Intelligent separate production well selection method based on interlayer interference dynamic characterization

Abstract

The invention discloses an intelligent separate production well selection method based on interlayer interference dynamic characterization. The method comprises the steps that S1, static parameters and dynamic parameters of a target multi-layer commingling production well are obtained; s2, constructing a reservoir afflux section pressure drop model and a wellbore flow section pressure drop model, and coupling the reservoir afflux section pressure drop and the wellbore flow section pressure drop to obtain a wellbore flow section pressure calculation formula; s3, constructing an oil reservoir seepage model; s4, performing liquid production history fitting on the basis of a wellbore flow section pressure calculation formula and the oil reservoir seepage model, and obtaining fitting formation parameters reflecting the current production dynamics; s5, calculating an interlayer interference coefficient; s6, setting a grading standard for carrying out interference grading on the oil well based on the interference coefficient; calculating the average interference coefficient of the oil well in the target time period, judging the interference level of the oil well in combination with the grading standard, and finally screening out the strong interference well which needs to preferentially implement intelligent separate production. According to the method, the objectivity and accuracy of intelligent separate production well selection are remarkably improved, and the applicability is high.

Description

Technical Field

[0001] This invention relates to the field of oil and gas field development technology, and in particular to an intelligent well selection method based on dynamic characterization of inter-layer interference. Background Technology

[0002] In the intelligent segregated production development of multi-layer oil wells, scientifically selecting potential wells requiring control valve installation is crucial for optimizing investment decisions, improving development efficiency, and reducing inter-layer interference. Dynamic characterization of inter-layer interference refers to the phenomenon in multi-layer synergistic production where, due to differences in the physical properties of each producing layer and competition for production pressure differentials, the overall well productivity is lower than the sum of the individual productivity of each layer. This effect not only causes production loss but also accelerates water channeling in high-permeability layers and inhibits the activation of low-permeability layers, severely impacting the balanced development and ultimate recovery rate of the reservoir. Therefore, dynamically characterizing the degree of inter-layer interference becomes a key basis for intelligent segregated production well selection.

[0003] Currently, well selection methods for intelligent production wells mainly include empirical judgment, static parameter comparison, and production capacity simulation based on simple superposition. Empirical judgment relies primarily on engineers' understanding of the block's geological characteristics and past development experience for subjective assessment. Static parameter comparison qualitatively evaluates the possibility of interference by comparing the differences in geological parameters across layers. Production capacity simulation based on simple superposition arithmetically adds the production capacities of each layer and compares this sum with the actual production to roughly estimate the degree of inter-layer interference. While these methods can be used for preliminary screening to some extent, they lack a deep understanding of the dynamic coupling mechanism between reservoir seepage and wellbore flow, thus failing to achieve real-time quantitative assessment of interference intensity.

[0004] Therefore, existing methods all suffer from the problem of difficulty in dynamically and continuously quantifying the degree of interlayer interference, which leads to insufficient basis for well selection, strong subjectivity, and affects the implementation effect and economy of the separate production measures. Summary of the Invention

[0005] To address the technical problem that existing methods lack dynamic and continuous quantitative characterization of inter-layer interference intensity during well selection in multi-layer commingled wells, leading to insufficient well selection basis, strong subjectivity, and directly affecting the implementation effectiveness and economy of separate well control measures, this invention provides an intelligent separate well selection method based on dynamic characterization of inter-layer interference. This method achieves quantitative evaluation and classification of interference intensity, improving the scientific rigor and accuracy of well selection, and providing a reliable basis for the precise application and promotion of intelligent separate well control technology.

[0006] The present invention provides an intelligent well selection method for separate production based on dynamic characterization of inter-layer interference. This method establishes a coupled pressure drop model between the reservoir inflow section and the wellbore flow section to obtain a pressure calculation formula for the wellbore flow section, and establishes an oil reservoir seepage model. Based on the wellbore flow section pressure calculation formula and the oil reservoir seepage model, production history is fitted to obtain fitted formation parameters reflecting the current production state. Based on the fitting results, a quantitative index characterizing the intensity of inter-layer interference—the interference coefficient—is defined and calculated to achieve a quantitative characterization of the interference intensity. Finally, based on the numerical range of the interference coefficient, strongly interfering wells that require priority for intelligent separate production are scientifically selected.

[0007] The method includes the following steps: S1. Obtain the static and dynamic parameters of the target multi-layered commingled well.

[0008] S2. Construct a pressure drop model for the reservoir inlet section and a pressure drop model for the wellbore flow section, and couple the pressure drop of the reservoir inlet section and the pressure drop of the wellbore flow section to obtain the formula for calculating the pressure of the wellbore flow section.

[0009] S3. Construct a reservoir seepage model that describes the relationship between pressure and production in each producing layer.

[0010] S4. Based on the wellbore flow section pressure calculation formula obtained in step S2 and the reservoir seepage model constructed in step S3, perform production history fitting to obtain the fitted formation parameters that reflect the current production dynamics.

[0011] S5. Calculate the inter-layer interference coefficient .

[0012] S6. Set based on interference coefficient The grading criteria for interference classification of oil wells are as follows: the average interference coefficient of the oil well within the target time period is calculated, the interference level of the oil well is determined based on the average interference coefficient and the grading criteria, and finally, based on the interference level of the oil well, the strongly interfering wells that need to be prioritized for intelligent segregation are selected.

[0013] Compared with the prior art, the advantages of the present invention are: (1) The method of the present invention combines mechanism modeling, dynamic fitting and quantitative evaluation to realize dynamic quantitative evaluation of the interlayer interference intensity during the well selection process of multi-layer combined wells, which improves the objectivity and accuracy of the well selection process and significantly improves work efficiency.

[0014] (2) The method of the present invention has strong applicability and can be integrated into the intelligent oilfield platform. It can be promoted in different oilfield multi-layer synergistic production scenarios, providing reliable technical support for the refined and intelligent development of offshore and onshore multi-layer oil reservoirs.

[0015] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Attached Figure Description

[0016] Figure 1 This is a technical roadmap for the intelligent well selection method based on dynamic characterization of inter-layer interference according to the present invention.

[0017] Figure 2 The diagram shows the production performance of wells A1, A2, and A3.

[0018] Figure 3 The graph shows the interference coefficient curves for wells A1, A2, and A3. Detailed Implementation

[0019] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0020] like Figure 1 As shown, the intelligent well selection method for production wells based on dynamic characterization of inter-layer interference of the present invention specifically includes the following steps: S1. Obtain the static and dynamic parameters of the target multi-layered commingled well.

[0021] The static parameters include the first... The fluid density of the layer, the first The permeability of the first layer, reservoir thickness of the first layer, The fluid viscosity, discharge radius and the first layer Formation pressure of the layer.

[0022] The dynamic parameters include the actual total oil well production, the first... Bottom-hole flowing pressure of the layer and the first Pump suction pressure of the layer; For layer numbering.

[0023] S2. Establish the pressure drop model of the reservoir inflow section and the pressure drop model of the wellbore flow section, and couple them to obtain the pressure calculation formula of the wellbore flow section.

[0024] Since fluids from different layers in the reservoir flow into the wellbore along the layer segments to form variable mass flow, the pressure drop in the wellbore needs to take into account the coupling relationship between the reservoir inflow section and the wellbore flow section. Therefore, a pressure drop model for the reservoir inflow section is established to describe the momentum and energy exchange when reservoir fluids flow into the wellbore. At the same time, a pressure drop model for the wellbore flow section is established to describe the variable mass flow in the wellbore. The pressure drop of the reservoir inflow section and the pressure drop of the wellbore flow section are coupled to obtain the formula for calculating the pressure of the wellbore flow section.

[0025] The reservoir inlet pressure drop model is as follows:

[0026] in, For the first Reservoir inflow pressure drop at wellbore, MPa; For the first Downstream pressure of the layer, MPa; For the first Upstream pressure of the layer, MPa; For the first The reservoir thickness of the layer, in meters; For the first Fluid density of the layer, kg / m 3 ; For the first The cross-sectional area of ​​the tubular column in the layer, m 2 ; For the first Reservoir inflow rate of layer, m 3 / s; For the first Downstream fluid velocity of the layer, m / s.

[0027] The pressure drop model for the wellbore flow section is as follows:

[0028]

[0029]

[0030]

[0031] In the formula, For the first Pressure drop in the wellbore flow section of the formation, MPa; For the first Gravity pressure drop in the flow section of the wellbore, MPa; For the first Frictional pressure drop in the flow section of the wellbore, MPa; For the first The pressure drop in the wellbore flow section of the layer is accelerated, MPa; Let be the fluid density of the i-th layer, kg / m³ 3 ; Let m be the reservoir thickness of the i-th layer; For the first The coefficient of frictional resistance of the layer is dimensionless. For the first The average fluid velocity of the layer, in m / s; denoted as the diameter of the tubing, in meters (m).

[0032] The formula for calculating the pressure in the wellbore flow section is as follows:

[0033] in, For the first Bottom-hole flowing pressure of the formation, MPa; The pressure is the pump suction pressure, in MPa.

[0034] S3. Based on the principle of potential superposition and Darcy's law, a reservoir seepage model is established to describe the relationship between pressure and production in each producing layer. The reservoir seepage model is as follows:

[0035] In the formula, For the first Formation pressure of the layer, MPa; For the first The fluid viscosity of the layer, mPa·s; For the first Permeability of the layer, mD; For the first Actual oil well production of the layer, m 3 / d; For the first The potential distribution of the layer is dimensionless. Let be the discharge radius, in meters. Let m be the reservoir thickness of the i-th layer.

[0036] S4. Based on the wellbore flow section pressure calculation formula obtained in step S2 and the reservoir seepage model constructed in step S3, perform production history fitting to obtain the fitted formation parameters that reflect the current production dynamics.

[0037] First, the first The pump suction pressure of the first layer, Well depth of layer, first The fluid density of the layer and the first Substituting the wellbore diameter of the first layer into the wellbore flow section pressure calculation formula obtained in step S2, the solution is iteratively obtained to calculate the first layer. Bottom-hole flowing pressure of the layer. Among them, Number the layers. Then use the first... Formation pressure of the first layer, The fluid viscosity of the first layer, The permeability of the first layer, The reservoir thickness and discharge radius of the first layer are calculated as follows: The bottom-hole flowing pressure of the layer is substituted into the reservoir seepage model constructed in step S3 to calculate the first layer. The fitted production of each layer is calculated; the fitted production of each layer is then summed to obtain the total fitted production of the oil well. During the historical fitting process, the formation pressure and permeability values ​​of each layer are repeatedly adjusted to ensure that the total fitted production of the oil well matches the actual production, and finally the fitted formation pressure and fitted permeability of each layer are determined.

[0038] S5. Calculate the inter-layer interference coefficient This enables quantitative characterization of interlayer interference coefficients.

[0039] To address the inter-layer interference phenomenon during multi-layer synergistic oil production, an interference coefficient is introduced. Quantitative characterization is performed. The core of this method is to use the minimum bottom-hole flowing pressure among all producing layers of an oil well as the common bottom-hole flowing pressure under ideal, undisturbed conditions. The calculation process is as follows: First, calculate the theoretical yield: [The text abruptly ends here, likely due to an incomplete sentence or a formatting error. The fluid viscosity of the first layer, The reservoir thickness and discharge radius of the layer are related to the first layer obtained in step S4. Fitted formation pressure of layer, first The fitted permeability and minimum bottom-hole flowing pressure of each layer are substituted into the reservoir seepage model constructed in step S3 for iterative solution to calculate the theoretical production of each layer under undisturbed conditions. The theoretical production of each layer under undisturbed conditions is summed to obtain the theoretical total production of the oil well under undisturbed conditions.

[0040] Secondly, calculate the actual output: [The text abruptly ends here, likely due to an incomplete sentence or a formatting error. The fluid viscosity of the first layer, The reservoir thickness and discharge radius of the layer are related to the first layer obtained in step S4. Fitted formation pressure of layer, first The fitted permeability of the layer and the bottom-hole flowing pressure during actual production are substituted into the reservoir seepage model constructed in step S3 for iterative solution to calculate the actual production of each layer when disturbed. The actual production of each layer when disturbed is summed to obtain the actual total production of the oil well when disturbed.

[0041] Finally, based on the theoretical total production and the actual total production, and according to the formula for calculating the interference coefficient, the interference coefficient of this oil well is calculated. , The calculation formula is as follows:

[0042] in, The oil well interference coefficient is dimensionless. m represents the theoretical total production of the oil well when it is undisturbed. 3 / d; m represents the actual total production when the oil well is disturbed. 3 / d.

[0043] S6. Interference classification and intelligent sampling decision-making; To quantitatively evaluate the degree of inter-layer interference, an interference coefficient based on the capacity loss rate is set. Grading criteria: When the capacity loss rate is below 20%, the corresponding preset interference coefficient is... A value <0.2 indicates a weakly interfering well; when the productivity loss rate is 20%~40%, the corresponding preset interference coefficient is 0.2≤ A value ≤0.4 indicates a well with moderate disturbance; when the production loss rate exceeds 40%, the corresponding preset disturbance coefficient is applied. >0.4 indicates a well with strong interference.

[0044] The average interference coefficient within the target time period is calculated. Based on the average interference coefficient and the aforementioned grading criteria, the interference level of the oil well is determined. For oil wells determined to be severely disturbed, intelligent shunting control valves are installed to suppress inter-layer interference and improve the utilization of each layer.

[0045] Application examples: Three wells (A1, A2, and A3) used in offshore water injection development were selected as research objects. Fluid density, reservoir thickness, fluid viscosity, and discharge radius of the three wells were obtained, as shown in Table 1. Based on the wellbore flow section pressure calculation formula and reservoir seepage model, production history was fitted. The fitting results of permeability and formation pressure for each layer, along with the calculated values ​​of the corresponding interference coefficients, are shown in Table 1. The production fitting effect is shown in... Figure 2 The interference coefficient curve can be found in the graph. Figure 3 The production fitting errors for wells A1, A2, and A3 were 3.15%, 4.37%, and 5.28%, respectively. After analyzing the interference coefficients of the three wells, well A1, with an interference coefficient greater than 0.4, is classified as a strongly interfering well and requires the installation of a smart production control valve to reduce inter-layer interference. Wells A2 and A3, with interference coefficients less than 0.4, do not require the installation of smart production control valves.

[0046] Table 1. Static and fitted parameters of wells A1, A2, and A3.

[0047] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for intelligent well selection based on dynamic characterization of inter-layer interference, characterized in that, Includes the following steps: S1. Obtain the static and dynamic parameters of the target multi-layered commingled well; S2. Construct a pressure drop model for the reservoir inlet section and a pressure drop model for the wellbore flow section, and couple the pressure drop of the reservoir inlet section and the pressure drop of the wellbore flow section to obtain the formula for calculating the pressure of the wellbore flow section; S3. Construct a reservoir seepage model that describes the relationship between pressure and production in each producing layer; S4. Based on the wellbore flow section pressure calculation formula obtained in step S2 and the reservoir seepage model constructed in step S3, perform production history fitting to obtain the fitted formation parameters that reflect the current production dynamics. S5. Calculate the inter-layer interference coefficient The calculation formula is as follows: in, The oil well interference coefficient is dimensionless. m represents the theoretical total production of the oil well when it is undisturbed. 3 / d; m represents the actual total production when the oil well is disturbed. 3 / d; S6. Set based on interference coefficient The grading criteria for interference classification of oil wells are as follows: the average interference coefficient of the oil well within the target time period is calculated, the interference level of the oil well is determined based on the average interference coefficient and the grading criteria, and finally, based on the interference level of the oil well, the strongly interfering wells that need to be prioritized for intelligent segregation are selected.

2. The intelligent well selection method based on dynamic characterization of inter-layer interference as described in claim 1, characterized in that, The static parameters include the first... The fluid density of the layer, the first The permeability of the first layer, reservoir thickness of the first layer, The fluid viscosity, discharge radius and the first layer Formation pressure of the layer; the dynamic parameters include actual total oil well production, the first layer... Bottom-hole flowing pressure of the layer and the first Pump suction pressure of the layer; For layer numbering.

3. The intelligent well selection method based on dynamic characterization of inter-layer interference as described in claim 2, characterized in that, The reservoir inlet pressure drop model is as follows: in, For the first Reservoir inflow pressure drop at wellbore, MPa; For the first Downstream pressure of the layer, MPa; For the first Upstream pressure of the layer, MPa; For the first The reservoir thickness of the layer, in meters; For the first Fluid density of the layer, kg / m 3 ; For the first Cross-sectional area of ​​the tubular column in the layer, m 2 ; For the first Reservoir inflow rate of layer, m 3 / s; For the first Downstream fluid velocity of the layer, m / s.

4. The intelligent well selection method based on dynamic characterization of inter-layer interference as described in claim 3, characterized in that, The pressure drop model for the wellbore flow section is as follows: In the formula, For the first Pressure drop in the wellbore flow section of the formation, MPa; For the first Gravity pressure drop in the flow section of the wellbore, MPa; For the first Frictional pressure drop in the flow section of the wellbore, MPa; For the first The pressure drop in the wellbore flow section of the layer is accelerated, MPa.

5. The intelligent well selection method based on dynamic characterization of inter-layer interference as described in claim 4, characterized in that, The formula for calculating the pressure in the wellbore flow section is as follows: in, For the first Bottom-hole flowing pressure of the formation, MPa; The pressure is the pump suction pressure, in MPa.

6. The intelligent well selection method based on dynamic characterization of inter-layer interference as described in claim 1, characterized in that, The reservoir seepage model is as follows: In the formula, For the first Formation pressure of the layer, MPa; For the first The fluid viscosity of the layer, mPa·s; For the first Permeability of the layer, mD; For the first Actual oil well production of the layer, m 3 / d; For the first The potential distribution of the layer is dimensionless. Let be the discharge radius, in meters (m). Let m be the reservoir thickness of the i-th layer.

7. The intelligent well selection method based on dynamic characterization of inter-layer interference as described in claim 1, characterized in that, In step S4, the fitted formation parameters that reflect the current production dynamics are obtained, including the fitted formation pressure and fitted permeability of each layer.

8. The intelligent well selection method based on dynamic characterization of inter-layer interference as described in claim 7, characterized in that, In step S5, the theoretical total production when the oil well is undisturbed. The calculation method is as follows: The first The fluid viscosity of the first layer, The reservoir thickness of the layer, the discharge radius, and the first layer obtained in step S4 Fitting formation pressure of layer and the first The fitted permeability and minimum bottomhole flowing pressure of each layer are substituted into the reservoir seepage model constructed in step S3 for iterative solution to calculate the theoretical production of each layer under undisturbed conditions. The theoretical production of each layer under undisturbed conditions is then summed to obtain the theoretical total production of the well when it is undisturbed. .

9. The intelligent well selection method based on dynamic characterization of inter-layer interference as described in claim 7, characterized in that, In step S5, the actual total production when the oil well is disturbed. The calculation method is as follows: The first The fluid viscosity of the first layer, The reservoir thickness of the layer, the discharge radius, and the first layer obtained in step S4 Fitting formation pressure of layer and the first Substituting the fitted permeability of the layers and the bottom-hole flowing pressure during actual production into the reservoir seepage model constructed in step S3, the model is iteratively solved to calculate the actual production rate of each layer under disturbance. The actual production rates of each layer under disturbance are then summed to obtain the total actual production rate of the well under disturbance. .

10. The intelligent well selection method based on dynamic characterization of inter-layer interference as described in claim 1, characterized in that, In step S6, based on the interference coefficient The specific grading criteria for disturbance classification of oil wells are as follows: When the capacity loss rate is less than 20%, the corresponding preset interference coefficient is... <0.2 indicates a well with weak interference; When the capacity loss rate is 20%~40%, the corresponding preset interference coefficient is 0.2≤ ≤0.4, classified as a moderately disturbed well; When the capacity loss rate is higher than 40%, the corresponding preset interference coefficient is... >0.4 indicates a well with strong interference.

Images