CN105528732B - Method for predicting productivity of gas testing well - Google Patents
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Abstract
The invention discloses a method for acquiring logging information of a reservoir in a research area; carrying out fluid identification on the logging information to screen out a gas testing horizon; acquiring reservoir characteristic data from logging data to perform characteristic analysis so as to determine that a spring section sensitive parameter is the product of resistivity and density neutron porosity difference and determine that a snakegourd base group sensitive parameter is the product of three porosity and resistivity; fitting the sensitive parameters of the snakegourd root library group with the gas yield per meter of the gas testing layer position to obtain a first productivity equation; fitting the first-section sensitive parameters of the spring with the gas production rate per meter of the gas test layer position to obtain a second capacity equation; the productivity prediction result of the reservoir in the research area is obtained by applying the first productivity equation and the second productivity equation, so that the technical problems that only the reservoir can be qualitatively optimized and more available information of the optimized reservoir is not obtained in the prior art are solved, the rapid quantitative prediction of the reservoir productivity is realized, and a basis is provided for the optimization of the next fracturing layer.
Description
Technical Field
The invention relates to the technical field of oil and gas development, in particular to a method for predicting productivity of a gas testing well.
Background
The logging is a method for measuring geophysical parameters by utilizing the geophysical characteristics such as electrochemical property, electric conduction property, acoustic property, radioactivity and the like of a rock stratum, and belongs to one of the applied geophysical methods (including heavy, magnetic, electric, seismic and nuclear). During petroleum drilling, logging must be performed after the drilling to the designed well depth, so as to obtain various petroleum geology and engineering technical data, which are used as original data of well completion and oil field development, namely logging data.
At present, the application of conventional logging information is mainly reservoir interpretation, and only reservoir optimization can be performed qualitatively, so that only the quality of the reservoir can be indicated qualitatively, and more available information of the reservoir optimization is lacked.
Disclosure of Invention
The invention aims to provide a method for predicting the productivity of a gas test well, which solves the technical problem that more available information of a preferable reservoir layer is not obtained in the prior art.
The embodiment of the invention provides a method for predicting productivity of a gas testing well, which comprises the following steps:
acquiring logging information of a reservoir in a research area;
carrying out fluid identification on the logging information to screen out a gas testing horizon;
obtaining reservoir characteristic data from the logging data to perform characteristic analysis so as to determine that a spring section sensitive parameter is the product of the resistivity and the density neutron porosity difference value and determine that a Melwhether library group sensitive parameter is the product of the three porosity and the resistivity;
fitting the sensitive parameters of the snakegourd pond group with the gas yield per meter of the gas testing layer position to obtain a first productivity equation;
fitting the first section of sensitive parameters and the gas yield per meter of the gas test layer position to obtain a second capacity equation;
and applying the first productivity equation and the second productivity equation to obtain a productivity prediction result of the reservoir in the research area.
Preferably, after said applying said first capacity equation and said second capacity equation to obtain a capacity forecast for said reservoir in said research area, said method further comprises:
and comparing the productivity prediction result with an actual gas testing result, and determining the error between the productivity prediction result and the actual gas testing result.
Preferably, before the fitting the sensitive parameters of the snakegourd library set and the gas production per meter of the gas test horizon to form a first energy production equation, the method further comprises the following steps:
calculating an accumulated permeability curve of each gas testing well by using the logging information;
taking the contribution amount larger than a preset percentage as the effective horizon thickness of the gas test horizon according to the cumulative permeability curve;
and dividing the gas testing yield of the gas testing layer by the effective thickness of the layer to obtain the gas yield per meter of the gas testing layer.
Preferably, before the fitting the sensitive parameters of the snakegourd library set and the gas production per meter of the gas test horizon to form a first energy production equation, the method further comprises the following steps:
estimating the effective thickness of the layer of the gas testing layer according to neutron density intersection;
and dividing the gas testing yield of the gas testing layer by the effective thickness of the layer to obtain the gas yield per meter of the gas testing layer.
Preferably, the test gas yield is specifically: single layer test gas yield or multi-layer combined test gas yield.
Preferably, the applying the first productivity equation and the second productivity equation to obtain the productivity prediction result for the reservoir in the research area comprises:
applying the first productivity equation and the second productivity equation to obtain a productivity prediction result map of the reservoir in the research area;
and applying the first productivity equation and the second productivity equation to obtain the single-layer productivity index of the reservoir in the research area.
Preferably, after the fitting the first section of sensitive parameters to the gas production rate per meter of the gas test horizon to obtain a first capacity equation, the method further comprises:
and performing reservoir classification on the wells without performing gas testing by using the first productivity equation and the second productivity equation.
Preferably, after the fitting the first section of sensitive parameters to the gas production rate per meter of the gas test horizon to obtain a first capacity equation, the method further comprises:
and predicting a natural productivity result and a fracturing productivity result for the well without gas testing by applying the first productivity equation and the second productivity equation.
One or more technical solutions provided by the embodiments of the present invention at least achieve the following technical effects or advantages:
the embodiment of the invention utilizes the logging data to carry out characteristic analysis on the reservoir stratum in the research area, determines the sensitive parameter of the spring section as the product of the resistivity and the porosity difference value of the density neutrons, and determining the sensitive parameter of the marcfort Log group as the product of the porosity and the resistivity, establishing a first capacity equation by applying the product of the difference between the resistivity and the porosity of neutrons in density and the gas production per meter, establishing a second capacity equation by applying the product of the porosity and the resistivity and the gas production per meter, and obtaining a capacity prediction result of the reservoir in the research area by applying the first capacity equation and the second capacity equation, so that the defect that the reservoir can only be qualitatively optimized by using conventional logging information can be overcome, and the technical problem that more available information of the optimized reservoir is not obtained in the prior art is solved, and further, rapid quantitative prediction of reservoir production energy is realized, and a basis is provided for the optimization of the next fracturing layer.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a flow chart of a method for predicting productivity of a test gas well according to an embodiment of the present invention;
FIG. 2 is a model diagram of a first capacity equation in an embodiment of the present invention;
FIG. 3 is a model diagram of a second capacity equation in an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a method for predicting productivity of a test gas well according to an embodiment of the present invention includes the following steps:
s101, obtaining logging information of a reservoir in a research area.
S102, carrying out fluid identification on the logging information to screen out a gas testing horizon.
Specifically, because the sensitivity of the logging parameter identification fluid property of different levels in the logging information is different, level screening is carried out on the gas testing well through fluid identification, and the gas testing conclusion is the depth section of the gas layer.
S103, obtaining reservoir characteristic data from the logging data to perform characteristic analysis so as to determine that the spring section sensitive parameter is the product of the resistivity and the density neutron porosity difference, and determine that the marcfort library group sensitive parameter is the product of the three-porosity and the resistivity.
In the specific implementation process, the reservoir in the research area is analyzed, if the reservoir is a complex fracture system, the number of the complex fracture system is large, the trend of different periods is different, and the stratum development of the western fracture area in the work area is complete. Specifically, the closed part of the structure of the urban deep 2 well region lacks an urban group, a sand river subgroup and a part of a snakegourd bank group stratum, the snakegourd bank group stratum is gradually thinned from west to east and pinches off in the urban deep 202 well region, the stratum above the spring head group is fully developed, and a section of spring sensitive parameter and a snakegourd bank group sensitive parameter are selected as productivity influencing factors for the storage layer which is the same as or similar to the reservoir layer of the research region.
Specifically, different sensitive parameters and a gas production capacity equation are established for correlation comparison analysis, and an optimal composite sensitive parameter and a gas production per meter are determined according to an analysis result to establish the capacity equation.
The section sensitive parameters of the spring in the preferred composite sensitive parameters determined by the correlation comparison are as follows: the product of the resistivity and the difference value of the density neutron porosity, and the susceptor group sensitive parameters are as follows: the product of the three porosities and the resistivity.
After the above S103 is executed, S104 is executed: and fitting the sensitive parameters of the marcforths storeroom group with the gas yield per meter of the gas testing horizon to obtain a first productivity equation.
Specifically, the gas production per meter is calculated in at least two embodiments, which are described below:
the first implementation mode comprises the following steps: the method comprises the following steps of calculating the gas production per meter by grouping sections, specifically:
step 1: and calculating the cumulative permeability curve of each gas testing well by using the logging data.
Step 2: and taking the contribution amount larger than the preset percentage as the effective thickness of the horizon of the gas testing horizon according to the cumulative permeability curve. For example, the thickness with the contribution amount greater than 90% is taken as the effective thickness of the horizon of the gas testing layer by using the cumulative permeability curve, and the size of the preset percentage can be adjusted according to actual requirements in the specific implementation process.
And step 3: and dividing the gas testing yield of the gas testing layer by the effective thickness of the layer of the gas testing layer to obtain the gas yield per meter of the gas testing layer.
Specifically, the gas test yield is a single-layer gas test yield or a multi-layer combined gas test yield, if the gas test layer position is a single layer when step 3 is executed, the gas test yield of the gas test layer position is a single-layer gas test yield, the single-layer gas test yield is specifically obtained by calculating the gas production per meter of the single-layer gas test layer without considering water in the single-layer gas test yield according to the following mode:
specifically, if the test is a multi-layer combined test in step 3, the test gas yield of the test gas layer is the average gas yield per meter of the combined test, and the average gas yield per meter is calculated according to the following mode:
wherein the gas production per meter of each gas testing layer in the multi-layer combined test fluctuates up and down in the calculated average gas production per meter, so that the following formula is established:
wherein n is the number of the layers to be tested; h is the effective thickness of the single-layer; q is the gas production per meter of each horizon; q is the total gas production of the test layer section.
In the implementation, the model for fitting the first capacity equation is shown in fig. 2, specifically, y is 0.3826e0.0074x，R20.7058, where X represents the spring sensitivity parameter as the product of the resistivity times the difference in density neutron porosity, y represents the gas production per meter, and R2Representing the correlation coefficient.
The second embodiment: the gas production per meter of the grouping section is calculated by the following steps in sequence:
step 1: and estimating the effective thickness of the layer of the gas testing layer according to neutron density intersection.
Step 2: and dividing the gas testing output of the gas testing layer by the effective thickness of the layer to obtain the gas yield per meter of the gas testing layer. In a specific implementation process, reference may be made to detailed description of step 3 in embodiment one for a specific implementation process of step 2 in this embodiment, and for brevity of the description, details are not repeated herein.
After the above step S103 is performed, step S105 is performed next: and fitting the first section of sensitive parameters of the spring with the gas production rate per meter of the gas test layer position to obtain a second capacity equation.
Specifically, fitting outThe model of the second capacity equation is shown with reference to fig. 3, where y is 0.0016x1.369，R20.9069, X represents the product of the three porosities and the resistivity, y represents the gas production per meter, R2And expressing the correlation coefficient, and fitting the first section of sensitive parameters and the gas yield per meter of the gas test layer position to obtain a second capacity equation based on the EXCEL.
In a specific implementation process, S104 and S105 are steps executed independently, and may be performed simultaneously or sequentially, so that the execution order of steps S104 and S105 is not limited herein.
Then, executing S106: and obtaining the productivity prediction result of the reservoir in the research area by applying the first productivity equation and the second productivity equation.
Specifically, the productivity prediction result comprises a productivity prediction result graph and a single-layer productivity index of a reservoir in a research area
S106 specifically includes the following steps: obtaining a productivity prediction result diagram of the reservoir in the research area by applying the first productivity equation and the second productivity equation; and obtaining the single-layer capacity index of the reservoir in the research area by applying the first capacity equation and the second capacity equation.
In a further embodiment, in order to check the rationality of the capacity forecast result, the method further includes the following steps after executing S106: and comparing the productivity prediction result with the actual gas testing result to determine the error between the productivity prediction result and the actual gas testing result.
For example, the total thickness of the gas testing horizon of the well roof bank group is 11.2m, the effective thickness of the horizon is 10.4m, and a productivity prediction result diagram and a single-layer productivity index are obtained by applying a first productivity equation and a second productivity equation, wherein the productivity prediction gas yield for the productivity prediction is 6.03km3, and the actual gas testing yield is 6.43km3And calculating that the relative error between the predicted gas production rate and the actual gas production rate of the capacity is 6.22 percent, so that the error is small.
In a further technical solution, after executing S105, the method further includes the following steps: and performing reservoir classification on the wells without testing gas by using the first capacity equation and the second capacity equation.
Reservoir classification is performed as follows: and classifying the reservoirs according to the capacity index.
In a further technical scheme: the following steps are also included after executing S105: and predicting a natural productivity result and a fracturing productivity result for the well without testing gas by applying the first productivity equation and the second productivity equation. And applying the capacity equation to the well without the test gas to obtain the capacity data of the well.
The gas production capacity of a single well is quantitatively predicted, and conventional qualitative research is converted into quantitative research.
Through one or more technical solutions provided by the embodiments of the present invention, at least the following technical effects or advantages are achieved:
the embodiment of the invention utilizes the logging data to carry out characteristic analysis on the reservoir stratum in the research area, determines the sensitive parameter of the spring section as the product of the resistivity and the porosity difference value of the density neutrons, and determining the sensitive parameter of the marcfort Log group as the product of the porosity and the resistivity, establishing a first capacity equation by applying the product of the difference between the resistivity and the porosity of neutrons in density and the gas production per meter, establishing a second capacity equation by applying the product of the porosity and the resistivity and the gas production per meter, and obtaining a capacity prediction result of the reservoir in the research area by applying the first capacity equation and the second capacity equation, so that the defect that the reservoir can only be qualitatively optimized by using conventional logging information can be overcome, and the technical problem that more available information of the optimized reservoir is not obtained in the prior art is solved, and further, rapid quantitative prediction of reservoir production energy is realized, and a basis is provided for the optimization of the next fracturing layer.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (8)
1. A gas well productivity prediction method based on a spring section sensitive parameter and a Melandros library group sensitive parameter is characterized by comprising the following steps:
acquiring logging information of a reservoir in a research area;
carrying out fluid identification on the logging information to screen out a gas testing horizon;
obtaining reservoir characteristic data from the logging data to perform characteristic analysis so as to determine that a spring section sensitive parameter is the product of the resistivity and the density neutron porosity difference value and determine that a Melwhether library group sensitive parameter is the product of the three porosity and the resistivity; the sensitive parameters of the spring-out section and the Mongolian snakegourd storehouse group are determined by establishing different sensitive parameters and a gas production capacity equation and performing correlation comparative analysis;
fitting the sensitive parameters of the snakegourd pond group with the gas yield per meter of the gas testing layer position to obtain a first productivity equation;
fitting the first section of sensitive parameters and the gas yield per meter of the gas test layer position to obtain a second capacity equation;
and applying the first productivity equation and the second productivity equation to obtain a productivity prediction result of the reservoir in the research area.
2. The method for predicting productivity of a test gas well according to claim 1, wherein after the applying the first and second productivity equations to obtain the prediction of productivity of the reservoir in the research area, the method further comprises:
and comparing the productivity prediction result with an actual gas testing result, and determining the error between the productivity prediction result and the actual gas testing result.
3. The test gas well productivity prediction method of claim 2, wherein before fitting the snakegourd bank set of sensitive parameters to the gas production rate per meter of the test gas horizon to a first productivity equation, the method further comprises:
calculating an accumulated permeability curve of each gas testing well by using the logging information;
taking the contribution amount larger than a preset percentage as the effective horizon thickness of the gas test horizon according to the cumulative permeability curve;
and dividing the gas testing yield of the gas testing layer by the effective thickness of the layer to obtain the gas yield per meter of the gas testing layer.
4. The test gas well productivity prediction method of claim 2, wherein before fitting the snakegourd bank set of sensitive parameters to the gas production rate per meter of the test gas horizon to a first productivity equation, the method further comprises:
estimating the effective thickness of the layer of the gas testing layer according to neutron density intersection;
and dividing the gas testing yield of the gas testing layer by the effective thickness of the layer to obtain the gas yield per meter of the gas testing layer.
5. The method for predicting productivity of a test gas well according to claim 3 or 4, wherein the test gas yield is specifically: single layer test gas yield or multi-layer combined test gas yield.
6. The method for predicting productivity of a test gas well according to claim 1, wherein the applying the first productivity equation and the second productivity equation to obtain the productivity prediction result for the reservoir in the research area comprises:
applying the first productivity equation and the second productivity equation to obtain a productivity prediction result map of the reservoir in the research area;
and applying the first productivity equation and the second productivity equation to obtain the single-layer productivity index of the reservoir in the research area.
7. The method for predicting productivity of a test gas well according to claim 1, wherein after fitting the one-section sensitive parameter to the gas production rate per meter of the test gas horizon to a second productivity equation, the method further comprises:
and performing reservoir classification on the wells without performing gas testing by using the first productivity equation and the second productivity equation.
8. The method for predicting productivity of a test gas well according to claim 1, wherein after fitting the one-section sensitive parameter to the gas production rate per meter of the test gas horizon to a second productivity equation, the method further comprises:
and predicting a natural productivity result and a fracturing productivity result for the well without gas testing by applying the first productivity equation and the second productivity equation.
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