Horizontal well position auxiliary screening method and computer equipment

Abstract

The invention relates to the technical field of oil reservoir engineering, in particular to a horizontal well position auxiliary screening method and computer equipment. The horizontal well position auxiliary screening method comprises the following steps that oil reservoir production potential data are obtained; calculating the reservoir production potential data by using a horizontal well position auxiliary screening algorithm to obtain a horizontal well position preliminary screening result; and unreasonable well positions in the horizontal well position primary screening result are removed, and a well position screening result is obtained. The method comprises the following steps: calculating oil reservoir production potential data by using a horizontal well position auxiliary screening algorithm to obtain a horizontal well position preliminary screening result; and screening the horizontal well position preliminary screening result, and further removing unreasonable well positions in the horizontal well position preliminary screening result, so as to obtain a well position screening result. By means of the method, the horizontal well position can be rapidly screened out from the oil reservoir production potential data, rapid auxiliary screening of the horizontal well position is achieved, the calculation complexity is greatly reduced, and the calculation efficiency is improved.

Description

Technical Field

[0001] The invention relates to the technical field of oil reservoir engineering, and in particular to a horizontal well location auxiliary screening method and computer equipment. Background Art

[0002] Oilfield development, especially offshore oilfield development, is a high-risk, high-investment business model that requires huge capital investment to support complex equipment construction, exploration, drilling, production and transportation. Therefore, compared with the development of onshore oilfields, the "few wells, high production" development strategy is mainly adopted in actual oilfield production. For reservoir engineers, the technical key to realizing the concept of "few wells, high production" lies in the reasonable selection of well locations.

[0003] Well site selection is a challenging optimization problem because it involves many static and dynamic parameters related to oilfield development, such as reservoir physical properties, heterogeneity, and fluid flow characteristic parameters. In recent years, many researchers have conducted extensive research on the well site selection problem. The existing technologies and methods can be divided into two categories: (1) numerical simulation screening methods based on reservoir production potential distribution maps; (2) screening methods based on artificial intelligence agent models. However, the existing methods have some shortcomings.

[0004] Numerical reservoir simulation is a very important and widely used technical tool for oilfield development. Numerical reservoir simulation uses mathematical models and computational techniques to simulate the flow and interaction of fluids within the reservoir, thereby simulating and predicting the production characteristics of the entire reservoir and a single well. The reservoir production potential map (or quality map method) is an intuitive graphical tool used to describe the differences in production potential in various areas of the reservoir. It is usually based on the results of numerical reservoir simulation and integrates some key static geological parameters and dynamic production parameters of the reservoir to visualize complex data and help reservoir engineers make more reasonable decisions. For the numerical simulation screening method based on the reservoir production potential distribution map, there is a problem of high complexity in numerical simulation calculation. When facing the problem of well location screening, a large number of case calculations need to be performed, resulting in low calculation efficiency. In addition, the existing potential calculation formula does not consider enough parameters, resulting in high uncertainty in the generated potential map.

[0005] The second existing method of well site screening is a screening method based on artificial intelligence proxy models. This method uses massive dynamic and static reservoir data to establish a data-driven model to replace the traditional reservoir geological model. This type of model is referred to as a proxy model. This screening method that relies on a neural network algorithm has high computational efficiency. However, based on existing research results, it can be found that this method is mainly applied to theoretical models or mechanism models, and the application effect is not good in large-scale real reservoirs, and the applicability to the actual production of oil fields is poor. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a horizontal well location auxiliary screening method and computer equipment.

[0007] The technical solution adopted by the present invention to solve the technical problem is: a horizontal well location auxiliary screening method, comprising the following steps:

[0008] Obtaining data on reservoir production potential;

[0009] Calculating the oil reservoir production potential data using a horizontal well location auxiliary screening algorithm to obtain a preliminary screening result of the horizontal well location;

[0010] The unreasonable well locations in the preliminary screening results of the horizontal well locations are eliminated to obtain the well location screening results.

[0011] Preferably, the step of eliminating unreasonable well locations in the preliminary screening results of horizontal well locations to obtain the well location screening results includes:

[0012] Eliminate well locations with unreasonable reservoir production potential in the initial screening results of horizontal well locations to obtain a reasonable well location set;

[0013] The production indexes of all well locations in the reasonable well location set are verified using a reservoir numerical simulator to obtain a well location screening result.

[0014] Preferably, the obtaining of reservoir production potential data includes:

[0015] Establish a formula for calculating reservoir production potential based on the relationship between target reservoir type, oil-water distribution relationship, structural characteristics and fluid flow characteristics;

[0016] Obtaining an oil saturation distribution map of a reservoir model and obtaining oil saturation distribution data;

[0017] Calculating the oil saturation distribution data using the oil reservoir production potential calculation formula to obtain an oil reservoir production potential distribution map;

[0018] The oil reservoir production potential distribution map is processed to generate matrix data to obtain the oil reservoir production potential data.

[0019] Preferably, the horizontal well location auxiliary screening algorithm is used to calculate the reservoir production potential data to obtain the initial screening results of the horizontal well location, including:

[0020] Determining a starting point element for the reservoir production potential data;

[0021] Starting from the starting point element, the oil reservoir production potential data is traversed using a matrix continuous element summation algorithm and a line segment length to obtain a well position line segment and a well position direction;

[0022] Obtaining the well position line segment in the oil reservoir production potential data and elements whose distance therefrom is other than the horizontal well spacing to obtain optional elements;

[0023] A new starting point element is determined from the optional elements, and the above steps are repeated until the number of the selected well location segments and the well location directions reaches the preset number of horizontal wells, thereby obtaining the initial screening result of the horizontal well locations.

[0024] Preferably, starting from the starting point element, using a matrix continuous element summation algorithm and a line segment length, traversing the reservoir production potential data to obtain a well location line segment and a well location direction includes:

[0025] In the matrix data of the reservoir production potential data, the starting point element is used as the starting point, and an element line segment in the matrix data direction of the corresponding element with the line segment length as the unit is obtained; the element line segment is composed of continuous elements;

[0026] Calculate the sum of the elements on each of the element segments;

[0027] The line segment with the largest sum is set as the well location line segment; the direction of the well location line segment is the well location direction.

[0028] Preferably, the reservoir production potential data includes two-dimensional matrix data; the matrix continuous element summation algorithm includes: a two-dimensional matrix continuous element summation algorithm;

[0029] For the two-dimensional matrix data, when the two-dimensional matrix continuous element summation algorithm obtains the element line segments, the matrix data has a maximum of eight directions.

[0030] Preferably, the reservoir production potential data includes three-dimensional matrix data; the matrix continuous element summation algorithm includes: a three-dimensional matrix continuous element summation algorithm;

[0031] For the three-dimensional matrix data, when the three-dimensional matrix continuous element summation algorithm obtains the element line segments, the matrix data has a maximum of twenty-six directions.

[0032] Preferably, the calculation formula for the oil reservoir production potential is as follows:

[0033] J m,n,p (t) = [S o,m,n,p (t)-S or ]·[P o,m,n,p (t)-P min ]

[0034] 1 / μ m,n,p ·K m,n,p ·φ m,n,p ·lnr m,n,p ·(h m,n,p / H)

[0035] (m=1,2,…,n x ; n=1,2,…,n y ; p=1,2,…,n z )

[0036] Among them, J m,n,p (t) is the reservoir production potential of the element (m, n, p) at time t; S o,m,n,p (t) is the oil saturation of the element (m, n, p) at time t; P o,m,n,p (t) is the pressure at the element (m, n, p) at time t; S or is the remaining oil saturation of the reservoir; min is the minimum bottom hole pressure; K m,n,p is the permeability at the element (m, n, p); r m,n,p is the distance from the element (m, n, p) to the nearest boundary; μ m,n,p is the viscosity at the element (m, n, p); φ m,n,p is the porosity at the element (m, n, p); h m,n,p is the formation thickness, which is the distance from the element (m, n, p) to the oil-water interface, representing the impact of structural elements on production potential; H is the distance from the highest point of the reservoir to the oil-water interface.

[0037] Preferably, the processing of the oil reservoir production potential distribution map to generate matrix data to obtain the oil reservoir production potential data includes:

[0038] Analyzing the oil reservoir production potential distribution map to obtain an oil reservoir model text;

[0039] Reading the reservoir model text line by line, expanding each line of data, and combining the expanded data into a first two-dimensional matrix data;

[0040] Converting the first two-dimensional matrix data into first one-dimensional matrix data;

[0041] The second two-dimensional matrix data is constructed according to the first one-dimensional matrix data so that the number of elements in each row of the second two-dimensional matrix data is consistent with the number of grids in each row of the reservoir model, thereby obtaining the reservoir production potential data.

[0042] A computer device comprises a processor and a memory storing a computer program, wherein the processor implements the steps of the above-mentioned horizontal well location auxiliary screening method when executing the computer program.

[0043] The implementation of the present invention has the following beneficial effects:

[0044] The present invention uses a horizontal well location auxiliary screening algorithm to calculate the reservoir production potential data to obtain a preliminary screening result of the horizontal well location; then the preliminary screening result of the horizontal well location is screened to further eliminate unreasonable well locations, thereby obtaining a well location screening result. The present invention can quickly screen out horizontal well locations from the reservoir production potential data, realize rapid auxiliary screening of horizontal well locations, greatly reduce the calculation complexity, and improve the calculation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0046] Figure 1 A flowchart of a horizontal well location auxiliary screening method in one embodiment;

[0047] Figure 2 A schematic diagram of a two-dimensional plane matrix in one embodiment;

[0048] Figure 3 Schematic diagram of the distribution of ignored grids in a two-dimensional matrix when the well spacing is 1 in one embodiment;

[0049] Figure 4 A schematic diagram of a three-dimensional plane matrix in an embodiment;

[0050] Figure 5 is an oil saturation distribution diagram of the third layer of the F reservoir model in one embodiment;

[0051] Figure 6 The oil reservoir production potential distribution map of the third layer of the F oil reservoir production potential model calculated by the oil reservoir production potential calculation formula in one embodiment;

[0052] Figure 7 A schematic diagram of a random two-dimensional matrix model and screening results in one embodiment;

[0053] Figure 8 A schematic diagram of a random three-dimensional matrix model and screening results in one embodiment;

[0054] Fig. 9 A schematic diagram of screening results of an algorithm considering the number of wells and well spacing in a random two-dimensional matrix model in one embodiment;

[0055] Fig.10 A schematic diagram of horizontal well location screening results in one embodiment;

[0056] Fig.11 A schematic diagram of an unreasonable well location in an embodiment. DETAILED DESCRIPTION

[0057] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, specific embodiments of the present invention are now described in detail with reference to the accompanying drawings.

[0058] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0059] The embodiment of the present invention provides a method for assisting in selecting the horizontal well location. Figure 1 As shown, the horizontal well location auxiliary screening method includes the following steps:

[0060] S1: Obtain reservoir production potential data.

[0061] It should be noted that reservoir production potential data usually include geological parameter data, well logging data, production data, seismic data, geological model data, reservoir potential assessment data, reservoir fluid distribution data, etc.

[0062] Among them, geological parameter data: including geological, geophysical and engineering parameters of oil reservoirs, such as porosity, permeability, reservoir burial depth and other key geological factors. Well logging data: various parameters measured downhole, such as resistivity, acoustic wave time difference, density, neutron porosity, etc. Production data: including historical production data such as oil well production, water well production, gas well production, etc. Seismic data: including seismic interpretation data, seismic phase and seismic attribute data. Geological model data: including geological structure model, reservoir attribute model, etc. Reservoir numerical simulation data: including historical fitting data in reservoir numerical simulation technology, such as static pressure, water content, gas-oil ratio and other parameters. Reservoir static and dynamic parameters: static parameters such as geological reserves and reservoir characteristics, dynamic parameters such as production change trend over time and before and after correlation. Reservoir fluid distribution data: including the distribution of oil, gas and water. Reservoir potential assessment data: including predicted reserves, reserve upgrade potential evaluation, remaining oil potential, etc.

[0063] In some executable embodiments, the reservoir production potential data includes a reservoir production potential distribution map.

[0064] Specifically, the oil reservoir production potential distribution map is a distribution map of oil reservoir production potential at various locations in a certain oil reservoir.

[0065] S2: Calculate the reservoir production potential data using the horizontal well location auxiliary screening algorithm to obtain the initial screening results of the horizontal well location.

[0066] Specifically, the initial screening result of the horizontal well location is a set of well location segments and their corresponding well location directions obtained by the horizontal well location auxiliary screening algorithm, which may specifically include the position, size and direction of the horizontal well location. The horizontal well location auxiliary screening algorithm calculates the reservoir potential data based on the matrix element fast summation method. The matrix element fast summation method includes basic matrix summation function, specified dimension summation, multidimensional array summation, parallel summation, etc. Horizontal wells include horizontal wells to be mined.

[0067] The selection is made according to the specific application scenario and the characteristics of the matrix to achieve the purpose of automatically and quickly calculating the reservoir potential.

[0068] S3: Eliminate unreasonable well locations in the initial screening results of horizontal well locations to obtain well location screening results.

[0069] It should be noted that if Fig.11 As shown, unreasonable well locations include well locations close to water bodies and reservoir boundaries. For example, the W-4 well location in the figure is an unreasonable well location, W-1, W-2, W-3, W-5, and W-6 are reasonable well locations, and the set of W-1, W-2, W-3, W-5, and W-6 is the well location screening result.

[0070] Specifically, by obtaining the assistance of well location screening results from the reservoir production potential distribution map, it is possible to achieve "few wells and high production" of the reservoir, reduce the manpower and material resources required for reservoir exploitation, reduce capital investment, and improve the return ratio of reservoir exploitation.

[0071] The present invention adopts the process of obtaining reservoir production potential data, calculating the auxiliary screening algorithm for horizontal well locations, and eliminating unreasonable well locations, which can improve the accuracy and efficiency of well location optimization, optimize well location deployment, reduce drilling risks and costs, and take into account ground conditions and reservoir control, thereby improving the economic benefits and cumulative oil production of the reservoir. Furthermore, the present invention calculates the reservoir production potential data through a horizontal well location auxiliary screening algorithm based on a fast summation method of matrix elements, thereby reducing the calculation complexity and improving the calculation efficiency. And because the calculation complexity of the horizontal well location auxiliary screening algorithm is low, it is easier to expand and can process larger reservoir data, so it has a wider applicability and stronger scalability.

[0072] In an executable embodiment, unreasonable well locations are eliminated from the initial screening results of horizontal well locations to obtain well location screening results, including:

[0073] The well locations with unreasonable reservoir production potential in the initial screening results of horizontal well locations are eliminated to obtain a reasonable well location set.

[0074] In some embodiments, if there are obviously unreasonable well locations in the initial screening results of horizontal well locations, they are removed to obtain a reasonable well location set, and the reasonable well location set is compared and verified.

[0075] The production indicators of all well locations in the reasonable well location set are verified using a reservoir numerical simulator to obtain the well location screening results.

[0076] Specifically, production indicators include cumulative oil production, water content, degree of recovery and other indicators.

[0077] The reservoir numerical simulator simulates the dynamic behavior of oil, gas and water in the reservoir by establishing a mathematical model of underground fluid flow and performing numerical solutions, thereby predicting the production performance and development effect of the reservoir. The reservoir numerical simulator verifies the production indicators of all wells in the reasonable well location set, and can select the optimal well location screening result from the reasonable well location set.

[0078] Understandably, a reasonable well location set is a set of well locations with reasonable well locations and high production, but high production potential does not represent excellent indicators such as recovery degree and water content. By verifying the production indicators of all well locations through the reservoir numerical simulator, the screening criteria can be further refined, such as setting the minimum threshold for cumulative oil production, the upper limit of water content, and the target range for recovery degree. Based on these refined standards, the reservoir numerical simulator will evaluate the production indicators of each well location to select the optimal well location that meets all conditions. It not only improves the accuracy of screening, but also helps to optimize the overall reservoir development plan and ensure the maximization of the economic benefits of oil field development.

[0079] In one executable embodiment, obtaining reservoir production potential data includes:

[0080] A formula for calculating reservoir production potential is established based on the relationship between target reservoir type, oil-water distribution relationship, structural characteristics and fluid flow characteristics.

[0081] Understandably, the reservoir production potential calculation formula established based on the relationship between the target reservoir type, oil-water distribution relationship, structural characteristics and fluid flow characteristics can more accurately evaluate the reservoir production potential. Horizontal wells are mainly used for the development of carbonate fracture reservoirs, reservoirs with gas caps or bottom water, thin layer reservoirs, low permeability reservoirs, heavy oil reservoirs and high water content artificial water injection reservoirs, etc., to increase the contact area between the wellbore and the formation and pass through the zone with good permeability to improve production capacity.

[0082] Obtain the oil saturation distribution map of the reservoir model and obtain the oil saturation distribution data.

[0083] It can be understood that the oil saturation distribution map is a geological map that shows the spatial distribution of oil saturation in the oil layer, which can intuitively reflect the distribution status and heterogeneity of oil, gas and water in the reservoir. The oil saturation distribution data may include the oil saturation values ​​of different areas in the oil layer, the quantitative relationship between porosity and oil saturation, the relationship between permeability and oil saturation, saturation correction data, saturation sum error, reservoir dynamic data, etc. The saturation correction data includes correcting the oil and water saturation by linear regression method, and extracting the difference in the corrected oil and water residual components. The reservoir dynamic data includes the change of oil saturation over time, which can extract the dynamic characteristics of the reservoir, such as the scope of water injection and the shape of water tongue.

[0084] In an executable embodiment, the oil saturation distribution map of the third layer of a certain F reservoir model is as follows: Figure 5 As shown. The F reservoir model effectively improves the reliability of rock physics type three-dimensional modeling through the modeling method of sedimentary phase coupling rock physics type, thereby improving the reliability of permeability and saturation models, and further improving the accuracy of oil saturation distribution data. Thereby improving the application effect of the present invention in large-scale real reservoirs and improving the actual production applicability in oil fields.

[0085] The reservoir production potential calculation formula is used to calculate the oil saturation distribution data to obtain the reservoir production potential distribution map.

[0086] First, according to the type and characteristics of the reservoir, a suitable formula for calculating the reservoir production potential is selected, which comprehensively considers key parameters such as porosity, permeability, and oil saturation. Then, the spatial data (oil saturation distribution data) in the oil saturation distribution map is input into the formula, and the production potential value of each grid block (element) is calculated through numerical simulation technology. Finally, the generated reservoir production potential distribution map intuitively shows the development value of different areas in the reservoir.

[0087] The reservoir production potential distribution map is processed to generate matrix data and obtain reservoir production potential data.

[0088] The preset algorithm is used to convert the reservoir production potential distribution map file into a file containing matrix data to obtain the reservoir production potential data. This can ensure data quality and improve analysis accuracy, thereby enhancing the use and analysis of reservoir model data.

[0089] In an executable embodiment, the horizontal well location auxiliary screening algorithm is used to calculate the reservoir production potential data to obtain the initial screening results of the horizontal well location, including:

[0090] The starting point element for determining reservoir production potential data.

[0091] Specifically, the reservoir production potential data consists of elements and dimensions, and the starting element is a preset given point in the reservoir production potential data.

[0092] Starting from the starting element, use the matrix consecutive element summation algorithm and the line segment length to traverse the reservoir production potential data to obtain the well position line segment and the well position direction.

[0093] Specifically, in some scenarios, the line segment length is the number of elements. The well position line segment is composed of consecutive elements in the reservoir production potential data, including information such as the position of the element in the reservoir production potential data and the value of the element. The length of the well position line segment is the length of the horizontal well.

[0094] In some scenarios, the well position line segment presents the position, size, and direction of the horizontal well in the form of a line segment on the reservoir production potential distribution.

[0095] Obtain the well position line segment in the reservoir production potential data and the elements outside the horizontal well spacing distance from it to get the optional elements.

[0096] As Figure 3 shown, the red line represents the horizontal well, and the × represents the elements excluded at a distance of 1 around the horizontal well. Excluding the elements of the well position line segment can ensure that when selecting subsequent line segments, the elements of the already screened line segments will not be selected repeatedly. Excluding the elements at a distance of the horizontal well spacing from the well position line segment can ensure that the well position line segments all meet the requirements of the horizontal well spacing.

[0097] Determine a new starting element from the optional elements, and repeat the above steps until the number of selected well position line segments and well position directions reaches the preset number of horizontal wells to obtain the preliminary screening result of the horizontal well well position.

[0098] It can be understood that the specific principle of the matrix consecutive element summation algorithm is as follows:

[0099] For a given matrix A, starting point (m, n), direction vector (dx, dy), line segment length l, and horizontal well spacing distance distance.

[0100] All_Ignored = Segment_Ignored ∪ {(sx, sy)∣0 ≤ k < l}

[0101] Among them, All_Ignored represents all ignored elements; Segment_Ignored represents the elements of the previous line segment; {(sx, sy)∣0 ≤ k < l} represents the set of ignored elements around the previous line segment.

[0102] sx = m + k·dx + surround_dx

[0103] sy = n + k·dy + surround_dy

[0104] Where surround_dx and surround_dy are integers from -distance to distance, representing the offsets in the x and y directions when searching for each position (m+k·dx,n+k·dy).

[0105] "distance" represents the minimum spacing between horizontal wells to ensure that the horizontal wells do not cross or overlap each other. The spacing between them will be greater than the "distance" value.

[0106] Furthermore, if the number of selected well location segments and well location directions does not reach the preset number of horizontal wells, but no other elements can be set as new starting points, the currently selected well location segments and well location directions are used as the initial screening results of horizontal well locations.

[0107] In an executable embodiment, starting from the starting element, the continuous element summation algorithm of the matrix and the line segment length are used to traverse the reservoir production potential data to obtain the well position line segment and the well position direction, including:

[0108] In the matrix data of the reservoir production potential data, the starting element is the starting point, and the element line segment in the matrix data direction of the corresponding element is obtained with the line segment length as the unit. The element line segment is composed of continuous elements.

[0109] It is understandable that when the line segment length is 3, the element line segment is composed of three consecutive elements. The 3 here is for illustration and is not intended to be limiting. It is set according to the needs of the reservoir.

[0110] Compute the sum of the elements on each element segment.

[0111] The line segment with the largest sum is set as the well location line segment; the direction of the well location line segment is the well location direction.

[0112] In an executable embodiment, the reservoir production potential data includes two-dimensional matrix data. The matrix continuous element summation algorithm includes: a two-dimensional matrix continuous element summation algorithm.

[0113] For two-dimensional matrix data, when the two-dimensional matrix continuous element summation algorithm obtains element segments, the matrix data has a maximum of eight directions.

[0114] It should be noted that the reservoir production potential data can be a two-dimensional plane matrix. The two-dimensional matrix continuous element summation algorithm specifically includes: starting from a given point (m, n) (starting element) in the two-dimensional plane matrix, searching for continuous point segments with a length of l (segment length) in all possible directions, and calculating the sum of these continuous points to find the segment with the largest value (well position segment). The specific principle of the two-dimensional matrix continuous element summation algorithm is as follows:

[0115] like Figure 2 As shown, assume that the two-dimensional plane matrix A consists of elements Amn and dimensions (M×N), where M is the number of rows and N is the number of columns. For a line segment S starting from a given point (m, n) and a given direction vector (dx, dy), it can be expressed as:

[0116] S(k)=A m+k·dx,n+k·dy ,for 0≤k <l

[0117] Among them, S(k) represents the kth element of the line segment S, and l represents the length of the line segment.

[0118] For a two-dimensional plane matrix, there are eight directions starting from a given point: [(1,0), (-1,0), (0,1), (0,-1), (1,1), (1,-1), (-1,1), (-1,-1)].

[0119] The formula for summing a line segment of length l is:

[0120]

[0121] The reservoir production potential data and the coordinates of the starting element (m, n) are set, the line segment length is set to l, and then the algorithm can automatically iterate the loop to calculate the sum of the line segments in all directions, and output the maximum value and direction of the line segment (well location direction).

[0122] In an executable embodiment, the reservoir production potential data is three-dimensional matrix data. The matrix continuous element summation algorithm includes: a three-dimensional matrix continuous element summation algorithm.

[0123] For three-dimensional matrix data, when the three-dimensional matrix continuous element summation algorithm obtains element segments, the matrix data directions are at most twenty-six directions.

[0124] It is understandable that the reservoir production potential data is a three-dimensional plane matrix. The reservoir is a three-dimensional spatial field, and the well location screening results based on the two-dimensional plane distribution field are not accurate. In order to better adapt to the actual situation of the reservoir, we extend the three-dimensional matrix continuous element summation algorithm to the three-dimensional space matrix and establish a three-dimensional matrix continuous element summation algorithm to make the well location screening results more accurate. The specific principle of the three-dimensional matrix continuous element summation algorithm is as follows:

[0125] like Figure 4 As shown, assume that the elements of the three-dimensional plane matrix A are Amnp, and the dimension is (M×N×P), where M is the number of rows, N is the number of columns, and P is the depth. From a given point (m,n,p) and a given direction vector (dx,dy,dz), define the line segment S as:

[0126] S(r)=A m+r·dx,n+r·dy,p+r·dz,for 0≤r <l

[0127] Among them, S(r) is the rth element of the line segment S, and l is the length of the line segment. In a three-dimensional matrix, there are 26 possible directions starting from a given starting point (see Table 1 for details).

[0128] Table 1

[0129]

[0130] The sum of line segments (element sets) of length l is:

[0131]

[0132] The horizontal well location auxiliary screening algorithm considering the number of horizontal wells and well spacing in the three-dimensional space matrix can find the optimal horizontal well location preliminary screening result by inputting the reservoir production potential data, line segment length, line segment number (preset number of horizontal wells) and distance (horizontal well spacing) parameters of the three-dimensional space matrix type. Among them, the line segment length represents the length of the horizontal well.

[0133] In one executable embodiment, the formula for calculating reservoir production potential is as follows:

[0134] J m,n,p (t) = [S o,m,n,p (t)-S or ]·[P o,m,n,p (t)-P min ]

[0135] 1 / μ m,n,p ·K m,n,p ·φ m,n,p ·lnr m,n,p ·(h m,n,p / H)

[0136] (m=1,2,…,n x ; n=1,2,…,n y ; p=1,2,…,n z )

[0137] Among them, J m,n,p (t) is the reservoir production potential at element (m, n, p) at time t; S o,m,n,p (t) is the oil saturation at the element (m, n, p) at time t; P o,m,n,p (t) is the pressure at the element (m, n, p) at time t; S or is the remaining oil saturation of the reservoir; min is the minimum bottom hole pressure; K m,n,p is the permeability at the element (m, n, p); r m,n,pis the distance from the element (m, n, p) to the nearest boundary; μ m,n,p is the viscosity at the element (m, n, p); φ m,n,p is the porosity at the element (m, n, p); h m,n,p is the formation thickness, which is the distance from the element (m, n, p) to the oil-water interface, also known as the structural position, representing the impact of the reservoir structural position on production potential; H is the distance from the highest point of the reservoir to the oil-water interface.

[0138] The oil reservoir production potential calculation formula constructed in this embodiment takes into account the relationship between the target oil reservoir type, oil-water distribution relationship, structural characteristics and fluid flow characteristics, and corrects and improves the existing production potential calculation formula. It can adapt to more oil reservoir types, expand the scope of application of the present invention, and improve accuracy.

[0139] In an executable embodiment, the reservoir production potential distribution map is processed to generate matrix data to obtain reservoir production potential data, including:

[0140] Analyze the reservoir production potential distribution map and obtain the reservoir model text.

[0141] It can be understood that the file generated by exporting the reservoir production potential distribution map from the reservoir model is the reservoir model text file. The reservoir model text usually contains a large amount of complex data. There are often some special formats of data representation in the export file, such as the use of asterisks (*) and zero values ​​(0), which represent repeated values ​​or missing values ​​in the data. Asterisks are usually used to indicate that a value appears repeatedly within a certain range, while zero values ​​may indicate missing or inapplicable data.

[0142] Read the reservoir model text line by line, expand each line of data, and combine the expanded data into the first two-dimensional matrix data;

[0143] Specifically, the reservoir model text is read line by line using a parsing function. The parsing function splits the line content into separate elements and checks for each element whether it contains a special mark - an asterisk (*). If the element contains an asterisk, it will be interpreted as a repeated value indicator, where the number before the asterisk indicates the number of repetitions, and the number after the asterisk (*) is the specific value of the repetition. The function will expand this value according to the specified number of repetitions and add it to a list. If the value is zero, it is replaced with Nan (representing a missing value) to avoid numerical calculation errors. If the element does not contain an asterisk, it will be directly converted to a floating point number and added to the list, and the zero value will also be replaced with Nan. The processed reservoir model text is combined into a two-dimensional list, representing the expanded reservoir model data (the first two-dimensional matrix data).

[0144] Convert the first two-dimensional matrix data into the first one-dimensional matrix data;

[0145] The second two-dimensional matrix data is constructed according to the first one-dimensional matrix data so that the number of elements in each row of the second two-dimensional matrix data is consistent with the number of grids in each row of the reservoir model, thereby obtaining the reservoir production potential data.

[0146] Furthermore, the second two-dimensional matrix data is written into a CSV file to facilitate subsequent analysis and processing. Each row of data is formatted as a row of the CSV file to ensure the order and readability of the data.

[0147] In some executable embodiments, a matrix continuity element summation algorithm is applied to random two-dimensional and three-dimensional matrix models.

[0148] In the first step, a two-dimensional matrix of size (11×11) is randomly generated, and each element is assigned a random integer between 0 and 100, such as Figure 7 and Figure 8 shown.

[0149] In the second step, the length of the line segment is set to 4, and the matrix continuous element summation algorithm of the present invention is used to loop through each element in the two-dimensional matrix. The algorithm calculates the sum of the elements traversed by the line segment, and then determines the maximum value, starting point, end point and direction of the line segment.

[0150] In the third step, a three-dimensional matrix of size (11×11×11) is randomly generated, and each element is assigned a random integer between 0 and 100. Similarly, the line segment length is set to 4, and the matrix continuous element summation algorithm of the present invention is used to loop through each element in the two-dimensional matrix. The result is as follows Figure 7 and Figure 8 As shown in the figure, the red line represents the result, and the result meets the requirements for the well position segment. In the two-dimensional matrix model, the optimal segment value is 354, the starting element is (3, 2), the end element is (6, 2), and the direction is (1, 0). In the three-dimensional matrix model, the optimal segment value is 387, the starting element is (7, 4, 0), the end element is (10, 4, 0), and the direction is (1, 0, 0).

[0151] In some executable embodiments, the parameters of the preset number of horizontal wells and horizontal well spacing are added on the basis of the matrix continuity element summation algorithm, and applied to the random two-dimensional matrix model.

[0152] In the first step, a two-dimensional matrix of size (20×20) was randomly generated, and each element was assigned a random integer between 0 and 100.

[0153] In the second step, the line segment length is set to 4, the preset number of horizontal wells is set to 6, and the horizontal well spacing is set to 2. The matrix continuous element summation algorithm considering the number of wells and the well spacing in the present invention is used to loop through each element in the two-dimensional matrix. The result is as follows Fig. 9 As shown in Table 2, the algorithm accurately identifies the best line segments while ensuring that the distances between these line segments are greater than or equal to the set threshold and that there is no crossing or overlapping.

[0154] Table 2

[0155] well name value Starting point coordinates End point coordinates direction W-1 347 (13,3) (16,6) (1,1) W-2 339 (4,2) (4,5) (0,1) W-3 339 (7,4) (7,7) (0,1) W-4 333 (7,10) (7,13) (0,1) W-5 330 (13,10) (13,13) (0,1) W-6 328 (1,3) (1,6) (0,1)

[0156] In some executable embodiments, the present invention is applied to an actual reservoir model of an offshore oil field F in the eastern region of the South China Sea.

[0157] The first step is to calculate the potential distribution of F reservoir using the reservoir potential calculation formula. The results are as follows: Figure 5 and Figure 6 shown. Figure 5 This is the oil saturation distribution map of the third layer of the F reservoir model. Figure 6 It is the reservoir production potential distribution map of the third layer of the F reservoir production potential model calculated by the reservoir potential calculation formula.

[0158] The second step is to export the calculated reservoir potential model to obtain the reservoir model text, and parse the reservoir production potential distribution map to obtain the reservoir model text. The reservoir model text is converted into a special format of data representation, 0 values ​​and missing values ​​are processed, and finally converted into a csv format with the same two-dimensional matrix data as the number of grids per row of the reservoir model to facilitate subsequent calculations.

[0159] The third step is to use the three-dimensional matrix continuous element summation algorithm in the present invention to quickly find the optimal horizontal well location based on the F reservoir potential model exported in csv format. The parameters of the screening algorithm are set as follows: the number of horizontal wells is preset to 5, the line segment length (horizontal segment length) = 15 grids (750 meters), and the horizontal well spacing = 4 grids (200 meters). The preliminary screening results of horizontal well locations are as follows: Fig.10 As shown in the figure, W-1, W-2, W-3, W-4, and W-5 are well location line segments in the preliminary screening results of horizontal well locations, representing horizontal well locations.

[0160] In the fourth step, if there are obviously unreasonable well locations in the preliminary well location screening results, they are manually removed and the remaining well location results are compared and verified. The remaining results are verified using a reservoir numerical simulator, and the comparison indicators include cumulative oil production, water content, recovery degree, etc., and finally the optimal well location screening results are obtained.

[0161] The present invention also provides a computer device, including a processor and a memory storing a computer program, and the processor implements the steps of the above-mentioned horizontal well location auxiliary screening method when executing the computer program.

[0162] The above embodiments only express the preferred implementation modes of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the patent scope of the present invention. It should be pointed out that, for ordinary technicians in this field, the above technical features can be freely combined without departing from the concept of the present invention, and several deformations and improvements can be made, which all belong to the protection scope of the present invention. Therefore, all equivalent changes and modifications made to the scope of the claims of the present invention should belong to the coverage of the claims of the present invention.

Claims

1. A horizontal well location auxiliary screening method, characterized in that: The following steps are involved: Obtaining data on reservoir production potential; Calculating the oil reservoir production potential data using a horizontal well location auxiliary screening algorithm to obtain a preliminary screening result of the horizontal well location; The unreasonable well locations in the preliminary screening results of the horizontal well locations are eliminated to obtain the well location screening results.

2. The horizontal well location auxiliary screening method according to claim 1, characterized in that: The step of eliminating unreasonable well locations in the preliminary screening results of horizontal well locations to obtain well location screening results includes: Eliminate well locations with unreasonable reservoir production potential in the initial screening results of horizontal well locations to obtain a reasonable well location set; The production indexes of all well locations in the reasonable well location set are verified using a reservoir numerical simulator to obtain a well location screening result.

3. The horizontal well location auxiliary screening method according to claim 1, characterized in that: The obtaining of oil reservoir production potential data comprises: Establish a formula for calculating reservoir production potential based on the relationship between target reservoir type, oil-water distribution relationship, structural characteristics and fluid flow characteristics; Obtaining an oil saturation distribution map of a reservoir model and obtaining oil saturation distribution data; Calculating the oil saturation distribution data using the oil reservoir production potential calculation formula to obtain an oil reservoir production potential distribution map; The oil reservoir production potential distribution map is processed to generate matrix data to obtain the oil reservoir production potential data.

4. The horizontal well location auxiliary screening method according to claim 3, characterized in that: The horizontal well location auxiliary screening algorithm is used to calculate the reservoir production potential data to obtain the initial screening results of the horizontal well location, including: Determining a starting point element for the reservoir production potential data; Starting from the starting point element, the oil reservoir production potential data is traversed using a matrix continuous element summation algorithm and a line segment length to obtain a well position line segment and a well position direction; Obtaining the well position line segment in the oil reservoir production potential data and elements whose distance therefrom is other than the horizontal well spacing to obtain optional elements; A new starting point element is determined from the optional elements, and the above steps are repeated until the number of the selected well location segments and the well location directions reaches the preset number of horizontal wells, thereby obtaining the initial screening result of the horizontal well locations.

5. The horizontal well location auxiliary screening method according to claim 4, characterized in that: The method of traversing the reservoir production potential data from the starting point element using a matrix continuous element summation algorithm and a line segment length to obtain a well position line segment and a well position direction includes: In the matrix data of the reservoir production potential data, the starting point element is used as the starting point, and an element line segment in the matrix data direction of the corresponding element with the line segment length as the unit is obtained; the element line segment is composed of continuous elements; Calculate the sum of the elements on each of the element segments; The line segment with the largest sum is set as the well location line segment; the direction of the well location line segment is the well location direction.

6. The horizontal well location auxiliary screening method according to claim 5, characterized in that: The reservoir production potential data includes two-dimensional matrix data; the matrix continuous element summation algorithm includes: a two-dimensional matrix continuous element summation algorithm; For the two-dimensional matrix data, when the two-dimensional matrix continuous element summation algorithm obtains the element line segments, the matrix data has a maximum of eight directions.

7. The horizontal well location auxiliary screening method according to claim 5, characterized in that: The reservoir production potential data includes three-dimensional matrix data; the matrix continuous element summation algorithm includes: a three-dimensional matrix continuous element summation algorithm; For the three-dimensional matrix data, when the three-dimensional matrix continuous element summation algorithm obtains the element line segments, the matrix data has a maximum of twenty-six directions.

8. The horizontal well location auxiliary screening method according to claim 4, characterized in that: The calculation formula for the reservoir production potential is as follows: Among them, J m,n,p (t) is the reservoir production potential of the element (m, n, p) at time t; S o,m,n,p (t) is the oil saturation of the element (m, n, p) at time t; P o,m,n,p (t) is the pressure at the element (m, n, p) at time t; S or is the remaining oil saturation of the reservoir; min is the minimum bottom hole pressure; K m,n,p is the permeability at the element (m, n, p); r m,n,p is the distance from the element (m, n, p) to the nearest boundary; μ m,n,p is the viscosity at the element (m, n, p); is the porosity at the element (m, n, p); h m,n,p is the formation thickness, which is the distance from the element (m, n, p) to the oil-water interface, representing the impact of structural elements on production potential; H is the distance from the highest point of the reservoir to the oil-water interface.

9. The horizontal well location auxiliary screening method according to claim 3, characterized in that: The step of processing the oil reservoir production potential distribution map to generate matrix data and obtaining the oil reservoir production potential data includes: Analyzing the oil reservoir production potential distribution map to obtain an oil reservoir model text; Reading the reservoir model text line by line, expanding each line of data, and combining the expanded data into a first two-dimensional matrix data; Converting the first two-dimensional matrix data into first one-dimensional matrix data; The second two-dimensional matrix data is constructed according to the first one-dimensional matrix data so that the number of elements in each row of the second two-dimensional matrix data is consistent with the number of grids in each row of the reservoir model, thereby obtaining the reservoir production potential data.

10. A computer device comprising a processor and a memory storing a computer program, characterized in that: The processor implements the steps of the horizontal well location auxiliary screening method according to any one of claims 1 to 9 when executing the computer program.

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