A reservoir oil content prediction model establishment method, prediction method and device

Abstract

This article provides a method for establishing a reservoir oil content prediction model, a prediction method and a device. The method includes: by analyzing the core physical property data of historical production wells in the work area and the monthly production data of a single well, determining the functional relationship between the production coefficient of the reservoir in the work area and the low water content oil production period, as well as the corresponding relationship between the target flow unit and the logging data, and the corresponding relationship between the target flow unit and the low water content oil production period, thereby determining the functional relationship between the production coefficient and the logging data in a preset oil content calculation formula, and then obtaining a reservoir oil content prediction model. When predicting other logging oil contents, it is only necessary to determine that the oil content of the reservoir to be tested can be accurately determined based on the logging data. This article can improve the accuracy of reservoir oil content prediction, which is of great significance for describing the dynamics of single well flooding and determining the water drive front.

Description

Technical Field

[0001] The present invention belongs to the field of oil and gas development, and specifically relates to a reservoir oil content prediction model establishment method, prediction method and device. Background Art

[0002] With the development of a large number of oil fields, the evaluation of water-flooded layers has become increasingly important. Well logging data can provide continuous physical and oil content data of the development layer, which is an important means of evaluating water-flooded layers. In low-permeability porous water-flooded layers, because fractures are not developed, the interconnected intergranular pores are the main channels for oil and water flow during the development process. However, there are many factors that affect the oil content of development wells, including reservoir heterogeneity, crude oil viscosity, injected water properties, injection-production relationship, well network layout and development time, which will affect the production oil content of development wells.

[0003] Core physical property analysis data and open hole well logging curves are static data reflecting the characteristics of the oil layer before development. After the oil field is put into water injection development, there are many factors that affect the production dynamics of the development wells. It is difficult to establish a method to characterize the water flooding dynamics of the reservoir by relying solely on logging curves without combining dynamic production data. At present, there is no universally applicable and accurate prediction method for the change of oil content in low permeability porous reservoirs after water flooding. Summary of the invention

[0004] In view of the above problems in the prior art, the purpose of this article is to provide a method for establishing a reservoir oil content prediction model, a prediction method and a device, which can improve the accuracy of reservoir oil content prediction.

[0005] In order to solve the above technical problems, the specific technical solutions of this article are as follows:

[0006] In one aspect, the present invention provides a method for establishing a reservoir oil content prediction model, the method comprising:

[0007] Obtain core physical property data, well logging data and monthly production data of target layers in historical production wells in the work area;

[0008] Dividing the target layer in the work area into flow units according to the core physical property data to obtain a plurality of flow units;

[0009] Determine the production coefficient corresponding to each historical production well in the work area according to the single-well monthly production data of the historical production wells;

[0010] Determine the target flow unit in each historical production well according to the unit thickness of each flow unit and the low water cut production period of each historical production well, and determine the corresponding relationship between the target flow unit and the well logging data, and the corresponding relationship between the target flow unit and the low water cut production period;

[0011] According to the production coefficient corresponding to each historical production well and the low water-cut production period of each historical production well, a functional relationship between the production coefficient and the low water-cut production period is obtained by fitting;

[0012] According to the correspondence between the target flow unit and the logging data, the correspondence between the target flow unit and the low water-cut oil production period, the functional relationship between the production coefficient and the low water-cut oil production period, and the preset oil content calculation formula, a reservoir oil content prediction model is obtained.

[0013] Furthermore, the core physical property data includes porosity and permeability;

[0014] According to the core physical property data, the target layer in the work area is divided into flow units to obtain multiple flow units, including:

[0015] According to the porosity and permeability, the flow zone index of the target layer in each historical production well is calculated;

[0016] Fitting to obtain a distribution probability cumulative curve of the flow zone index;

[0017] According to the change of the slope of the distribution probability cumulative curve, the flow zone index is divided into a plurality of unit types;

[0018] According to the unit type, the target layer in each historical production well is divided into a plurality of flow units.

[0019] Further, the target flow unit in each historical production well is determined according to the unit thickness of each flow unit and the low water cut production period of each historical production well, including:

[0020] According to the unit thickness of each flow unit, the thickness ratio of each flow unit in each historical production well is calculated;

[0021] The corresponding relationship between the thickness proportion of the same type of flow units in different historical production wells and the low water content production period and the corresponding fitting coefficients are obtained by fitting;

[0022] The flow unit corresponding to the unit type in the corresponding relationship with the highest fitting coefficient is taken as the target flow unit.

[0023] Furthermore, the logging data includes natural gamma curves and acoustic time difference curves;

[0024] Determining the correspondence between the target flow unit and the logging data includes:

[0025] Obtaining well logging data of the location of the target flow unit in each historical production well;

[0026] Calculate the shale content of the target flow unit in each historical production well according to the natural gamma curve in the well logging data;

[0027] Calculate and obtain the effective porosity of the target flow unit in each historical production well according to the shale content and the acoustic time difference curve in the logging data;

[0028] The effective porosity and acoustic time difference curves of the target flow unit in all historical production wells are counted to obtain the effective porosity range and the acoustic time difference value range, and establish a corresponding relationship between the data set composed of the effective porosity range and the acoustic time difference value range and the target flow unit.

[0029] Further, the production coefficient includes a time coefficient and a flow coefficient;

[0030] The function relationship between the production coefficient and the low water cut oil production period is obtained by fitting according to the production coefficient corresponding to each historical production well and the low water cut oil production period of each historical production well, including:

[0031] The functional relationship between the time coefficient and the low water content oil production period was obtained by fitting;

[0032] The functional relationship between the flow coefficient and the low water content production period was obtained by fitting.

[0033] Furthermore, the reservoir oil content prediction model is obtained according to the correspondence between the target flow unit and the logging data, the correspondence between the target flow unit and the low water cut oil production period, the functional relationship between the production coefficient and the low water cut oil production period, and the preset oil content calculation formula, including:

[0034] Determine a calculation function of the low water cut production period with respect to the well logging data according to the corresponding relationship between the target flow unit and the well logging data, and the corresponding relationship between the target flow unit and the low water cut production period;

[0035] Determine the calculation function of the production coefficient with respect to the well logging data according to the calculation function of the low water cut production period with respect to the well logging data and the functional relationship between the production coefficient and the low water cut production period;

[0036] The calculation function of the production coefficient with respect to the logging data is introduced into a preset oil content calculation formula to obtain a reservoir oil content prediction model.

[0037] Furthermore, the preset oil content calculation formula is expressed by the following formula:

[0038]

[0039] Among them, f ois the average monthly production oil content; α and k are both production coefficients, a is the time coefficient, k is the flow coefficient; t D is dimensionless time, q in is the injection rate, t is the real time, V p is the injection volume.

[0040] On the other hand, this article also provides a reservoir oil content prediction method, the method comprising:

[0041] Acquire real-time production data and logging data of the reservoir to be tested, wherein the production data includes injection rate, real time and injection volume;

[0042] The real-time production data and the logging data are brought into the reservoir oil content prediction model established by the above-mentioned method to predict the real-time oil content in the reservoir to be tested.

[0043] On the other hand, this article also provides a reservoir oil content prediction model establishment device, the device comprising:

[0044] The data acquisition module is used to obtain core physical property data, well logging data and monthly production data of single wells in the target layer of the historical production wells in the work area;

[0045] A flow unit division module is used to divide the target layer in the work area into flow units according to the core physical property data to obtain multiple flow units;

[0046] A production coefficient determination module, used to determine the production coefficient corresponding to each historical production well in the work area according to the single well monthly production data of the historical production well;

[0047] A target flow unit generation module is used to determine the target flow unit in each historical production well according to the unit thickness of each flow unit and the low water-cut production period of each historical production well, and determine the corresponding relationship between the target flow unit and the well logging data, and the corresponding relationship between the target flow unit and the low water-cut production period;

[0048] A fitting module, for fitting the functional relationship between the production coefficient and the low water cut oil production period according to the production coefficient corresponding to each historical production well and the low water cut oil production period of each historical production well;

[0049] The model building module is used to obtain a reservoir oil content prediction model based on the corresponding relationship between the target flow unit and the logging data, the corresponding relationship between the target flow unit and the low water content oil production period, the functional relationship between the production coefficient and the low water content oil production period, and a preset oil content calculation formula.

[0050] On the other hand, this document also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described above when executing the computer program.

[0051] By adopting the above technical scheme, a reservoir oil content prediction model establishment method, prediction method and device described in this article analyze the core physical property data of historical production wells and the monthly production data of single wells in the work area to determine the functional relationship between the production coefficient of the reservoir in the work area and the low water content oil production period, as well as the corresponding relationship between the target flow unit and the logging data, and the corresponding relationship between the target flow unit and the low water content oil production period, thereby determining the functional relationship between the production coefficient and the logging data in the preset oil content calculation formula, and then obtaining the reservoir oil content prediction model. When predicting other logging oil contents, it is only necessary to determine that the oil content of the reservoir to be tested can be accurately determined based on the logging data. This article can improve the accuracy of reservoir oil content prediction, which is of great significance for describing the single well flooding dynamics and determining the water drive front.

[0052] In order to make the above and other purposes, features and advantages of this article more obvious and easy to understand, the following specifically cites preferred embodiments and describes them in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of this article or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of this article. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 A schematic diagram of an implementation environment of the method provided in the embodiments of this article is shown;

[0055] Figure 2 A schematic diagram showing the steps of a method for establishing a reservoir oil content prediction model provided in an embodiment of this invention is shown;

[0056] Figure 3 A schematic diagram of the flow unit division steps in the embodiment of this article is shown;

[0057] Figure 4 A schematic diagram showing the relationship between the average moisture content in the production month and the production month in an embodiment of this invention is shown;

[0058] Figure 5 A schematic diagram of the flow unit division result in one embodiment of this invention is shown;

[0059] Figure 6 A schematic diagram showing the target flow unit thickness ratio and low water cut oil production period fitting results in one embodiment of this invention is shown;

[0060] Figure 7 A schematic diagram of the steps for determining the corresponding relationship between the well logging data and the target flow unit in the embodiment of this invention is shown;

[0061] Figure 8 A schematic diagram showing the division results of different flow unit division methods in an embodiment of this invention is shown;

[0062] Fig. 9 The comparison between the fitted oil content and the actual monthly average oil content during the flooding process of the X1 well in one embodiment of this invention is shown;

[0063] Fig.10 A schematic diagram showing the fitting results of the time coefficient and the low water content oil production period in an embodiment of this invention is shown;

[0064] Fig.11 A schematic diagram of the steps for establishing a reservoir oil content prediction model in the embodiment of this article is shown;

[0065] Fig.12 A schematic diagram of the structure of a reservoir oil content prediction model establishment device provided in an embodiment of this invention is shown;

[0066] Fig.13 A schematic diagram of the steps of a reservoir oil content prediction method provided in an embodiment of this invention is shown;

[0067] Fig.14 A schematic diagram of the structure of a reservoir oil content prediction device provided in an embodiment of this invention is shown;

[0068] Fig.15 A schematic diagram of the structure of a computer device provided in an embodiment of this invention is shown.

[0069] Description of the accompanying symbols:

[0070] 10. Client;

[0071] 20. Server;

[0072] 30. Network;

[0073] 100. Data acquisition module;

[0074] 200. Flow unit division module;

[0075] 300. Production coefficient determination module;

[0076] 400. target flow unit generation module;

[0077] 500, fitting module;

[0078] 600, model building module;

[0079] 700. a module for acquiring reservoir data to be tested;

[0080] 800, prediction module;

[0081] 1502. Computer equipment;

[0082] 1504. Processor;

[0083] 1506. Memory;

[0084] 1508, driving mechanism;

[0085] 1510, input / output module;

[0086] 1512. Input devices;

[0087] 1514. Output device;

[0088] 1516. Presentation equipment;

[0089] 1518. Graphical user interface;

[0090] 1520, network interface;

[0091] 1522. Communication link;

[0092] 1524. Communication bus. DETAILED DESCRIPTION

[0093] The following will be combined with the drawings in the embodiments of this article to clearly and completely describe the technical solutions in the embodiments of this article. Obviously, the described embodiments are only part of the embodiments of this article, not all of the embodiments. Based on the embodiments of this article, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this article.

[0094] It should be noted that the terms "first", "second", etc. in the specification and claims of this article and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of this article described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, device, product or equipment that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0095] In the prior art, the prediction of production oil content during the development of oil field development wells is generally based on static data such as core physical property analysis data and open hole well logging curves. However, this method is difficult to accurately analyze the changes in oil content of low permeability porous reservoirs after water flooding, which affects the oil and gas production work of subsequent development wells.

[0096] In order to solve the above problems, the embodiments of this specification provide a method for establishing a reservoir oil content prediction model. By using the model established by this method, the oil content in the reservoir can be accurately and reliably predicted. Figure 1 As shown, it is a schematic diagram of the implementation environment of the method, which may include a client 10 and a server 20. The client 10 and the server 20 are connected via a network 30, and data interaction can be achieved through the network 30.

[0097] The client 10 is used to obtain data of the target layer of the historical production concentration in the work area, and the data may include core physical property data, well logging data and monthly production data of a single well, and send the data to the server 20 through the network 30. The server 20 establishes a reservoir oil content prediction model according to a preset model establishment method. Specifically, by analyzing the core physical property data and monthly production data of the historical production wells in the work area, the functional relationship between the production coefficient and the low water content oil production period of the reservoir in the work area, as well as the correspondence between the target flow unit and the well logging data, and the correspondence between the target flow unit and the low water content oil production period, thereby determining the functional relationship between the production coefficient and the well logging data in the preset oil content calculation formula, and then obtaining the reservoir oil content prediction model. When predicting other well logging oil contents, it is only necessary to determine that the oil content of the reservoir to be tested can be accurately determined based on the well logging data. This article can improve the accuracy of reservoir oil content prediction.

[0098] In an optional embodiment, the server 20 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), as well as big data and artificial intelligence platforms.

[0099] In an optional embodiment, the client 10 can establish a reservoir oil content prediction model in conjunction with the server 20. Specifically, the client 10 can include but is not limited to electronic devices such as smart phones, desktop computers, tablet computers, laptop computers, smart speakers, digital assistants, augmented reality (AR) / virtual reality (VR) devices, smart wearable devices, etc. Optionally, the operating system running on the electronic device can include but is not limited to Android system, IOS system, Linux, Windows, etc.

[0100] In addition, it should be noted that Figure 1 What is shown is only one application environment provided by the present disclosure. In actual applications, other application environments may also be included, such as the establishment of a reservoir oil content prediction model, which may also be implemented on the client 10.

[0101] Specifically, the embodiments of this invention provide a method for establishing a reservoir oil content prediction model. By using the model established by this method, the oil content in the reservoir can be accurately and reliably predicted. Figure 2 This is a schematic diagram of the steps of a method for establishing a reservoir oil content prediction model provided in the embodiments of this article. This specification provides the method operation steps described in the embodiments or flow charts, but more or fewer operation steps may be included based on conventional or non-creative labor. The order of steps listed in the embodiments is only one way of executing the steps among many orders, and does not represent the only order of execution. When the actual system or device product is executed, it can be executed in the order of the methods shown in the embodiments or the drawings or in parallel. Specifically, Figure 2 As shown, the method may include:

[0102] S101: Obtain core physical property data, well logging data and monthly production data of single wells of target layers in historical production wells in the work area;

[0103] S102: dividing the target layer in the work area into flow units according to the core physical property data to obtain a plurality of flow units;

[0104] S103: determining the production coefficient corresponding to each historical production well in the work area according to the monthly production data of the single well of the historical production well;

[0105] S104: determining a target flow unit in each historical production well according to the unit thickness of each flow unit and the low water cut production period of each historical production well, and determining a corresponding relationship between the target flow unit and the well logging data, and a corresponding relationship between the target flow unit and the low water cut production period;

[0106] S105: fitting a functional relationship between the production coefficient and the low water cut production period according to the production coefficient corresponding to each historical production well and the low water cut production period of each historical production well;

[0107] S106: Obtain a reservoir oil content prediction model according to the corresponding relationship between the target flow unit and the logging data, the corresponding relationship between the target flow unit and the low water-cut oil production period, the functional relationship between the production coefficient and the low water-cut oil production period, and a preset oil content calculation formula.

[0108] It can be understood that the embodiments of this specification analyze the core physical property data, logging data and monthly production data of single wells in the historical production wells in the work area to obtain the reservoir oil content for predicting other well logging data in the work area, thereby improving the accuracy of oil content prediction, and at the same time, it can also realize the dynamic evaluation of oil content during the production process, thereby improving the adaptability and application scope of the model.

[0109] It should be noted that the work area in the embodiments of this specification may be a low-permeability porous water-flooded layer. Since fractures are not developed, the connected intergranular pores are the main channels for oil and water flow during the development process. Therefore, the accuracy of oil content prediction in low-permeability reservoirs can be improved by analyzing the pore structure in the reservoir.

[0110] The target horizon may be a specific reservoir position in the stratum of the work area, such as the Chang 6 reservoir of the Triassic Yanchang Formation.

[0111] In the embodiments of this specification, the core physical property data may include porosity and permeability; accordingly, Figure 3 As shown, according to the core physical property data, the target layer in the work area is divided into flow units to obtain multiple flow units, including:

[0112] S201: Calculate and obtain the flow zone index of the target layer in each historical production well according to the porosity and permeability;

[0113] S202: fitting to obtain a distribution probability cumulative curve of the flow zone index;

[0114] S203: dividing the flow zone index into a plurality of unit types according to the change in the slope of the distribution probability cumulative curve;

[0115] S204: Divide the target layer in each historical production well into multiple flow units according to the unit type.

[0116] Among them, the flow zone index (FZI) can reflect the pore structure characteristics of the reservoir, and the flow unit can be divided by its distribution probability. The calculation process of the flow zone index is as follows:

[0117] Step 1.1: Calculate the reservoir quality index based on the porosity and permeability of the reservoir sample core; the calculation formula is as follows:

[0118]

[0119] Among them, RQI is the reservoir quality index, K is the permeability, the unit is mD; φ is the gas-measured porosity, and the gas-measured porosity φ is a decimal.

[0120] Step 2.1: Calculate the flow zone index based on the reservoir quality index and the permeability of the reservoir sampling core; the calculation formula is as follows:

[0121]

[0122] Among them, FZI is the flow zone index.

[0123] In the same historical development well, since core sampling can be performed at different positions of the target layer (for example, there are different sampling points in the thickness direction), the flow zone index can be used to determine the reservoir pore structure characteristics in the target layer in each historical development well, and then the flow unit can be divided according to the flow zone index.

[0124] Based on the multiple flow zone indicators of the target layer in each historical development well, the distribution probability cumulative curve of the flow zone indicators is fitted through statistical analysis methods, and the target layer is divided into different unit types according to the change of the slope of the curve.

[0125] The slope change of the curve can be that the position where the slope changes greatly can be the unit type division point. For example, the slope change range can be set, and the slope between two adjacent coordinate points is calculated in turn, and the difference between the current slope and the previous slope is calculated. When the difference is not within the slope change range, any coordinate point in the current slope is used as the unit type division point, so that the unit type division based on the slope change can be realized, ensuring that reservoirs with the same or similar pore structure properties are divided into the same type of flow unit, thereby improving the accuracy of flow unit division.

[0126] For example, take a well group in the ultra-low permeability X well area in the Ordos Basin that is developed by water injection as an example. The development layer is the Chang 6 reservoir of the Triassic Yanchang Formation, which was put into production and water injection development in 1997. The conventional well logging curves, core physical property analysis porosity, permeability and monthly production data of the well and the well group are obtained, and the well logging curves are standardized. Well logging curves, monthly average water production rate data of oil wells, and number of months in production can be collected for all wells in the study area. In some wells, core physical property analysis porosity and permeability data are also collected in the study layer. When the oil field is developed by water injection, the monthly average water content of the production wells is very low and is in a waterless oil production period. The relationship between the monthly average water content of the production of well X1 in the X well area and the production month, such as Figure 4 As shown, the low water content oil production period in the well (i.e. the oil production period or month when the reservoir water content is lower than 20%) can be determined.

[0127] The above formulas (1) and (2) can be used to obtain the reservoir quality index RQI and flow zone index FZI in different development wells in the study area, and the distribution probability of the flow zone index is cumulatively plotted. The slope change of the curve obtained by fitting can be obtained as follows: Figure 5 The three-segment line graph shown in FIG. 1 shows that the target layer is divided into three types of flow units, namely, type I, type II, and type III flow units, so that the target layer in each historical development well can be divided into three flow units.

[0128] In the embodiment of this specification, the method of determining the target flow unit in each historical production well according to the unit thickness of each flow unit and the low water cut oil production period of each historical production well includes:

[0129] According to the unit thickness of each flow unit, the thickness ratio of each flow unit in each historical production well is calculated;

[0130] The corresponding relationship between the thickness proportion of the same type of flow units in different historical production wells and the low water content production period and the corresponding fitting coefficients are obtained by fitting;

[0131] The flow unit corresponding to the unit type in the corresponding relationship with the highest fitting coefficient is taken as the target flow unit.

[0132] It can be understood that the pore characteristics of different flow units are different. By analyzing the relationship between different flow units and the water content during the production process, the flow unit can be effectively used to predict the water content or oil content of the same flow unit in other locations in the same work area. Moreover, in the same work area (study area), due to the characteristics of continuous extension of the formation, reservoirs with the same pore characteristics will also have a certain continuity or correlation in other lithological characteristics, such as the thickness of the reservoir. The reservoir thickness ratio of the same type of flow units may have a certain consistency. Therefore, the embodiment of this specification establishes a corresponding relationship between the thickness ratio of different types of flow units and the low water content oil production period, thereby determining the flow unit type that has a functional relationship with the low water content oil production period, thereby ensuring the reliability and accuracy of the subsequent prediction model establishment.

[0133] In the embodiment of the present specification, the thickness ratio of the flow unit may be the ratio of the thickness of different flow units in the historical development well to the total thickness of the target layer in the historical development well.

[0134] For example, a certain well group in the ultra-low permeability X well area of ​​the Ordos Basin for water injection development is taken as an example for explanation. Three flow units can be obtained through the above unit type division. According to the unit type division method, the Chang 6 reservoir of the 5 production wells in the well group where the certain well in the X well area is located is divided into flow units. The proportion of the thickness of different flow units of each well to the total thickness of the reservoir and the low water production period with an average monthly water content of less than 20% are counted. The results are shown in Table 1 below.

[0135] Table 1 Thickness ratio of different flow units in each well in the study area and low water production period

[0136]

[0137] Through the above steps, the functional relationship between the three types of flow units and the low water cut oil production period and the corresponding fitting coefficients are fitted in sequence, and it can be obtained that the thickness proportion of the third (i.e., III) type of flow unit has a good linear relationship with the low water cut oil production period, such as Figure 6 As shown, it is a schematic diagram of the functional relationship obtained by fitting between the thickness proportion of the third type of flow unit and the low water-cut oil production period (or duration). Therefore, the thickness proportion of the third type of flow unit can be used to predict the low water-cut oil production period.

[0138] In the embodiment of this specification, the logging data includes natural gamma curve and acoustic time difference curve; accordingly, Figure 7 As shown, determining the correspondence between the target flow unit and the logging data includes:

[0139] S301: Acquire well logging data of the location of the target flow unit in each historical production well;

[0140] S302: Calculate the shale content of the target flow unit in each historical production well according to the natural gamma curve in the well logging data;

[0141] S303: Calculate and obtain the effective porosity of the target flow unit in each historical production well according to the shale content and the acoustic time difference curve in the well logging data;

[0142] S304: Count the effective porosity and acoustic time difference curves of the target flow unit in all historical production wells, obtain the effective porosity range and the acoustic time difference value range, and establish a corresponding relationship between the data set consisting of the effective porosity range and the acoustic time difference value range and the target flow unit.

[0143] It can be understood that in the actual mining process, the sampling and mining efficiency of core samples are relatively low. Therefore, in order to improve the prediction of the oil content in the reservoir to be tested or the production well, the oil content in the development well can be predicted through the logging data. Specifically, by determining the correspondence between the historical logging data in the historical production well and the determined target flow unit, and then in the same work area, the target flow unit in the production well to be tested is determined through the logging data of the production well to be tested, and then the corresponding prediction process is performed, thereby improving the efficiency of determining the target flow unit in the reservoir to be tested or the production well, and then improving the accuracy of the oil content prediction.

[0144] The logging data may also include natural potential and well diameter curves, etc.

[0145] Alternatively, the shale content of the target flow unit in the historical production well is calculated by the following formula:

[0146]

[0147] Among them, V sh is the mud content, GR is the natural gamma curve, GR min is the minimum value of the natural gamma curve, GR max is the maximum value of the natural gamma curve.

[0148] The effective pore size of the target flow unit in the historical production well is calculated by the following formula:

[0149]

[0150] Where Por is the effective porosity of the reservoir, Δt ma is the acoustic time difference of the sandstone skeleton, Δt f is the acoustic time difference of the pore fluid, Δt sh is the acoustic time difference of the pure mudstone section, Δt is the acoustic time difference curve, V sh The mud content.

[0151] The above formula (3) and formula (4) can be used to obtain the effective porosity range in the target flow unit in all historical production wells, and the range of the acoustic time difference curve in the target flow unit in all historical production wells can also be determined based on the acoustic time difference curve combined with the position of the target flow unit, and then the corresponding relationship between the data set consisting of the effective porosity range and the acoustic time difference value range and the target flow unit can be established. In other words, for the production well to be tested, the effective porosity and acoustic time difference curve at different positions can be obtained by calculating the well logging data, and the position of the target flow unit in the target layer in the production well to be tested can be determined based on the corresponding relationship between the data set consisting of the effective porosity range and the acoustic time difference value range and the target flow unit established above, thereby improving the efficiency of determining the target flow unit in the reservoir or production well to be tested.

[0152] Of course, the logging data of other types of flow units can also be processed to obtain the corresponding effective porosity and acoustic time difference, so that the characteristics of different types of flow units can be intuitively distinguished. For example, as shown in Table 2 below, the division conditions of different flow units based on logging data are shown.

[0153] Table 2 Division conditions of different flow units based on well logging data

[0154]

[0155] In actual work, any historical mining well in the X scenic area can also be selected as a comparison for detection, and the flow unit division based on core physical property data and the flow unit division based on well logging curves are compared. Figure 8 The figure shows the comparison results. It can be seen that the flow unit division results based on well logging curves are well consistent with those based on core analysis.

[0156] In the embodiment of this specification, the production coefficient includes a time coefficient and a flow coefficient. Accordingly, the functional relationship between the production coefficient and the low water cut oil production period is obtained by fitting according to the production coefficient corresponding to each historical production well and the low water cut oil production period of each historical production well, including:

[0157] The functional relationship between the time coefficient and the low water content oil production period was obtained by fitting;

[0158] The functional relationship between the flow coefficient and the low water content production period was obtained by fitting.

[0159] It can be understood that the production coefficient is a parameter that affects the oil content during the production process of the production well, which can be specifically a time coefficient and a flow coefficient, and can be obtained by fitting the monthly production data of a single well in historical production wells. The specific fitting process is a commonly used method in the field of oil and gas production, such as fitting through a pre-configured fitting program through Matlab programming. The specific fitting process is not limited in the embodiments of this specification.

[0160] The monthly production data of a single well may include water content, oil content, production, reservoir heterogeneity, crude oil viscosity, properties of injected water, injection-production relationship, well network layout and development time, etc. Different fitting parameters are selected according to the needs of fitting.

[0161] Through the above steps, it can be known that the target flow unit in the development well to be tested can be determined through the logging data, and then the corresponding low water-cut oil production period is determined on the basis of the established correspondence between the target flow unit and the low water-cut oil production period, and then the production coefficient of the target layer in the development well to be tested is calculated by fitting the functional relationship between the production coefficient (i.e., time coefficient and flow coefficient) and the low water-cut oil production period, thereby realizing that the production coefficient in the production well to be tested can be calculated through the corresponding characteristics of the target flow unit (such as thickness ratio), so as to realize the prediction of the oil content in the production well to be tested.

[0162] For example, by fitting the production data of different production wells in the X well area, the dimensionless time conversion coefficient ( where q in is the injection rate, V p The injection volume) is set to 0.008, the actual number of months is converted into dimensionless time, the monthly production data of the five production wells in the study area during the water injection development process are fitted, the time coefficient α and the single well flow coefficient k are determined, and the single well fitting coefficient R is calculated. 2 and the average flow coefficient k of the well area, the results are shown in Table 3, the production coefficient (i.e., the water flooding dynamic model parameters) and the fitting effect of the reservoir oil content prediction model of each well in the study area.

[0163] Table 3 Production coefficient and fitting effect of reservoir oil content prediction model for each well in the study area

[0164]

[0165] It can be seen from Table 3 that the historical data fitting effect of oil content based on the method provided in the embodiments of this specification is significant, and there is a strong linear relationship between the time coefficient and the low water content oil production period. Fig. 9 As shown in Figure 1, the comparison between the fitted oil content and the actual monthly average oil content during the flooding process of Well X1 is shown in Figure 2. Fig.10As shown, it can be determined that the correlation coefficient between the time coefficient and the low water-cut oil production period is 0.987, which has a strong correlation. The functional relationship between the time coefficient and the low water-cut oil production period can be fitted. There is no obvious linear relationship between the flow coefficient and the low water-cut oil production period, that is, the flow coefficient varies less in the same work area, and it can be determined as a fixed value, such as the average value of the flow coefficient in historical production wells in the work area.

[0166] In the embodiments of this specification, Fig.11 As shown, according to the corresponding relationship between the target flow unit and the logging data, the corresponding relationship between the target flow unit and the low water cut oil production period, the functional relationship between the production coefficient and the low water cut oil production period, and the preset oil cut calculation formula, a reservoir oil cut prediction model is obtained, including:

[0167] S401: determining a calculation function of the low water cut production period with respect to the well logging data according to the corresponding relationship between the target flow unit and the well logging data, and the corresponding relationship between the target flow unit and the low water cut production period;

[0168] S402: determining a calculation function of the production coefficient with respect to the well logging data according to the calculation function of the low water cut production period with respect to the well logging data and a functional relationship between the production coefficient and the low water cut production period;

[0169] S403: Substitute the calculation function of the production coefficient with respect to the logging data into a preset oil content calculation formula to obtain a reservoir oil content prediction model.

[0170] It can be understood that the embodiment of this specification determines the corresponding well logging data in the target flow unit through the analysis of historical production wells in the work area, and then obtains the correspondence between the low water-cut oil production period and the well logging data (or a calculation function) based on the correspondence between the target flow unit and the low water-cut oil production period. In fact, the target flow unit is determined through the well logging data, and the low water-cut oil production period is determined through the target flow unit. Then, the functional relationship between the production coefficient and the low water-cut oil production period is combined to obtain the calculation function of the production coefficient with respect to the well logging data, thereby obtaining the production coefficient (i.e., the time coefficient and the flow coefficient) in the preset oil content calculation formula, thereby realizing the establishment of a reservoir oil content prediction model.

[0171] Optionally, the preset oil content calculation formula is expressed by the following formula:

[0172]

[0173] Among them, f o is the average monthly production oil content; α and k are both production coefficients, a is the time coefficient, k is the flow coefficient; t D is dimensionless time, q in is the injection rate, t is the real time, V p is the injection volume.

[0174] A method for establishing a reservoir oil content prediction model provided in an embodiment of the present specification analyzes core physical property data, logging data and monthly production data of single wells of historical production wells in a work area to determine the functional relationship between the production coefficient of the reservoir in the work area and the low water-cut oil production period, as well as the correspondence between the target flow unit and the logging data, and the correspondence between the target flow unit and the low water-cut oil production period, thereby determining the functional relationship between the production coefficient and the logging data in a preset oil content calculation formula, and then obtaining a reservoir oil content prediction model. When predicting other logging oil contents, it is only necessary to determine that the oil content of the reservoir to be tested can be accurately determined based on the logging data. This invention can improve the accuracy of reservoir oil content prediction.

[0175] Based on the same inventive concept, the embodiment of this specification also provides a reservoir oil content prediction model establishment device, such as Fig.12 As shown, the device comprises:

[0176] The data acquisition module 100 is used to acquire core physical property data, well logging data and monthly production data of a single well of a target layer in a historical production well in the work area;

[0177] A flow unit division module 200 is used to divide the target layer in the work area into flow units according to the core physical property data to obtain multiple flow units;

[0178] The production coefficient determination module 300 is used to determine the production coefficient corresponding to each historical production well in the work area according to the monthly production data of the single well of the historical production well;

[0179] The target flow unit generation module 400 is used to determine the target flow unit in each historical production well according to the unit thickness of each flow unit and the low water cut production period of each historical production well, and determine the corresponding relationship between the target flow unit and the well logging data, and the corresponding relationship between the target flow unit and the low water cut production period;

[0180] A fitting module 500 is used to fit the functional relationship between the production coefficient and the low water cut production period according to the production coefficient corresponding to each historical production well and the low water cut production period of each historical production well;

[0181] The model building module 600 is used to obtain a reservoir oil content prediction model based on the correspondence between the target flow unit and the logging data, the correspondence between the target flow unit and the low water-cut oil production period, the functional relationship between the production coefficient and the low water-cut oil production period, and a preset oil content calculation formula.

[0182] The beneficial effects obtained by the above-mentioned device are consistent with the beneficial effects obtained by the above-mentioned method, and will not be described in detail in the embodiments of this specification.

[0183] Based on the above-mentioned method for establishing a reservoir oil content prediction model, the present specification also provides a reservoir oil content prediction method, such as Fig.13 As shown, the method includes:

[0184] S501: Acquire real-time production data and logging data of the reservoir to be tested, wherein the production data includes injection rate, real time and injection volume;

[0185] S502: The real-time production data and the logging data are brought into the reservoir oil content prediction model established by the above-mentioned method to predict the real-time oil content in the reservoir to be tested.

[0186] It can be understood that this step is the application of the above-mentioned method to establish a model. It should be noted that the historical production wells involved in the establishment of the reservoir oil content prediction model and the production wells corresponding to the reservoir to be tested should belong to the same work area. This can ensure that the reservoir pore structure and reservoir lithology characteristics in the same work area are similar, thereby ensuring the accuracy of the oil content prediction.

[0187] In the actual prediction process, the corresponding relationship between the logging data and the target flow unit in the reservoir oil content prediction model, as well as the corresponding relationship between the thickness ratio of the target flow unit and the low water content oil production period can be determined first, so as to obtain the effective porosity range and acoustic wave time difference range in the target flow unit. The logging data in the reservoir to be tested are processed to obtain the effective porosity and acoustic wave time difference at different positions, and the area falling within the effective porosity range and acoustic wave time difference range is determined as the target flow unit, so as to obtain the thickness ratio of the target flow unit. Then, the low water content oil production period of the reservoir to be tested is determined in combination with the corresponding relationship between the thickness ratio of the target flow unit and the low water content oil production period; and then, according to the functional relationship between the production coefficient and the low water content oil production period in the reservoir oil content prediction model, the production coefficient is calculated, and then the calculated production coefficient and the real-time production data of the reservoir to be tested are brought into the above formula (5) to predict the oil content of the reservoir to be tested.

[0188] Accordingly, this paper also provides a reservoir oil content prediction device, such as Fig.14 As shown, the device comprises:

[0189] The test reservoir data acquisition module 700 is used to acquire real-time production data and logging data of the test reservoir, wherein the production data includes injection rate, real time and injection volume;

[0190] The prediction module 800 is used to bring the real-time production data and the logging data of the reservoir to be tested into the reservoir oil content prediction model established by the above-mentioned method to predict the real-time oil content in the reservoir to be tested.

[0191] like Fig.15 As shown, a computer device provided in an embodiment of this invention, the apparatus in this invention can be a computer device in this embodiment, and the method of this invention is executed. The computer device 1502 may include one or more processors 1504, such as one or more central processing units (CPUs), and each processing unit may implement one or more hardware threads. The computer device 1502 may also include any memory 1506, which is used to store any kind of information such as code, settings, data, etc. Non-limiting, for example, the memory 1506 may include any one or more combinations of the following: any type of RAM, any type of ROM, flash memory device, hard disk, optical disk, etc. More generally, any memory may use any technology to store information. Further, any memory may provide volatile or non-volatile retention of information. Further, any memory may represent a fixed or removable component of the computer device 1502. In one case, when the processor 1504 executes an associated instruction stored in any memory or a combination of memories, the computer device 1502 may perform any operation of the associated instruction. The computer device 1502 also includes one or more drive mechanisms 1508 for interacting with any storage, such as a hard disk drive mechanism, an optical disk drive mechanism, etc.

[0192] The computer device 1502 may also include an input / output module 1510 (I / O) for receiving various inputs (via input devices 1512) and for providing various outputs (via output devices 1514). A specific output mechanism may include a presentation device 1516 and an associated graphical user interface (GUI) 1518. In other embodiments, the input / output module 1510 (I / O), the input device 1512, and the output device 1514 may not be included, and the computer device 1502 may be used as a computer device in a network. The computer device 1502 may also include one or more network interfaces 1520 for exchanging data with other devices via one or more communication links 1522. One or more communication buses 1524 couple the components described above together.

[0193] The communication link 1522 may be implemented in any manner, for example, through a local area network, a wide area network (e.g., the Internet), a point-to-point connection, etc., or any combination thereof. The communication link 1522 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc. governed by any protocol or combination of protocols.

[0194] Corresponds to Figure 2 , Figure 3 , Figure 7 , Fig.11 and Fig.13 For any of the methods shown, the embodiments of this document also provide a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are executed.

[0195] The embodiment of the present invention also provides a computer readable instruction, wherein when the processor executes the instruction, the program therein causes the processor to execute the following Figure 2 , Figure 3 , Figure 7 , Fig.11 and Fig.13 Any of the methods shown.

[0196] It should be understood that in the various embodiments of this document, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this document.

[0197] It should also be understood that in the embodiments of this article, the term "and / or" is only a description of the association relationship of the associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0198] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this article.

[0199] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0200] In the several embodiments provided herein, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, or can be electrical, mechanical or other forms of connection.

[0201] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of this article.

[0202] In addition, each functional unit in each embodiment of this invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of software functional unit.

[0203] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this article is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of this article. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0204] Specific embodiments are used in this article to illustrate the principles and implementation methods of this article. The description of the above embodiments is only used to help understand the methods and core ideas of this article. At the same time, for general technicians in this field, according to the ideas of this article, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as a limitation on this article.

Claims

1. A method for establishing a reservoir oil content prediction model, characterized in that: The method comprises: Obtain core physical property data, well logging data and monthly production data of target layers in historical production wells in the work area; Dividing the target layer in the work area into flow units according to the core physical property data to obtain a plurality of flow units; Determine the production coefficient corresponding to each historical production well in the work area according to the single-well monthly production data of the historical production wells; Determine the target flow unit in each historical production well according to the unit thickness of each flow unit and the low water cut production period of each historical production well, and determine the corresponding relationship between the target flow unit and the well logging data, and the corresponding relationship between the target flow unit and the low water cut production period; According to the production coefficient corresponding to each historical production well and the low water-cut production period of each historical production well, a functional relationship between the production coefficient and the low water-cut production period is obtained by fitting; According to the corresponding relationship between the target flow unit and the logging data, the corresponding relationship between the target flow unit and the low water cut oil production period, the functional relationship between the production coefficient and the low water cut oil production period, and the preset oil cut calculation formula, a reservoir oil cut prediction model is obtained, including: Determine a calculation function of the low water cut oil production period with respect to the well logging data according to the corresponding relationship between the target flow unit and the well logging data, and the corresponding relationship between the target flow unit and the low water cut oil production period; Determine the calculation function of the production coefficient with respect to the well logging data according to the calculation function of the low water cut production period with respect to the well logging data and the functional relationship between the production coefficient and the low water cut production period; Substituting the calculation function of the production coefficient with respect to the logging data into a preset oil content calculation formula to obtain a reservoir oil content prediction model; The preset oil content calculation formula is expressed by the following formula: ， Among them, f0 is the average monthly production oil content; α and k are both production coefficients, α is the time coefficient, and k is the flow coefficient; t D is dimensionless time, t D =(q in ×t) / V p ,q in is the injection rate, t is the real time, V p is the injection volume.

2. The method according to claim 1, characterized in that The core physical property data include porosity and permeability; According to the core physical property data, the target layer in the work area is divided into flow units to obtain multiple flow units, including: According to the porosity and permeability, the flow zone index of the target layer in each historical production well is calculated; Fitting to obtain a distribution probability cumulative curve of the flow zone index; According to the change of the slope of the distribution probability cumulative curve, the flow zone index is divided into a plurality of unit types; According to the unit type, the target layer in each historical production well is divided into a plurality of flow units.

3. The method according to claim 1, characterized in that The method of determining the target flow unit in each historical production well according to the unit thickness of each flow unit and the low water content production period of each historical production well comprises: According to the unit thickness of each flow unit, the thickness ratio of each flow unit in each historical production well is calculated; The corresponding relationship between the thickness proportion of the same type of flow units in different historical production wells and the low water content production period and the corresponding fitting coefficients are obtained by fitting; The flow unit corresponding to the unit type in the corresponding relationship with the highest fitting coefficient is taken as the target flow unit.

4. The method according to claim 1, characterized in that The logging data include natural gamma curve and acoustic time difference curve; Determining the correspondence between the target flow unit and the logging data includes: Obtaining well logging data of the location of the target flow unit in each historical production well; Calculate the shale content of the target flow unit in each historical production well according to the natural gamma curve in the well logging data; Calculate and obtain the effective porosity of the target flow unit in each historical production well according to the shale content and the acoustic time difference curve in the logging data; The effective porosity and acoustic time difference curves of the target flow unit in all historical production wells are counted to obtain the effective porosity range and the acoustic time difference value range, and establish a corresponding relationship between the data set composed of the effective porosity range and the acoustic time difference value range and the target flow unit.

5. The method according to claim 1, characterized in that The production coefficient includes a time coefficient and a flow coefficient; The function relationship between the production coefficient and the low water cut oil production period is obtained by fitting according to the production coefficient corresponding to each historical production well and the low water cut oil production period of each historical production well, including: The functional relationship between the time coefficient and the low water content oil production period was obtained by fitting; The functional relationship between the flow coefficient and the low water content production period was obtained by fitting.

6. A method for predicting reservoir oil content, characterized in that: The method comprises: Acquire real-time production data and logging data of the reservoir to be tested, wherein the production data includes injection rate, real time and injection volume; The real-time production data and the logging data are brought into a reservoir oil content prediction model established by the method described in any one of claims 1 to 5 to predict the real-time oil content in the reservoir to be tested.

7. A reservoir oil content prediction model establishment device, characterized in that: The device comprises: The data acquisition module is used to obtain core physical property data, well logging data and monthly production data of single wells in the target layer of the historical production wells in the work area; A flow unit division module is used to divide the target layer in the work area into flow units according to the core physical property data to obtain multiple flow units; A production coefficient determination module, used to determine the production coefficient corresponding to each historical production well in the work area according to the single well monthly production data of the historical production well; A target flow unit generation module is used to determine the target flow unit in each historical production well according to the unit thickness of each flow unit and the low water-cut production period of each historical production well, and determine the corresponding relationship between the target flow unit and the well logging data, and the corresponding relationship between the target flow unit and the low water-cut production period; A fitting module, for fitting the functional relationship between the production coefficient and the low water cut oil production period according to the production coefficient corresponding to each historical production well and the low water cut oil production period of each historical production well; The model building module is used to obtain a reservoir oil content prediction model according to the corresponding relationship between the target flow unit and the logging data, the corresponding relationship between the target flow unit and the low water content oil production period, the functional relationship between the production coefficient and the low water content oil production period, and a preset oil content calculation formula, including: Determine a calculation function of the low water cut production period with respect to the well logging data according to the corresponding relationship between the target flow unit and the well logging data, and the corresponding relationship between the target flow unit and the low water cut production period; Determine the calculation function of the production coefficient with respect to the well logging data according to the calculation function of the low water cut production period with respect to the well logging data and the functional relationship between the production coefficient and the low water cut production period; Substituting the calculation function of the production coefficient with respect to the logging data into a preset oil content calculation formula to obtain a reservoir oil content prediction model; The preset oil content calculation formula is expressed by the following formula: ， Among them, f0 is the average monthly production oil content; α and k are both production coefficients, α is the time coefficient, and k is the flow coefficient; t D is dimensionless time, t D =(q in ×t) / V p ,q in is the injection rate, t is the real time, V p is the injection volume.

8. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 5 or claim 6 is implemented.

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