Productivity prediction method for developing fault block low-permeability reservoir based on multistage fracturing horizontal well

Abstract

The invention relates to the technical field of exploration and development of low-permeability reservoirs, in particular to a productivity prediction method for developing fault block low-permeability reservoirs based on a multistage fractured horizontal well, which comprises the following steps of: preparing productivity data, and determining the relationship between productivity and geological parameters according to a material balance equation; horizontal well fracturing parameters are prepared and screened, factors influencing productivity are qualitatively analyzed according to a horizontal well productivity formula, and fracturing parameters related to the fracturing effect are determined; calculating the grey relational degree of each factor based on a grey relational degree method; calculating a weight coefficient corresponding to each factor by adopting a weighted average method based on the correlation degree of each factor; determining an analogy relation between the EUR and the half-year yield by utilizing linear regression analysis; and calculating the EUR of the low-permeability reservoir oil well of which the production time is less than one year by using an analogy relation between the EUR and the half-year yield. According to the method, the EUR prediction coincidence rate reaches 0.8 or above, and a reference basis is provided for oil field block economic development scheme deployment.

Description

Technical Field

[0001] The invention relates to the technical field of low-permeability oil reservoir exploration and development, and is a production capacity prediction method for developing fault block low-permeability oil reservoirs based on multi-stage fracturing horizontal wells. Background Art

[0002] In recent years, the exploration and development of low-permeability oil and gas reservoirs has become a new development direction in the oil and gas field. Segmented multi-cluster fracturing + horizontal wells are currently the most commonly used reservoir transformation methods. The accuracy of production capacity prediction can affect the deployment of oilfield development plans and economic decisions.

[0003] The factors affecting the productivity of fractured horizontal wells in low permeability reservoirs are mainly divided into geological factors, engineering factors and well pattern factors. A lot of previous work has been done, mainly as follows: Lang Zhaoxin et al. applied the reset potential theory and potential superposition principle to derive the productivity formula of fractured horizontal wells with multiple fractures, and demonstrated the relationship between fracture length, density and horizontal well productivity. Lin Wang et al. used numerical simulation methods to study the main controlling factors of fractured horizontal well development effects on tight oil reservoirs.

[0004] At present, the main EUR methods for low permeability reservoirs are empirical model method and analogy method. The empirical model method mainly includes SPED, PowerLaw, Apr, Duong index model, etc. The analogy method is to compare and calculate the EUR (Economic Ultrua Rate) of similar blocks based on the EUR values ​​of developed wells in blocks with similar geological conditions. The above methods for predicting and evaluating the productivity of fractured horizontal wells in low permeability reservoirs have the following disadvantages:

[0005] (1) Disadvantages of the empirical model method: Currently, the flowback time and production system adjustment period after fracturing of horizontal wells are relatively long, and generally a prediction can only be made after one year.

[0006] (2) Disadvantages of the analogy method: Low permeability reservoirs have strong reservoir heterogeneity, and the formation pressure coefficient, permeability, porosity, and microfracture development parameters vary greatly, resulting in the unreliability of using the analogy method to predict EUR. Summary of the invention

[0007] The present invention provides a method for predicting the production capacity of fault block low-permeability reservoirs based on multi-stage fracturing horizontal well development, which overcomes the shortcomings of the above-mentioned prior art and can effectively solve the problems in the prior art of single fluid interpretation method for tight gas reservoirs, weak pertinence and low compliance rate.

[0008] The technical solution of the present invention is achieved through the following measures: A method for predicting the production capacity of a fault block low permeability reservoir developed by staged multi-cluster fracturing horizontal wells, comprising the following steps:

[0009] S1. Prepare capacity data and determine the relationship between capacity and geological parameters based on the material balance equation;

[0010] S2. Prepare and screen horizontal well fracturing parameters, qualitatively analyze factors affecting production capacity based on the horizontal well production capacity formula, and determine fracturing parameters related to fracturing effect;

[0011] S3, calculate the grey correlation degree of each factor based on the grey correlation method;

[0012] S4. Based on the correlation of each factor, the corresponding weight coefficient of each factor is calculated by using the weighted average method;

[0013] S5. Determine the analogy relationship between EUR and half-year output using linear regression analysis;

[0014] S6. Use the analogy between EUR and half-year production to calculate the EUR of low-permeability reservoir wells that have been put into production for less than one year.

[0015] The following are further optimizations and / or improvements to the above technical solutions:

[0016] The above step S1 specifically includes the following steps:

[0017] Prepare production capacity data, use empirical models to predict the cumulative oil production of each well, and then split the cumulative oil production of a single well into individual wells and individual layers based on the results of stratified oil testing or formation coefficients;

[0018] The correlation between the half-year cumulative production and geological parameters is guided by the material balance method; according to the material balance equation of the comprehensive driving reservoir, it is simplified to obtain:

[0019] ΔV og =NB oi c eff Δp+W

[0020] Where, △V og c is the underground oil and gas volume of the reservoir eff is the effective compression coefficient of the comprehensive driving reservoir, N is the geological reserves of the reservoir, B oi is the volume coefficient of formation crude oil, △P is the pressure drop between the original formation pressure and the current formation pressure, and W is the water invasion.

[0021] The above step S2 specifically includes the following steps:

[0022] Prepare and screen horizontal well fracturing parameters, qualitatively analyze factors affecting productivity based on horizontal well productivity formula, and determine fracturing parameters related to fracturing effect;

[0023] When considering that the horizontal well penetrates the entire reservoir, the steady-state flow production formula of the unfractured horizontal well is:

[0024]

[0025] In the formula, q is the oil well production, k is the formation permeability, h is the oil layer thickness, L is the horizontal section length, r w is the wellbore radius, u is the fluid viscosity, and △p is the production pressure difference.

[0026] In the above step S2, the Pearson correlation coefficient method is used to analyze the correlation between various factors and the cumulative output for half a year.

[0027] In the above step S3, porosity, oil layer thickness, oil saturation, mobility, horizontal section length, fracturing length, number of fracturing stages, average sand volume per stage, average liquid volume per stage, total liquid usage, sand addition volume, and number of fracturing clusters are selected for grey correlation analysis to obtain the grey correlation degree of each factor.

[0028] In the above step S3, the horizontal well length, the average sand volume of a single stage, the average liquid volume of a single stage, the fracturing length, the number of fracturing stages, the average sand volume of a single stage, and the average liquid volume of a single stage are standardized, and the standardized format is:

[0029]

[0030] Where N nor is the standardized value of the variable, N is the original value of the variable, and N min is the minimum value of the block variable, N max The minimum value of the variable in this block.

[0031] In the above step S4, a dimensionless index is calculated based on the weight coefficient, namely:

[0032]

[0033]

[0034] In the formula, w i is the weight coefficient corresponding to each factor, x i is a dimensionless factor, n is the number of factors, r i is the correlation degree of each factor, DI is a dimensionless index;

[0035] The linear relationship between the dimensionless index and the half-year output is established using the least squares method, namely:

[0036] q=a*DI+b

[0037] In the above formula, q is the cumulative output for half a year, a and b are regression coefficients, and DI is a dimensionless index.

[0038] The above step S5 specifically includes the following steps:

[0039] For oil wells that have been in production for more than half a year, the traditional method is used to calculate EUR; the analogy relationship between EUR and half-year production is determined using linear regression analysis;

[0040] According to the relationship between EUR, half-year production and reservoir thickness, the regression formula is established as follows:

[0041] EUR=a*q+b*h+c

[0042] In the formula, EUR is the economic final output, q is the half-year output of a single well, h is the oil layer thickness, and a, b, and c are regression coefficients;

[0043] This regression formula is an analogy between EUR and half-year output.

[0044] The purpose of the present invention is to provide a method for predicting the production capacity of a fractured horizontal well for which the EUR calculation cannot be calculated by the empirical model due to the short production time. In the practical application of low permeability in a certain block, the present invention is suitable for calculating the EUR when the production time is short and the empirical model method cannot be used, and the present invention has higher accuracy than the analogy method. The EUR prediction compliance rate reaches more than 0.8, which provides a reference basis for the deployment of economic development plans for oil field blocks. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Attached Figure 1 Schematic diagram of a flow chart of an embodiment of the present invention.

[0046] Attached Figure 2 This is the correlation heat map of Pearson correlation coefficient method.

[0047] Attached Figure 3 It is the correlation curve diagram of each factor and the half-year output (resolution coefficient p=0.5).

[0048] Attached Figure 4 It is the EUR fitting diagram of the embodiment of the present invention. DETAILED DESCRIPTION

[0049] The present invention is not limited by the following embodiments, and specific implementation methods can be determined based on the technical solution of the present invention and actual conditions.

[0050] In the present invention, for the convenience of description, the description of the relative position relationship of each component is described according to the layout of the drawings in the specification, such as: the position relationship of front, back, up, down, left, right, etc. is determined according to the layout direction of the drawings in the specification.

[0051] The present invention will be further described below in conjunction with embodiments and drawings:

[0052] Embodiment 1: As attached Figure 1 , 2As shown in , 3 and 4, the production capacity prediction method for developing fault block low permeability reservoirs by segmented multi-cluster fractured horizontal wells includes the following steps:

[0053] S1. Prepare capacity data and determine the relationship between capacity and geological parameters based on the material balance equation. Step S1 specifically includes the following steps:

[0054] Prepare production capacity data, use empirical models to predict the cumulative oil production of each well, and then split the cumulative oil production of a single well into individual wells and individual layers based on the results of stratified oil testing or formation coefficients;

[0055] The correlation between the half-year cumulative production and geological parameters is guided by the material balance method; according to the material balance equation of the comprehensive driving reservoir, it is simplified to obtain:

[0056] ΔV og =NB oi C eff Δp+W

[0057] Where, △V og is the underground oil and gas volume of the reservoir, m 3 ;c eff is the effective compressibility coefficient of the comprehensive driving reservoir, MPa -1 , N is the geological reserves of the reservoir, B oi is the formation crude oil volume coefficient, △P is the pressure drop between the original formation pressure and the current formation pressure, and W is the water invasion. This shows that the cumulative oil production is related to geological reserves, effective compression coefficient, crude oil volume coefficient, reservoir pressure drop and reservoir water storage; under the same well pattern deployment and fluid properties, the cumulative oil production is related to porosity, oil layer thickness and oil saturation.

[0058] Among them, the material balance equation of the above-mentioned comprehensive driving reservoir is a basic equation in the principle of reservoir engineering, which is a prior art and will not be repeated here.

[0059] S2, preparing and screening horizontal well fracturing parameters, qualitatively analyzing the factors affecting the production capacity according to the horizontal well production capacity formula, and determining the fracturing parameters related to the fracturing effect. Step S2 specifically includes the following steps:

[0060] Prepare and screen horizontal well fracturing parameters, qualitatively analyze the factors affecting productivity based on the horizontal well productivity formula, and determine the fracturing parameters related to the fracturing effect; staged multi-cluster fracturing of horizontal wells greatly increases the contact area between the wellbore and the reservoir. At the same time, the cracks formed by fracturing increase the seepage channels of the reservoir fluid, expand the oil leakage range of the horizontal well, and improve the productivity of the horizontal well.

[0061] When considering that the horizontal well penetrates the entire reservoir, the steady-state flow production formula of the unfractured horizontal well is:

[0062]

[0063] In the above formula, q is the oil well production, k is the formation permeability, h is the oil layer thickness, L is the horizontal section length, r w is the wellbore radius, μ is the fluid viscosity, and △p is the production pressure difference.

[0064] It can be concluded that the horizontal well production is related to fluid mobility, production pressure difference, reservoir thickness, well diameter, horizontal section length L and well spacing. When considering fracturing, it can be considered that the cracks run through the entire reservoir, and the horizontal well production is related to the scale of fracturing. Parameters that affect the fracturing effect include: fracturing length, number of fracturing stages, average sand volume per stage, average liquid volume per stage, total liquid used, sand addition volume, and number of fracturing clusters.

[0065] In step S2, the Pearson correlation coefficient method is used to analyze the correlation between various factors and the cumulative output for half a year.

[0066] S3. The grey correlation degree of each factor is calculated based on the grey correlation method. The form of each factor in the reservoir formula should be fully considered when selecting parameters. In step S3, porosity, oil layer thickness, oil saturation, mobility (k / μ), horizontal section length, fracturing length, number of fracturing stages, average sand volume per stage, average liquid volume per stage, total liquid usage, sand addition volume, and number of fracturing clusters are selected for grey correlation analysis to obtain the grey correlation degree of each factor.

[0067] The porosity and oil saturation parameters of horizontal wells should be calculated based on the average value of each perforation section and then weighted averaged according to the length of the perforation section. In order to facilitate the establishment of a multi-factor model, some influencing factors can be dimensionless, such as oil saturation, porosity, total fluid consumption, and sand addition. The dimensionless oil layer porosity is equal to the ratio of the porosity of the layer in a single well to the average oil layer porosity of the layer in the block. The dimensionless oil layer saturation is equal to the ratio of the oil saturation of the layer in the block to the average oil saturation of the layer in the block. The dimensionless total fluid consumption is equal to the ratio of the total fluid consumption of the layer in a single well to the average total fluid consumption of the layer in the area. The dimensionless sand addition is equal to the ratio of the sand addition of the layer in a single well to the average sand addition of the layer in the area.

[0068] In step S3, the horizontal well length, the average sand volume of a single stage, the average liquid volume of a single stage, the fracturing length, the number of fracturing stages, the average sand volume of a single stage, and the average liquid volume of a single stage are standardized, and the standardized format is:

[0069]

[0070] Where N nor is the standardized value of the variable, N is the original value of the variable, and N min is the minimum value of the block variable, N max The minimum value of the variable in this block.

[0071] Table 1 is a grey relational analysis table when the resolution coefficient p=0.5.

[0072] Table 1 Grey relational analysis table (resolution coefficient p = 0.5)

[0073]

[0074] S4. Based on the correlation of each factor, the weight coefficient of each factor is calculated by weighted average method. In step S4, a dimensionless index is calculated based on the weight coefficient, namely:

[0075]

[0076]

[0077] In the formula, w i is the weight coefficient corresponding to each factor, x i is a dimensionless factor, n is the number of factors, r i It is the correlation degree of each factor, and DI is a dimensionless index.

[0078] The linear relationship between the dimensionless index and the half-year output is established using the least squares method, namely:

[0079] q=a*DI+b

[0080] In the above formula, q is the cumulative output for half a year, a and b are regression coefficients, and DI is a dimensionless index.

[0081] S5, using linear regression analysis to determine the analogy relationship between EUR and half-year output. Step S5 specifically includes the following steps:

[0082] For oil wells that have been put into production for more than half a year, the traditional method is used to calculate EUR; the analogy between EUR and half-year production is determined by linear regression analysis. There is a certain linear relationship between half-year production and EUR, but the correlation coefficient is low. The reason for the analysis may be that the thickness of the oil layer affects the decline of the oil well in the later stage, thus affecting the prediction of EUR.

[0083] According to the relationship between EUR, half-year production and reservoir thickness, the regression formula is established as follows:

[0084] EUR=a*q+b*h+c

[0085] In the formula, EUR is the economic final output, q is the half-year output of a single well, h is the oil layer thickness, and a, b, and c are regression coefficients. This regression formula is the analogy between EUR and half-year output.

[0086] S6. Use the analogy between EUR and half-year production to calculate the EUR of low-permeability reservoir wells that have been put into production for less than one year.

[0087] In the specific application practice of the embodiment of the present invention, the production capacity prediction method of a low-permeability oil reservoir block includes the following steps: using the empirical model method to calculate the EUR of all horizontal wells in the block; standardizing 11 factors (i.e., the number of fracturing clusters, the amount of sand added, the total amount of liquid used, the average liquid volume of a single stage, the average sand volume of a single stage, the number of fracturing stages, the length of the horizontal section, the oil saturation, the mobility, the thickness of the oil layer, and the porosity) to calculate the DI; establishing a linear relationship between the dimensionless index and the half-year production; according to the relationship between the EUR and the half-year production and the oil layer thickness, a regression formula is established; the formula for determining the model is:

[0088] Y=-186129.662+0.13*x1+10431.854*x2

[0089] The purpose of the embodiment of the present invention is to provide a production capacity prediction method for fractured horizontal wells that cannot use empirical models to calculate EUR when the production time is short. In the actual application of low permeability in a certain block, the embodiment of the present invention is suitable for calculating EUR when the production time is short and the empirical model method cannot be used, and the embodiment of the present invention is more accurate than the analogy method. The EUR prediction compliance rate reaches more than 0.8, which provides a reference for the deployment of economic development plans for oil field blocks.

[0090] The above technical features constitute the embodiments of the present invention, which have strong adaptability and implementation effect. Non-essential technical features can be added or reduced according to actual needs to meet the requirements of different situations.

Claims

1. A method for predicting the production capacity of fault-block low-permeability reservoirs by staged multi-cluster fracturing horizontal wells, characterized in that The following steps are involved: S1. Prepare capacity data and determine the relationship between capacity and geological parameters based on the material balance equation; S2. Prepare and screen horizontal well fracturing parameters, qualitatively analyze factors affecting production capacity based on the horizontal well production capacity formula, and determine fracturing parameters related to fracturing effect; S3, calculate the grey correlation degree of each factor based on the grey correlation method; S4. Based on the correlation of each factor, the corresponding weight coefficient of each factor is calculated by using the weighted average method; S5. Determine the analogy relationship between EUR and half-year output using linear regression analysis; S6. Use the analogy between EUR and half-year production to calculate the EUR of low-permeability reservoir wells that have been put into production for less than one year.

2. The method for predicting the production capacity of a fault block low permeability reservoir based on multi-stage fracturing horizontal well development according to claim 1, characterized in that Step S1 specifically includes the following steps: Prepare production capacity data, use empirical models to predict the cumulative oil production of each well, and then split the cumulative oil production of a single well into individual wells and individual layers based on the results of stratified oil testing or formation coefficients; The correlation between the half-year cumulative production and geological parameters is guided by the material balance method; according to the material balance equation of the comprehensive driving reservoir, it is simplified to obtain: ΔV og =NB oi c eff Δp+W Where, △V og is the underground oil and gas volume produced by the reservoir, c eff is the effective compression coefficient of the comprehensive driving reservoir, N is the geological reserves of the reservoir, B oi is the volume coefficient of formation crude oil, △P is the pressure drop between the original formation pressure and the current formation pressure, and W is the water invasion.

3. The method for predicting the production capacity of a fault block low permeability reservoir based on multi-stage fracturing horizontal well development according to claim 1, characterized in that Step S2 specifically includes the following steps: Prepare and screen horizontal well fracturing parameters, qualitatively analyze factors affecting productivity based on horizontal well productivity formula, and determine fracturing parameters related to fracturing effect; When considering that the horizontal well penetrates the entire reservoir, the steady-state flow production formula of the unfractured horizontal well is: In the formula, q is the oil well production, k is the formation permeability, h is the oil layer thickness, L is the horizontal section length, r w is the wellbore radius, u is the fluid viscosity, and △p is the production pressure difference.

4. The method for predicting the production capacity of a fault block low permeability reservoir based on multi-stage fracturing horizontal well development according to claim 1, 2 or 3, characterized in that In step S2, the Pearson correlation coefficient method is used to analyze the correlation between each factor and the cumulative output for half a year.

5. The method for predicting the production capacity of fault block low permeability reservoirs based on multi-stage fracturing horizontal well development according to claim 1, 2 or 3, characterized in that In step S3, porosity, oil layer thickness, oil saturation, mobility, horizontal section length, fracturing length, number of fracturing stages, average sand volume per stage, average liquid volume per stage, total liquid usage, sand addition volume, and number of fracturing clusters are selected for grey correlation analysis to obtain the grey correlation degree of each factor.

6. The method for predicting the production capacity of a fault block low permeability reservoir based on multi-stage fracturing horizontal well development according to claim 4, characterized in that In step S3, porosity, oil layer thickness, oil saturation, mobility, horizontal section length, fracturing length, number of fracturing stages, average sand volume per stage, average liquid volume per stage, total liquid usage, sand addition volume, and number of fracturing clusters are selected for grey correlation analysis to obtain the grey correlation degree of each factor.

7. The method for predicting the production capacity of a fault block low permeability reservoir based on multi-stage fracturing horizontal well development according to claim 5, characterized in that In step S3, the horizontal well length, the average sand volume of a single stage, the average liquid volume of a single stage, the fracturing length, the number of fracturing stages, the average sand volume of a single stage, and the average liquid volume of a single stage are standardized, and the standardized format is: Where N nor is the standardized value of the variable, N is the original value of the variable, and N min is the minimum value of the block variable, N max The minimum value of the variable in this block.

8. The method for predicting the productivity of fault block low permeability reservoirs based on multi-stage fracturing horizontal well development according to claim 1, 2, 3, 6 or 7, characterized in that In step S4, a dimensionless index is calculated based on the weight coefficient, namely: In the formula, w i is the weight coefficient corresponding to each factor, x i is a dimensionless factor, n is the number of factors, r i is the correlation degree of each factor, and DI is a dimensionless index; The linear relationship between dimensionless index and half-year output is established by using the least squares method, namely: q=a*DI+b In the above formula, q is the cumulative output for half a year, a and b are regression coefficients, and DI is a dimensionless index.

9. The method for predicting the productivity of fault block low permeability reservoirs based on multi-stage fracturing horizontal well development according to claim 1, 2, 3, 6 or 7, characterized in that Step S5 specifically includes the following steps: For oil wells that have been in production for more than half a year, the traditional method is used to calculate EUR; the analogy relationship between EUR and half-year production is determined using linear regression analysis; According to the relationship between EUR, half-year production and reservoir thickness, the regression formula is established as follows: EUR=a*q+b*h+c In the formula, EUR is the economic final output, q is the half-year output of a single well, h is the oil layer thickness, and a, b, and c are regression coefficients; This regression formula is an analogy between EUR and half-year output.

10. The method for predicting the production capacity of fault block low permeability reservoirs based on multi-stage fracturing horizontal well development according to claim 4, characterized in that Step S5 specifically includes the following steps: For oil wells that have been in production for more than half a year, the traditional method is used to calculate EUR; the analogy relationship between EUR and half-year production is determined using linear regression analysis; According to the relationship between EUR, half-year production and reservoir thickness, the regression formula is established as follows: EUR=a*q+b*h+c In the formula, EUR is the economic final output, q is the half-year output of a single well, h is the oil layer thickness, and a, b, and c are regression coefficients; This regression formula is an analogy between EUR and half-year output.

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