Method and device for determining oil extraction speed of edge water loose oil reservoir

Abstract

The invention discloses a method and device for determining the oil extraction speed of an edge water loose oil reservoir, and the method comprises the steps: carrying out a conventional displacement sand production experiment for a target oil reservoir, and obtaining a sand production rate-water content relation curve; a preset sand control filter screen displacement sand production experiment is conducted on the target oil reservoir, and a permeability-water content relation curve is obtained; according to the permeability-water content relation curve, a skin coefficient-water content relation curve is obtained through calculation based on a preset seepage model; a plurality of oil production speeds are preset, and a water content prediction curve corresponding to each oil production speed is obtained through prediction by using a preset oil reservoir mechanism numerical model; predicting and fitting to obtain a regression curve of the oil production speed and the recovery degree by combining the curve and using a preset oil reservoir mechanism numerical model; and determining an inflection point of the regression curve to obtain the optimal oil production speed of the target oil reservoir. According to the method, the internal relation of the oil production speed, the water content, the sand production rate, the permeability and the recovery degree is established, and the optimal oil production speed of the edge water loose oil reservoir is determined.

Description

TECHNICAL FIELD

[0001] The present application relates to a method and device for determining the oil production rate of a reservoir with edge water. BACKGROUND

[0002] Reservoirs with edge water unconsolidated sandstone are a typical type of high-permeability, high-yield reservoir. Such reservoirs usually have abundant edge water, which can provide stable pressure support for the reservoir, thereby maintaining a high oil well productivity. However, due to its high permeability, this type of reservoir faces special challenges during development, especially in terms of sand production and water control.

[0003] Unconsolidated sandstone is prone to sand production during production due to its loose structure. This phenomenon not only causes wear and tear of wellbore equipment, increasing maintenance costs, but also causes sand accumulation, blocking flow channels, and thus affecting the production capacity of the oil well. At the same time, the presence of edge water makes the water cut rise rapidly during production, further exacerbating the sand production problem.

[0004] Currently, the development of reservoirs with edge water unconsolidated sandstone often adopts a low oil production rate to avoid sand production. However, this strategy often fails to fully exploit the production potential of the reservoir. On the other hand, a high oil production rate, while increasing short-term production, also accelerates the rise of water cut, leading to an increase in sand production, sand migration, and blockage of flow channels near the sand control pipe in the wellbore, ultimately affecting the long-term productivity of the oil well. SUMMARY

[0005] To determine the optimal oil production rate, the present application provides a method and device for determining the oil production rate of a reservoir with edge water unconsolidated sandstone.

[0006] In a first aspect, the present application provides a method for determining the oil production rate of a reservoir with edge water unconsolidated sandstone, which can include:

[0007] Performing a conventional displacement sand production experiment on the target reservoir to obtain a sand production rate-water cut relationship curve;

[0008] Performing a preset sand control screen displacement sand production experiment on the target reservoir to obtain a permeability-water cut relationship curve;

[0009] According to the permeability-water cut relationship curve, a skin factor-water cut relationship curve is calculated based on a preset seepage model;

[0010] Pre-set multiple oil production rates, and use a preset reservoir mechanism numerical model to predict the water cut prediction curve corresponding to each oil production rate;

[0011] The regression curve of the oil production rate and the recovery percentage is predicted and fitted by using a preset reservoir mechanism numerical model, in combination with the sand production rate-moisture content relationship curve, the permeability-moisture content relationship curve, the skin factor-moisture content relationship curve and the moisture content prediction curve corresponding to each oil production rate.

[0012] The inflection point of the regression curve is determined to obtain the optimal oil production rate of the target reservoir.

[0013] In one or some optional embodiments of the application, the regression curve of the oil production rate and the recovery percentage is predicted and fitted by using a preset reservoir mechanism numerical model, in combination with the sand production rate-moisture content relationship curve, the permeability-moisture content relationship curve, the skin factor-moisture content relationship curve and the moisture content prediction curve corresponding to each oil production rate, including:

[0014] Based on the moisture content prediction curve corresponding to each oil production rate, the skin factor, the sand production rate and the permeability corresponding to each oil production rate at different moisture contents are determined in combination with the sand production rate-moisture content relationship curve, the permeability-moisture content relationship curve and the skin factor-moisture content relationship curve.

[0015] The recovery percentage at a preset future time corresponding to each oil production rate is predicted by using a preset reservoir mechanism numerical model according to the skin factor, the sand production rate and the permeability corresponding to each oil production rate at different moisture contents.

[0016] The regression curve of the oil production rate and the recovery percentage is fitted based on the recovery percentage of the target reservoir at a preset future time corresponding to each oil production rate.

[0017] In one or some optional embodiments of the application, the preset sand prevention screen displacement sand production experiment is performed on the target reservoir to obtain the permeability-moisture content relationship curve, including:

[0018] The sandstone core sample of the target reservoir and the sand filling pipe with a sand prevention screen installed at the outlet end are obtained.

[0019] The original permeability is measured based on the sandstone core sample.

[0020] The sand production experiment is performed by using the sandstone core sample and the sand filling pipe with a sand prevention screen installed at the outlet end based on a plurality of preset moisture content conditions to obtain the permeability corresponding to each moisture content.

[0021] The permeability-moisture content relationship curve is determined based on the permeability corresponding to each moisture content.

[0022] In one or some optional embodiments of the embodiments of the present application, the skin factor-water cut relationship curve is obtained based on a preset seepage model according to the permeability-water cut relationship curve, and the method comprises the following steps of:

[0023] The skin factor corresponding to each permeability is calculated according to the following formula based on the permeability in the permeability-water cut relationship curve:

[0024]

[0025] In the formula, S is the skin factor, K is the original permeability, K S is the permeability in the permeability-water cut relationship curve, r s is the leakage rate corresponding to the oil drainage radius, r w is the wellbore radius.

[0026] The skin factor-water cut relationship curve is obtained by combining the permeability-water cut relationship curve and the skin factor corresponding to each permeability.

[0027] In one or some optional embodiments of the embodiments of the present application, the preset reservoir mechanism numerical model is obtained by the following method:

[0028] A full-reservoir numerical model is established.

[0029] A typical well group block is cut in the full-reservoir numerical model as an initial reservoir mechanism numerical model.

[0030] In the initial reservoir mechanism numerical model, a Fetkovic water body with a volume greater than that of the initial reservoir mechanism numerical model is set to obtain a preset reservoir mechanism numerical model.

[0031] In one or some optional embodiments of the embodiments of the present application, the establishment of the full-reservoir numerical model comprises the following steps of:

[0032] Geological data, logging data and seismic data are collected for the target reservoir.

[0033] Lithology modeling and structure modeling are performed based on the geological data, logging data and seismic data to obtain an initial full-reservoir geological static model.

[0034] The initial full-reservoir geological static model is coarsened, initialized and added with vertical flow performance data to obtain a basic full-reservoir geological static model.

[0035] An initial full-reservoir dynamic model is established based on the basic full-reservoir geological static model.

[0036] Optimize parameters in the initial full reservoir dynamic model based on the obtained historical data, and perform parameter correction to obtain a basic full reservoir dynamic model;

[0037] Perform multi-round interactive iteration based on the basic full reservoir geological static model and the basic full reservoir dynamic model, and integrate to obtain a full reservoir numerical model.

[0038] In a second aspect, an embodiment of the present application provides a device for determining the oil production rate of a loose oil reservoir with edge water, which can include:

[0039] A first experimental module is configured to perform a conventional sand displacement experiment on a target reservoir to obtain a sand displacement amount-water cut relationship curve;

[0040] A second experimental module is configured to perform a preset sand prevention screen sand displacement experiment on the target reservoir to obtain a permeability-water cut relationship curve;

[0041] A first calculation module is configured to calculate a skin factor-water cut relationship curve based on a preset seepage model according to the permeability-water cut relationship curve;

[0042] A first prediction module is configured to preset a plurality of oil production rates and predict a water cut prediction curve corresponding to each oil production rate using a preset reservoir mechanism numerical model;

[0043] A second prediction module is configured to combine the sand displacement amount-water cut relationship curve, the permeability-water cut relationship curve, the skin factor-water cut relationship curve, and the water cut prediction curve corresponding to each oil production rate, and use a preset reservoir mechanism numerical model to predict and fit a regression curve of the oil production rate and the recovery degree;

[0044] A turning point determination module is configured to determine a turning point of the regression curve to obtain an optimal oil production rate of the target reservoir.

[0045] In a third aspect, an embodiment of the present application provides a computer readable storage medium having a computer program / instruction stored thereon, which, when executed by a processor, implements the method for determining the oil production rate of a loose oil reservoir with edge water as described above.

[0046] In a fourth aspect, an embodiment of the present application provides a computer program product including a computer program / instruction, which, when executed by a processor, implements the method for determining the oil production rate of a loose oil reservoir with edge water as described above.

[0047] In a fifth aspect, an embodiment of the present application provides a computer device including a memory, a processor, and a computer program stored on the memory, wherein the processor, when executing the computer program, implements the method for determining the oil production rate of a loose oil reservoir with edge water as described above.

[0048] The beneficial effects of the above technical solutions provided by the embodiments of the present application at least include:

[0049] The embodiment of the present application provides a method for determining the oil production rate of a marginal water loose reservoir, which comprises the following steps: performing a conventional sand displacement experiment, a preset sand prevention filter screen sand displacement experiment and a preset percolation model on a target reservoir to obtain a sand displacement amount-water cut relationship curve, a permeability-water cut relationship curve and a skin factor-water cut relationship curve; a preset reservoir mechanism numerical model is established by using a numerical simulation technology; the sand displacement amount-water cut relationship curve, the permeability-water cut relationship curve and the skin factor-water cut relationship curve are combined to predict the recovery degree under different recovery degrees, and a regression curve of the oil production rate and the recovery degree is fitted; and the optimal oil production rate of the target reservoir is obtained based on the inflection point of the regression curve.

[0050] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application can be realized and attained by particularly pointed out in the written description and the accompanying drawings.

[0051] The technical solutions of the present application will be further described in detail below with the help of the accompanying drawings and embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0052] The accompanying drawings are used to provide a further understanding of the present application, and constitute a part of the specification, and are used to explain the present application together with the embodiments of the present application, and do not constitute a limitation to the present application. In the drawings:

[0053] Figure 1 The step schematic diagram of the method for determining the oil production rate of a marginal water loose reservoir provided by the embodiments of the present application is shown in the figure;

[0054] Figure 2 The anti-pressure strength-water cut relationship curve schematic diagram provided by the embodiments of the present application is shown in the figure;

[0055] Figure 3 The sand displacement amount-water cut relationship curve schematic diagram provided by the embodiments of the present application is shown in the figure;

[0056] Figure 4 The sand cake schematic diagram of the outlet end of the sand filling pipe in the preset sand prevention filter screen sand displacement experiment provided by the embodiments of the present application is shown in the figure;

[0057] Figure 5 A schematic diagram of the relationship between permeability, sand production and water cut provided for the embodiment of the present application is shown in Figure 1;

[0058] Figure 6 A schematic diagram of the relationship between the amplitude of permeability change and water cut provided for the embodiment of the present application is shown in Figure 2;

[0059] Figure 7 A schematic diagram of the relationship between the skin factor and water cut provided for the embodiment of the present application is shown in Figure 3;

[0060] Figure 8 A schematic diagram of the water cut prediction curve corresponding to multiple oil production rates provided for the embodiment of the present application is shown in Figure 4;

[0061] Figure 9 A schematic diagram of the process for establishing a full reservoir numerical model provided for the embodiment of the present application is shown in Figure 5;

[0062] Figure 10 A schematic diagram of the preset reservoir mechanism data model provided for the embodiment of the present application is shown in Figure 6;

[0063] Figure 11 A schematic diagram of the regression curve of oil production rate and recovery degree provided for the embodiment of the present application is shown in Figure 7;

[0064] Figure 12 A schematic diagram of the structure of the device for determining the oil production rate of a edge water unconsolidated reservoir provided for the embodiment of the present application is shown in Figure 8. DETAILED DESCRIPTION

[0065] Exemplary embodiments of the present disclosure will be described below in greater detail with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be accurately conveyed to those skilled in the art.

[0066] The inventors have found that, in the prior art, for the development of edge water unconsolidated sandstone reservoirs, low oil production rates cannot exploit the potential of reservoir development; high oil production rates accelerate water cut, leading to intensified sand production, causing sand particles to migrate and plug the flow channels near the wellbore sand control pipe, affecting the productivity of oil wells. It is of great significance to determine the reasonable oil production rate under the condition of wellbore sand control for such reservoirs to achieve water control and sand prevention. The oil production rate is the main control factor for the development of such reservoirs with rising water cut, and the increase in water cut is an important factor affecting sand production, which in turn affects the reservoir properties and thus the development effect. The prior art lacks systematic research on the correlation of the above influencing factors, making it difficult to effectively determine the optimal oil production rate of such reservoirs. Based on this, the inventors have made further research and development to make the present application, which provides a method and device for determining the oil production rate of an edge water unconsolidated reservoir.

[0067] Embodiment one

[0068] The embodiment one of the present application provides a method for determining the oil production rate of edge water loose reservoir, referring to the figure, Figure 1 The method can comprise the following steps S101-S106:

[0069] S101: performing a conventional sand displacement experiment on the target reservoir to obtain a sand displacement amount-water cut relationship curve.

[0070] S102: performing a preset sand screen sand displacement experiment on the target reservoir to obtain a permeability-water cut relationship curve.

[0071] S103: obtaining a skin factor-water cut relationship curve based on a preset seepage model according to the permeability-water cut relationship curve.

[0072] S104: presetting a plurality of oil production rates, and using a preset reservoir mechanism numerical model to predict a water cut prediction curve corresponding to each oil production rate.

[0073] S105: combining the sand displacement amount-water cut relationship curve, the permeability-water cut relationship curve, the skin factor-water cut relationship curve and the water cut prediction curve corresponding to each oil production rate, using the preset reservoir mechanism numerical model, predicting and fitting to obtain a regression curve of the oil production rate and the recovery degree.

[0074] S106: determining the inflection point of the regression curve to obtain the optimal oil production rate of the target reservoir.

[0075] The embodiment of the present application provides a method for determining the oil production rate of edge water loose reservoir, which obtains a sand displacement amount-water cut relationship curve, a permeability-water cut relationship curve and a skin factor-water cut relationship curve by performing a conventional sand displacement experiment, a preset sand screen sand displacement experiment and a preset seepage model on the target reservoir, establishes a preset reservoir mechanism numerical model by using numerical simulation technology, combines the sand displacement amount-water cut relationship curve, the permeability-water cut relationship curve and the skin factor-water cut relationship curve to predict the recovery degree under different recovery degrees, and fits to obtain a regression curve of the oil production rate and the recovery degree, and obtains the optimal oil production rate of the target reservoir based on the inflection point of the regression curve. The method realizes integrated research of geology and development engineering by the fusion and carding of multiple specialties, multiple technologies and multiple information, innovates the evaluation method of the optimal oil production rate under the influence of multiple factors, breaks through the traditional evaluation of the oil production rate by using only a single influencing factor, establishes the internal relationship of five indexes of the oil production rate, the water cut, the sand displacement amount, the permeability and the recovery degree, and determines the optimal oil production rate of the edge water loose reservoir.

[0076] The application has been successfully applied to an oilfield development project, the reservoir rapidly increases production and stably produces a yield of 5 million tons / year, the sand control and water control effect is obvious, the average sand content of oil wells is less than 0.1%, the comprehensive water content of the reservoir is controlled to be less than 3%, and the recovery degree during the water-free oil production period is more than 17%, which plays a key role in realizing peak production and maintaining stable production of the project.

[0077] In the embodiment of the application, the inventor carried out a series of innovative experimental researches when studying the development of the edge water loose reservoir, the traditional triaxial compression mechanics experiment mainly focuses on the mechanical properties of the rock under different stress conditions, the inventor introduced the change of the water content in the triaxial compression mechanics experiment, and combined with the conventional sand displacement experiment, the law of formation sand production under different water content conditions was further explored, so as to reveal the influence mechanism of the increase of the water content on the sand production, and provide a new scientific basis for the follow-up research of the edge water loose reservoir development.

[0078] The triaxial compression mechanics experiment under different water content conditions is carried out for the target reservoir to explore the change of the mechanical properties of the loose reservoir rock under different water content and the influence mechanism on the formation sand production. The triaxial compression mechanics experiment under different water content conditions is realized through the following steps:

[0079] The sandstone core sample of the target reservoir is obtained.

[0080] According to the set water content, the corresponding oil pump speed and water pump speed are selected, and the oil and water are injected into the sandstone core sample according to the set proportion, so that the water content reaches the preset value.

[0081] A triaxial compression machine is prepared, which includes an axial loading system, a confining pressure loading system, a pore water loading system, a high temperature system, a deformation measurement and control system and a displacement measurement and control system.

[0082] The sandstone core sample with different water content is installed in the triaxial compression machine, uniform confining pressure is applied to the sandstone core sample, and axial stress is gradually applied until the sandstone core sample breaks or reaches the preset experimental endpoint. The stress, strain, displacement and other values of the sandstone core sample under different stress conditions are monitored in real time during the experiment.

[0083] In the embodiment of the application, the triaxial compression mechanics experiment explores the change of the mechanical properties of the loose reservoir rock under different water content and the influence mechanism on the formation sand production, and the variation characteristics of the sand production with the water content are characterized.

[0084] In a specific embodiment, the compression strength-water content relationship curve obtained by the triaxial compression mechanics experiment is as shown in Figure 2As shown in the figure, the horizontal axis is water cut, the vertical axis is compressive strength, and three different permeability (500 mD, 2000 mD and 8000 mD) loose sand core samples are used. As can be seen from the figure, with the increase of water cut, the compressive strength decreases, which indicates that with the increase of water cut, the rock gradually softens, the formation strength decreases, the stability becomes poor, and the possibility of sand production caused by particle peeling increases, indicating that the sand production in the edge water loose reservoir is positively correlated with the water cut.

[0085] In the step S101, the conventional displacement sand production experiment is implemented through the following steps S1015-S1018.

[0086] S1011: Obtain the sand core sample and the sand pack tube of the target reservoir.

[0087] S1012: Measure the original permeability based on the sand core sample.

[0088] S1013: Based on the preset multiple water cut conditions, use the sand core sample and the sand pack tube to perform the displacement sand production experiment, and obtain the sand production corresponding to each water cut.

[0089] S1014: Determine the sand production-water cut relationship curve based on the sand production corresponding to each water cut.

[0090] In the embodiment of the application, according to the conclusion of the triaxial compressive mechanical experiment, the sand production and the water cut are positively correlated, and based on the conclusion, the conventional displacement sand production experiment is performed to quantitatively characterize the change mechanism of the sand production with the water cut.

[0091] In a specific embodiment, the conventional displacement sand production experiment equipment can include a horizontal flow pump, an intermediate container, a six-way valve, a sand pack tube, a pressure detection system and a collection device, and the specific implementation method can adopt the detailed description in the prior art. The sand production-water cut relationship curve obtained by the conventional displacement sand production experiment is as shown in Figure 3 As shown in the figure, the horizontal axis is water cut, the vertical axis is sand production, and seven injection pressure conditions (0.9 psi, 1 psi, 1.1 psi, 1.3 psi, 1.6 psi, 2.5 psi and 2.9 psi) are tested in the experiment. As can be seen from the figure, with the increase of water cut, the sand production increases.

[0092] In the step S102, the preset sand screen displacement sand production experiment is performed for the target reservoir to obtain the permeability-water cut relationship curve. Specifically, the following steps S1021-S1024 are included:

[0093] S1021: Obtain the sand core sample and the sand pack tube with a sand screen installed at the outlet end of the target reservoir.

[0094] S1022: Measure the original permeability based on the sand core sample.

[0095] S1023: Based on the preset water cut conditions, using the sandstone core sample and the sand filling pipe with the sand control screen installed at the outlet end to perform the sand displacement experiment, to obtain the permeability corresponding to each water cut.

[0096] S1024: Based on the permeability corresponding to each water cut, determining the permeability-water cut relationship curve.

[0097] In the embodiment of the present application, the influence mechanism of the sand production amount on the reservoir properties under different water cuts is revealed through the preset sand control screen displacement sand experiment, the variation law of the permeability with the water cut is quantitatively characterized, and the internal relationship among the water cut, the sand production amount and the permeability is established.

[0098] In a specific embodiment, the sand cake schematic diagram of the outlet end of the sand filling pipe in the preset sand control screen displacement sand experiment is as shown in Figure 4 It can be seen that there is an obvious mud cake near the outlet end of the sand filling pipe after the experiment, because in order to simulate the condition of installing the sand control pipe in the wellbore when the edge water loose reservoir is produced, the sand control screen is installed at the outlet end of the sand filling pipe in the experiment, and the sand control screen blocks the sand body from flowing out, so the mud cake is formed near the outlet end of the sand filling pipe.

[0099] But because of the sand control screen, the sand body is blocked from flowing out, so the sand production amount at the outlet end of the sand filling pipe cannot be tested, and only the permeability can be tested, therefore, based on the sand production amount-water cut relationship curve obtained in the above step S101, the influence mechanism of the sand production amount on the leakage rate after the sand control screen is installed can be quantitatively characterized, so as to obtain the permeability-water cut relationship curve. Taking the water injection pressure of 1.6 psi as an example, the corresponding relationship between the permeability, the sand production amount and the water cut obtained by the preset sand control screen displacement sand experiment is as shown in Figure 5 The horizontal axis in the figure is the water cut, the left vertical axis is the permeability, and the right vertical axis is the sand production amount. When the water cut is 20%, 40%, 60% and 80%, the corresponding measured sand production amounts are 680 mg / 100g, 820 mg / 100g, 880 mg / 100g and 900 mg / 100g, and the permeabilities are 4251 mD, 3697 mD, 2541 mD and 1894 mD, respectively. It can be seen that the permeability presents a decreasing trend with the increase of the water cut.

[0100] In the embodiment of the present application, after the permeability-water cut relationship curve is obtained by performing the preset sand control screen displacement sand experiment, the permeability change principle can also be analyzed based on the permeability data of the inlet end and the outlet end of the sand filling pipe retained in the preset sand control screen displacement sand experiment. In a specific embodiment, the relationship curve between the permeability change range of the inlet end and the outlet end of the sand filling pipe and the water cut is as shown in Figure 6As shown in the figure, the horizontal axis represents the moisture content, the left vertical axis represents the permeability variation, and the right vertical axis represents the sand output. It can be seen that as the moisture content increases, the permeability at the inlet of the sand-filled pipe tends to increase, with a maximum increase of 26%. This is because the increased moisture content intensifies sand movement, creating a high-permeability channel at the inlet of the sand-filled pipe, thus increasing the permeability. At the same time, as the moisture content increases, the permeability at the outlet of the sand-filled pipe tends to decrease, especially after the moisture content reaches 40%, the decrease becomes more significant, with a maximum decrease of 59%. The fundamental reason is that a sand filter screen is installed at the outlet of the sand-filled pipe, which blocks the sand from flowing out and forms a sand cake near the outlet, increasing the flow resistance and thus reducing the permeability.

[0101] In step S103 above, based on the permeability-moisture content relationship curve, the quantitative relationship between permeability and skin coefficient under different moisture contents affected by sand discharge is calculated based on a preset seepage model, i.e., the skin coefficient-moisture content relationship curve.

[0102] Specifically, the skin coefficient corresponding to each permeability can be calculated based on the permeability in the permeability-water content curve and the skin coefficient calculation formula in the preset seepage model, i.e., Formula 1 below:

[0103]

[0104] In the formula, S is the epidermal coefficient, K is the original permeability, and K S r represents the permeability in the permeability-water content curve. s r is the oil drain radius corresponding to the leakage rate. w Where is the radius of the wellbore.

[0105] In Formula 1, the original permeability, the leakage rate, the corresponding oil drain radius, and the wellbore radius are all obtained during the sand displacement experiment of the preset sand control filter in step S102 above.

[0106] In one specific embodiment, a schematic diagram of the skin coefficient-moisture content relationship coefficient is shown below. Figure 7 As shown in the figure, the horizontal axis represents water content, the left vertical axis represents permeability, and the right vertical axis represents skin coefficient. The initial permeability at the outlet of the sand-filled pipe before displacement was 4621 mD, and the skin coefficient was 0. After the experiment, when the water content was 20%, 40%, 60%, and 80%, the permeabilities were 4251 mD, 3697 mD, 2541 mD, and 1894 mD, respectively. The skin coefficients calculated using Formula 1 were 0.5, 1.4, 4.5, and 7.9, respectively. The results indicate that the skin coefficient tends to increase with increasing water content. This is mainly because as the water content increases, sand migration is intensified, and the sand cake formed near the outlet of the sand-filled pipe leads to a decrease in permeability, resulting in a corresponding increase in the skin coefficient.

[0107] In step S104 above, multiple oil production rates are preset, and a preset reservoir mechanism numerical model is used to predict the water cut prediction curve for a future period of time under each oil production rate.

[0108] In one specific embodiment, the water cut prediction curves corresponding to multiple oil recovery rates are as follows: Figure 8 As shown in the figure, the horizontal axis represents time, and the vertical axis represents water cut. Using a pre-defined reservoir mechanism numerical model, the predicted water cut curves for five oil production rates (1.4%, 1.9%, 2.3%, 2.8%, and 3.2%) were generated between 2021 and 2040. It can be seen that the lower the oil production rate, the slower the increase in water cut.

[0109] The pre-defined reservoir mechanism numerical model is obtained through the following methods, specifically including the following steps S1041-S1043:

[0110] S1041: Establish a numerical model of the entire reservoir.

[0111] Step S1041 specifically includes the following steps S10411-S10416:

[0112] S10411: Collect geological data, well logging data, and seismic data for the target oil reservoir.

[0113] Specifically, this can involve collecting geological data, well logging data, and seismic data for the target reservoir. Geological data includes stratigraphic division, lithological distribution, and sedimentary environment, while well logging data includes porosity, permeability, and density. Seismic data includes source information and seismic records, which are used to determine the structure and distribution of the reservoir.

[0114] S10412: Based on geological data, well logging data, and seismic data, lithological modeling and structural modeling are performed to obtain an initial static geological model of the entire reservoir.

[0115] Specifically, it can be done by establishing a lithological model based on geological data, well logging data, and seismic data through interpolation methods, determining the lithological properties of each grid unit, establishing a geological structure model of the reservoir, including geological features such as faults and folds, and obtaining an initial static geological model of the entire reservoir.

[0116] S10413: The initial full reservoir geological static model is coarsened and initialized, and vertical flow performance data is added to obtain the basic full reservoir geological static model.

[0117] Step S10413 specifically includes the following steps:

[0118] Firstly, the entire reservoir region in the initial full-reservoir geological static model is divided into a limited number of grid cells to simplify the model and reduce the amount of calculation, and then, based on the data in the initial full-reservoir geological static model, lithology, permeability, porosity and other attributes are assigned to the grid cells after roughing.

[0119] Secondly, the initial full-reservoir geological static model after roughing is initialized, which includes setting initial conditions and fluid physical behavior data, to provide basic data for subsequent reservoir development models. Among them, the initial conditions include parameters such as initial pressure, saturation and temperature of the reservoir, and the fluid physical behavior data includes fluid PVT (Pressure-Volume-Temperature) data and relative permeability curves, which are used to simulate the property changes of fluids under different pressures and temperatures, and the relative permeability relationship of multiphase fluids in the reservoir.

[0120] In the initialization, the reservoir also needs to be zoned. It can be divided into several regions according to geological characteristics, fluid properties and engineering requirements, to ensure that the fluid and rock properties in each region are relatively consistent, thereby simplifying the calculation and improving the simulation efficiency.

[0121] Thirdly, vertical flow performance (VFP) data is added to the initial full-reservoir geological static model after initialization. The vertical flow performance data is a curve used to describe the fluid flow behavior in the wellbore, and the vertical flow performance data applies formation conditions and fluid PVT data to wellbore flow simulation to predict the changes of wellbore pressure and flow rate during the oil production process.

[0122] S10414: Based on the basic full-reservoir geological static model, an initial full-reservoir dynamic model is established.

[0123] Specifically, dynamic modeling can be performed based on the basic full-reservoir geological static model to obtain an initial full-reservoir dynamic model. Dynamic modeling refers to simulating fluid flow, pressure changes and wellbore effects in the reservoir based on the basic full-reservoir geological static model to predict the performance of the reservoir during the oil production process.

[0124] S10415: Based on the obtained historical data, the parameters in the initial full-reservoir dynamic model are optimized and corrected to obtain a basic full-reservoir dynamic model.

[0125] Specifically, this step S10415 includes two parts, parameter optimization and parameter field correction.

[0126] The parameter optimization includes comparing simulation data of the initial full reservoir dynamic model with historical data, adjusting parameters in the initial full reservoir dynamic model by using an optimization algorithm (such as the least square method), and minimizing the prediction error. The historical data includes production, water production, Production Logging Tool (PLT) data, and Modular Formation Dynamics Tester (MDT) pressure data.

[0127] The parameter field correction includes further adjusting and optimizing the parameter field in the initial full reservoir dynamic model after the parameter optimization by using an optimization algorithm according to actual detection data, and adjusting the spatial distribution of the parameter field to improve the accuracy of the model in the actual formation. The actual detection data refers to data obtained in an oil and gas field or a wellbore by actual measurement and monitoring means.

[0128] S10416: Multi-round interactive iteration is performed based on the basic full reservoir geological static model and the basic full reservoir dynamic model, and a full reservoir numerical model is obtained by integration.

[0129] Specifically, the basic full reservoir geological static model can be adjusted based on the simulation result of the basic full reservoir dynamic model, then a new initial full reservoir dynamic model is re-established according to the adjusted basic full reservoir geological static model, and the above step S10415 is executed based on the new initial full reservoir dynamic model to obtain a new basic full reservoir dynamic model.

[0130] The process of adjusting the full reservoir static model and establishing the optimized full reservoir dynamic model is repeated and iterated until the accuracy of the two models reaches the expected accuracy, and a full reservoir numerical model is obtained by integration.

[0131] In order to facilitate those skilled in the art to understand the present scheme, the specific implementation process of the method for establishing a full reservoir numerical model provided by steps S10411-S10416 of the embodiments of the present application is described more clearly and completely below, with reference to Figure 9 As shown in FIG. 1, the process includes: the first step is to establish a full reservoir geological static model, corresponding to the above steps S10411-S10412, the second step is to coarsen, the third step is initialization and VFP, corresponding to the above steps S10413, the fourth step is to establish a full reservoir dynamic model, corresponding to the above steps S10414, the fifth step is history matching, the sixth step is parameter field correction, corresponding to the above steps S10415, after multi-round interactive iteration of the static and dynamic models, a full reservoir numerical model is finally formed, corresponding to the above step S10416.

[0132] S1042: A typical well group block is cut in the full reservoir numerical model as an initial reservoir mechanism numerical model.

[0133] S1043: In the initial reservoir mechanism numerical model, a Fetkovic water body with a volume greater than that of the initial reservoir mechanism numerical model is set to obtain a preset reservoir mechanism numerical model.

[0134] In a specific embodiment, the step S1043 can set a Fetkovic water body greater than 50 times the volume of the initial reservoir mechanism numerical model on the boundary of the initial reservoir mechanism numerical model to represent edge water and simulate a strong edge water displacement environment.

[0135] In a specific embodiment, the preset reservoir mechanism data model obtained by the above steps S1041-S1043 is as shown in FIG. 9, wherein a total of 9 oil wells are used for production, and the average permeability of the near-well reservoir of the oil well is 4600 mD, which is close to the original permeability of the sand core sample used in the above steps S101-S102 experiment, that is, the experimental parameters have similarity with the parameters of the preset reservoir mechanism data model, further verifying the reliability of the experimental results. Figure 10

[0136] In the above step S105, the regression curve of the oil production rate and the recovery degree is predicted and fitted using the preset reservoir mechanism numerical model in combination with the sand production rate-water cut relationship curve, the permeability-water cut relationship curve, the skin factor-water cut relationship curve, and the water cut prediction curve corresponding to each oil production rate. Specifically, the following steps S1051-S1053 are included:

[0137] S1051: Based on the water cut prediction curve corresponding to each oil production rate, in combination with the sand production rate-water cut relationship curve, the permeability-water cut relationship curve, and the skin factor-water cut relationship curve, the skin factor, the sand production rate, and the permeability corresponding to each oil production rate at different water cuts are determined.

[0138] S1052: According to the skin factor, the sand production rate, and the permeability corresponding to each oil production rate at different water cuts, the preset future time recovery degree corresponding to each oil production rate is predicted using the preset reservoir mechanism numerical model.

[0139] S1053: Based on the recovery degree of the target reservoir corresponding to each oil production rate at the preset future time, the regression curve of the oil production rate and the recovery degree is fitted.

[0140] In a specific embodiment, the oil production rate includes 1.4%, 1.9%, 2.3%, 2.8%, and 3.2%, and the preset future time is 2040. The recovery degree corresponding to each oil production rate is shown in Table 1.

[0141] Table 1: Recovery degree corresponding to oil production rate

[0142] ​

[0143] Based on the recovery degree of the target reservoir corresponding to each production rate shown in Table 1 in 2040, the regression curve of the production rate and the recovery degree obtained by fitting is as shown in FIG. 1, in which the abscissa is the production rate, the ordinate is the recovery degree, and the dotted line is the regression curve of the production rate and the recovery degree. It can be seen that, as the production rate increases, the recovery degree has a trend of increasing, but there is an inflection point, indicating that the recovery degree has a maximum value. Figure 11

[0144] In the step S106, the inflection point of the regression curve is determined to obtain the optimal production rate of the target reservoir.

[0145] In a specific embodiment, taking the regression curve of the production rate and the recovery degree as shown in FIG. 1 as an example, as the production rate increases, the recovery degree has a trend of increasing, and when the production rate increases to a certain value, the recovery degree approaches the maximum value, that is, the inflection point of the regression curve, and the corresponding production rate is the optimal production rate. In this example, the optimal production rate is 2.761%. Figure 11

[0146] In the embodiments of the present application, the steps S105-S106 adopt the numerical simulation technology to establish a mechanism model, predict the recovery degree under different skin factors and production rates, and create a development technical policy chart of the skin factor, the production rate and the recovery degree under the wellbore sand control condition based on the regression curve of the production rate and the recovery degree, thereby guiding the evaluation of the optimal production rate of the edge water loose reservoir.

[0147] Embodiment Two

[0148] Based on the same inventive concept, the embodiments of the present application also provide a device for determining the production rate of an edge water loose reservoir. Referring to FIG. 2, the device comprises: Figure 12

[0149] The first experimental module 101 is configured to perform a conventional displacement sand production experiment on the target reservoir to obtain a sand production amount-water cut relationship curve.

[0150] The second experimental module 102 is configured to perform a preset sand control screen displacement sand production experiment on the target reservoir to obtain a permeability-water cut relationship curve.

[0151] The first calculation module 103 is configured to calculate a skin factor-water cut relationship curve based on a percolation model according to the permeability-water cut relationship curve.

[0152] The first prediction module 104 is configured to preset a plurality of production rates and predict a water cut prediction curve corresponding to each production rate by using a preset reservoir mechanism numerical model.

[0153] ​​​The second prediction module 105 is configured to use a preset reservoir mechanism numerical model to predict and fit a regression curve of the oil production rate and the recovery degree in combination with the sand production rate-moisture content curve, the permeability-moisture content curve, the skin factor-moisture content curve and the moisture content prediction curve corresponding to each oil production rate.

[0154] The inflection point determination module 106 is configured to determine an inflection point of the regression curve to obtain an optimal oil production rate of the target reservoir.

[0155] Embodiment three

[0156] Based on the same inventive concept, the embodiments of the present application also provide a computer readable storage medium, which stores a computer program / instruction, and the computer program / instruction is executed by a processor to implement the method for determining the oil production rate of the edge water loose reservoir as described in the above embodiment one.

[0157] Embodiment four

[0158] Based on the same inventive concept, the embodiments of the present application also provide a computer program product, which includes a computer program / instruction, and the computer program / instruction is executed by a processor to implement the method for determining the oil production rate of the edge water loose reservoir as described in the above embodiment one.

[0159] Embodiment five

[0160] Based on the same inventive concept, the embodiments of the present application also provide a computer device, which includes a memory, a processor and a computer program stored in the memory, and the processor executes the computer program to implement the method for determining the oil production rate of the edge water loose reservoir as described in the above embodiment one.

[0161] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage, etc.) containing computer-usable program code.

[0162] The present application is described in reference to the accompanying drawings, which use flow diagrams and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flow diagrams and / or block diagrams, and combinations of blocks in the flow diagrams and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flow diagrams and / or block diagrams block or blocks. Figure 1 one or more flow or flow diagrams and / or block or blocks. Figure 1 one or more flow or flow diagrams and / or block or blocks.

[0163] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flow diagrams and / or block diagrams block or blocks. Figure 1 one or more flow or flow diagrams and / or block or blocks. Figure 1 one or more flow or flow diagrams and / or block or blocks.

[0164] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flow diagrams and / or block diagrams block or blocks. Figure 1 one or more flow or flow diagrams and / or block or blocks. Figure 1 one or more flow or flow diagrams and / or block or blocks.

[0165] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their legal equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A method of determining the rate of oil recovery for a rimmed reservoir, characterized by, The method comprises the following steps: a conventional sand production experiment is performed on the target oil reservoir to obtain a sand production rate-water cut relationship curve; a preset sand control screen sand production experiment is performed on the target oil reservoir to obtain a permeability-water cut relationship curve; an apparent skin factor-water cut relationship curve is calculated based on a preset percolation model according to the permeability-water cut relationship curve; a plurality of oil production rates are preset, and a preset reservoir mechanism numerical model is used to predict a water cut prediction curve corresponding to each oil production rate; a regression curve of the oil production rate and the recovery degree is predicted and fitted by using the preset reservoir mechanism numerical model in combination with the sand production rate-water cut relationship curve, the permeability-water cut relationship curve, the apparent skin factor-water cut relationship curve and the water cut prediction curve corresponding to each oil production rate; a turning point of the regression curve is determined to obtain the optimal oil production rate of the target oil reservoir.

2. The method of claim 1, wherein, The method comprises the following steps: based on the water cut prediction curve corresponding to each oil production rate, the sand production rate-water cut relationship curve, the permeability-water cut relationship curve and the apparent skin factor-water cut relationship curve, the skin factor, the sand production rate and the permeability corresponding to each oil production rate at different water cuts are determined; based on the skin factor, the sand production rate and the permeability corresponding to each oil production rate at different water cuts, the recovery degree of the target oil reservoir at a preset future time corresponding to each oil production rate is predicted by using the preset reservoir mechanism numerical model; based on the recovery degree of the target oil reservoir at the preset future time corresponding to each oil production rate, the regression curve of the oil production rate and the recovery degree is fitted.

3. The method of claim 1, wherein, The method comprises the following steps: sandstone core samples of the target oil reservoir and a sand filling pipe with a sand control screen installed at an outlet end are obtained; an original permeability is measured based on the sandstone core samples; based on a plurality of preset water cut conditions, the sandstone core samples and the sand filling pipe with the sand control screen installed at the outlet end are used to perform a sand production experiment to obtain a permeability corresponding to each water cut; based on the permeability corresponding to each water cut, a permeability-water cut relationship curve is determined.

4. The method of claim 1, wherein, The method comprises the following steps: based on the permeability in the permeability-water cut relationship curve, an apparent skin factor corresponding to each permeability is calculated according to the following formula: where S is the skin factor, K is the original permeability, K S is the permeability in the permeability-moisture relationship curve, r s is the leakage rate corresponding to the drainage radius, r w is the wellbore radius; based on the permeability-water cut relationship curve and the apparent skin factor corresponding to each permeability, an apparent skin factor-water cut relationship curve is obtained.

5. The method of claim 1, wherein, The preset reservoir mechanism numerical model is obtained in the following manner: a full reservoir numerical model is established; a typical well group block is cut in the full reservoir numerical model as an initial reservoir mechanism numerical model; In the initial oil reservoir mechanism numerical model, a Fetkovic water body with a volume greater than that of the initial oil reservoir mechanism numerical model is set to obtain a preset oil reservoir mechanism numerical model.

6. The method of claim 5, wherein, The establishing of the full oil reservoir numerical model comprises: For the target oil reservoir, geological data, logging data and seismic data are collected; Based on the geological data, logging data and seismic data, lithology modeling and structure modeling are performed to obtain an initial full oil reservoir geological static model; The initial full oil reservoir geological static model is coarsened, initialized and vertical flow performance data is added to obtain a basic full oil reservoir geological static model; Based on the basic full oil reservoir geological static model, an initial full oil reservoir dynamic model is established; Based on the obtained historical data, the parameters in the initial full oil reservoir dynamic model are optimized and parameter correction is performed to obtain a basic full oil reservoir dynamic model; Based on the basic full oil reservoir geological static model and the basic full oil reservoir dynamic model, multiple rounds of interactive iteration are performed to obtain a full oil reservoir numerical model.

7. An apparatus for determining the rate of oil production for a rimmed oil reservoir, comprising: Comprise: A first experimental module for performing a conventional displacement sand production experiment on a target oil reservoir to obtain a sand production amount-water cut relationship curve; A second experimental module for performing a preset sand prevention filter displacement sand production experiment on a target oil reservoir to obtain a permeability-water cut relationship curve; A first calculation module for calculating a skin factor-water cut relationship curve based on a preset percolation model according to the permeability-water cut relationship curve; A first prediction module for predicting a water cut prediction curve corresponding to each oil production rate using a preset oil reservoir mechanism numerical model under a preset plurality of oil production rates; A second prediction module for predicting and fitting a regression curve of oil production rate and recovery degree using a preset oil reservoir mechanism numerical model in combination with the sand production amount-water cut relationship curve, the permeability-water cut relationship curve, the skin factor-water cut relationship curve and the water cut prediction curve corresponding to each oil production rate; A turning point determination module for determining the turning point of the regression curve to obtain the optimal oil production rate of the target oil reservoir.

8. A computer readable storage medium having stored thereon computer programs / instructions, characterized in that, The computer program / instructions are executed by the processor to implement the method for determining the oil production rate of the edge water unconsolidated oil reservoir according to any one of claims 1-6.

9. A computer program product comprising computer programs / instructions, characterized in that, The computer program / instructions are executed by the processor to implement the method for determining the oil production rate of the edge water unconsolidated oil reservoir according to any one of claims 1-6.

10. A computer device comprising a memory, a processor, and a computer program stored on the memory, wherein the computer program comprises instructions that, when executed by the processor, cause the processor to perform the method of any one of claims 1-9. The processor executes the computer program to implement the method for determining the oil production rate of the edge water unconsolidated oil reservoir according to any one of claims 1-6.

Images