Three-dimensional fine geological model vertical grid optimization method

Abstract

The invention relates to the technical field of oil and gas field development, in particular to a three-dimensional fine geologic model vertical grid optimization method, which comprises the following steps of: analyzing the relative position of an interlayer in a small layer according to a logging interpretation data table, and judging the plane distribution range of the interlayer according to an adjacent well; the relative positions of the sand body and the interlayer interface are calculated, the relative position sorting data are screened, and stratigraphic grid division is completed; carrying out vertical grid division on the geological model according to the screened relative position sorting data, and establishing a three-dimensional structure model capable of depicting a minimum sand body and an interlayer; and predicting the sand body and the interlayer by adopting a sequential indication method to obtain a three-dimensional lithofacies model reflecting the sand body, the mudstone and the interlayer. According to the method, the problems of thin interbed reservoir and interlayer information loss and incompleteness and the like in a traditional geologic model can be effectively solved; a thin interbed reservoir and an interlayer can be better displayed in a reservoir model according to a structural model established by a fine geologic model vertical grid optimization method.

Description

Technical Field

[0001] The invention relates to the technical field of oil and gas field development, and is a three-dimensional fine geological model vertical grid optimization method. Background Art

[0002] Three-dimensional fine geological modeling technology can display the development characteristics of reservoirs in three dimensions. As an important technology for reservoir description, it has been widely used in major oil fields. With the continuous improvement of oil field development, there are more and more thin interbedded reservoirs, and the role of interlayers on oil and gas seepage is becoming more and more important. Therefore, the study of thin interbedded reservoirs and interlayers has become the focus of fine geological research, and it is urgent to establish a three-dimensional fine geological model that can reflect thin interbedded reservoirs and interlayers.

[0003] Thin interbedded reservoirs are generally sand-mud interbeds, with many sand layers but thin thickness, in a sheet-like shape; interlayers are generally located between layers, which are stable mudstones with long extension distances and small thickness variations; interlayers are generally developed within layers, with short extension distances and large thickness variations. According to conventional structural modeling methods, the description of thin interbedded reservoirs and interlayers requires the determination of the minimum sand body thickness or the division of the vertical grid according to the thickness of the interlayer, and the use of vertical grid equal-proportion division to make the vertical grid size consistent. In this way, when the thickness of the sand body or interlayer is small, the vertical grid will be over-subdivided, resulting in a huge total number of grids in the structural model, so that the calculation speed of the attribute model is very slow. The number of grids in the geological model is very large, which is not conducive to the subsequent numerical simulation of the reservoir. The geological model must be coarsened to reduce the number of horizontal and vertical grids, which will cause the loss or incomplete information of thin sand bodies and interlayers in the geological model, resulting in the model being unable to correctly characterize the sedimentary rhythm characteristics inside the reservoir, and will cause the reservoir attribute model to be inaccurate, which will have a great impact on the subsequent numerical simulation results and cannot correctly reflect the seepage law of the fluid in the reservoir and the remaining oil distribution characteristics of the reservoir in the middle and late stages of development.

[0004] Therefore, how to finely characterize thin interbedded reservoirs and interlayers is an important research topic in geological modeling technology. At present, there are many modeling methods for thin interbedded reservoirs and interlayers, such as using high-resolution sequence stratigraphy as a guide to establish reservoir interlayer models, or combining seismic data to study reservoir configuration characteristics, and study the connectivity of reservoir configuration elements in dense well network well areas, quantitatively characterize the internal configuration characteristics of reservoirs, or combine reservoir configuration research ideas to establish a river phase configuration model to characterize the internal interlayers of reservoirs. The above methods can characterize thin interbedded reservoirs and interlayers, but these studies are more about the division of sedimentary phases, lithofacies or configuration units, using vertical grid equal-proportion division, and do not involve vertical grid optimization in structural modeling. At the same time, the models established by these studies will cause the loss of information on thin interbedded reservoirs and interlayers after model coarsening.

[0005] A Chinese patent document with publication number CN112052591A discloses a method for fine characterization and embedded modeling of interlayers under reservoir configuration constraints. The invention mainly establishes an interlayer model under reservoir configuration constraints and embeds the model into the original reservoir model. This can achieve complete retention of interlayer information after the reservoir model is coarsened, but it cannot finely characterize the three-dimensional spatial distribution of interlayers. Summary of the invention

[0006] The present invention provides a three-dimensional fine geological model vertical grid optimization method, which overcomes the shortcomings of the above-mentioned prior art and can effectively solve the problem in the prior art that the later stage reservoir numerical simulation cannot accurately describe the distribution characteristics of the remaining oil.

[0007] The technical solution of the present invention is achieved by the following measures: A three-dimensional fine geological model vertical grid optimization method is carried out according to the following steps:

[0008] The first step is to identify the thin interlayer reservoir and interlayer of a single well using core phase and logging phase, and obtain a logging interpretation data table. The interlayer includes interlayer and interlayer, and the interlayer includes mud interlayer, physical interlayer and calcareous interlayer, and the interlayer is mainly mud interlayer;

[0009] The second step is to count the top and bottom depths of the small layer, the top and bottom depths of the sand body, and the top and bottom depths and thickness of the interlayer according to the logging interpretation data table, create a matrix for drilling, sand body, and interlayer data analysis, analyze the relative position of the interlayer in the small layer, and determine the plane distribution range of the interlayer according to the adjacent wells;

[0010] The third step is to create a two-dimensional matrix to store the relative position and sorting data of the top and bottom of the sand body, convert the relative position of the interlayer in the small layer into the relative position of the top and bottom of the interlayer, and calculate the relative position of the interface between the sand body and the interlayer, screen the relative position sorting data, convert the interlayer relative position data into the vertical grid ratio, and complete the stratigraphic grid division;

[0011] The fourth step is to analyze the maximum and minimum values ​​of the formation thickness according to the formation grid division, so that the minimum formation thickness in the grid is smaller than the minimum thickness of the sand body or the minimum thickness of the interlayer (in actual operation, the smaller value of the minimum thickness of the sand body and the minimum thickness of the interlayer can be selected for comparison with the minimum formation thickness), ensure that the vertical grid accuracy meets the needs of depicting the sand body, and perform vertical grid division of the geological model according to the screened relative position sorting data to establish a three-dimensional structural model that can depict the minimum sand body and interlayer;

[0012] The fifth step is to use the sequential indication method to predict the sand bodies and interlayers, obtain the spatial distribution of sand bodies and interlayers, obtain a three-dimensional lithofacies model reflecting sand bodies, mudstones and interlayers, and realize the vertical grid optimization of the three-dimensional fine geological model.

[0013] The following are further optimizations and / or improvements to the above technical solutions:

[0014] In the second step above, matrix W and matrix A are created. Matrix W stores all well names in the work area, and matrix A stores the depth data of the small layer top, sand body top, sand body bottom and small layer bottom of each well.

[0015] W=[W1 W2 W3 … W j ](1 <j≤N)

[0016]

[0017] Among them, the number of columns N of the two-dimensional matrix is ​​the number of wells drilled in the work area, and the number of rows M of the two-dimensional matrix is ​​the number of sand bodies in the well with the most sand bodies drilled in the small layer.

[0018] In the third step above, the specific operation of creating a two-dimensional matrix to store the relative position and sorting data of the top and bottom of the sand body is as follows: create a two-dimensional matrix B to store the formation thickness data from the top and bottom of the sand body to the top of the small layer of each well, matrix C to store the relative position data from the top and bottom of multiple sand bodies to the top of the small layer of each well, matrix D to store the sorted data of the relative position of each well, and filter it, matrix E to store the maximum and minimum values ​​of the formation thickness corresponding to the filtered relative position sorting data, specifically expressed as:

[0019]

[0020]

[0021]

[0022]

[0023] The formula for obtaining the matrix B data is:

[0024] B ij =A ij -A 1j (1≤i≤M, 1≤j≤N)

[0025] The matrix C data is obtained by the formula:

[0026]

[0027] The formula for obtaining the maximum and minimum values ​​of the formation thickness corresponding to the relative position sorting data after optimization in the matrix E is:

[0028] E 2k =B max ×E 1k (1≤k≤M×N)

[0029] E 3k =Bmin ×E 1k (1≤k≤M×N)

[0030] Among them, B max and B min The maximum and minimum values ​​of the formation thickness are screened out from the B matrix respectively.

[0031] The above process of screening the data after the relative position sorting of each well stored in the matrix D is as follows: based on the matrix D, the thinnest formation grid thickness corresponding to each grid ratio thickness from small to large is calculated. If the thinnest formation grid thickness value is less than the minimum grid thickness requirement, the grid ratio interface is eliminated, and the remaining data is the filtered relative position sorting data.

[0032] In the third step, the relative position data of the interlayer is converted into the vertical grid ratio by dividing the formation thickness data from the top and bottom of the sand body to the top of the small layer of each well by the thickness of the small layer of the well.

[0033] The present invention provides a fine geological model vertical grid optimization method, which can effectively solve the problems of loss and incomplete information of thin interbedded reservoirs and interlayers in traditional geological models; at the same time, the structural model established according to the fine geological model vertical grid optimization method can better display the thin interbedded reservoirs and interlayers in the reservoir model. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Attached Figure 1 The present invention is a flowchart of the vertical grid optimization method for a three-dimensional fine geological model.

[0035] Attached Figure 2 This is a single well interlayer interface identification diagram in Example 6 of the present invention.

[0036] Attached Figure 3 Schematic diagram of the multi-well interlayer interface in Example 6 of the present invention.

[0037] Attached Figure 4 This is a schematic diagram of the structural model before vertical grid subdivision in Example 6 of the present invention.

[0038] Attached Figure 5 Schematic diagram of the structural model after vertical grid optimization in Example 6 of the present invention.

[0039] Attached Figure 6 Schematic diagram of single well grid division after vertical grid optimization in Example 6 of the present invention.

[0040] Attached Figure 7 Schematic diagram of the lithofacies model including sandstone, mudstone and interlayer in Example 6 of the present invention. DETAILED DESCRIPTION

[0041] The present invention is not limited by the following embodiments, and specific implementation methods can be determined based on the technical solution of the present invention and actual conditions.

[0042] The present invention will be further described below in conjunction with embodiments:

[0043] Embodiment 1: The vertical grid optimization method of the three-dimensional fine geological model is carried out according to the following steps:

[0044] The first step is to identify the thin interlayer reservoir and interlayer of a single well using core phase and logging phase, and obtain a logging interpretation data table. The interlayer includes interlayer and interlayer, and the interlayer includes mud interlayer, physical interlayer and calcareous interlayer, and the interlayer is mainly mud interlayer;

[0045] The second step is to count the top and bottom depths of the small layer, the top and bottom depths of the sand body, and the top and bottom depths and thickness of the interlayer according to the logging interpretation data table, create a matrix for drilling, sand body, and interlayer data analysis, analyze the relative position of the interlayer in the small layer, and determine the plane distribution range of the interlayer according to the adjacent wells;

[0046] The third step is to create a two-dimensional matrix to store the relative position and sorting data of the top and bottom of the sand body, convert the relative position of the interlayer in the small layer into the relative position of the top and bottom of the interlayer, and calculate the relative position of the interface between the sand body and the interlayer, screen the relative position sorting data, convert the interlayer relative position data into the vertical grid ratio, and complete the stratigraphic grid division;

[0047] The fourth step is to analyze the maximum and minimum values ​​of the formation thickness according to the formation grid division, so that the minimum formation thickness in the grid is less than the minimum thickness of the sand body or the minimum thickness of the interlayer (according to the logging interpretation data table), ensure that the vertical grid accuracy meets the needs of describing the sand body, and perform vertical grid division of the geological model according to the screened relative position sorting data to establish a three-dimensional structural model that can describe the minimum sand body and interlayer;

[0048] The fifth step is to use the sequential indication method to predict the sand bodies and interlayers, obtain the spatial distribution of sand bodies and interlayers, and obtain a three-dimensional lithofacies model reflecting sand bodies, mudstones and interlayers.

[0049] Embodiment 2: As an optimization of the above embodiment, in the second step, a matrix W and a matrix A are created, the matrix W stores all the well names in the work area, and the matrix A stores the sublayer top, sand body top, sand body bottom and sublayer bottom depth data of each well;

[0050] W=[W1 W2 W3 … W j ] (1 <j≤N)

[0051]

[0052] Among them, the number of columns N of the two-dimensional matrix is ​​the number of wells drilled in the work area, and the number of rows M of the two-dimensional matrix is ​​the number of sand bodies in the well with the most sand bodies drilled in the small layer.

[0053] Embodiment 3: As an optimization of the above embodiment, in the third step, the specific operation of creating a two-dimensional matrix to store the relative position and sorting data of the top and bottom of the sand body is as follows: creating a two-dimensional matrix B to store the formation thickness data from the top and bottom of the sand body to the top of the small layer of each well, the matrix C to store the relative position data from the top and bottom of multiple sand bodies to the top of the small layer of each well, the matrix D to store the sorted data of the relative position of each well, and filter it, the matrix E to store the maximum and minimum formation thickness corresponding to the filtered relative position sorting data, specifically expressed as:

[0054]

[0055]

[0056]

[0057]

[0058] The formula for obtaining the matrix B data is:

[0059] B ij =A ij -A 1j (1≤i≤M,1≤j≤N)

[0060] The matrix C data is obtained by the formula:

[0061]

[0062] The formula for obtaining the maximum and minimum values ​​of the formation thickness corresponding to the relative position sorting data after optimization in the matrix E is:

[0063] E 2k =B max ×E 1k (1≤k≤M×N)

[0064] E 3k =B min ×E 1k (1≤k≤M×N)

[0065] Among them, B max and B min The maximum and minimum values ​​of the formation thickness are screened out from the B matrix respectively.

[0066] Example 4: As an optimization of the above example, the process of screening the data after the relative position sorting of each well stored in matrix D is: based on matrix D, calculate the thinnest formation grid thickness corresponding to each grid ratio thickness from small to large, if the thinnest formation grid thickness value is less than the minimum grid thickness requirement, eliminate the grid ratio interface, and the remaining data is the filtered relative position sorting data.

[0067] Example 5: As an optimization of the above example, in the third step, the interlayer relative position data is converted into a vertical grid ratio. The specific operation is: divide the formation thickness data from the top and bottom of multiple sand bodies to the top of the small layer of each well by the thickness of the small layer of the well.

[0068] Example 6: Taking Shanshan Oilfield Block B as an example, the vertical grid optimization method of the three-dimensional fine geological model of the present invention is specifically analyzed and explained. The study area is located in the southern part of the Tuha Basin. The internal structure of the block is simple, which is a dome anticline and has two faults.

[0069] The main target layer in the study area is the Jurassic Sanjianfang Formation. The main sedimentary background is the delta front and the prodelta subfacies. There are three main microfacies: underwater distributary channel, underwater distributary channel and prodelta mud. The main S sandstone group is mainly fine sandstone, followed by silt-fine sandstone. The particles are mostly angular-subcircular, with severe debris weathering, moderate sorting and rounding, point-line contact, and an average porosity of 9.76%. The average effective permeability is 0.35×10 -3 μm 2 The average sand thickness is 24m, the shape of the logging curve is mainly "box-shaped", the sand body thickness is stable, and the continuity is good. There are mud and calcareous interlayers developed inside the target layer, among which mud interlayers are mainly developed and the interlayers are stably developed. The interlayers are mainly mud and calcareous interlayers, and the interlayers are unstable.

[0070] The vertical grid optimization method of the three-dimensional fine geological model is carried out in the following steps:

[0071] The first step is to use core phase and logging phase to identify thin interbedded reservoirs and interlayers in a single well.

[0072] The core phase and logging phase are used to identify the interlayer interface. The logging response characteristics of different lithology interlayers are shown in Table 1. The core phase data are used to identify and interpret the interlayer interface in the vertical direction of a single well, including mud interlayers, calcareous interlayers and physical interlayers. By establishing the corresponding relationship between the core phase and the logging phase, an interpretation model of the interlayer interface in the vertical logging of a single well is established, and the interpretation model is used to identify and interpret the interlayer interface in the uncored well; the logging response characteristics of the mud interlayer are: high natural gamma GR curve value, low resistivity RD curve value, high acoustic time difference AC curve value, increased wellbore curve CAL, and a diameter expansion feature; the logging response characteristics of the calcareous interlayer are: the resistivity RD curve value is higher than the oil layer, and the acoustic time difference AC curve value is low; the logging response characteristics of the physical interlayer are: compared with the reservoir, the natural gamma GR curve value is high, the acoustic time difference AC curve value is low, and the resistivity RD curve value is low, which is equivalent to the dry layer characteristics, see Figure 2 .

[0073] The second step is to count the top and bottom depths of the small layer, the top and bottom depths of the sand body, the top and bottom depths and thickness of the interlayer, analyze the relative position of the interlayer in the small layer, and determine the plane distribution range of the interlayer according to the adjacent wells. Figure 3 .

[0074] The third step is to convert the position of the interlayer in the small layer into the relative position of the top and bottom of the interlayer, calculate the relative position of the sand body and the interlayer interface, and convert the interlayer relative position data into the vertical grid ratio.

[0075] The fourth step is to analyze the maximum and minimum values ​​of the stratum after grid division according to the obtained E matrix, so that the minimum grid thickness can be less than the minimum thickness of the sand body or the minimum thickness of the interlayer, thereby ensuring that the vertical grid accuracy meets the needs of describing the sand body. Taking the S23 sublayer as an example, there are 142 grid ratio data divided according to the sand body and interlayer, and the grid ratio after screening is 46 data, some of which are shown in Table 2.

[0076] The fifth step is to use the sequential indication method to predict the sand bodies and interlayers, and obtain the spatial distribution of sand bodies and interlayers, thereby obtaining a three-dimensional lithofacies model reflecting sand bodies, mudstones and interlayers.

[0077] ① Establishment of structural model

[0078] The vertical depth and thickness data of the target sand bodies and interlayers of the single well in the study area were imported into the geological modeling software and used as basic data. The trend surface model of the top and bottom of each sublayer of the S2 sand layer group was compiled using the Make Surface module in the reservoir geological modeling software Petrel platform. The trend of the trend surface model should follow the structural trend of the reservoir model. According to the structural modeling method, the structural model of the S23 sublayer was established using the Make zones module, see Figure 4 .

[0079] ② Optimization of vertical grid

[0080] Use the Layering module to perform mesh subdivision, select the Fractions subdivision method, and import the filtered mesh proportions obtained in the fourth step into the software. Take the S23 sublayer as an example, Figure 5 To construct the model after optimizing the subdivision of the S23 layer using the Layering module, Figure 6 This is the display of the subdivided optimized vertical grid on a well section. The selected grid ratio is 46 data, specifically:

[0081] 3318,3431,4846,1418,833,4491,1411,910,763,1860,4180,2717,2886,2480,2991,4172,2367,1916,1394,1597,4066,941,1042,1709,827,855,1637,994,675,2317,995,976,1272,1346,2619,2125,2330,1589,1709,2626,873,602,1676,837,6757,6621.

[0082] The optimized grid can describe thin sand bodies or interlayers larger than 0.10 m, and the vertical description grid is reduced for thicker sand bodies. The advantage of this is that compared with evenly dividing the vertical grid to 0.10 m, the number of vertical grids is greatly reduced, and both thin interlayer sand bodies and thick sand bodies can be described.

[0083] ③Establishment of interlayer model

[0084] Based on the identification data of sandstone, mudstone and interlayer of a single well obtained in the first step above, the interlayer is assigned a value of "2", the sandstone is assigned a value of "1", and the mudstone is assigned a value of "0". The sandstone, mudstone and interlayer data of a single well are discretized into a three-dimensional grid model.

[0085] Based on the discrete single-well sandstone, mudstone and interlayer data, the sequential indication method in the Faciesmodeling module of the Petrel platform is used to interpolate and predict the sandstone, mudstone and interlayer, so as to obtain the spatial distribution of sand bodies, mudstones and interlayers, and then obtain the three-dimensional lithofacies model. Figure 7 The interlayer model in this modeling method can completely retain the interlayer information inside the reservoir. Since the vertical grid of the model is optimized, the total number of grids in the model is greatly reduced, and the model can be directly used for later reservoir numerical simulation, providing a more refined geological model data body for later reservoir numerical simulation.

[0086] In summary, the present invention provides a three-dimensional fine geological model vertical grid optimization method, which performs vertical grid optimization on sand bodies, especially thin interbedded reservoirs and interlayers. The established structural model can characterize thin sand bodies in small layers with fewer vertical grids, and can finely characterize the three-dimensional spatial distribution of interlayers. The three-dimensional lithofacies model containing sandstone, mudstone and interlayer can better retain the interlayer information. The established reservoir model can be directly used for later numerical simulation of oil reservoirs, overcoming the deficiency of missing or incomplete interlayer information caused by coarsening of conventional reservoir models, and has important guiding significance for the later numerical simulation of oil reservoirs to study the distribution characteristics of remaining oil.

[0087] The above technical features constitute the embodiments of the present invention, which have strong adaptability and implementation effect. Non-essential technical features can be added or reduced according to actual needs to meet the requirements of different situations.

[0088] Table 1

[0089]

[0090] Table 2

[0091]

Claims

1. A vertical grid optimization method for a three-dimensional fine geological model, characterized in that Follow these steps: The first step is to identify the thin interlayer reservoir and interlayer of a single well using core phase and logging phase, and obtain a logging interpretation data table. The interlayer includes interlayer and interlayer, and the interlayer includes mud interlayer, physical interlayer and calcareous interlayer, and the interlayer is mainly mud interlayer; The second step is to count the top and bottom depths of the small layer, the top and bottom depths of the sand body, and the top and bottom depths and thickness of the interlayer according to the logging interpretation data table, create a matrix for drilling, sand body, and interlayer data analysis, analyze the relative position of the interlayer in the small layer, and determine the plane distribution range of the interlayer according to the adjacent wells; The third step is to create a two-dimensional matrix to store the relative position and sorting data of the top and bottom of the sand body, convert the relative position of the interlayer in the small layer into the relative position of the top and bottom of the interlayer, and calculate the relative position of the interface between the sand body and the interlayer, screen the relative position sorting data, convert the interlayer relative position data into the vertical grid ratio, and complete the stratigraphic grid division; The fourth step is to analyze the maximum and minimum values ​​of the formation thickness according to the formation grid division, so that the minimum formation thickness in the grid is less than the minimum thickness of the sand body or the minimum thickness of the interlayer, and to perform vertical grid division of the geological model according to the screened relative position sorting data, and to establish a three-dimensional structural model that can depict the minimum sand body and interlayer; The fifth step is to use the sequential indication method to predict the sand bodies and interlayers, obtain the spatial distribution of sand bodies and interlayers, and obtain a three-dimensional lithofacies model reflecting sand bodies, mudstones and interlayers.

2. The vertical grid optimization method of a three-dimensional fine geological model according to claim 1, characterized in that In the second step, matrix W and matrix A are created. Matrix W stores all well names in the work area, and matrix A stores the depth data of the small layer top, sand body top, sand body bottom and small layer bottom of each well. <h2 style=";text-align:left;direction:ltr">W=[W1 W2 W3 … W<h2 style=";text-align:left;direction:ltr"> j <h2 style=";text-align:left;direction:ltr"> (1<j≤N) Among them, the number of columns N of the two-dimensional matrix is ​​the number of wells drilled in the work area, and the number of rows M of the two-dimensional matrix is ​​the number of sand bodies in the well with the most sand bodies drilled in the small layer.

3. The vertical grid optimization method for a three-dimensional fine geological model according to claim 1 or 2, characterized in that In the third step, the specific operation of creating a two-dimensional matrix to store the relative position and sorting data of the top and bottom of the sand body is as follows: create a two-dimensional matrix B to store the formation thickness data from the top and bottom of the sand body to the top of the small layer of each well, matrix C to store the relative position data of multiple sand bodies from the top and bottom to the top of the small layer of each well, matrix D to store the sorted data of the relative position of each well, and filter it, matrix E to store the maximum and minimum values ​​of the formation thickness corresponding to the filtered relative position sorting data, specifically expressed as: The formula for obtaining the matrix B data is: B ij =A ij -A 1j (1≤i≤M,1≤j≤N) The matrix C data is obtained by the formula: The formula for obtaining the maximum and minimum values ​​of the formation thickness corresponding to the relative position sorting data after optimization in the matrix E is: E 2k =B max ×E 1k (1≤k≤M×N) B 3k =B min ×E 1k (1≤k≤M×N) Among them, B max and B min The maximum and minimum values ​​of the formation thickness are screened out from the B matrix respectively.

4. The vertical grid optimization method of a three-dimensional fine geological model according to claim 3 is characterized in that The process of screening the data after the relative position sorting of each well stored in matrix D is as follows: based on matrix D, the thinnest formation grid thickness corresponding to each grid ratio thickness from small to large is calculated. If the thinnest formation grid thickness value is less than the minimum grid thickness requirement, the grid ratio interface is eliminated, and the remaining data is the screened relative position sorting data.

5. The vertical grid optimization method for a three-dimensional fine geological model according to claim 1, 2 or 4, characterized in that In the third step, the interlayer relative position data are converted into vertical grid proportions. The specific operation is as follows: the formation thickness data from the top and bottom of multiple sand bodies to the top of the small layer of each well are divided by the thickness of the small layer of the well.

6. The vertical grid optimization method of a three-dimensional fine geological model according to claim 3, characterized in that In the third step, the interlayer relative position data are converted into vertical grid proportions. The specific operation is as follows: the formation thickness data from the top and bottom of multiple sand bodies to the top of the small layer of each well are divided by the thickness of the small layer of the well.

Images