Lithofacies paleogeography single factor analysis method, medium, equipment and product

Abstract

The invention discloses a lithofacies paleogeography single factor analysis method, medium, equipment and product, and relates to the technical field of lithofacies paleogeography analys.The method comprises the steps that lithofacies description, well position coordinates and stratigraphic stratification data in logging interpretation data are obtained; performing binary classification on lithofacies types according to whether the lithofacies description accords with the definition of the target lithofacies; based on the stratigraphic data and the binary lithofacies type, three single factor indexes of each well are calculated, and the three single factor indexes are the total stratum thickness, the target lithofacies cumulative thickness and the rock-to-ground ratio; according to the well position coordinate data and the three single-factor indexes, well points in the whole area are paired in pairs, experimental variation function values under different lag distances are calculated, and experimental variation scatter diagrams of the three single-factor indexes are drawn respectively; kriging gridding interpolation is carried out on the scatter points, contour maps of the three single-factor indexes are drawn, and lithofacies paleogeography analysis is carried out by using the three contour maps. The lithofacies paleogeography single factor analysis is realized based on the logging interpretation data.

Description

Technical Field

[0001] This invention relates to the field of lithofacies paleogeographic analysis technology, and in particular to a single-factor analysis method, medium, equipment and product for lithofacies paleogeographic analysis. Background Technology

[0002] Lithofacies paleogeographic research is a crucial foundation for oil and gas field exploration and development. Its core objective is to reconstruct the sedimentary environment patterns and spatial distribution of sand bodies during geological history. In the field of sedimentology, the classic "single-factor analysis with multiple maps" method is currently the most mainstream and scientific research approach. This method posits that lithofacies paleogeographic maps are not generated out of thin air, but must be based on the quantitative statistical analysis and mapping of various single geological factors that reflect sedimentary characteristics, such as lithology, stratigraphic thickness, sand-to-soil ratio (lith-to-soil ratio), paleontology, and sedimentary structures.

[0003] Single-factor maps are an intermediate result of lithofacies paleogeographic research and are the cornerstone that determines the accuracy of the final results. By compiling contour maps of single-factor indicators (such as sandstone thickness, stratigraphic thickness, and sand-to-soil ratio) at different coordinates, geologists can quantitatively analyze the supply direction of sediment sources, the intensity and variation of hydrodynamic forces, and the size of the containment space. They can then comprehensively consider and determine facies boundaries, and finally compile lithofacies paleogeographic maps.

[0004] Currently, in mature oilfield blocks, the acquisition of single-factor data mainly relies on well logging data. Existing technology is capable of using well logging curves to invert or interpret the vertical lithological profile of a single well. Geologists typically use office software such as Excel to compile single-well data, and then import it into software such as Surfer or Petrel for interpolation and plotting.

[0005] Although well logging interpretation technology is relatively mature, the following problems still exist in the process of converting "well logging interpretation data" into "lithofacial paleogeographic single-factor mapping": Existing well logging interpretation results usually only provide the lithological names of the layers (such as "argillaceous siltstone"), which are discrete text data. Traditional processing methods often only count a single indicator (such as only counting sandstone thickness).

[0006] The quantitative classification standards for lithological data are inconsistent and inefficient: lithological descriptions generated by well logging interpretation are often very complex and non-standardized. Before performing single-factor statistics, these complex descriptions must be manually classified into standard single-factor categories such as "sand" and "mud." Faced with massive amounts of well data, manual classification is prone to errors and is extremely time-consuming.

[0007] The disconnect between data-driven and mapping systems: In existing processes, data cleaning, index calculation, grid interpolation and mapping are usually performed in different software, lacking an integrated method that is directly driven by well logging interpretation data and automatically publishes single-factor maps from text cleaning. Summary of the Invention

[0008] The purpose of this invention is to address the lack of well logging interpretation data-driven single-factor analysis of lithofacies paleogeography in existing technologies, and to propose a single-factor analysis method for lithofacies paleogeography, comprising the following steps: S1. Obtain lithofacies description, well location coordinate data, and formation layering data from well logging interpretation data; S2. Define the target lithofacies and classify the lithofacies type into binary categories based on whether the lithofacies description matches the definition of the target lithofacies. S3. Based on stratigraphic data and binary lithofacies type, calculate three single-factor indicators for each well. The three single-factor indicators are total stratigraphic thickness, cumulative thickness of target lithofacies, and lithofacies ratio. S4. Based on the well location coordinate data and the three single-factor indicators, pair up all well points in the area, calculate the experimental variation function values ​​under different lag distances, and draw scatter plots of the experimental variation of the three single-factor indicators respectively. S5. Perform Kriging grid interpolation on the scattered points, draw contour maps of the three single-factor indicators, and use the three contour maps to conduct lithofacies paleogeographic analysis.

[0009] Furthermore, S2 specifically refers to: Define a set of characteristic keywords for the target lithofacies. If the string describing the lithofacies contains any characteristic keyword of the target lithofacies, define the lithofacies type as 1; otherwise, define it as 0.

[0010] Furthermore, the formula for calculating the total thickness of the strata is:

[0011] Where H represents the total formation thickness of a single well, and These represent the bottom and top depths of the target layer in a single well, respectively.

[0012] Furthermore, the formula for calculating the cumulative thickness of the target lithofacies is as follows:

[0013] Where h represents the cumulative thickness of the target lithofacies in a single well, and N is the total number of single sand layers within the target section of a single well. and These are the bottom and top boundaries of the i-th single sand layer within the target interval of a single well. Indicates lithofacies type, Indicates the target lithofacies. This indicates a non-target lithofacies.

[0014] Furthermore, the formula for calculating the rock-soil ratio is:

[0015] Where R represents the rock-soil ratio, h represents the cumulative thickness of the target lithofacies in a single well, and H represents the total formation thickness in a single well.

[0016] Furthermore, the experimental variogram value is:

[0017] in, This represents the experimental variation function value. Indicates distance as The number of well points and Indicates distance as The single-factor index of the well point pair refers to any one of the total formation thickness, cumulative target lithofacies thickness, and rock-soil ratio. d represents the lag distance, which refers to the center value of the interval after grouping the well distances according to the preset step size.

[0018] The present invention also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described lithofacies paleogeographic single-factor analysis method.

[0019] The present invention also proposes an electronic device, including a processor and a memory, wherein the processor and the memory are interconnected, wherein the memory is used to store a computer program, the computer program including computer-readable instructions, and the processor is configured to invoke the computer-readable instructions to execute the above-described lithofacies paleogeographic single-factor analysis method.

[0020] The present invention also proposes a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the above-described lithofacies paleogeographic single-factor analysis method.

[0021] The beneficial effects of the technical solution provided by this invention are: This invention extracts binary lithofacies types from well logging interpretation data. Based on stratigraphic stratification data and binary lithofacies types, it obtains three single-factor indicators, transforming unstructured natural language descriptions into structured numerical variables that can be used for lithofacies-to-geomorphic ratio cascade calculations, thus completing the transformation from qualitative description to quantitative characterization. Geostatistical analysis is performed on well location coordinate data and single-factor indicators to calculate variogram values. Range, sill values, and nugget values ​​are obtained from the variogram scatter plot. Kriging is used for gridded interpolation, and contour maps of the three single-factor indicators are drawn. This addresses the problem of existing technologies lacking single-factor lithofacies paleogeographic analysis driven by well logging interpretation data, achieving quantitative characterization of the target lithofacies sedimentary characteristics. Attached Figure Description

[0022] Figure 1This is a flowchart of the lithofacies paleogeographic single-factor analysis method according to an embodiment of the present invention; Figure 2 This is the spatial variation function fitting curve of the rock-soil ratio in an embodiment of the present invention; Figure 3 This is a single-factor contour map of formation thickness according to an embodiment of the present invention; Figure 4 This is a single-factor contour map of the target lithofacies thickness according to an embodiment of the present invention; Figure 5 This is a single-factor contour map of the target lithofacies-lithogeogeographic ratio in an embodiment of the present invention; Figure 6 This is a block diagram of an electronic device according to an exemplary embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0024] The flowchart of the lithofacies paleogeographic single-factor analysis method of this invention is as follows: Figure 1 Specifically, it includes the following steps: S1. Obtain lithofacies description, well location coordinates, and formation stratification data from well logging interpretation data.

[0025] For the acquired multi-source raw data, the following data processing methods are adopted: different encoding methods are used for adaptive reading of different data; invalid records with empty coordinate values, negative well depths, or missing lithological description fields are removed; using "well number" as the unique primary key, an inner join algorithm is used to align and fuse the spatial attributes of well location data with the geological attributes of stratification and lithofacies data.

[0026] S2. Define the target lithofacies. Based on whether the lithofacies description conforms to the definition of the target lithofacies, classify the lithofacies type into binary categories. Specifically, define a set of feature keywords for the target lithofacies. If the string of the lithofacies description contains any feature keyword of the target lithofacies, define the lithofacies type as 1; otherwise, define it as 0.

[0027] Lithofacies descriptions in well logging interpretation data are typically in natural language, such as "calc-bearing silty mudstone," and cannot be directly used for numerical calculations. First, based on sedimentary grain size classification standards, the target lithofacies is defined as three grain sets, with the following keywords: The keyword is: sand, representing water kinetic energy deposition; The keyword is: mud, representing still water sedimentation; The key word is: carbonate, representing chemical deposition.

[0028] The keyword mapping algorithm is executed, and each lithofacies description string is traversed. If any of the keywords sand, mud, and carbonate are present in the string, the lithofacies type of the layer is defined as 1; otherwise, it is defined as 0.

[0029] S3. Based on stratigraphic stratification data and binary lithofacies types, calculate three single-factor indicators for each well. The three single-factor indicators are total stratigraphic thickness, cumulative thickness of target lithofacies, and lithofacies ratio.

[0030] For each well, three single-factor indicators with clear geophysical significance were calculated. The top and bottom boundary data were extracted from the stratified data, and the total formation thickness was calculated using the following formula:

[0031] Where H represents the total formation thickness of a single well, and These represent the bottom and top depths of the target formation in a single well, respectively. The total formation thickness characterizes the sedimentary containment space at that well point during geological history.

[0032] Example data: The target formation depth of this well is 5114.92 meters at the bottom and 4971.97 meters at the top. Calculations show... rice.

[0033] Based on the target lithofacies and lithofacies type classification defined in step S2, the thicknesses of all single sand layers identified as target lithofacies (granular rock) within this well section are weighted and accumulated. The formula for calculating the cumulative thickness of the target lithofacies is as follows:

[0034] Where h represents the cumulative thickness of the target lithofacies in a single well, and N is the total number of single sand layers within the target section of a single well. and These are the bottom and top boundaries of the i-th single sand layer within the target interval of a single well. Indicates lithofacies type, Indicates the target lithofacies. This indicates a non-target lithofacies. The cumulative thickness of the target lithofacies characterizes the absolute abundance of the source supply at that well point.

[0035] Example data: Through automatic accumulation by the program, multiple sets of sandstone and siltstone are developed in this well section, with a total thickness of rice.

[0036] The lithofacies ratio is the ratio of the cumulative thickness of the target lithofacies to the total thickness of the strata. The calculation formula is:

[0037] Where R represents the lithofacies ratio, h represents the cumulative thickness of the target lithofacies in a single well, and H represents the total stratigraphic thickness in a single well. The lithofacies ratio eliminates the influence of uneven stratigraphic thickness and directly reflects the strength of sedimentary hydrodynamics and the type of microfacies (the higher the ratio, the more it indicates the channel center or the main body of the floodplain).

[0038] The rock-to-land ratio (R) of the present invention is derived from the lithological thickness (h) divided by the stratum thickness (H), forming a correlation in the data.

[0039] Example data: .

[0040] S4. Based on the well location coordinate data and the three single-factor indicators, pair up all well points in the area, calculate the experimental variation function values ​​under different lag distances, and draw scatter plots of the experimental variation for the three single-factor indicators respectively.

[0041] The experimental variation function value is:

[0042] in, This represents the experimental variation function value. Indicates distance as The number of well points and Indicates the lag distance as The single-factor index for well point pair refers to any one of the following: total formation thickness, cumulative thickness of target lithofacies, and rock-soil ratio.

[0043] The hysteresis distance refers to the center value of the interval after grouping the well distances according to a preset step size. Its specific acquisition process is as follows: First, using the well location coordinates... Calculate the straight-line distance between any two wells in the work area to form a well distance matrix for the entire area; then, based on the exploration range and well density of the work area, pre-determine a step size to divide all the above well distances into several discrete segments (i.e., distance intervals). This represents the center value or scale of these distance intervals. In the calculation... At that time, the system will search and summarize all spacings that fall within the specified range. For well point pairs within a given interval, the square mean of the differences in their single-factor indices is calculated, thereby eliminating the randomness of a single well point pair and revealing the statistical regularity of sedimentary parameters as spatial distance increases.

[0044] To ensure that the interpolation results conform to geological patterns rather than random smoothing, this embodiment calculates the experimental variation function values ​​for the calculated single-factor indicators and plots an experimental variation scatter plot.

[0045] S5. Perform Kriging grid interpolation on the scattered points, draw contour maps of the three single-factor indicators, and use the three contour maps to conduct lithofacies paleogeographic analysis.

[0046] A spherical model was selected to fit the experimental scatter points. Optimization algorithms, such as the least squares method, were used to approximate the calculated discrete experimental variogram scatter points, thereby obtaining a continuous function curve that characterizes the spatial continuity of sedimentary parameters across the entire area. The purpose of this fitting process was to analyze three key spatial structural parameters: range, sill value, and nugget value. These parameters form the mathematical basis for subsequently constructing the Kriging interpolation equations and determine the weighting strategy for the interpolation results. The spatial variogram fitting curve for the lithofacies ratio is referenced... Figure 2 The fitting results show that the range of the rock-geological ratio data is approximately 6500 meters, and the sill value is approximately 0.04. The data exhibits strong spatial autocorrelation within a 6.5-kilometer range and has a small nugget value, indicating that the well logging interpretation data is of high quality and suitable for high-precision interpolation using the Kriging algorithm.

[0047] Using the ordinary kriging algorithm, discrete well point data, i.e., the three single-factor indices, are mapped to... In a high-resolution grid matrix.

[0048] Specifically as follows: 1. Construct the spatial distance matrix against Each node to be estimated in the grid matrix First, calculate the relationship between this node and all known well points within the search radius. The Euclidean distance vector between (the i-th well points, i=1,2,...,n) And the known distance matrix between each pair of well points. .

[0049] 2. Distance-semivariance mapping transformation The spherical model curve obtained through fitting and the three structural parameters (range) determined by it are used. , base value Gold value The geometric distance described above is then transformed into a semivariance value representing spatial correlation. The transformation formula is as follows:

[0050] in, This represents the semivariance value between the i-th and j-th wells. Let be the element in the i-th row and j-th column of the distance matrix D, representing the distance between the i-th well point and the j-th well point.

[0051] Using this formula, the system will use the geometric distance matrix. Transform into semivariance matrix Variable range The sill value determines the maximum spatial range by which a known point influences an unknown point. With gold value The proportion determines the sensitivity of weight allocation.

[0052] 3. Construct and solve the Kriging equations. To satisfy the conditions of minimizing the variance of the estimation error and being unbiased, we construct a system of ordinary Kriging linear equations:

[0053] In the formula, Represents the Lagrange multiplier. The semivariance value between the i-th well point and the grid node to be estimated (both are calculated in step 2).

[0054] 4. Weighted Valuation Generation Solving the above system of equations yields the optimal weight vector for that grid node. Finally, a linear weighted summation is performed. This allows for accurate predictions of the stratigraphic thickness, lithological thickness, or rock-soil ratio at that grid location. .

[0055] Three single-factor planar plots were generated for the target lithofacies of granulite, as follows: Single-factor contour map of formation thickness in this embodiment of the invention Figure 3 The darker the color, the thicker the stratum, visually demonstrating the subsidence extent at the sedimentation center.

[0056] Reference to the single-factor contour map of grain rock thickness in this embodiment of the invention Figure 4 The thickness of the material deposit in the display area is shown.

[0057] Reference to the single-factor contour map of the granular rock-geological ratio in this embodiment of the invention Figure 5 . Figure 5 High-value zones of medium yellowish-brown and dark green (lithological ratio) The distribution is clearly striped, indicating the location of underwater distributary channels or sandbars with strong sedimentary hydrodynamics; while large areas of dark blue and cyan low-value zones ( This map precisely corresponds to fine-grained sedimentary areas such as interchannel bays or floodplains. It visually reveals the spatial distribution of the reservoir's "skeleton."

[0058] In one exemplary embodiment, a computer-readable storage medium is included, which stores a computer program that, when executed by a processor, implements the above-described lithofacies paleogeographic single-factor analysis method.

[0059] Please see Figure 6In one exemplary embodiment, the device further includes an electronic device including at least one processor, at least one memory, and at least one communication bus.

[0060] The memory contains a computer program, which includes computer-readable instructions. The processor calls the computer-readable instructions stored in the memory through the communication bus to execute the aforementioned lithofacies paleogeographic single-factor analysis method.

[0061] In one exemplary embodiment, a computer program product is proposed, including a computer program / instruction that, when executed by a processor, implements the steps of the above-described lithofacies paleogeographic single-factor analysis method.

[0062] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A single-factor analysis method for lithofacies paleogeography, characterized in that, Includes the following steps: S1. Obtain lithofacies description, well location coordinate data, and formation layering data from well logging interpretation data; S2. Define the target lithofacies and classify the lithofacies type into binary categories based on whether the lithofacies description matches the definition of the target lithofacies. S3. Based on stratigraphic data and binary lithofacies type, calculate three single-factor indicators for each well. The three single-factor indicators are total stratigraphic thickness, cumulative thickness of target lithofacies, and lithofacies ratio. S4. Based on the well location coordinate data and the three single-factor indicators, pair up all well points in the area, calculate the experimental variation function values ​​under different lag distances, and draw scatter plots of the experimental variation of the three single-factor indicators respectively. S5. Perform Kriging grid interpolation on the scattered points, draw contour maps of the three single-factor indicators, and use the three contour maps to conduct lithofacies paleogeographic analysis.

2. The single-factor analysis method for lithofacies paleogeography according to claim 1, characterized in that, S2 specifically refers to: Define a set of characteristic keywords for the target lithofacies. If the string describing the lithofacies contains any characteristic keyword of the target lithofacies, define the lithofacies type as 1; otherwise, define it as 0.

3. The single-factor analysis method for lithofacies paleogeography according to claim 1, characterized in that, The formula for calculating the total thickness of a stratum is: Where H represents the total formation thickness of a single well, and These represent the bottom and top depths of the target layer in a single well, respectively.

4. The single-factor analysis method for lithofacies paleogeography according to claim 1, characterized in that, The formula for calculating the cumulative thickness of the target lithofacies is: Where h represents the cumulative thickness of the target lithofacies in a single well, and N is the total number of single sand layers within the target section of a single well. and These are the bottom and top boundaries of the i-th single sand layer within the target interval of a single well. Indicates lithofacies type, Indicates the target lithofacies. This indicates a non-target lithofacies.

5. The single-factor analysis method for lithofacies paleogeography according to claim 1, characterized in that, The formula for calculating the rock-soil ratio is: Where R represents the rock-soil ratio, h represents the cumulative thickness of the target lithofacies in a single well, and H represents the total formation thickness in a single well.

6. The single-factor analysis method for lithofacies paleogeography according to claim 1, characterized in that, The experimental variation function value is: in, This represents the experimental variation function value. Indicates distance as The number of well points and Indicates distance as The single-factor index of the well point pair refers to any one of the total formation thickness, cumulative target lithofacies thickness, and rock-soil ratio. d represents the lag distance, which refers to the center value of the interval after grouping the well distances according to the preset step size.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it implements the method as described in any one of claims 1-6.

8. An electronic device, characterized in that, The device includes a processor and a memory, the processor being interconnected with the memory, wherein the memory is used to store a computer program, the computer program including computer-readable instructions, and the processor is configured to invoke the computer-readable instructions to perform the method as described in any one of claims 1-6.

9. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1-6.

Images