A Classification and Evaluation Method for Common Heavy Oil Reservoirs

Abstract

This invention discloses a method for classifying and evaluating ordinary heavy oil reservoirs, comprising the following steps: 1) selecting sample wells from oil wells in a block or well area; 2) obtaining the pseudo-flow coefficient F of the reservoir in the sample wells. pcr Surface crude oil viscosity u o Initial production capacity q o The method involves several steps: 3) Listing all selected sample well numbers and their corresponding three parameters to form a cluster analysis sample set; 4) Based on reservoir characteristics and development practice, setting the number of reservoir classification categories to n, and generating a reservoir cluster analysis phylogenetic map; 5) Performing regression analysis on the clustering results to obtain discrimination models for different types of reservoirs and determine the category to which the reservoir belongs. The reservoir classification and evaluation method for ordinary heavy oil reservoirs provided by this invention can effectively distinguish reservoir differences, guide the adoption of different development strategies and methods for the reservoir, and improve the level of crude oil development.

Description

Technical Field

[0001] This invention relates to reservoir evaluation technology, and more particularly to a method for classifying and evaluating ordinary heavy oil reservoirs. Background Technology

[0002] Currently, there are three main methods for classifying and evaluating oil reservoirs. The first method mainly uses static parameters for classification and evaluation, commonly employing methods such as microscopic pore throat parameters and seismic attributes. However, this method suffers from limitations such as a small number of sample points and poor seismic quality, resulting in low accuracy. The second method evaluates reservoirs by studying the single-factor relationship between production capacity or output and static parameters. This method considers fewer factors and is difficult to conduct comprehensive multi-parameter evaluations. The third method uses relevant laboratory experimental test parameters from core wells as the research object and employs statistical analysis methods for classification and evaluation. However, this method is limited by the richness of core data and the collection of experimental test data, resulting in poor economy and timeliness.

[0003] It is challenging to conduct reservoir evaluation by comprehensively considering reservoir properties, fluid properties, and production dynamic parameters. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method for classifying and evaluating ordinary heavy oil reservoirs, addressing the deficiencies in the existing technology.

[0005] The technical solution adopted by this invention to solve its technical problem is: a method for classifying and evaluating ordinary heavy oil reservoirs, comprising the following steps:

[0006] 1) Select sample wells from oil wells in the block or well area;

[0007] The principles for selecting sample wells are as follows:

[0008] The sample well is a single-production directional well (a vertical well that only perforates a single layer); the sample well is an early production well in a block or well area; the sample well has a water cut of less than 20% in the early stage of production; the sample well has crude oil viscosity analysis data; the sample well has been in stable production for more than one year and can obtain initial production, without any production enhancement measures during the period;

[0009] 2) Obtain the pseudo-flow coefficient F of the sample well reservoir pcr Surface crude oil viscosity u o Initial production capacity q o parameter;

[0010] Specifically as follows:

[0011] pseudo-flow coefficient F pcr ,

[0012]

[0013] in,

[0014] K o (s w ) = K ro (s w )K a ;

[0015] K ro (s w K represents the relative permeability of the oil phase, obtained from relative permeability curve testing. a The reservoir air permeability is obtained from well logging or laboratory experiments; u o h represents the surface crude oil viscosity, obtained from full analysis of crude oil samples taken from the wellhead; h represents the effective perforation thickness of the production well, obtained from well logging interpretation; q represents... o This represents the initial production capacity of the oil well, calculated as the daily average of the production during the last three months of continuous production over a period of 12 months.

[0016] 3) List all selected sample well numbers and their corresponding three parameters to form a cluster analysis sample set;

[0017] 4) Based on reservoir characteristics and development practice, set the number of reservoir classification categories to n, and generate a reservoir cluster analysis hierarchy diagram;

[0018] 5) Use the Bayesian discriminant model in SPSS software to regress the clustering results and obtain the discriminant model F for different types of reservoirs. i Substitute the reservoir parameters into the respective reservoir discrimination models to calculate F. i The value is used to determine the reservoir category F according to the principle of the maximum value. m ;

[0019] The discrimination models for different types of reservoirs are as follows:

[0020] F i =a i ×u o +b i ×F pcr +c i ×q o +A i

[0021] F max =max(F1,F2,…F i )

[0022] Where i is the reservoir classification number, i = 1, 2, 3, ..., n; a i b is the viscosity term constant for the classification regression of the i-th type of reservoir; i c is the constant of the pseudo-flow coefficient term in the regression of the i-th type of reservoir; i Let A be the constant of the productivity term in the regression of the i-th type of reservoir;i Let F be the constant for the classification regression of the i-th type of reservoir. max F was calculated for this reservoir in different reservoir discrimination models. i The maximum value, for example, F max =F2 means that the reservoir belongs to the second type of reservoir.

[0023] The beneficial effects of this invention are:

[0024] The reservoir classification and evaluation method for ordinary heavy oil reservoirs provided by this invention reduces the time, manpower, and material resources required to obtain pore throat parameters through a large number of indoor experiments; at the same time, it avoids the defects of unreasonable evaluation results and difficulty in providing development guidance caused by using a single factor for reservoir classification and evaluation. It can effectively distinguish reservoir differences, guide reservoirs to adopt different development strategies and methods, and improve the level of crude oil development. Attached Figure Description

[0025] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings:

[0026] Figure 1 This is a flowchart of a method according to an embodiment of the present invention;

[0027] Figure 2 This is a cluster analysis phylogenetic diagram in the reservoir classification and evaluation of this invention.

[0028] Figure 3 This is the reservoir classification and evaluation discriminant function in this embodiment of the invention. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0030] like Figure 1 As shown, a method for classifying and evaluating ordinary heavy oil reservoirs includes the following steps:

[0031] 1) Select sample wells from oil wells in the block or well area;

[0032] The principles for selecting sample wells are as follows:

[0033] The sample well is a single-production directional well (a vertical well that only perforates a single layer); the sample well is an early production well in a block or well area; the sample well has a water cut of less than 20% in the early stage of production; the sample well has crude oil viscosity analysis data; the sample well has been in stable production for more than one year and can obtain initial production, without any production enhancement measures during the period;

[0034] 2) Obtain the pseudo-flow coefficient F of the sample well reservoir pcrSurface crude oil viscosity u o Initial production capacity q o parameter;

[0035] Specifically as follows:

[0036] pseudo-flow coefficient F pcr ,

[0037]

[0038] in,

[0039] K o (s w ) = K ro (s w )K a ;

[0040] K ro (s w K represents the relative permeability of the oil phase, obtained from relative permeability curve testing. a The reservoir air permeability is obtained from well logging or laboratory experiments; u o h represents the surface crude oil viscosity, obtained from full analysis of crude oil samples taken from the wellhead; h represents the effective perforation thickness of the production well, obtained from well logging interpretation; q represents... o This represents the initial production capacity of the oil well, calculated as the daily average of the production during the last three months of continuous production over a period of 12 months.

[0041] 3) List all selected sample well numbers and their corresponding three parameters to form a cluster analysis sample set;

[0042] Table 1 List of Three Parameters

[0043]

[0044] 4) Based on reservoir characteristics and development practice, set the number of reservoir classification categories to n, and generate a reservoir cluster analysis hierarchy diagram;

[0045] Using the cluster analysis function of SPSS software, based on reservoir characteristics and development practice, the number of reservoir classification categories, n, was set. The spatial distance of each parameter was calculated using the Mahalanobis distance model, and the Ward method (sum of squared deviations) was employed for clustering. Then, a reservoir cluster analysis hierarchy diagram was generated. Figure 2 As shown;

[0046] 5) Use the Bayesian discriminant model in SPSS software to regress the clustering results and obtain the discriminant model F for different types of reservoirs. i Substitute the reservoir parameters into the respective reservoir discrimination models to calculate F. i The value is used to determine the reservoir category F according to the principle of the maximum value. m ;

[0047] The discrimination models for different types of reservoirs are as follows:

[0048] F i =a i ×u o +b i ×F pcr +c i ×q o +A i

[0049] F max =max(F1,F2,…F i )

[0050] Where i is the reservoir classification number, i = 1, 2, 3, ..., n; a i b is the viscosity term constant for the classification regression of the i-th type of reservoir; i c is the constant of the pseudo-flow coefficient term in the regression of the i-th type of reservoir; i Let A be the constant of the productivity term in the regression of the i-th type of reservoir; i Let F be the constant for the classification regression of the i-th type of reservoir. max F was calculated for this reservoir in different reservoir discrimination models. i The maximum value, for example, F max =F2 means that the reservoir belongs to the second type of reservoir.

[0051] The Bayesian discriminant model obtained from data regression in this embodiment is as follows: Figure 3 .

[0052] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A general heavy oil reservoir reservoir classification evaluation method, characterized in that, Comprise the following steps: 1) select sample wells in block or well area oil wells; 2) Obtain sample well reservoir pseudo-flow coefficient , surface crude oil viscosity , initial deliverability parameters; Specific as follows: Pseudo-flow coefficient ； Wherein, ； Kro is the oil phase relative permeability, obtained from relative permeability curves test; Krg is the gas phase relative permeability, obtained from relative permeability curves test; μo is the surface crude oil viscosity, obtained from wellhead crude oil sampling full analysis test; h is the producing well pay thickness, obtained from well logging interpretation; Qo is the oil well initial productivity, which is the average daily production of the last three months of the continuous production for 12 months. 3) all selected sample well numbers and their corresponding three parameters are listed to form a cluster analysis sample set; 4) according to the characteristics and development practice of the reservoir, set the number of reservoir classification categories as n, and generate a reservoir cluster analysis pedigree chart; 5) Perform regression analysis on the clustering results to obtain discrimination models for different types of reservoirs. Substitute the reservoir parameters into each reservoir discrimination model to calculate. The value is used to determine the reservoir category according to the principle of the maximum value. ; Wherein, the discriminant model of different types of reservoirs is as follows: = × + × + × + wherein, is the reservoir classification number, ; is the viscosity term constant of the reservoir classification regression for the reservoir classification regression; is the pseudo flow coefficient term constant of the reservoir classification regression for the reservoir classification regression; is the deliverability term constant of the reservoir classification regression for the reservoir classification regression; is the constant of the reservoir classification regression for the reservoir classification regression, is the maximum value of the reservoir calculated by different reservoir discrimination models. ​ 2. The method according to claim 1, wherein, In the step 1), the sample well selection principles are as follows: The sample well is a single directional well; The sample well is an early production well in the block or well area; The sample well is a well with water content less than 20% at the early stage of production; The sample well has crude oil viscosity analysis test data; The sample well has been stably produced for more than one year, the initial production can be calculated, and there is no stimulation measure during the period.

3. The method according to claim 1, wherein, In the step 5), the cluster results are regressed by using the Bayesian discriminant model in the spss software.

4. A server comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the general heavy oil reservoir classification and evaluation method in any one of claims 1 to 3.

5. A computer readable storage medium, characterized in that, The computer readable storage medium stores computer instructions, which are used to make the processor execute the general heavy oil reservoir classification and evaluation method in any one of claims 1 to 3.

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