Tight sandstone reservoir productivity logging prediction method based on productivity sensitive parameters

Abstract

The invention belongs to the technical field of tight sandstone reservoir productivity prediction and exploration, and discloses a tight sandstone reservoir productivity logging prediction method based on productivity sensitive parameters. Comprising the following steps: finely classifying and evaluating a reservoir, constructing productivity sensitive parameters, obtaining a relationship between productivity and an energy storage coefficient by adopting a linear regression method, a principal component analysis method and a normalization method, and verifying the goodness of fit of the method. The method is high in operability, the productivity can be rapidly and accurately predicted through the tight sandstone reservoir productivity logging prediction method based on the productivity sensitive parameters, the favorable sandstone reservoir can be determined, and the method has important scientific guiding significance in the aspects of old well review, sandstone oil and gas resource exploration and the like.

Description

Technical Field

[0001] The present invention belongs to the technical field of productivity prediction exploration of tight sandstone reservoirs, and particularly relates to a logging prediction method for the productivity of tight sandstone reservoirs based on productivity sensitive parameters. Background Art

[0002] The productivity prediction of oil and gas reservoirs is a technology for comprehensively evaluating the oil production capacity of reservoirs. Generally speaking, productivity is a comprehensive index of the dynamic characteristics of oil and gas reservoirs, which is a certain dynamic balance reached during the mutual restriction process between the production potential of oil and gas reservoirs and various factors.

[0003] At present, there is no developed quantitative evaluation model for logging prediction of the productivity of tight sandstone reservoirs at home and abroad, and it is often roughly evaluated in an empirical way. Therefore, a fast and accurate logging prediction method for productivity is of great significance for the fine exploration and development of tight sandstone reservoirs. Summary of the Invention

[0004] In order to overcome the deficiencies of the prior art, the present invention provides a logging prediction method for the productivity of tight sandstone reservoirs based on productivity sensitive parameters, which can timely explore and develop tight sandstone reservoirs.

[0005] The above object of the present invention is achieved by the following technical solutions: A logging prediction method for the productivity of tight sandstone reservoirs based on productivity sensitive parameters, comprising the following steps:

[0006] 1. Select the oil testing wells in the study area, introduce the Flow Zone Index (FZI), carry out fine classification and evaluation of the reservoir, and divide the reservoir into three categories;

[0007] 2. Combine the oil testing and production testing data, analyze the logging response characteristics of different productivities, construct productivity sensitive parameters, and productivity is a comprehensive reaction of multiple parameters such as porosity, effective thickness, and saturation, that is, the energy storage coefficient;

[0008] 3. Use the linear regression method, principal component analysis method, and normalization method for some oil testing wells in the study area to obtain the relationship between productivity and the energy storage coefficient;

[0009] 4. Select another part of the oil testing wells in the study area, repeat steps 1-3, and verify the method coincidence degree.

[0010] Further, the FZI described in step 1 is a comprehensive determination parameter that combines rock structure and pore throat characteristics, etc., and can more accurately describe the heterogeneous characteristics of the oil reservoir. The larger the FZI value, the better the pore throat matching relationship.

[0011] Further, in step 1, the reservoir is divided into three categories. The specific classification method is as follows: for the first category, porosity > 15%, permeability > 10 mD, flow unit > 14, displacement pressure < 0.35 mPa, median pressure of mercury saturation < 1.0 mPa, average value of pore radius > 1.0 micron, AC > 235 μs / m, DEN ≤ 2.45 g / cm 3 ; for the second category, porosity > 10%, permeability > 1 mD, flow unit > 4, displacement pressure 0.35 - 0.5 mPa, median pressure of mercury saturation 1 - 3.6 mPa, average value of pore radius 0.3 - 1.0 micron, AC 228 - 235 μs / m, DEN 2.45 - 2.5 g / cm 3 ; for the third category, porosity > 9%, permeability > 0.1 mD, flow unit > 2, displacement pressure > 2.8 mPa, median pressure of mercury saturation < 3.6 mPa, average value of pore radius < 0.3 micron, AC 217 - 228 μs / m, DEN 2.5 - 2.52 g / cm 3 .

[0012] Further, step 3 specifically analyzes the relationship between production capacity and energy storage coefficient by using the following formula:

[0013]

[0014]

[0015] where Sj is the energy storage coefficient of the j-th type of reservoir; Φi is the porosity of the i-th sub-layer; Soi is the oil saturation of the i-th sub-layer; Hi is the effective thickness of the i-th sub-layer; Aj is the weighted value of the reservoir coefficient of the j-th type of reservoir.

[0016] The beneficial effects of the present invention compared with the prior art are as follows: this method has strong operability. Through this production logging prediction method for tight sandstone reservoirs based on production capacity sensitive parameters, rapid and accurate prediction of production capacity can be achieved, favorable sandstone reservoirs can be determined, and it has important scientific guiding significance for aspects such as rechecking old wells and exploring sandstone oil and gas resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention will be further described below in conjunction with the drawings and specific embodiments

[0018] Figure 1 is the relationship diagram between porosity and permeability;

[0019] Figure 2 is the relationship diagram between LogFZI and frequency;

[0020] Figure 3 is the mercury injection curve diagram;

[0021] Figure 4It is a graph showing the relationship between sensitive parameters and oil testing production;

[0022] Figure 5 It is a conceptual diagram of the energy storage coefficient weighted production prediction method. Specific implementation manners

[0023] The present invention will be described in detail below through specific embodiments, but the protection scope of the present invention is not limited. Unless otherwise specified, the experimental methods adopted in the present invention are all conventional methods, and the experimental equipment, materials, reagents, etc. used can be obtained from commercial channels.

[0024] Embodiment 1

[0025] A method for predicting well logging production of tight sandstone reservoirs based on production-sensitive parameters, comprising the following steps:

[0026] Step 1: The Yangdachengzi oil layer reservoir in Xinmiao area has low permeability and strong heterogeneity. In a set of sandstone reservoirs, the contribution weight values of different lithology and physical property intervals to the reservoir production capacity are different. Therefore, to accurately predict the reservoir production capacity, it is necessary to first carry out fine reservoir classification and evaluation, and here the parameter of flow zone index (FZI) is introduced. FZI is a comprehensive judgment parameter that combines rock structure and pore throat characteristics, etc., and can accurately describe the heterogeneous characteristics of the oil reservoir. The larger the FZI value, the better the pore throat matching relationship. Using FZI combined with the selected tight reservoir characteristic parameters, well logging response characteristics and oil testing results from mercury injection experiment data, a classification standard for pore structure is comprehensively obtained, and some oil testing wells in the study area are selected to comprehensively classify the reservoir into three categories.

[0027] Step 2: Combining oil testing and production testing data, analyze, summarize and conclude the well logging response characteristics of reservoirs with different production capacities, and construct production-sensitive parameters. Generally speaking, production capacity is a comprehensive index of the dynamic characteristics of oil and gas reservoirs, and it is a certain dynamic balance achieved during the mutual restriction process between the production potential of oil and gas reservoirs and various factors. The size of production capacity is restricted by multiple factors such as the geological conditions of the reservoir, engineering transformation technology, and environmental impact, and its own geological conditions depend on the lithology, physical properties and oil-bearing properties of the reservoir, and are finally determined by establishing a relationship between oil testing data and various reservoir parameters. Production capacity is a comprehensive reflection of multiple parameters such as porosity, effective thickness, and saturation.

[0028] Step 3: On the basis of fine well logging interpretation, considering that different sub-layers of the same sand body unit have different contributions to production capacity, the same sand body unit is finely classified according to the reservoir classification standard, and the reservoir is longitudinally segmented and classified, and then the energy storage coefficient of each sub-layer is determined, and the weights of various energy storage coefficients are determined by multiple regression.

[0029] By applying the weights of the energy storage coefficients of different types of reservoirs obtained, the cumulative production capacity of the target interval can be determined.

[0030]

[0031]

[0032] $S_j$ is the energy storage coefficient of the $j$-th type of reservoir;

[0033] $\Phi_i$ is the porosity of the $i$-th sub-layer;

[0034] $S_{oi}$ is the oil saturation of the $i$-th sub-layer;

[0035] $H_i$ is the effective thickness of the $i$-th sub-layer;

[0036] $A_j$ is the weighted value of the reservoir coefficient of the $j$-th type of reservoir.

[0037] Using the above formulas, the results are obtained for 17 oil testing wells in the study area by linear regression, principal component analysis, and normalization methods.

[0038] Result formula: $y = 0.845\times(0.429\times S$ Ⅰ $+ 0.388\times S$ Ⅱ $+ 0.183\times S$ Ⅲ )

[0039] Therefore, the logging productivity prediction mainly considers three key parameters: ① The classification levels of each contributing layer (Class I, Class II, Class III); ② The fine interpreted energy storage coefficient of the sub-layer; ③ The contribution rate (weight) of sub-layers at different levels to productivity.

[0040] Step 4: Select other oil testing wells in the study area to verify (i.e., repeat Steps 1 - 3) the error rate of this method and determine the degree of fit of this method.

[0041] Table 1 Reservoir Classification Statistical Table

[0042]

[0043]

[0044] The above-established logging prediction method for the productivity of tight sandstone reservoirs based on productivity-sensitive parameters has established a logging method for quickly and accurately predicting productivity and effectively determining high-quality sandstone reservoirs. It solves the problem of difficult productivity prediction for tight sandstone reservoirs. The whole method is highly operable and has important scientific guiding significance for aspects such as rechecking old wells and exploring sandstone oil and gas resources.

[0045] The above-described embodiments are only the preferred embodiments of the present invention and not all the feasible embodiments of the present invention. For those of ordinary skill in the art, any obvious changes made without departing from the principles and spirit of the present invention should be considered to be included within the scope of protection of the claims of the present invention.

Claims

1. A method for predicting the productivity of tight sandstone reservoirs based on productivity-sensitive parameters, characterized in that, It includes the following steps: S1. Select the oil testing wells in the study area, introduce FZI, conduct fine classification and evaluation of the reservoir, and divide the reservoir into three categories; S2. Combine the oil testing and production testing data, analyze the logging response characteristics of different production capacities, construct the production capacity sensitive parameters. The production capacity is the comprehensive reaction of multiple parameters such as porosity, effective thickness, and saturation, that is, the energy storage coefficient; S3. Use the linear regression method, principal component analysis method, and normalization method for some of the oil testing wells in the study area to obtain the relationship between the production capacity and the energy storage coefficient; S4. Select another part of the oil testing wells in the study area, repeat steps S1 - S3, and verify the method coincidence degree.

2. The productivity logging prediction method for tight sandstone reservoirs based on productivity-sensitive parameters according to claim 1, wherein The FZI described in step S1 is a comprehensive determination parameter that combines rock structure and pore throat characteristics, etc.

3. The productivity logging prediction method for tight sandstone reservoirs based on productivity-sensitive parameters according to claim 2, characterized in that The larger the FZI value, the better the pore throat matching relationship.

4. The productivity logging prediction method for tight sandstone reservoirs based on productivity-sensitive parameters according to claim 1, wherein In step S1, the reservoir is divided into three categories through porosity, permeability, flow unit, displacement pressure, median pressure of mercury saturation, average value of pore radius, AC, and DEN.

5. The productivity logging prediction method for tight sandstone reservoirs based on productivity-sensitive parameters according to claim 4, wherein The porosity of the first type of reservoir is > 15%, the permeability is > 10 mD, the flow unit is > 14, the displacement pressure is < 0.35 mPa, the median pressure of mercury saturation is < 1.0 mPa, the average value of pore radius is > 1.0 micron, AC > 235 μs / m, DEN ≤ 2.45 g / cm 3 .

6. The productivity logging prediction method for tight sandstone reservoirs based on productivity-sensitive parameters according to claim 4, wherein The porosity of the second type of reservoir is >10%, the permeability is >1 mD, the flow unit is >4, the displacement pressure is 0.35 - 0.5 mPa, the median pressure of mercury saturation is 1 - 3.6 mPa, the average value of pore radius is 0.3 - 1.0 microns, AC is 228 - 235 μs / m, and DEN is 2.45 - 2.5 g / cm 3 .

7. The productivity logging prediction method for tight sandstone reservoirs based on productivity-sensitive parameters according to claim 4, wherein The porosity of the third type of reservoir is >9%, the permeability is >0.1 mD, the flow unit is >2, the displacement pressure is >2.8 mPa, the median pressure of mercury saturation is <3.6 mPa, the average value of pore radius is <0.3 microns, AC is 217 - 228 microseconds / meter, and DEN is 2.5 - 2.52 g / cm 3 .

8. The productivity logging prediction method for tight sandstone reservoirs based on productivity-sensitive parameters according to claim 1, characterized in that, Step 3 specifically analyzes and obtains the relationship between the production capacity and the energy storage coefficient by using the following formula: Among them, Sj is the energy storage coefficient of the jth type of reservoir; Φi is the porosity of the ith small layer; Soi is the oil saturation of the ith small layer; Hi is the effective thickness of the ith small layer; Aj is the weighted value of the reservoir coefficient of the jth type of reservoir.

Images