CN112800581B - Modeling research method for fine geological model of oil field - Google Patents
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Abstract
The application discloses a modeling research method of an oil field fine geological model, and relates to the technical field of oil gas development. The modeling method comprises the following steps: s1, acquiring oilfield modeling basic data; s2, based on oilfield modeling basic data, four sub-models are established, wherein the sub-models are respectively: constructing a grid model, a sedimentary microphase model, a reservoir attribute model and a fracture model, wherein the fracture model comprises a plurality of fracture sub-models which are classified and dimensioned for modeling; s3, equivalently combining and constructing a grid model, a sedimentary microphase model, a reservoir attribute model and a fracture model to form a high-precision three-dimensional geological model; s4, correcting the three-dimensional geological model; s5, storing the three-dimensional geological model into a cloud storage area. The application adopts a plurality of classification scale modeling crack sub-models to reflect and predict the crack characteristics of the oil field. And simultaneously, in the correction and calibration process of the three-dimensional geological model, the model is corrected by using the historical data of the mined oil well and the region as different characteristics of standard.
Description
Technical Field
The application relates to the technical field of oil gas development, in particular to a modeling research method of an oil field fine geological model.
Background
Oilfield geologic modeling is a comprehensive generalization of the structure of the oilfield, the reservoir, and the fluid properties therein, as well as a continued and final outcome display of reservoir descriptions. In oil gas development, the geologic model not only provides geologic basis for static and dynamic analysis of oil deposit underground, but also provides a basic geologic framework for numerical simulation in oil deposit engineering research. The accurate geological model is designed, so that the target in-place accuracy of the on-site drilling track design can be improved during the development of drilling, the drilling effect can be greatly improved, the drilling cost is reduced, and the overall benefit of oilfield development is improved.
In a complex formation characterization field, represented by the east Bardaa field, there are many different types of reservoir regions: such as carbonate reservoirs with high porosity and low permeability characteristics; the oil-water distribution is complex, and the sandstone reservoir has the characteristics of medium-high porosity and medium-high permeability. The oil field has complex structural characteristics, and a finer address model needs to be established to know the structural characteristics, reservoir spread, reservoir physical properties, non-average property, crack distribution and other characteristics of the oil field, so that better guidance is made to the development of the oil field.
Disclosure of Invention
In view of this, in the field of oil and gas development technology, a finer geologic model is required to guide the development of oil fields. Therefore, the application provides a modeling research method for an oil field fine geological model, which solves the problems by using the following technical points:
the modeling research method of the oil field fine geological model is characterized by comprising the following steps of: s1, acquiring oilfield modeling basic data; s2, based on oilfield modeling basic data, four sub-models are established, wherein the sub-models are respectively: constructing a grid model, a sedimentary microphase model, a reservoir attribute model and a fracture model, wherein the fracture model comprises a plurality of fracture sub-models which are classified and dimensioned for modeling; s3, equivalently combining and constructing a grid model, a sedimentary microphase model, a reservoir attribute model and a fracture model to form a high-precision three-dimensional geological model; s4, correcting the three-dimensional geological model; s5, storing the three-dimensional geological model into a cloud storage area.
As described above, the application provides a fine geologic modeling research method applied to an oil field, and the high-precision three-dimensional geologic model provided by the method comprises a structural grid model, a sedimentary microphase model, a reservoir attribute model, a fracture model and other sub-models. By developing a fine study of structural features, the underlying lattice and fracture system distribution is implemented to build the structural lattice model. The deposition microphase model predicts the spread of the deposition phase by performing feature recognition on the deposition phase to establish. Reservoir spread characteristic research, reservoir physical property and pore microscopic characteristic research and reservoir heterogeneity characteristic research are required before the reservoir attribute model is established. To reflect the effect of the fracture on the physical properties of the reservoir, the present geologic model also includes a fracture model. The fracture model is constructed based on core data, imaging logging data, seismic data, dynamic monitoring data and the like. The cracks can be divided into structural cracks and diagenetic cracks according to geological causes, and the structural cracks are mainly controlled by the structural effect and have a certain scale and shape; the diagenetic cracks are formed in the process of depositing or diagenetic, the distribution range is limited, and the horizontal cracks are the main. Classification is therefore required when modeling cracks. The dimensions of the fracture are also different, and there is a causal relationship between the different dimensions of the fracture, for example, a large-scale fracture has causal constraints on a small-scale fracture. Thus, modeling characterization of a fracture requires characterization of it on a classified and scale by building several fracture sub-models. And the fourth step of the modeling method corrects the three-dimensional geologic model, so that the established three-dimensional geologic model can accurately reflect the real attribute condition of the geology of the oil and gas reservoir.
The further technical scheme is as follows:
the oilfield modeling base data includes: seismic information, core data, well logging interpretation, geophysical interpretation. In this technical feature, the modeling base data is not limited to the listed data materials. Seismic information, core data, and log interpretation can be used to describe the development of single fractures. The geophysical interpretation effort can be used to develop studies of sedimentary facies and reservoir development.
Since the accurate construction grid model is the correct root of an oil reservoir model, the follow-up work is meaningful under the correct construction model. It is therefore provided that the construction grid model comprises a layer model and a fault model. The layer model displays the microstructure patterns of each small layer, and can reflect the release relation and the superposition patterns of each small layer.
The method for correcting the three-dimensional geological model by means of proofreading comprises the following steps:
s401 retrieving historical data of characteristics i of produced oil wells and areas of the oil field simulated by the three-dimensional geological modelWherein i represents the feature type, i takes values from 1 to m, m is the total number of the feature types to be evaluated, e represents that the data type belongs to historical data, and t represents the time coordinate where the value is located; the initial moment of the selection of feature i, i.e. history data at t=0 +.>Input as input data to a three-dimensional geologic model for simulation to obtain simulation data of feature i>Wherein i represents the feature type, i takes values from 1 to m, m is the total number of the feature types to be evaluated, a represents that the data type belongs to analog data, and t represents the time coordinate where the numerical value is located;
s402 calculating historical data of feature iAnalog data from feature i->Average value delta of errors between i ，Wherein n is the total number of time coordinates;
s403 determining a reference error accuracy delta, an average value delta of errors for a total of m different features i i Give authority lambda i And summed to obtain an overall error delta,if delta is larger than delta, the three-dimensional model is disqualified, the next correction operation is carried out, if delta is smaller than delta, the three-dimensional model is qualified, and S5, the operation of storing the three-dimensional geological model into a cloud storage area is carried out;
s404 calculates a second order error f between the history data of the feature i and the simulation data of the feature i i ，Wherein n is the total number of data;
s405 second order error f for different features i i Give authority lambda i Establishing a screening function MaxF (i), maxF(i)＝max[λ 1 f 1 ；λ 2 f 2 ；......λ m f m ]Screening out the characteristic type i with the largest second-order error value after weighting;
s406, aiming at the feature type, positioning a sub-model related to the generation of the feature, and correcting each related sub-model to form a new three-dimensional geological model;
s407 repeats steps S401 to 406 until the total error δ is less than the reference error accuracy Δ.
As described above, since the produced wells and regions already have rich actual production data, their historical data includes a large number of different types of sample values that vary over the course of production time. Step S401 therefore selects the history data of a particular feature type i at a particular time tAs a standard value for correction contrast, the letter e is used to distinguish the history data from the subsequent analog data. The initial moment of the selection of feature i, i.e. history data at t=0 +.>The characteristic data is input as input data into a three-dimensional geological model for simulation, so that simulation data of the characteristic changing along with time, namely +.>Where a is an indication that the data type is analog data. In step S401, the characteristic, the history data and the simulation data corresponding to each other in time are selected so as to perform the mutual contrast correction operation subsequently.
Step S402 determines historical data of the computational feature iAnalog data from feature i->Average value delta of errors between i The method comprises the steps of carrying out a first treatment on the surface of the Step S403 for different featuresThe error averages of i are weighted and compared with the determined reference error accuracy delta. Steps S402 and S403 together constitute a comparison and judgment step. In the existing operation of three-dimensional geologic model precision inspection, a probability distribution consistency inspection method of single type features is generally adopted, and the efficiency is still further improved. Meanwhile, in the steps of the scheme, the total errors obtained by weighting the characteristic errors are calculated and judged, and the total errors are used for judging, so that the accuracy of the whole three-dimensional model comprising the plurality of sub-models can be reflected. In this scheme, the error averages of different features are weighted by λ before summing i 。
And when the error is found to not meet the precision requirement, performing the next correction step. Step S404 is calculating a second order error between the history data of feature i and the simulation data of feature iCompared with the error average value delta in step S402 i Higher order errors can more reflect the magnitude of the difference in error between different features i. S405 second order error f for different features i i Give authority lambda i Establishing a screening function MaxF (i) =max [ lambda ] 1 f 1 ；λ 2 f 2 ；......λ m f m ]And screening out the characteristic type i with the largest second-order error value after weighting. This step enables fast localization of the analog data error maximum feature i. And then, a positioning sub-model is carried out on the characteristic i, and a new three-dimensional geological model is obtained by targeted modification. S407 repeats steps S401 to 406 until the total error δ is less than the reference error accuracy Δ. In the repeated, re-contrast correction process of step S407, since the three-dimensional geologic model has been modified, the deviation value of the feature i generating the maximum error in the above step tends to decrease, and if the overall error still does not meet the error accuracy, in the subsequent screening process of the screening function, another feature generating the maximum relative error value may be screened out, and the correction is performed again for the feature,and repeating the steps, all the characteristics can be adjusted, so that the precision of the three-dimensional geological model meets the standard precision requirement to the greatest extent.
The characteristics i comprise porosity, permeability, oil production of an oil field and water content of a single well.
Compared with the prior art, the application has the beneficial effects that:
the application is scientific and reasonable. Compared with the prior art, the method adopts a plurality of fracture sub-models which are classified and dimensioned for modeling to reflect and predict the fracture characteristics of the oil field. And simultaneously, in the correction and calibration process of the three-dimensional geological model, the model is corrected by using the historical data of the mined oil well and the region as different characteristics of standard. The error average value is used to reflect the error precision of the whole three-dimensional model, and the efficiency of the whole precision error judgment is improved. The second-order error is adopted for comparison, the error magnitude among different types of features can be more clearly compared, the feature with the largest deviation degree can be positioned by utilizing the screening function, and meanwhile, the sub-model with the construction error can be more accurately positioned by utilizing the feature.
Drawings
The accompanying drawings, which are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the principles of the application. In the drawings:
FIG. 1 is a step diagram of a modeling study method of an oilfield fine geologic model of the present application.
Detailed Description
For the purpose of making apparent the objects, technical solutions and advantages of the present application, the present application will be further described in detail with reference to the following examples and the accompanying drawings, wherein the exemplary embodiments of the present application and the descriptions thereof are for illustrating the present application only and are not to be construed as limiting the present application.
Example 1:
as shown in fig. 1, a modeling research method of an oilfield fine geological model is characterized by comprising the following steps: s1, acquiring oilfield modeling basic data; s2, based on oilfield modeling basic data, four sub-models are established, wherein the sub-models are respectively: constructing a grid model, a sedimentary microphase model, a reservoir attribute model and a fracture model, wherein the fracture model comprises a plurality of fracture sub-models which are classified and dimensioned for modeling; s3, equivalently combining and constructing a grid model, a sedimentary microphase model, a reservoir attribute model and a fracture model to form a high-precision three-dimensional geological model; s4, correcting the three-dimensional geological model; s5, storing the three-dimensional geological model into a cloud storage area.
As described above, the application provides a fine geologic modeling research method applied to an oil field, and the high-precision three-dimensional geologic model provided by the method comprises a structural grid model, a sedimentary microphase model, a reservoir attribute model, a fracture model and other sub-models. By developing a fine study of structural features, the underlying lattice and fracture system distribution is implemented to build the structural lattice model. The deposition microphase model predicts the spread of the deposition phase by performing feature recognition on the deposition phase to establish. Reservoir spread characteristic research, reservoir physical property and pore microscopic characteristic research and reservoir heterogeneity characteristic research are required before the reservoir attribute model is established. To reflect the effect of the fracture on the physical properties of the reservoir, the present geologic model also includes a fracture model. The fracture model is constructed based on core data, imaging logging data, seismic data, dynamic monitoring data and the like. The cracks can be divided into structural cracks and diagenetic cracks according to geological causes, and the structural cracks are mainly controlled by the structural effect and have a certain scale and shape; the diagenetic cracks are formed in the process of depositing or diagenetic, the distribution range is limited, and the horizontal cracks are the main. Classification is therefore required when modeling cracks. The dimensions of the fracture are also different, and there is a causal relationship between the different dimensions of the fracture, for example, a large-scale fracture has causal constraints on a small-scale fracture. Thus, modeling characterization of a fracture requires characterization of it on a classified and scale by building several fracture sub-models. And the fourth step of the modeling method corrects the three-dimensional geologic model, so that the established three-dimensional geologic model can accurately reflect the real attribute condition of the geology of the oil and gas reservoir.
Example 2:
this example is further defined on the basis of example 1:
the oilfield modeling base data includes: seismic information, core data, well logging interpretation, geophysical interpretation. In this technical feature, the modeling base data is not limited to the listed data materials. Seismic information, core data, and log interpretation can be used to describe the development of single fractures. The geophysical interpretation effort can be used to develop studies of sedimentary facies and reservoir development.
Since the accurate construction grid model is the correct root of an oil reservoir model, the follow-up work is meaningful under the correct construction model. It is therefore provided that the construction grid model comprises a layer model and a fault model. The layer model displays the microstructure patterns of each small layer, and can reflect the release relation and the superposition patterns of each small layer.
The method for correcting the three-dimensional geological model by means of proofreading comprises the following steps:
s401 retrieving historical data of characteristics i of produced oil wells and areas of the oil field simulated by the three-dimensional geological modelWherein i represents the feature type, i takes values from 1 to m, m is the total number of the feature types to be evaluated, e represents that the data type belongs to historical data, and t represents the time coordinate where the value is located; the initial moment of the selection of feature i, i.e. history data at t=0 +.>Input as input data to a three-dimensional geologic model for simulation to obtain simulation data of feature i>Wherein i represents the feature type, i takes values from 1 to m, m is the total number of the feature types to be evaluated, a represents that the data type belongs to analog data, and t represents the time coordinate where the numerical value is located;
s402 calculating historical data of feature iAnalog data from feature i->Average value delta of errors between i ，Wherein n is the total number of time coordinates;
s403 determining a reference error accuracy delta, an average value delta of errors for a total of m different features i i Give authority lambda i And summed to obtain an overall error delta,if delta is larger than delta, the three-dimensional model is disqualified, the next correction operation is carried out, if delta is smaller than delta, the three-dimensional model is qualified, and S5, the operation of storing the three-dimensional geological model into a cloud storage area is carried out;
s404 calculates a second order error f between the history data of the feature i and the simulation data of the feature i i ，Wherein n is the total number of data;
s405 second order error f for different features i i Give authority lambda i Establishing a screening function MaxF (i), wherein MaxF (i) =max [ lambda ] 1 f 1 ；λ 2 f 2 ；......λ m f m ]Screening out the characteristic type i with the largest second-order error value after weighting;
s406, aiming at the feature type, positioning a sub-model related to the generation of the feature, and correcting each related sub-model to form a new three-dimensional geological model;
s407 repeats steps S401 to 406 until the total error δ is less than the reference error accuracy Δ.
As described above, since the produced wells and regions already have rich actual production data,the historical data includes a number of different types of sample values that vary with the time of production. Step S401 therefore selects the history data of a particular feature type i at a particular time tAs a standard value for correction contrast, the letter e is used to distinguish the history data from the subsequent analog data. The initial moment of the selection of feature i, i.e. history data at t=0 +.>The characteristic data is input as input data into a three-dimensional geological model for simulation, so that simulation data of the characteristic changing along with time, namely +.>Where a is an indication that the data type is analog data. In step S401, the characteristic, the history data and the simulation data corresponding to each other in time are selected so as to perform the mutual contrast correction operation subsequently.
Step S402 determines historical data of the computational feature iAnalog data from feature i->Average value delta of errors between i The method comprises the steps of carrying out a first treatment on the surface of the Step S403 weights and sums the error averages of the different features i and compares them with the determined reference error accuracy Δ. Steps S402 and S403 together constitute a comparison and judgment step. In the existing operation of three-dimensional geologic model precision inspection, a probability distribution consistency inspection method of single type features is generally adopted, and the efficiency is still further improved. Meanwhile, in the steps of the scheme, the total errors obtained by weighting the characteristic errors are calculated and judged, and the total errors are used for judging, so that the accuracy of the whole three-dimensional model comprising the plurality of sub-models can be reflected. Also because of the different importance of the different features, in this solutionThe error averages of different features are respectively weighted lambda before summing the error averages i 。
And when the error is found to not meet the precision requirement, performing the next correction step. Step S404 is calculating a second order error between the history data of feature i and the simulation data of feature iCompared with the error average value delta in step S402 i Higher order errors can more reflect the magnitude of the difference in error between different features i. S405 second order error f for different features i i Give authority lambda i Establishing a screening function MaxF (i) =max [ lambda ] 1 f 1 ；λ 2 f 2 ；......λ m f m ]And screening out the characteristic type i with the largest second-order error value after weighting. This step enables fast localization of the analog data error maximum feature i. And then, a positioning sub-model is carried out on the characteristic i, and a new three-dimensional geological model is obtained by targeted modification. S407 repeats steps S401 to 406 until the total error δ is less than the reference error accuracy Δ. In the repeating, comparing and correcting process of step S407 again, since the three-dimensional geological model has been modified, the deviation value of the feature i generating the maximum error in the above steps will be reduced, at this time, if the overall error still does not meet the error precision, in the subsequent screening process of the screening function, another feature generating the maximum relative error value may be screened out, and correction is performed on the feature again, so that all the features are adjusted, so that the precision of the three-dimensional geological model meets the standard precision requirement to the greatest extent.
The characteristics i comprise porosity, permeability, oil production of an oil field and water content of a single well.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the application, and is not meant to limit the scope of the application, but to limit the application to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the application are intended to be included within the scope of the application.
Claims (4)
1. The modeling research method of the oil field fine geological model is characterized by comprising the following steps of:
s1, acquiring oilfield modeling basic data;
s2, based on oilfield modeling basic data, four sub-models are established, wherein the sub-models are respectively: constructing a grid model, a sedimentary microphase model, a reservoir attribute model and a fracture model, wherein the fracture model comprises a plurality of fracture sub-models which are classified and dimensioned for modeling;
s3, equivalently combining and constructing a grid model, a sedimentary microphase model, a reservoir attribute model and a fracture model to form a high-precision three-dimensional geological model;
s4, correcting the three-dimensional geological model;
s5, storing the three-dimensional geological model to a cloud storage;
the method for correcting the three-dimensional geological model by means of proofreading comprises the following steps:
s401 retrieving historical data of characteristics i of produced oil wells and areas of the oil field simulated by the three-dimensional geological model
Wherein i represents the type of the feature, i takes values from 1 to m, m is the total number of the evaluated feature types, e represents the type of the data belonging to the historical data, and t represents the time coordinate of the data;
the initial moment of the selection of feature i, i.e. the history data at t=0Input into a three-dimensional geological model as input data for simulation to obtain simulation data of the characteristic i
Wherein i represents the type of the feature, i takes values from 1 to m, m is the total number of the evaluated feature types, a represents the type of the data belongs to analog data, and t represents the time coordinate of the data;
s402 calculating historical data of feature iAnalog data from feature i->Average value delta of errors between i ，
Wherein n is the total number of time coordinates;
s403 determining a reference error accuracy delta, an average value delta of errors for a total of m different features i i Give authority lambda i And summed to obtain an overall error delta,
if delta is larger than delta, the three-dimensional geological model is disqualified, the next correction operation is carried out, if delta is smaller than delta, the three-dimensional geological model is qualified, and S5 the operation of storing the three-dimensional geological model into a cloud storage area is carried out;
s404 calculates a second order error f between the history data of the feature i and the simulation data of the feature i i ，
Wherein n is the total number of data;
s405 second order error f for different features i i Give authority lambda i Establishing a screening function MaxF (i),
MaxF(i)＝max[λ 1 f 1 ；λ 2 f 2 ......λ m f m ]，
screening out the characteristic type i with the largest second-order error value after weighting;
s406, aiming at the type of the feature, positioning the sub-model related to the generation of the feature, and correcting each related sub-model to form a new three-dimensional geological model;
s407 repeats steps S401 to 406 until the total error δ is less than the reference error accuracy Δ.
2. The method for modeling study of an oilfield fine geologic model of claim 1, wherein the oilfield modeling base data comprises: seismic information, core data, logging interpretation results, and geophysical interpretation results.
3. A method of modeling a fine geologic model of an oil field as defined in claim 1, wherein the structural grid model comprises a bedding model and a fault model.
4. The method for modeling study of fine geologic model of an oil field as defined in claim 1, wherein the characteristics i comprise porosity, permeability, oil production from the oil field and water content from the individual wells.
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