Single-well full-lifecycle production classification prediction method for deepwater oilfield

Abstract

The present application relates to a deepwater oilfield single well full life cycle production classification prediction method, comprising the following steps: establishing a deepwater oilfield production well yield type judgment standard based on a deepwater turbidite reservoir configuration mode; establishing a deepwater oilfield production well water-free stage yield prediction model; introducing a reservoir coefficient and an endpoint value correction coefficient to correct the generalized water cut prediction model, and establishing a new water cut prediction model considering reservoir factors; according to the flow distribution equation, combining the new type of oil-water relative permeability ratio representation relationship, the production well water-bearing stage yield prediction model is derived; the deepwater oilfield single well full life cycle yield segmented prediction model is established; and the model parameter value is determined by fitting a large amount of actual static and dynamic data of a typical deepwater oilfield. The present application has clear principle, objective calculation result and strong operability, and can finely predict the yield change rule of single well full life cycle under different reservoir configuration modes, and provide theoretical basis and technical support for oilfield development adjustment.

Description

Technical Field

[0001] This invention relates to the field of oil and gas extraction, and more specifically to a high-precision classification and prediction method for the entire life cycle production of a single well in a deepwater oilfield. Background Technology

[0002] Due to the high development costs and risks of deepwater oilfields, "fewer wells, higher production, and refined management" have become crucial for maximizing the economic benefits of deepwater oilfields. Therefore, oilfield managers need to accurately grasp the production changes of each individual well throughout its entire lifecycle to provide a cognitive foundation for achieving high production with fewer wells and refined management in deepwater oilfields. Because deepwater oilfields often employ ultra-large injection-production well spacing (on the order of kilometers), water drive sweep patterns are significantly influenced by reservoir configuration. Furthermore, for deepwater turbidite reservoirs, the formation process is often influenced by hydrodynamics and evolutionary stages, resulting in a complex and diverse sedimentary pattern of multiple superimposed channel sand bodies. Therefore, the production changes of individual wells throughout their entire lifecycle in deepwater oilfields are complex and varied, with significant differences between wells. Traditional prediction methods do not consider the impact of reservoir configuration on production, resulting in low prediction accuracy and failing to meet the needs of refined management in deepwater oilfields. Summary of the Invention

[0003] To address the aforementioned problems, the purpose of this invention is to provide a high-precision classification and prediction method for the full life cycle production of a single well in deepwater oilfields. This method can be used to accurately predict the full life cycle production of a single well under the influence of reservoir configuration, providing technical support for the refined management of deepwater oilfields.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] A method for classifying and predicting the full life-cycle production of a single well in a deepwater oilfield, characterized by the following steps:

[0006] Based on the deep-water turbidite reservoir configuration model, establish a standard for judging the production type of a single well in a deep-water oilfield;

[0007] Based on the production well type judgment criteria, a production well waterless stage prediction model for single wells in deepwater oilfields is established by using the flow rate equation and combining the oil-water relative permeability ratio characterization formula.

[0008] The reservoir coefficient and endpoint correction coefficient are introduced into the waterless stage production prediction model of the production well to establish a water-bearing stage production model that considers reservoir factors; and

[0009] The production model for the waterless stage and the production model for the water-bearing stage are combined to establish a segmented prediction model for the full life cycle production of a single well in a deepwater oilfield.

[0010] Based on the life-cycle production variation pattern of single wells in deepwater oilfields, production wells in deepwater oilfields are classified as follows:

[0011] The same-layer interconnected type, the injection and production well group is located in the same-phase channel sand body or leaf sand body, the reservoir properties of the same-phase channel or leaf sand body are relatively homogeneous, and the water drive front advance is uniform.

[0012] The composite connectivity type has poorer connectivity than the same-layer connectivity type, strong heterogeneity between different phases of channel sand bodies, and faster water-drive front advancement than the same-layer connectivity type.

[0013] The cross-layer interconnected type involves injection and production well groups located within sand bodies of different phases. These sand bodies exhibit strong heterogeneity, and the water drive front advances rapidly along high-permeability strips.

[0014] The present invention has the following advantages due to the adoption of the above technical solutions:

[0015] Accurately predict the production capacity change patterns of production wells under different reservoir characteristics in oilfields, support the needs of refined management of oilfields, slow down production decline, and effectively release the production capacity of production wells. Attached Figure Description

[0016] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. In the drawings:

[0017] Figure 1 This is a schematic diagram illustrating the water cut variation of well P1 according to an embodiment of the present invention;

[0018] Figure 2 This is a schematic diagram illustrating the water cut variation of well P2 according to an embodiment of the present invention;

[0019] Figure 3 This is a schematic diagram illustrating the water cut variation of well P3 according to an embodiment of the present invention;

[0020] Figure 4 This is a schematic diagram of the water-bearing stage production prediction results of well P1 according to an embodiment of the present invention;

[0021] Figure 5 This is a schematic diagram of the water-bearing stage production prediction results of well P5 according to an embodiment of the present invention;

[0022] Figure 6 This is a schematic diagram of the water-bearing stage production prediction results of well P6 according to an embodiment of the present invention;

[0023] Figure 7 This is a schematic diagram illustrating the life-cycle production prediction results of wells P1 / P2 / P3 according to an embodiment of the present invention; and

[0024] Figure 8 This is a schematic diagram illustrating the field implementation effect of an embodiment of the present invention at the AKPO oilfield. Detailed Implementation

[0025] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the invention and to fully convey the scope of the invention to those skilled in the art.

[0026] To achieve the above objectives, the present invention adopts the following technical solution: a high-precision classification and prediction method for the entire life cycle production of a single well in a deepwater oilfield, comprising the following steps:

[0027] 1) Determine the production type of deep-water oilfield production wells based on the deep-water turbidite reservoir configuration.

[0028] Based on current research, deep-water turbidite reservoir configurations can be classified into three types: intra-layer connectivity, composite connectivity, and inter-layer connectivity.

[0029] Analysis of actual data from typical oilfields reveals that reservoir characteristics differ significantly under different reservoir configurations, resulting in different production patterns throughout the entire life cycle of a single well.

[0030] Based on the different production change patterns throughout the entire life cycle, deepwater oilfield production wells are divided into three categories.

[0031] Sensitivity analysis revealed that the main controlling factors for production well type were reservoir configuration and reservoir breakthrough coefficient. Correlation analysis determined the correspondence between production well type and main controlling factors (Table 1).

[0032] Table 1 shows the classification of production wells in turbidite reservoirs in deepwater oilfields.

[0033] Table 1

[0034]

[0035] Using Table 1, the type of well can be determined based on the reservoir configuration pattern and reservoir inrush coefficient, providing a basis for selecting appropriate production prediction model parameter values ​​in subsequent steps.

[0036] The same-layer interconnected type means that the injection and production well groups are located in the same phase of channel sand body or leaf sand body. The reservoir connectivity is good, the reservoir properties of the same phase channel or leaf sand body are relatively homogeneous, and the water drive front advances evenly. Therefore, Class A wells generally have high initial production, long waterless stage, and rapid production decline in the water-bearing stage, showing a "convex shape".

[0037] Composite interconnected wells have slightly poorer reservoir connectivity than interconnected production wells in the same layer. They exhibit strong heterogeneity between different phases of waterway sand bodies and faster water drive front advancement than interconnected wells in the same layer. Therefore, Class C wells generally have lower initial production than interconnected production wells in the same layer, shorter waterless phase, and relatively slower production decline in the water-bearing phase, exhibiting an "S-shaped" pattern.

[0038] Cross-layer connectivity type means that the injection and production well groups are located in different phases of sand bodies. The reservoir properties and connectivity are poor, and the heterogeneity of different phases of sand bodies is strong. The water drive front advances rapidly along the high-permeability strip. Therefore, Class B wells generally have low initial production, short waterless stage, and slow production decline in the water-bearing stage, which is "concave".

[0039] Formula for calculating reservoir protrusion coefficient:

[0040]

[0041] In the formula, T k —Reservoir inrush coefficient, f; K m —Maximum reservoir permeability, D; K a — Average reservoir permeability, D.

[0042] 2) Based on the analysis of actual data from typical oilfields, establish a production prediction model for the waterless stage of production wells in deepwater oilfields.

[0043] ① Production during the waterless stage is less affected by reservoir factors and can be predicted using traditional production capacity models.

[0044]

[0045] In the formula, Q i —Initial production from the production well, m 3 / d;K e —Effective permeability of the reservoir, D; h—Effective thickness of the reservoir, m; Δp—Production pressure differential, MPa; μ o — Formation crude oil viscosity, mPa·s; B o —Crude oil volume coefficient; R—Injection-production well distance, m; r—Well radius, m; S—Skin coefficient.

[0046] ② Establish a prediction model for water breakthrough timing in deepwater oilfields to determine the production time during the waterless phase.

[0047] Analysis of the water breakthrough timing of actual production wells in typical oilfields reveals that, for the same type of production well, the stronger the internal heterogeneity of the reservoir, the earlier the water breakthrough occurs. Furthermore, the timing of water breakthrough is related to the reservoir's surge coefficient T. k It exhibits good log-correlation. A prediction model for water breakthrough timing in different types of production wells in deepwater oilfields was established through fitting a large amount of actual data.

[0048] Class A production wells:

[0049] t i =-2.5ln(T) k +8.7 (3)

[0050] Class B production wells:

[0051] t i =-5.1ln(T) k +10.8 (4)

[0052] Class C production wells:

[0053] t i =-12.1ln(T) k )+20.5 (5)

[0054] In the formula, t i —Duration of the dry phase in production wells, in years.

[0055] 3) Introduce reservoir coefficient and endpoint value correction coefficient to modify the generalized water cut prediction model and establish a new water cut prediction model that considers reservoir factors.

[0056] Based on Willhite's formula for relative permeability of the oil phase and Wyllie's formula for relative permeability of the oil and water phases in water-wet rocks, Gao Wenjun et al. derived a generalized water cut prediction model. However, this model is not accurate enough in predicting the initial water cut and water cut rise rate of production wells, and the parameter calculation process is complex.

[0057] To further improve prediction accuracy, this patent introduces a heterogeneity coefficient λ and a dominant phase coefficient α to modify the generalized water cut prediction model based on dynamic analysis of a large amount of actual production data, and establishes a new water cut prediction model that considers reservoir factors.

[0058]

[0059] Among them, the heterogeneity coefficient λ and the dominant phase coefficient α are functions of the reservoir advance coefficient and the thickness ratio of the dominant phase, respectively.

[0060] λ=A1ln(T k )+A2 (7)

[0061] α=B1ln(T d )+B2 (8)

[0062] By combining formulas (5) to (7), a new water cut prediction model considering reservoir factors is established (Equation 9).

[0063]

[0064]

[0065] In the formula, f w —Moisture content, %; t—Production time, years; λ—Heterogeneity coefficient, f; α—Dominant phase coefficient, f; p, b, c—Model parameters; T d —Percentage of dominant facies in reservoir, %; H d —Thickness of dominant facies phase in reservoir, m; H t —Total reservoir thickness, m; A1, A2, B1, B2 —Model parameters.

[0066] 4) Based on the flow fraction equation and the new characterization formula of the oil-water relative permeability ratio, the production prediction model for the water-bearing stage of the production well is derived.

[0067] Considering that oilfields mostly adopt constant pressure differential production methods, and without considering the effects of gravity and capillary force, based on the flow-part equation and combined with a new characterization formula for the oil-water relative permeability ratio, the correlation between the dimensionless production rate and water cut of production wells is obtained (Equation 11):

[0068]

[0069] In the formula, q is the dimensionless output, f is the output, and C1, C2, C3 are the model parameters.

[0070] Dimensionless production is the ratio of the production at any point during the water-bearing stage of a production well to the initial production of that well.

[0071]

[0072] In the formula, Q t —Production rate at any point during the water-bearing stage of the production well, m 3 / d;Q i —Initial production from the production well, m 3 / d, the initial production is the production of the production well during the waterless production period, which can be calculated using the traditional directional well production capacity prediction model (Equation 1).

[0073] Combining equations (11) and (12) with equation (1), we obtain the relationship between the production output of a well during the water-cut stage and the water cut:

[0074]

[0075] By combining the novel water cut prediction model (Equation 9) that considers reservoir factors with the relationship between production well output and water cut during the water cut stage (Equation 13), a production well output prediction model for the water cut stage is established:

[0076]

[0077] in:

[0078] K e—Effective permeability of the reservoir;

[0079] h—effective reservoir thickness;

[0080] Δp — Production pressure difference;

[0081] μ o —Crude oil viscosity;

[0082] B o —Crude oil volume coefficient;

[0083] R—supply radius;

[0084] r—wellbore radius;

[0085] S—epidermal coefficient.

[0086] 5) Combine the production models of the waterless stage and the water-bearing stage to establish a segmented prediction model for the full life cycle production of a single well in a deepwater oilfield.

[0087]

[0088] By analyzing dynamic and static data, the parameter values ​​for the life-cycle production prediction model of different types of single wells in deepwater oilfields were determined. Multivariate nonlinear fitting was performed using a large amount of actual dynamic and static data from typical deepwater oilfields to determine the parameter values ​​for the life-cycle production prediction model of different types of single wells in deepwater oilfields.

[0089] Table 2 shows the parameter values ​​for the production prediction model of different types of production wells in deepwater oilfields during the water-cutting stage.

[0090] Table 2

[0091]

[0092] The invention will now be described in detail using three typical production wells of different types, P1, P2, and P3, from a deepwater oilfield in West Africa as examples.

[0093] This invention proposes a high-precision classification and prediction method for the entire life cycle production of a single well in deepwater oilfields, comprising the following steps:

[0094] 1) Determine the production type of production wells P1, P2, and P3 based on the deep-water turbidite reservoir configuration.

[0095] Based on typical oilfield reservoir studies, the reservoir configurations of production wells P1, P2, and P3 are intra-layer connectivity, composite connectivity, and inter-layer connectivity, respectively, with breakthrough coefficients of 2.3, 3.5, and 4.2. Table 1 can be used to determine the production type of production wells P1, P2, and P3.

[0096] Table 3 shows the production well types P1, P2, and P3.

[0097] Table 3

[0098]

[0099] 2) Based on the actual conditions of production wells P1, P2, and P3, predict the production output of P1, P2, and P3 during the waterless stage.

[0100] ①Predict the initial output of P1, P2, and P3 using the traditional production capacity model (Equation 1).

[0101] Table 4 shows the initial production calculation results for wells P1, P2, and P3.

[0102] Table 4

[0103]

[0104] ② Establish a prediction model for water breakthrough timing in deepwater oilfields to determine the production time for the waterless stages P1, P2, and P3.

[0105] Using equations (2) to (4), the production time of the anhydrous stages P1, P2, and P3 can be predicted and determined respectively:

[0106] Table 5 shows the production time during the waterless stage of wells P1, P2, and P3.

[0107] Table 5

[0108]

[0109] 3) Predict the water cut changes of production wells P1, P2, and P3 using a novel water cut prediction model that considers reservoir factors.

[0110] A novel water cut prediction model (Equation 9) considering reservoir factors was used to predict the water cut changes of production wells P1, P2, and P3, as shown in the figure. Comparison with the actual water cut measurement points of production wells P1, P2, and P3 revealed that the water cut prediction accuracy can reach 90%.

[0111] Table 6 shows the heterogeneity coefficients and dominant phase coefficients for wells P1, P2, and P3.

[0112] Table 6

[0113]

[0114] The water cut changes of production well P1 are as follows Figure 1 As shown, where:

[0115]

[0116] Water cut changes in production well P2 as follows Figure 2 As shown, where:

[0117]

[0118] Water cut changes in production well P3 as follows Figure 3 As shown, where:

[0119]

[0120] 4) Use the production prediction model for the water-bearing stage of production wells to predict the changes in production output during the water-bearing stages of production wells P1, P2, and P3.

[0121] Using the production well water-cutting stage production prediction model (Equation 14), the production changes of production wells P1, P2, and P3 during the water-cutting stages are predicted, such as... Figure 1-3 As shown, by comparing with the actual production measurement points of production wells P1, P2, and P3, it was found that the production prediction accuracy during the water-bearing stage can reach 90%.

[0122] Production changes during the water-cutting stage of production well P1 are as follows: Figure 4 As shown, where:

[0123]

[0124] Production changes during the water-cutting stage of production well P2 are as follows: Figure 5 As shown, where:

[0125]

[0126] Production changes during the water-cutting stage of production well P3 are as follows: Figure 6 As shown, where:

[0127]

[0128] 5) Based on the production prediction results of the waterless stage and the water-bearing stage, the full life cycle production prediction results of production wells P1, P2, and P3 are obtained.

[0129] Based on the predicted production time, production rate during the waterless stage, and production rate during the water-bearing stage, the total lifetime production of production wells P1, P2, and P3 is obtained, as follows: Figure 7 As shown.

[0130] The concept of this invention patent has been applied in the AKPO oilfield, a typical deep-water oilfield in West Africa. This oilfield has a water depth exceeding 1200m and is primarily composed of complex channel deposits, with localized development of lobed sand bodies, classifying it as a complex channel sedimentary body. The formation fluid is volatile oil, with slight differences in fluid properties along vertical lines. The crude oil's surface density is 0.8 g / cm³. 3 The formation crude oil viscosity is 0.21 mPa·s. Most of the production wells in the main oilfield of this oilfield have entered the medium-to-high water-cut stage, and the production capacity of the production wells has declined significantly. The adjustment measures taken need to be more targeted and applicable.

[0131] The method provided in this patent accurately predicts the production capacity variation patterns of production wells under different reservoir characteristics in the oilfield, guiding the oilfield to take targeted adjustment measures and supporting the oilfield's need for refined management. Figure 8 As shown, based on the prediction results, the oilfield successfully guided the implementation of injection-production optimization adjustments for five production wells between 2015 and 2017, slowing down production decline, effectively releasing the production capacity of production wells, and increasing oil production by 523 cubic meters per day. 3 / d, with a cumulative increase of 450,000 cubic meters of oil.

[0132] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for classifying and predicting the full life-cycle production of a single well in a deepwater oilfield, characterized in that, Includes the following steps: 1) Determine the production type of deep-water oilfield production wells based on the deep-water turbidite reservoir configuration model; 2) Based on the analysis of actual data from typical oilfields, establish a production prediction model for the waterless stage of production wells in deepwater oilfields; 3) Introduce reservoir coefficient and endpoint value correction coefficient to modify the generalized water cut prediction model and establish a new water cut prediction model that considers reservoir factors; 4) Based on the flow rate equation and the new characterization formula of the oil-water relative permeability ratio, the production prediction model for the water-cut stage of the production well is derived. 5) Combine the production model for the waterless stage and the production model for the water-bearing stage of the production well to establish a segmented prediction model for the full life cycle production of a single well in a deepwater oilfield. The expression for this model is: In the formula, Q t — Production output at any point during the water-bearing stage of a production well; K e —Effective permeability of the reservoir; —Effective reservoir thickness; —Production pressure differential; —Crude oil viscosity; —Crude oil volume coefficient; R—supply radius; r—wellbore radius; S—epidermal coefficient; t i —Duration of the waterless phase in the production well; t —Production time; , , —Model parameters; A 1 , A 2 , B 1 , B 2—Model parameters; T d —The proportion of thickness of the dominant phase in the reservoir; p, b, c —Model parameters; T k This represents the reservoir advance coefficient.

2. The method for classifying and predicting the full life cycle production of a single well in a deepwater oilfield according to claim 1, characterized in that, In step 1), based on the life-cycle production variation pattern of a single well in a deepwater oilfield, the production well types are classified as follows: Class A: The deep-water turbidite reservoir has a connected layer configuration. The injection and production well groups are located in the same channel sand body or leaf sand body. The reservoir properties of the same channel or leaf sand body are relatively homogeneous, and the water drive front advances evenly. Type B: The deep-water turbidite reservoir configuration is a composite interconnected type. The connectivity of the composite interconnected type is worse than that of the same-layer interconnected type. The heterogeneity between different phases of channel sand bodies is strong, and the water drive front advances faster than that of the same-layer interconnected type. Class C: The deep-water turbidite reservoir has a cross-layer interconnected structure, with injection and production well groups located in different phases of sand bodies. The sand bodies in different phases are highly heterogeneous, and the water drive front advances rapidly along high-permeability strips.

3. The method for classifying and predicting the full life-cycle production of a single well in deepwater oilfields according to claim 2, characterized in that, In step 1), a prediction model for the water breakthrough timing of different types of production wells in deepwater oilfields is established through data fitting, wherein... Class A production wells conform to formula (3): （3） Type B production wells conform to formula (4): （4） Class C production wells conform to formula (5): （5） in, t i Indicates the duration of the waterless phase in the production well; T k This represents the reservoir advance coefficient.

4. The method for classifying and predicting the full life cycle production of a single well in a deepwater oilfield according to claim 1, characterized in that, In step 3), a heterogeneity coefficient λ and a dominant phase coefficient are introduced. α The water cut prediction model is revised to establish a water cut prediction model that considers reservoir factors, wherein: The heterogeneity coefficient λ conforms to equation (7): （7） Dominant phase coefficient α Conformation (8): （8） The water cut prediction model considering reservoir factors conforms to equation (9): （9） In the formula, f w —Moisture content; t —Production time; λ —Heterogeneity coefficient; α— Dominant phase coefficient; p, b, c —Model parameters; T d —The proportion of thickness of the dominant phase in the reservoir; H d —Thickness of dominant phases in the reservoir; H t —Total reservoir thickness; A 1 , A 2 , B 1 , B 2—Model parameters.

5. The method for classifying and predicting the full life cycle production of a single well in a deepwater oilfield according to claim 4, characterized in that, In step 4), without considering the effects of gravity and capillary force, the water cut prediction model considering reservoir factors is combined with the relationship between the production well's water cut stage and the water cut variation to establish a production well water cut stage production prediction model that conforms to equation (14): （14） in, Q t — Production output at any point during the water-bearing stage of a production well; C 1 , C 2 , C 3—Model parameters; K e —Effective permeability of the reservoir; h—effective reservoir thickness; Δp — Production pressure difference; μ o —Crude oil viscosity; B o —Crude oil volume coefficient; R—supply radius; r—wellbore radius; S—epidermal coefficient.

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