Thin stratum thickness prediction method and system based on cause method, and medium

Abstract

The invention discloses a thin stratum thickness prediction method and system based on a cause method and a medium, and relates to the technical field of stratum thickness prediction, and the method comprises the steps: S10, analyzing stratum distribution main control factors; s20, constructing boundary description; s30, performing qualitative recovery on the stratigraphic geologic model in combination with a stratigraphic distribution rule and an earthquake depicted structural boundary to obtain a stratigraphic thickness trend model; s40, establishing a stratum thickness prediction data model in combination with the drilled stratum thickness data and the stratum thickness trend model, and performing iteration on the stratum thickness prediction data model; s50, according to the actual drilling well deviation, the well bottom coordinate of the top of the target stratum is calculated; and S60, key parameters of the well bottom and the coordinate boundary are calculated, and then the key parameters are input into the stratum thickness prediction data model to predict the stratum thickness. The stratum thickness is predicted by introducing a sedimentology principle and a cause method, the logicality is high, and the method is more reasonable; meanwhile, the requirement for seismic data quality is lowered, compatibility is high, and seismic data are prevented from being over-processed.

Description

Technical Field

[0001] This invention relates to the field of formation thickness prediction technology, specifically to a method, system, and medium for predicting the thickness of thin formations based on genetic methods. Background Technology

[0002] Identifying deep and thin strata is challenging due to the limited resolution of seismic data. Previous methods for thin-layer identification have primarily relied on increasing seismic data resolution or using high-resolution inversion methods. However, high-resolution methods struggle to predict strata deeper than 5,000 meters and less than 20 meters thick. Inversion methods, on the other hand, are overly constrained by well data; when well points are sparse, the reliability of the inversion results significantly decreases. Furthermore, traditional methods often fail to accurately characterize the spatial distribution of thin strata in complex geological regions, leading to discrepancies between interpretation results and actual geological conditions, thus impacting subsequent exploration, development, and drilling-while-drilling decisions. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method, system and medium for predicting the thickness of thin strata based on the genetic method.

[0004] The objective of this invention is achieved through the following technical solution: In a first aspect, this application discloses a method for predicting the thickness of thin strata based on genetic methods, comprising the following steps: S10. Analysis of the main controlling factors of stratigraphic distribution, clarifying the structural zone where the well is located, clarifying the structural background, structural evolution process and sedimentary background of the structural zone based on literature research, and restoring the main controlling factors and sedimentary processes of the target strata. S20. Structural boundary characterization: Based on the seismic data of the structural zone at the well location, the data is preprocessed, and seismic attributes are extracted along the top boundary of the target layer according to the characteristics of key structural points to characterize the boundary location of the structural zone. S30. Combining the stratigraphic distribution pattern and the seismic characterization of tectonic boundaries, the stratigraphic geological model is qualitatively restored to obtain the stratigraphic thickness trend model and the relative thickness of the stratigraphy is predicted. S40. Combine the drilled formation thickness data with the formation thickness trend model to establish a formation thickness prediction data model, and iterate the formation thickness prediction data model according to the formation thickness of the drilled wells. S50. Calculate the bottom coordinates of the top of the target layer based on the actual well inclination. S60. Calculate the key parameters of the well bottom and coordinate boundary, and then input them into the formation thickness prediction data model to predict the formation thickness.

[0005] Based on the first aspect, the preprocessing described in step S20 includes fault enhancement and structural filtering. Fault enhancement means highlighting the response characteristics of discontinuous structures through seismic data, and structural filtering means suppressing noise and preserving structural information along the stratigraphic phase axis.

[0006] Based on the first aspect, the key structural point features mentioned in step S20 include stratigraphic angular unconformities, stratigraphic structures with overcutting and undercutting, and areas with chaotic seismic facies.

[0007] Based on the first aspect, in step S30, the sedimentary tectonic background is clarified by surveying regional literature, and the stratigraphic geological model is qualitatively restored. In the stratigraphic geological model, the sedimentary thickness of the strata is negatively correlated with the distance of the main controlling fault. That is, the sedimentary thickness H is calculated by the formula H=Hmax-D*λ, where λ represents the coefficient, D represents the distance between the well site and the main controlling fault, and Hmax represents the maximum sedimentary thickness.

[0008] Based on the first aspect, in step S40, based on actual drilling, the distance between the actual well and the main control fault and the actual sedimentary thickness of the target layer at the known well location are statistically analyzed and input into the stratigraphic geological model for iteration.

[0009] Based on the first aspect, in step S50, the bottom coordinates (X1, Y1) of the top of the target layer are calculated according to the well inclination of the actual drilled well using the formulas X1=X+ΔDx and Y1=Y+Δdy. Here, ΔDx represents the horizontal coordinate closure distance, and its calculation formula is ΔDx=sin(Δβ)* ΔD, X is the horizontal coordinate of the wellhead, Y is the vertical coordinate of the wellhead, Δdy represents the vertical coordinate closure distance, and its calculation formula is Δdy= cos(Δβ)* ΔD, where ΔD represents the bottom coordinate closure distance and Δβ represents the azimuth angle.

[0010] Based on the first aspect, in step S60, the distance of the main control fracture is calculated based on the bottom coordinates of the top of the target layer obtained in step S50, and finally the result is input into the iterated formation thickness prediction data model to predict the formation thickness.

[0011] Secondly, this application discloses a thin-layer formation thickness prediction system based on genetic methods, used in the aforementioned thin-layer formation thickness prediction method based on genetic methods, comprising: The analysis module is used to determine the structural zone where the well is located and to reconstruct the main controlling factors and sedimentary processes of the target strata. The boundary characterization module is used to preprocess seismic data of well location structural zones, extract seismic attributes, and characterize the boundary locations of structural zones. The formation thickness prediction data model building module is used to characterize structural boundaries, qualitatively restore the formation geological model, obtain the formation thickness trend model, and combine the drilled formation thickness data with the formation thickness trend model to establish a formation thickness prediction data model. The coordinate calculation module is used to calculate the bottom coordinates of the top of the target layer based on the actual well inclination. The prediction module is used to predict formation thickness based on key parameters at the bottom of the well and coordinate boundaries.

[0012] Thirdly, this application discloses a computer-readable storage medium storing computer-executable instructions. When the computer-executable instructions are loaded and executed by a processor, they implement the thin-layer formation thickness prediction method based on the genetic method as described above.

[0013] The beneficial effects of this invention are: 1) This application introduces sedimentological principles and uses genetic methods to predict stratigraphic thickness, which is logically sound and more reasonable. At the same time, this application reduces the requirements for seismic data quality, has strong compatibility, and avoids over-processing of seismic data.

[0014] 2) This application is highly timely. Compared with the inversion method, it reduces various steps such as calibration, rock physics analysis, and inversion parameter adjustment. At the same time, it has higher accuracy and a wider range of application scenarios. The relevant results can guide well location tracking while drilling, well location deployment, and well trajectory design.

[0015] 3) Through continuous iteration of the model in this application, it can also be applied to the implementation of subsequent well locations, continuously improving the database and increasing accuracy. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a thin-layer formation thickness prediction method based on genetic analysis, according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the formation thickness from drilled well data, representing an embodiment of the present invention for a method for predicting thin formation thickness based on genetic methods. Figure 3 This is a schematic diagram illustrating the measurement of the vertical distance from the bottom of the well to the edge of the platform according to an embodiment of the present invention; Figure 4 This is a schematic diagram illustrating the correlation between the thickness of the paved surface and the distance to the edge of the platform in an embodiment of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and 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.

[0018] This invention discloses a genetic method, system, and medium for predicting thin-layer formation thickness. The genetic method, based on sedimentological principles, establishes a formula for predicting formation thickness by statistically analyzing the relationship between the distance of well points from the platform margin and formation thickness, thereby enabling quantitative prediction of thin-layer formation thickness. The advantages of this method are that it is independent of seismic data resolution and well density, avoiding errors caused by insufficient seismic data quality, inversion methods, and well density. It is simpler to operate and more reliable. The flowchart of the method is shown below. Figure 1 As shown, it includes the following steps: S10. Analysis of the main controlling factors of stratigraphic distribution, clarifying the structural zone where the well is located, clarifying the structural background, structural evolution process and sedimentary background of the structural zone based on literature research, and restoring the main controlling factors and sedimentary processes of the target strata. S20. Structural boundary characterization: Based on the seismic data of the structural zone at the well location, the data is preprocessed, and seismic attributes are extracted along the top boundary of the target layer according to the characteristics of key structural points to characterize the boundary location of the structural zone. S30. Combining the stratigraphic distribution pattern and the seismic characterization of tectonic boundaries, the stratigraphic geological model is qualitatively restored to obtain the stratigraphic thickness trend model and the relative thickness of the stratigraphy is predicted. S40. Combine the drilled formation thickness data with the formation thickness trend model to establish a formation thickness prediction data model, and iterate the formation thickness prediction data model according to the formation thickness of the drilled wells. S50. Calculate the bottom coordinates of the top of the target layer based on the actual well inclination. S60. Calculate the key parameters of the well bottom and coordinate boundary, and then input them into the formation thickness prediction data model to predict the formation thickness.

[0019] For example, the preprocessing described in step S20 includes fault enhancement and structural filtering, wherein fault enhancement means highlighting the response characteristics of discontinuous structures through seismic data, and structural filtering means suppressing noise and retaining structural information along the stratigraphic phase axis.

[0020] For example, the key structural features mentioned in step S20 include angular unconformities, stratigraphic structures with overlap and overcut, and chaotic seismic facies. An angular unconformity is characterized by a significant difference in attitude and regional sedimentary discontinuity between the overlying new strata and the underlying old strata, often manifested as two adjacent strata intersecting at a large angle. The undercut and overlap is a special type of unconformity, characterized by the underlying strata being cut off (undercut) and the overlying strata overlapping (overlap) on the unconformity surface. Chaotic seismic facies at the interface between two adjacent strata is associated with intense tectonic deformation or abrupt changes in sedimentary environment.

[0021] For example, in step S30, the sedimentary tectonic background is clarified by surveying regional literature, and the stratigraphic geological model is qualitatively reconstructed. In the stratigraphic geological model, the sedimentary thickness of the strata is negatively correlated with the distance to the main controlling fault. That is, the sedimentary thickness H is calculated by the formula H=Hmax-D*λ, where λ represents a coefficient, D represents the distance between the well site and the main controlling fault, and Hmax represents the maximum sedimentary thickness. The sedimentary thickness is greater near the main controlling fault and smaller away from the main controlling fault.

[0022] For example, in step S40, based on actual drilling, the distance between the actual well and the main controlling fault and the actual sedimentary thickness of the target layer at the known well location are statistically analyzed, and these are input into the formation geological model for iteration. A columnar diagram of formation thickness based on drilled well data is shown below. Figure 2 As shown. A single well column, top and bottom of the Maidiping formation, is used to calculate the thickness of the Maidiping formation. This involves identifying the top and bottom interfaces of the Maidiping formation using single-well logging data, and the difference between the two is the apparent thickness of the formation. Since the wellbore trajectory of the deviated well is not vertical, the apparent thickness needs to be corrected to the vertical thickness of the formation using inclination and azimuth data. If there is an angle between the formation surface and the wellbore trajectory, further correction is needed based on the formation dip angle. A vertical trajectory is best, ideally perpendicular to the platform margin, meaning the wellbore trajectory should be kept as vertical as possible (well inclination angle close to 0°) to reduce thickness correction errors caused by the deviated well. The platform margin is a high-energy sedimentary facies zone at the edge of a carbonate platform. Vertical drilling can more accurately reflect the lateral distribution and vertical stacking relationship of the platform margin, which is beneficial for reservoir evaluation. In this example, the study area is selected as the Gaomo area of ​​the Sichuan Basin. The Deng 4 member belongs to the upper part of the Dengying Formation of the Sinian System, which is a key section for deep-ultra-deep natural gas exploration in the Sichuan Basin. The upper part of the Dengying Formation contains the thin-layered Maidiping Formation, which is relatively thin and its thickness distribution is unstable. Predicting the thickness of the Maidiping Formation is crucial for successful well drilling targeting the Dengying Formation. Due to the limited resolution of seismic data, the seismic response characteristics of the Maidiping Formation are unclear, and effective reflection phase axes are difficult to form at the top and bottom of the formation, making it impossible to directly predict the thickness of the Maidiping Formation using seismic data. The Qiongzhusi Formation is an important stratigraphic unit of the Lower Cambrian in the Sichuan Basin and its surrounding areas, and together with the Maidiping Formation, it constitutes an exploration target. The stratigraphic layering table is shown in Table 1, and the well inclination table for the Maidiping Formation section is shown in Table 2.

[0023] Table 1: Stratigraphic Stratification Table Table 2: Well Inclination Table For example, in step S50, based on the well inclination of the actual drilled well, the bottom coordinates (X1, Y1) of the top of the target layer are calculated using the formulas X1=X+ΔDx and Y1=Y+Δdy, where ΔDx represents the horizontal coordinate closure distance, calculated by the formula ΔDx=sin(Δβ)*ΔD, and Δdy represents the vertical coordinate closure distance, calculated by the formula Δdy=cos(Δβ)*ΔD. X is the horizontal coordinate of the wellhead, Y is the vertical coordinate of the wellhead, ΔD represents the bottom closure distance, and Δβ represents the azimuth angle.

[0024] For example, in step S60, based on the bottom-hole coordinates of the top of the target layer obtained in step S50, the distance to the main control fracture is calculated. A schematic diagram of the measurement of the vertical distance from the bottom of the well to the platform edge is shown below. Figure 3 As shown in the figure (W-1 / 2 / 3 represents the bottom of the well, and D1 / 2 / 3 represents the vertical distance from the bottom of the well to the platform edge), the results are finally input into the iterated formation thickness prediction data model to predict the formation thickness. The thickness statistics of the Maidiping Formation are shown in Table 3.

[0025] Table 3: Thickness Statistics of the Wheat Field Pine Group For example, based on actual drilling data, the combined thickness of the Canglangpu Formation (Cang) and the Qiongzhusi Formation (Qiong) is significantly correlated with the location of the drilling structure. The sedimentary thickness of the Cangqiong Formation is controlled by paleogeography; areas with greater thickness are mostly distributed on paleogeographic slopes or rift edges. Two-level slope break zones may control the depositional range of sediments, leading to thickness differences. The sedimentary thickness of the Maidiping Formation is negatively correlated with the distance to the platform boundary zone of the fourth member of the Dengying Formation (Dengsi), as illustrated in the diagram below. Figure 4 As shown, the Maidiping Formation is thicker near the platform margin and thinner further away from the platform margin. This invention uses a genetic prediction method based on sedimentological principles. By statistically analyzing the relationship between the distance of well points from the platform margin and the formation thickness, a formation thickness prediction formula is established, thereby completing the quantitative prediction of thin formation thickness.

[0026] This application also discloses a thin-layer formation thickness prediction system based on genetic methods, used in the aforementioned thin-layer formation thickness prediction method based on genetic methods, comprising: The analysis module is used to determine the structural zone where the well is located and to reconstruct the main controlling factors and sedimentary processes of the target strata. The boundary characterization module is used to preprocess seismic data of well location structural zones, extract seismic attributes, and characterize the boundary locations of structural zones. The formation thickness prediction data model building module is used to characterize structural boundaries, qualitatively restore the formation geological model, obtain the formation thickness trend model, and combine the drilled formation thickness data with the formation thickness trend model to establish a formation thickness prediction data model. The coordinate calculation module is used to calculate the bottom coordinates of the top of the target layer based on the actual well inclination. The prediction module is used to predict formation thickness based on key parameters at the bottom of the well and coordinate boundaries.

[0027] This application also discloses a computer-readable storage medium storing computer-executable instructions, which, when loaded and executed by a processor, implement the thin-layer formation thickness prediction method based on the genetic method as described above.

[0028] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A method for predicting the thickness of thin strata based on genetic methods, characterized in that, Includes the following steps: S10. Analysis of the main controlling factors of stratigraphic distribution, clarifying the structural zone where the well is located, clarifying the structural background, structural evolution process and sedimentary background of the structural zone based on literature research, and restoring the main controlling factors and sedimentary processes of the target strata. S20. Structural boundary characterization: Based on the seismic data of the structural zone at the well location, the data is preprocessed, and seismic attributes are extracted along the top boundary of the target layer according to the characteristics of key structural points to characterize the boundary location of the structural zone. S30. Combining the stratigraphic distribution pattern and the seismic characterization of tectonic boundaries, the stratigraphic geological model is qualitatively restored to obtain the stratigraphic thickness trend model and the relative thickness of the stratigraphy is predicted. S40. Combine the drilled formation thickness data with the formation thickness trend model to establish a formation thickness prediction data model, and iterate the formation thickness prediction data model according to the formation thickness of the drilled wells. S50. Calculate the bottom coordinates of the top of the target layer based on the actual well inclination. S60. Calculate the key parameters of the well bottom and coordinate boundary, and then input them into the formation thickness prediction data model to predict the formation thickness.

2. The method for predicting the thickness of thin strata based on genetic methods according to claim 1, characterized in that: The preprocessing described in step S20 includes fault enhancement and structural filtering. Fault enhancement means highlighting the response characteristics of discontinuous structures through seismic data, and structural filtering means suppressing noise and preserving structural information along the stratigraphic phase axis.

3. The method for predicting the thickness of thin strata based on genetic methods according to claim 1, characterized in that: The key structural features mentioned in step S20 include stratigraphic unconformities, stratigraphic structures with overcutting and undercutting, and areas with chaotic seismic facies.

4. The method for predicting the thickness of thin strata based on genetic methods according to claim 1, characterized in that: In step S30, the sedimentary tectonic setting is clarified by surveying regional literature, and the stratigraphic geological model is qualitatively reconstructed. In the stratigraphic geological model, the sedimentary thickness of the strata is negatively correlated with the distance of the main controlling fault, i.e., by the formula H=H max -D*λ calculates the formation depositional thickness H, where λ represents a coefficient, D represents the distance between the well site and the main controlling fault, and H... max This indicates the maximum thickness of the sedimentary strata.

5. The method for predicting the thickness of thin strata based on genetic methods according to claim 1, characterized in that: In step S40, based on actual drilling, the distance between the actual well and the main control fault and the actual sedimentary thickness of the target layer at the known well location are statistically analyzed and input into the stratigraphic geological model for iteration.

6. The method for predicting the thickness of thin strata based on genetic methods according to claim 1, characterized in that: In step S50, based on the well inclination of the actual drilled well, the bottom coordinates (X1, Y1) of the top of the target layer are calculated using the formulas X1=X+ΔDx and Y1=Y+Δdy. Here, ΔDx represents the horizontal coordinate closure distance, which is calculated using the formula ΔDx=sin(Δβ)*ΔD, and Δdy represents the vertical coordinate closure distance, which is calculated using the formula Δdy=cos(Δβ)*ΔD. X is the horizontal coordinate of the wellhead, Y is the vertical coordinate of the wellhead, ΔD represents the bottom coordinate closure distance, and Δβ represents the azimuth angle.

7. The method for predicting the thickness of thin strata based on genetic methods according to claim 1, characterized in that: In step S60, based on the bottom-hole coordinates of the top of the target layer obtained in step S50, the distance of its main control fracture is calculated, and finally the result is input into the iterated formation thickness prediction data model to predict the formation thickness.

8. A thin-layer formation thickness prediction system based on genetic methods, used in the thin-layer formation thickness prediction method based on genetic methods as described in any one of claims 1-7, characterized in that, include: The analysis module is used to determine the structural zone where the well is located and to reconstruct the main controlling factors and sedimentary processes of the target strata. The boundary characterization module is used to preprocess seismic data of well location structural zones, extract seismic attributes, and characterize the boundary locations of structural zones. The formation thickness prediction data model building module is used to characterize structural boundaries, qualitatively restore the formation geological model, obtain the formation thickness trend model, and combine the drilled formation thickness data with the formation thickness trend model to establish a formation thickness prediction data model. The coordinate calculation module is used to calculate the bottom coordinates of the top of the target layer based on the actual well inclination. The prediction module is used to predict formation thickness based on key parameters at the bottom of the well and coordinate boundaries.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which, when loaded and executed by a processor, implement a thin-layer formation thickness prediction method based on genetic method as described in any one of claims 1-7.

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