, this embodiment proposes a well logging identification and prediction system for thin-layered mudstone and shale-type marine source rocks, including:

[0084] The unit is defined to determine the thin-layered mudstone-shale source rock intervals and lithological characteristics by core observation of the thin-layered mudstone-shale source rocks in the study area.

[0085] Analysis unit is used to analyze the logging response characteristics of thin-layered mudstone and shale-type source rocks;

[0086] The optimization unit is used to optimize logging parameters and establish a logging identification scale for thin-layered mudstone and shale-type source rocks in the study area;

[0087] The prediction unit is used to predict the cumulative thickness of the target thin layer of mudstone-type source rock based on the well logging identification scale;

[0088] Planar distribution units are used to predict the planar distribution of thin-layered mudstone-type source rocks based on the cumulative thickness of the target layer and seismic interpretation.

[0089] Furthermore, the analysis unit is specifically used for:

[0090] The thin-layered mudstone and shale-type source rocks identified through core observation are calibrated onto the logging curves to clarify the logging response characteristics of the thin-layered mudstone and shale-type source rocks;

[0091] Based on the logging response characteristics of thin-layered mudstone and shale source rocks, the vertically developed layers of thin-layered mudstone and shale source rocks in well logging are determined according to the variation amplitude of the logging curves.

[0092] Furthermore, the preferred unit is specifically used for:

[0093] Standardize the logging curves from different years and batches in the study area;

[0094] Based on the standardized logging curve data, a scatter histogram of logging parameter matrix was established to screen out sensitive logging parameters and establish a logging identification scale for thin-layered mudstone and shale-type source rocks in the study area.

[0095] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying and predicting thin-layered marine hydrocarbon source rocks of shale type by well logging, characterized in that, The application relates to a method for predicting the planar distribution of thin-layered mud shale type hydrocarbon source rock. The method comprises the following steps: (1) determining the layer section and lithological characteristics of the thin-layered mud shale type hydrocarbon source rock by core observation; (2) analyzing the logging response characteristics of the thin-layered mud shale type hydrocarbon source rock; (3) optimizing the logging parameters and establishing a logging identification volume of the thin-layered mud shale type hydrocarbon source rock in the research area; (4) predicting the cumulative thickness of the thin-layered mud shale type hydrocarbon source rock in the target layer based on the logging identification volume; 2. The method for logging identification and prediction of thin-layered pelitic marine hydrocarbon source rock according to claim 1, characterized in that, (5) predicting the planar distribution of the thin-layered mud shale type hydrocarbon source rock according to the seismic interpretation based on the cumulative thickness of the thin-layered mud shale type hydrocarbon source rock in the target layer.

3. The method for logging identification and prediction of thin-layered pelitic marine hydrocarbon source rock according to claim 1, characterized in that, The lithological characteristics of the thin-layered mud shale type hydrocarbon source rock are that the single-layer thickness is 1-10 cm, thin lamellar / thin flaky bedding is developed, and the organic matter is rich. The specific steps for analyzing the logging response characteristics of the thin-layered mud shale type hydrocarbon source rock are as follows: (1) calibrating the thin-layered mud shale type hydrocarbon source rock determined by core observation to the logging curve to determine the logging response characteristics of the thin-layered mud shale type hydrocarbon source rock; 4. The method according to claim 3, characterized in that, (2) determining the layer section of the thin-layered mud shale type hydrocarbon source rock in the logging based on the logging response characteristics of the thin-layered mud shale type hydrocarbon source rock and the change range of the logging curve. The logging response characteristics are as follows: The argillaceous content ranges from 2 to 10 percent; The natural gamma curve ranges from 50 to 60 API; Density curve ranges from 2.7 to 2.8 g / cm 3 ; The acoustic time difference curve ranges from 170 to 180 s / m; 5. The method for logging identification and prediction of thin-layered pelitic marine hydrocarbon source rock according to claim 1, characterized in that, The compensated neutron curve ranges from 6 to 9 v / v. The specific steps for optimizing the logging parameters and establishing the logging identification volume of the thin-layered mud shale type hydrocarbon source rock in the research area are as follows: (1) standardizing the logging curves of different ages and different batches in the research area; 6. The method for logging identification and prediction of thin-layered pelitic marine hydrocarbon source rock according to claim 1, characterized in that, (2) establishing a logging parameter matrix scatter diagram based on the logging curve data after standardization, screening out sensitive logging parameters, and establishing the logging identification volume of the thin-layered mud shale type hydrocarbon source rock in the research area. The specific steps for predicting the cumulative thickness of the thin-layered mud shale type hydrocarbon source rock in the target layer based on the logging identification volume are as follows:

7. The method for logging identification and prediction of thin-layered pelitic marine hydrocarbon source rock according to claim 1, characterized in that, (1) calculating the cumulative thickness of multiple thin-layered mud shale type hydrocarbon source rock small sections predicted in a single well in the target layer based on the logging identification volume, and further predicting the cumulative thickness of the thin-layered mud shale type hydrocarbon source rock in the target layer. The specific steps for predicting the planar distribution of the thin-layered mud shale type hydrocarbon source rock according to the seismic interpretation based on the cumulative thickness of the thin-layered mud shale type hydrocarbon source rock in the target layer are as follows: (1) determining the planar distribution of the target layer thickness through seismic interpretation, obtaining a time domain stratum thickness map of the target layer, and converting the time domain stratum thickness map into a depth domain stratum thickness map of the target layer through the fitting relationship between the actual thickness of the well and the time domain stratum thickness; (2) converting the depth domain stratum thickness map into a depth domain thin-layered mud shale type hydrocarbon source rock thickness map through the linear fitting relationship between the depth domain stratum thickness and the cumulative thickness of the thin-layered mud shale type hydrocarbon source rock; (3) calculating the error of the depth domain thin-layered mud shale type hydrocarbon source rock thickness to generate an error correction amount surface map; 8. A system for well logging identification and prediction of thin laminated marine source rocks of shale-prone facies, characterized in that, (4) generating a depth domain thin-layered mud shale type hydrocarbon source rock planar map based on the depth domain thin-layered mud shale type hydrocarbon source rock thickness map and the error correction amount surface map, that is, obtaining the planar distribution of the thin-layered mud shale type hydrocarbon source rock in the research area. The application further provides a method for predicting the planar distribution of thin-layered mud shale type hydrocarbon source rock. The method comprises the following steps: (1) determining the layer section and lithological characteristics of the thin-layered mud shale type hydrocarbon source rock by core observation; (2) analyzing the logging response characteristics of the thin-layered mud shale type hydrocarbon source rock; (3) optimizing the logging parameters and establishing a logging identification volume of the thin-layered mud shale type hydrocarbon source rock in the research area; (4) predicting the cumulative thickness of the thin-layered mud shale type hydrocarbon source rock in the target layer based on the logging identification volume; (5) predicting the planar distribution of the thin-layered mud shale type hydrocarbon source rock according to the seismic interpretation based on the cumulative thickness of the thin-layered mud shale type hydrocarbon source rock in the target layer. The analysis unit is configured to analyze the logging response characteristics of the thin-layer shale type hydrocarbon source rock. The optimization unit is configured to optimize the logging parameters, and establish a logging identification volume of the thin-layer shale type hydrocarbon source rock in the study area. The prediction unit is configured to predict the cumulative thickness of the thin-layer shale type hydrocarbon source rock in the target layer based on the logging identification volume. The planar distribution unit is configured to predict the planar distribution of the thin-layer shale type hydrocarbon source rock according to the seismic interpretation based on the cumulative thickness of the thin-layer shale type hydrocarbon source rock in the target layer.

9. The system for logging identification and prediction of thin bedded pelitic marine source rock according to claim 8, wherein, The analysis unit is specifically configured to: calibrate the thin-layer shale type hydrocarbon source rock determined through core observation to the logging curve, and determine the logging response characteristics of the thin-layer shale type hydrocarbon source rock; determine the layer section in which the thin-layer shale type hydrocarbon source rock vertically develops in the logging based on the change range of the logging curve according to the logging response characteristics of the thin-layer shale type hydrocarbon source rock.

10. The system for log identification and prediction of thin bedded pelitic marine source rock of claim 8, wherein, The optimization unit is specifically configured to: standardize the logging curves of different ages and different batches in the study area; establish a logging parameter matrix scatter diagram based on the logging curve data after the standardization processing, screen out sensitive logging parameters, and establish a logging identification volume of the thin-layer shale type hydrocarbon source rock in the study area.

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