CN111488666A - Gas reservoir horizontal well productivity prediction model establishing and predicting method and device - Google Patents
Gas reservoir horizontal well productivity prediction model establishing and predicting method and device Download PDFInfo
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Abstract
The invention provides a method and a device for establishing and predicting a gas reservoir horizontal well productivity prediction model, wherein when the horizontal well productivity prediction model is established, reservoir factors and horizontal section drilling factors of a horizontal well are considered, an orthogonal method is adopted to calculate correlation coefficients of the reservoir factors and the horizontal section drilling factors of the horizontal well and actual productivity, and the reservoir factors and the horizontal section drilling factors with the correlation coefficients larger than a set threshold value are selected as active factors, so that the establishment of the productivity prediction model between a main control factor and the actual productivity is completed.
Description
Technical Field
The invention belongs to the technical field of oil and gas field development, and particularly relates to a method and a device for establishing and predicting a gas reservoir horizontal well productivity prediction model.
Background
At present, the horizontal well technology becomes a key technology and an important means for the development of a compact sandstone gas reservoir, and is more and more widely applied to the development of a gas field. The method for timely and accurately predicting the capacity of the tight sandstone gas reservoir horizontal well is an important basis for dynamic optimization deployment of the horizontal well, dynamic analysis and adjustment of gas field development.
The existing gas reservoir horizontal well productivity prediction model only considers the influence of horizontal well reservoir physical property factors, but does not consider the influence of horizontal well drilling factors on productivity, wherein the reservoir physical property factors generally comprise porosity, permeability, gas saturation and the like; the horizontal segment drilling factors generally comprise the length of a horizontal segment, the length of sandstone, the length of a gas layer display segment, average total hydrocarbon and the like. Production practices show that the drilling factors of the horizontal well can effectively reflect the change of the physical properties of the reservoir and the strength and the heterogeneity of the reservoir, and are also key parameters influencing the productivity of the reservoir.
Therefore, how to comprehensively consider the productivity influence factors of the horizontal well from the basic geological characteristics of the gas reservoir, determine the main productivity influence factors, predict the productivity in advance and are particularly important for the efficient development of the compact gas reservoir.
Disclosure of Invention
The invention aims to provide a gas reservoir horizontal well productivity prediction model establishing and predicting method and device, which are used for solving the problem that the productivity prediction result has errors due to incomplete consideration of influence factors in the existing horizontal well productivity prediction.
In order to achieve the aim, the invention provides a gas reservoir horizontal well productivity prediction model establishing method, which comprises the following steps:
acquiring reservoir factors, horizontal section drilling factors and actual productivity of a drilled horizontal well;
calculating the correlation coefficients of reservoir factors, horizontal section drilling factors and actual productivity by adopting an orthogonal test method; selecting reservoir factors and horizontal section drilling factors with the correlation coefficient larger than a set threshold value as main control factors;
and (3) establishing a capacity prediction model between the main control factors and the actual capacity by using the main control factors as sample data and adopting a linear regression method.
The invention also provides a gas reservoir horizontal well productivity prediction model establishing device which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the gas reservoir horizontal well productivity prediction model establishing method is realized when the processor executes the computer program.
According to the method, when the horizontal well productivity prediction model is established, reservoir factors and horizontal section drilling factors of the horizontal well are considered, correlation coefficients of the reservoir factors and the horizontal section drilling factors of the horizontal well and actual productivity are calculated by adopting an orthogonal method, the reservoir factors and the horizontal section drilling factors of which the correlation coefficients are larger than a set threshold value are selected as active factors, and therefore the establishment of the prediction model between the main control factors and the actual productivity is completed.
As a further improvement of the establishing method and the establishing device, in order to make the obtained productivity prediction model more accurate, if the difference between the predicted productivity of the horizontal well to be measured and the actual productivity of the horizontal well to be measured is greater than a set error, the main control factor of the horizontal well to be measured is added to the sample data, and a linear regression method is adopted to establish the productivity prediction model.
As a further improvement of the building method and the building apparatus, in order to obtain the productivity prediction model, an expression of the productivity prediction model is as follows:
Qpred＝α*(a1*I1+a2*I2+a3*I3+…+an*In)+β
wherein Qpred predicts productivity for horizontal wells, I1、I2、I3、I4……InIs a master factor, n is the number of the master factors α, β, a1、a2、a3……anRepresenting the relevant parameters in the fitting process.
The invention also provides a gas reservoir horizontal well productivity prediction method, which comprises the following steps:
acquiring reservoir factors, horizontal section drilling factors and actual productivity of a drilled horizontal well;
calculating the correlation coefficients of reservoir factors, horizontal section drilling factors and actual productivity by adopting an orthogonal test method; selecting reservoir factors and horizontal section drilling factors with the correlation coefficient larger than a set threshold value as main control factors;
taking the main control factor as sample data, and establishing a capacity prediction model between the main control factor and the actual capacity by adopting a linear regression method;
and acquiring the main control factor of the horizontal well to be tested, substituting the main control factor of the horizontal well to be tested into the productivity prediction model, and calculating to obtain the predicted productivity of the horizontal well to be tested.
The invention also provides a gas reservoir horizontal well productivity prediction device which comprises a memory, a processor and a computer program which is stored on the memory and can be operated on the processor, wherein the gas reservoir horizontal well productivity prediction method is realized when the processor executes the computer program.
According to the method, when the horizontal well productivity prediction model is established, reservoir factors and horizontal section drilling factors of the horizontal well are considered, the considered factors are relatively comprehensive, the established horizontal well productivity prediction model is relatively accurate, the productivity of the horizontal well predicted according to the horizontal well productivity prediction model is relatively accurate, the horizontal well productivity prediction precision is improved, the optimal deployment of the well position of a target area is facilitated, and the effective development of the horizontal well is promoted.
As a further improvement on the prediction method and the prediction device, in order to enable the obtained productivity prediction model to be more accurate, if the difference value between the predicted productivity of the horizontal well to be tested and the actual productivity of the horizontal well to be tested is larger than a set error, the main control factor of the horizontal well to be tested is added into the sample data, and a linear regression method is adopted to establish the productivity prediction model.
In order to obtain a productivity prediction model, the expression of the productivity prediction model is as follows:
Qpred＝α*(a1*I1+a2*I2+a3*I3+…+an*In)+β
wherein Qpred predicts productivity for horizontal wells, I1、I2、I3、I4……InIs a master factor, n is the number of the master factors α, β, a1、a2、a3……anRepresenting the relevant parameters in the fitting process.
Drawings
FIG. 1 is a flow chart of a gas reservoir horizontal well productivity prediction method of the present invention;
FIG. 2 is a schematic diagram showing the relationship between the actual production capacity of natural gas and the sandstone length displayed by gas measurement;
FIG. 3 is a schematic representation of the actual production of natural gas as a function of average total hydrocarbons according to the present invention;
FIG. 4 is a schematic diagram of the relationship between the actual production capacity and the gas saturation of natural gas according to the present invention;
FIG. 5 is a schematic representation of the actual production of natural gas as a function of the effective thickness of the reservoir in accordance with the present invention;
FIG. 6 is a schematic diagram of the actual capacity of natural gas versus horizontal segment length of the present invention;
FIG. 7 is a schematic diagram of the relationship between the actual capacity and the predicted capacity of natural gas according to the present invention.
Detailed Description
The following further describes embodiments of the present invention with reference to the drawings:
the embodiment of the capacity prediction model establishing method comprises the following steps:
the method for establishing the gas reservoir horizontal well productivity prediction model comprises the following steps:
1) selecting horizontal wells needing productivity test after fracturing as horizontal wells to be tested, namely the horizontal wells are drilled, and then obtaining reservoir factors, horizontal section drilling factors and corresponding productivity test indexes of the drilled horizontal wells, wherein the corresponding productivity test indexes mainly refer to actual productivity of the horizontal wells, and forming the reservoir factors, the horizontal section drilling factors and the actual productivity of the drilled horizontal wells into a productivity sample database;
here, reservoir factors for a drilled horizontal well include the effective thickness D of the reservoir, the porosity of the reservoir
Permeability K, gas saturation Sg. The effective thickness D of the reservoir is measured according to the well guide hole or the logging interpretation data of the surrounding adjacent wells; porosity of
The permeability K and the gas saturation Sg are obtained by calculation, and the calculation process is as follows: porosity of
The permeability K is calculated according to a region empirical formula, and the gas saturation Sg is calculated according to an Archie formula.
The horizontal section drilling factor comprises the length H of the horizontal section0Sandstone length H1Gas layer display segment length H, average total hydrocarbons QT. Wherein, the length H of the horizontal section0The length from the landing point of the horizontal well to the depth of the drilled well; sandstone length H1The lengths of the coarse sandstone, the medium sandstone and the fine sandstone encountered by drilling the horizontal section of the horizontal well are finished; the length H of the gas layer display segment is obtained by accumulating the lengths of the well segments with the gas measurement total hydrocarbon net value of more than 0.5 percent in the horizontal segment; the average total hydrocarbon QT is obtained by weighted average calculation according to the total hydrocarbon value and the well section length of the horizontal well logging while drilling.
The actual capacity is represented by Q, and the acquisition process of the actual capacity Q comprises the following steps: and selecting the horizontal well subjected to the capacity test after fracturing, and calculating the actual capacity Q of the horizontal well according to the tested daily production data (including daily gas production, oil pressure, casing pressure and the like) of the horizontal well.
2) Performing capacity influence single factor analysis by using a capacity sample database, and calculating a correlation coefficient R of reservoir factors, horizontal section drilling factors and actual capacity by adopting an orthogonal test method according to the reservoir factors, the horizontal section drilling factors and the actual capacity of the drilled horizontal well; selecting reservoir factors and horizontal section drilling factors with the correlation coefficient R larger than a set threshold value as main control factors, wherein the main control factors are I1、I2、I3、I4… … shows that the threshold value is set to 0.8 in this embodiment, but as another embodiment, the threshold value may be selected to have another value according to actual requirements.
3) After the master control factors are determined, the master control factors of the horizontal well to be measured in the productivity sample database are used as independent variables, the corresponding actual productivity of the horizontal well to be measured is used as dependent variables, a multiple linear regression fitting method is adopted, and a productivity prediction model between the master control factors and the actual productivity of the horizontal well to be measured is obtained, wherein the productivity prediction model is expressed as Qpred- α (a)1*I1+a2*I2+a3*I3+…+an*In) + β, wherein n is the number of the master factors in the capacity prediction model, α, β, a1、a2、a3……anRepresenting the relevant parameters in the fitting process, α, β, a1、a2、a3……anAll are dimensionless, a1*I1+a2*I2+a3*I3+…+an*InPartial definitionIs a comprehensive index PPI of reservoir capacity potential, namely PPI ═ a1*I1+a2*I2+a3*I3+…+an*InThe PPI is used for evaluating the productivity potential of the horizontal well to be tested, and the larger the comprehensive index of the productivity potential of the reservoir is, the larger the productivity potential of the horizontal well to be tested is. As another embodiment, the expression of the capacity prediction model can be rewritten as: qpred ═ a1*I1+a2*I2+a3*I3+…+an*In+β。
The embodiment of the capacity prediction method comprises the following steps:
as shown in fig. 1, after the productivity prediction model is established by the method, the corresponding parameters of the horizontal well to be measured are input into the productivity prediction model to obtain the productivity of the horizontal well to be measured. Since the process of creating the capacity prediction model is the same as that of the above-described prediction model creation method, it is not described herein again.
It should be noted that when the productivity prediction model is used for predicting the actual natural gas productivity of the horizontal well to be measured, if the difference between the predicted productivity of the horizontal well to be measured and the actual productivity of the horizontal well to be measured is larger than a set error, the main control factor of the horizontal well to be measured is added into the productivity sample database, and the linear regression method is adopted to reestablish the productivity prediction model. In this embodiment, the linear regression may be a curve fitting method such as a least square method, a gradient least square method, or the like.
The embodiment of the capacity prediction model establishing device comprises:
the invention also provides a gas reservoir horizontal well productivity prediction model establishing device which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the gas reservoir horizontal well productivity prediction model establishing method is realized when the processor executes the computer program. Since the specific implementation of the gas reservoir horizontal well productivity model building method has been clearly and specifically described, the specific implementation of the gas reservoir horizontal well productivity prediction model building device is not described herein again.
The embodiment of the capacity prediction device comprises:
the invention also provides a gas reservoir horizontal well productivity prediction device which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the gas reservoir horizontal well productivity prediction method is realized when the processor executes the computer program. Since the specific implementation of the method for predicting the productivity of the gas reservoir horizontal well has been described, the specific implementation of the device for predicting the productivity of the gas reservoir horizontal well is not repeated here.
The capacity prediction process of a certain gas field in a certain area is described by adopting the capacity prediction method of the invention for the gas field in the certain area:
(1) utilizing the productivity sample database and using the effective thickness D and the porosity of the reservoir
Permeability K, gas saturation Sg, horizontal segment length H0Sandstone length H1And carrying out sensitivity analysis on parameters such as the length H of the gas layer display segment, the average total hydrocarbon QT and the like and the actual productivity Q by an orthogonal test method, and determining productivity influence factors with the correlation coefficient R being not less than 0.80 as the length H of the gas layer display segment, the average total hydrocarbon QT, the gas saturation Sg and the effective thickness D of the reservoir, namely taking the length H of the gas layer display segment, the average total hydrocarbon QT, the gas saturation Sg and the effective thickness D of the reservoir as main control factors. 2-5 are graphs showing the relationship between the actual capacity of a batch of gas reservoir horizontal wells of the gas field and the length of a gas layer display segment (corresponding to the length of sandstone displayed by gas logging in FIG. 2), the length of a horizontal segment, average total hydrocarbon, gas saturation and effective thickness of a reservoir (corresponding to the thickness of the gas layer in FIG. 5), and the graphs show that the actual capacity of natural gas of the gas reservoir horizontal wells is positively correlated with all parameters, and the correlation coefficient R is more than 0.80; fig. 6 is a schematic diagram of a relationship between actual capacity of natural gas and a horizontal section length of the same gas reservoir horizontal well, and it is shown that a correlation coefficient R between the actual capacity of natural gas and the horizontal section length is less than 0.80.
(2) Obtaining the length H of the display segment of the gas layer
Selecting a batch of horizontal wells subjected to capacity testing after fracturing, wherein the block characteristic related to the horizontal wells is mainly a compact sandstone gas reservoir at present, generally adopting fracturing production to ensure the capacity effect of a single well, and obtaining the length H of a gas layer display section according to the length accumulation of a well section with the gas logging total hydrocarbon net value greater than 0.5% in the horizontal section.
(3) Obtaining average Total hydrocarbons QT
And obtaining average total hydrocarbon QT by weighted average calculation according to the gas logging while drilling value of the horizontal well and the well section length.
(4) Obtaining the saturation Sg of gas
And calculating the gas saturation Sg of the reservoir according to the logging information of the surrounding adjacent wells, and obtaining the average gas saturation according to the effective thickness of the reservoir.
(5) Measuring the effective thickness D of a reservoir
Loading the well pilot hole or surrounding adjacent well logging interpretation data to specific professional software according to the well pilot hole or surrounding adjacent well logging interpretation data (because some horizontal wells are pilot holes and some are pilot holes, if the horizontal wells are pilot holes, parameters of pilot hole data are selected, and if the horizontal wells are pilot holes, parameters of surrounding adjacent well data are selected), and measuring the effective thickness D of the reservoir according to the effective thickness identification electrical parameter standard.
(6) Establishing a productivity prediction model
Adopting a multivariate linear regression fitting method to four parameters of the length H of the gas layer display segment, the average total hydrocarbon QT, the gas saturation Sg and the effective thickness D of the reservoir to form a productivity prediction model, and substituting specific numerical values of the length H of the gas layer display segment, the average total hydrocarbon QT, the gas saturation Sg and the effective thickness D of the reservoir into the productivity prediction model to obtain the productivity prediction model: qpred0.0057 PPI-25.1, from which the natural gas production is predicted to be QpredWherein, PPI is a comprehensive index of reservoir productivity potential, and the expression is as follows: PPI 4 × H +58 × QT +39 × Sg +70 × D.
(7) Prediction of natural gas productivity of new well (horizontal well to be tested)
For the horizontal well to be tested, the logging information is comprehensively utilized to identify the natural gas layer, then the parameter index values of the length H of the display segment of the gas layer, the average total hydrocarbon QT, the gas saturation Sg, the effective thickness D of the reservoir and the like are obtained, and the productivity prediction module in the step (6) is usedModel, the natural gas productivity Q which can be obtained after reservoir transformation can be predictedpred. FIG. 7 is a schematic diagram of the relationship between the predicted capacity and the actual capacity of natural gas, wherein the actual capacity of natural gas is positively correlated with the predicted capacity.
Table 1 is a comparison table of predicted capacity and actual capacity after fracturing for a batch of new wells. Except that the predicted results of the 2 wells are not accordant, the predicted results of the other wells are accordant, and the coincidence rate is 90%. It should be noted that, for the reservoir in the region, the error between the actual capacity of the fracturing test gas natural gas and the prediction result is 20%, which is calculated to be in line.
TABLE 1 comparison table of actual and predicted capacity of new well
(8) Perfecting productivity prediction model
After the fracturing gas testing is completed, the actual capacity of the natural gas can be obtained. And (4) comparing the obtained actual capacity with the predicted capacity, if the error of the prediction result exceeds the acceptable range, adding the relevant parameters of the well reservoir capacity and the actual capacity data into a capacity sample database, and repeating the step (6) to obtain a corrected capacity prediction model. After more new well prediction, detection and perfection, the productivity prediction precision can be continuously improved.
The specific embodiments are given above, but the present invention is not limited to the above-described embodiments. The basic idea of the present invention lies in the above basic scheme, and it is obvious to those skilled in the art that no creative effort is needed to design various modified models, formulas and parameters according to the teaching of the present invention. Variations, modifications, substitutions and alterations may be made to the embodiments without departing from the principles and spirit of the invention, and still fall within the scope of the invention.
Claims (8)
1. The method for establishing the gas reservoir horizontal well productivity prediction model is characterized by comprising the following steps of:
acquiring reservoir factors, horizontal section drilling factors and actual productivity of a drilled horizontal well;
calculating the correlation coefficients of reservoir factors, horizontal section drilling factors and actual productivity by adopting an orthogonal test method; selecting reservoir factors and horizontal section drilling factors with the correlation coefficient larger than a set threshold value as main control factors;
and (3) establishing a capacity prediction model between the main control factors and the actual capacity by using the main control factors as sample data and adopting a linear regression method.
2. The method for establishing the gas reservoir horizontal well productivity prediction model according to claim 1, wherein if the difference between the predicted productivity of the horizontal well to be measured and the actual productivity of the horizontal well to be measured is larger than a set error, the main control factor of the horizontal well to be measured is added to the sample data, and a linear regression method is adopted to establish the productivity prediction model.
3. The method for establishing the gas reservoir horizontal well productivity prediction model according to claim 1 or 2, wherein the productivity prediction model has an expression as follows:
Qpred＝α*(a1*I1+a2*I2+a3*I3+…+an*In)+β
wherein Qpred predicts productivity for horizontal wells, I1、I2、I3、I4……InIs a master factor, n is the number of the master factors α, β, a1、a2、a3……anRepresenting the relevant parameters in the fitting process.
4. The gas reservoir horizontal well productivity prediction method is characterized by comprising the following steps:
acquiring reservoir factors, horizontal section drilling factors and actual productivity of a drilled horizontal well;
calculating the correlation coefficients of reservoir factors, horizontal section drilling factors and actual productivity by adopting an orthogonal test method; selecting reservoir factors and horizontal section drilling factors with the correlation coefficient larger than a set threshold value as main control factors;
taking the main control factor as sample data, and establishing a capacity prediction model between the main control factor and the actual capacity by adopting a linear regression method;
and acquiring the main control factor of the horizontal well to be tested, substituting the main control factor of the horizontal well to be tested into the productivity prediction model, and calculating to obtain the predicted productivity of the horizontal well to be tested.
5. The gas reservoir horizontal well productivity prediction method according to claim 4, wherein if the difference between the predicted productivity of the horizontal well to be measured and the actual productivity of the horizontal well to be measured is larger than a set error, the main control factor of the horizontal well to be measured is added to the sample data, and a linear regression method is adopted to establish the productivity prediction model.
6. The gas reservoir horizontal well productivity prediction method according to claim 4 or 5, wherein the productivity prediction model has an expression as follows:
Qpred＝α*(a1*I1+a2*I2+a3*I3+…+an*In)+β
wherein Qpred predicts productivity for horizontal wells, I1、I2、I3、I4……InIs a master factor, n is the number of the master factors α, β, a1、a2、a3……anRepresenting the relevant parameters in the fitting process.
7. A gas reservoir horizontal well productivity prediction model building device, comprising a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the gas reservoir horizontal well productivity prediction model building method according to any one of claims 1 to 3.
8. A gas reservoir horizontal well productivity prediction device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to perform the gas reservoir horizontal well productivity prediction method according to any one of claims 4 to 6.
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