CN112800581A - Modeling research method of oil field fine geological model - Google Patents
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Abstract
The invention discloses a modeling research method of an oil field fine geological model, and relates to the technical field of oil and gas development. The modeling method comprises the following steps: s1, acquiring oil field modeling basic data; s2, based on the oil field modeling basic data, four sub models are established, which are respectively: constructing a lattice model, a sedimentary microfacies model, a reservoir attribute model and a fracture model, wherein the fracture model comprises a plurality of fracture sub-models which are classified and scaled for modeling; s3 equivalently merging the structural grid model, the sedimentary microfacies model, the reservoir attribute model and the fracture model into a high-precision three-dimensional geological model; s4, carrying out proofreading and correction on the three-dimensional geological model; s5 stores the three-dimensional geological model in a cloud storage area. The invention adopts a plurality of crack submodels which are classified and scaled to carry out modeling to reflect and predict the crack characteristics of the oil field. Meanwhile, in the process of correcting and correcting the three-dimensional geological model, historical data of the produced oil well and the area are used as standards to correct the model according to different characteristics.
Description
Technical Field
The invention relates to the technical field of oil and gas development, in particular to a modeling research method of an oil field fine geological model.
Background
Oilfield geologic modeling is a comprehensive overview of the formation, reservoir, and fluid properties within an oilfield, and is also a continuing and ultimate outcome display of the reservoir description. In oil and gas development, the geological model not only provides geological basis for the static and dynamic analysis of the underground of an oil reservoir, but also provides a basic geological framework for numerical simulation in the oil reservoir engineering research. An accurate geological model is designed, so that the target center in-place accuracy of on-site drilling track design can be improved during drilling development, the drilling effect can be greatly improved, the drilling cost is reduced, and the overall benefit of oilfield development is improved.
In complex formation characteristic fields, represented by the Dongbagada field, there are many different types of reservoir regions: for example, carbonate reservoirs characterized by high porosity and low permeability; the sandstone reservoir has the characteristics of complex oil-water distribution, medium-high porosity and medium-high permeability. The oil field has complex structural characteristics, a more precise address model needs to be established to know the structural characteristics, the reservoir spreading, the reservoir physical properties, the non-mean property, the fracture distribution and other characteristics of the oil field, and better guidance is provided for the development of the oil field.
Disclosure of Invention
Therefore, in the technical field of oil and gas development, a more detailed geological model needs to be provided to guide the development of an oil field. Therefore, the invention provides a modeling research method of an oil field fine geological model, which solves the problems by the following technical points:
a modeling research method for an oil field fine geological model is characterized by comprising the following steps: s1, acquiring oil field modeling basic data; s2, based on the oil field modeling basic data, four sub models are established, which are respectively: constructing a lattice model, a sedimentary microfacies model, a reservoir attribute model and a fracture model, wherein the fracture model comprises a plurality of fracture sub-models which are classified and scaled for modeling; s3 equivalently merging the structural grid model, the sedimentary microfacies model, the reservoir attribute model and the fracture model into a high-precision three-dimensional geological model; s4, carrying out proofreading and correction on the three-dimensional geological model; s5 stores the three-dimensional geological model in a cloud storage area.
As mentioned above, the invention provides a fine geological modeling research method applied to oil fields, and the high-precision three-dimensional geological model provided by the method comprises sub-models such as a structural lattice model, a sedimentary microfacies model, a reservoir property model and a fracture model. And implementing the distribution of the bottom grid and the fracture system by developing the fine research of the structural characteristics to establish the structural grid model. The sedimentary microfacies model is built by performing feature recognition on sedimentary facies and predicting the distribution of the sedimentary facies. Before the reservoir attribute model is established, reservoir spreading characteristic research, reservoir physical property and pore microscopic characteristic research and reservoir heterogeneity characteristic research need to be carried out. In order to reflect the influence of the fractures on the physical properties of the reservoir, the local mass model also comprises a fracture model. The fracture model is constructed based on data such as rock core data, imaging logging data, seismic data, dynamic monitoring data and the like. The cracks can be divided into tectonic cracks and diagenetic cracks according to geological causes, and the tectonic cracks are mainly controlled by tectonic action and have certain scales and shapes; the diagenetic cracks are formed in the process of deposition or diagenetic, have limited distribution range and are mainly horizontal cracks. Therefore, classification is required when modeling the fracture. The size of the fracture is different, and different sizes of fractures have causal connection, for example, large-size fractures have causal constraint on small-size fractures. Therefore, the modeling and characterization of the fracture needs to be performed in a classified scale by establishing a plurality of fracture sub-models. And correcting the three-dimensional geological model in the fourth step of the modeling method, so that the established three-dimensional geological model can more accurately and precisely reflect the real attribute condition of the oil and gas reservoir geology.
The further technical scheme is as follows:
the oil field modeling basic data comprises: seismic information, core data, well logging interpretation results and geophysical interpretation results. In this technical feature, the modeling-base data is not limited to the listed data materials. Seismic information, core data and well logging interpretation results can be used to describe the development of a single fracture. The geophysical interpretation effort can be used to develop a study of dephasing and reservoir spread.
Because the accurate structure grid model is the correct root of the reservoir model, the subsequent work performed under the correct structure model is meaningful. It is therefore provided that the construction grid model comprises a layer model and a fault model. The layer model displays the micro-structure style of each small layer, and simultaneously can reflect the release relation and the overlapping style of each small layer.
The method for correcting the three-dimensional geological model comprises the following steps:
s401, retrieving historical data of characteristics i of the exploited oil wells and the areas of the oil field simulated by the three-dimensional geological model
Wherein i represents the feature type, i takes values from 1 to m, m is the total number of the evaluated feature types, e represents that the data type belongs to historical data, and t represents the time coordinate of the numerical value; selecting historical data at the initial time of the feature i, i.e. when t is 0
Inputting the data into a three-dimensional geological model as input data to be simulated to obtain simulated data of the characteristic i
Wherein i represents the feature type, i takes values from 1 to m, m is the total number of the evaluated feature types, a represents that the data type belongs to analog data, and t represents the time coordinate of the numerical value;
s402, calculating historical data of the characteristic i
Analog data with feature i
Mean value of error δ betweeni，
Wherein n is the total number of the time coordinates;
s403, determining reference error precision delta, error average value delta of different characteristics i with m in totaliTo give authority lambdaiAnd the sum is taken to the total error delta,
if delta is larger than delta, the three-dimensional model is disqualified, and the next correction operation is carried out, if delta is smallIf the value is equal to delta, the three-dimensional model is qualified, and S5 is carried out to store the three-dimensional geological model to a cloud storage area;
s404, calculating a second-order error f between the historical data of the characteristic i and the simulation data of the characteristic ii，
Wherein n is the total number of the data number;
s405 second order error f for different characteristics iiTo give authority lambdaiEstablishing a screening function MaxF (i), MaxF (i) max [ lambda ]1f1；λ2f2；......λmfm]Screening out the characteristic type i with the maximum second-order error value after weighting;
s406, aiming at the feature type, positioning the sub-models related to the feature generation, 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 smaller than the reference error precision Δ.
As noted above, because the wells and zones that have been produced already have abundant 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 historical data of a particular feature type i at a particular time t
As a standard value for correction and comparison, the letter e is used to distinguish between the history data and the subsequent simulation data. Selecting historical data at the initial time of the feature i, i.e. when t is 0
The data is input into a three-dimensional geological model as input data to be simulated, and simulated data of the characteristic changing along with time can be obtained, namely
Where a is an indication that the data type is analog data. When the feature is selected in step S401Historical data and analog data which correspond to each other are used for carrying out mutual comparison and correction operation in the following process.
Step S402 of determining the history data of the calculation feature i
Analog data with feature i
Mean value of error δ betweeni(ii) a Step S403 weights and sums the error average values of different features i, and compares the weighted and summed error average values with the determined reference error accuracy Δ. Steps S402 and S403 together constitute a comparison and determination step. In the operation of the existing three-dimensional geological model precision test, a probability distribution consistency test means of a single type of feature is generally adopted, and the efficiency has a further improved space. Meanwhile, in the steps of the scheme, the overall error obtained by weighting the multiple characteristic errors is calculated and judged, and the accuracy of the integral three-dimensional model comprising the multiple sub-models can be reflected by utilizing the overall error for judgment. Since the importance of different features is different, in the scheme, the error average values of different features are weighted before the error average values are summedi。
And when the error is found not to meet the precision requirement, carrying out the next correction step. Step S404 is to calculate a second-order error between the historical data of the feature i and the simulation data of the feature i
Compared to the error average value δ in step S402iHigher order errors can reflect more the magnitude of the difference in error between different features i. S405 second order error f for different characteristics iiTo give authority lambdaiEstablishing a screening function MaxF (i) ═ max [ lambda ]1f1；λ2f2；......λmfm]And screening out the characteristic type i with the maximum second-order error value after weighting. This step enables fast localization of the maximum signature i of the simulated data error. Subsequently, the characteristic i is determinedAnd the bit sub-model is modified in a targeted manner to obtain a new three-dimensional geological model. S407 repeats steps S401 to 406 until the total error δ is smaller than the reference error precision Δ. In the process of repeating and comparing again and correcting the three-dimensional geological model in step S407, since the three-dimensional geological model has been modified, it is imperative that the deviation value of the feature i generating the largest error in the above steps is reduced, at this time, if the total error still does not satisfy the error accuracy, in the subsequent screening function screening process, another new feature generating the largest relative error value may be screened out, and the feature is corrected again, and so repeatedly, all the features can be adjusted, so that the accuracy of the three-dimensional geological model can maximally satisfy the standard accuracy requirement.
The characteristics i comprise porosity, permeability, oil field oil production and single well water content.
Compared with the prior art, the invention has the beneficial effects that:
the invention is scientific and reasonable. Compared with the prior art, the method adopts a plurality of classification and scale-division modeling fracture sub-models to reflect and predict the fracture characteristics of the oil field. Meanwhile, in the process of correcting and correcting the three-dimensional geological model, historical data of the produced oil well and the area are used as standards to correct the model according to different characteristics. The error average value is used to reflect the error precision of the whole three-dimensional model, and the efficiency of judging the whole precision error is improved. The method has the advantages that the second-order errors are adopted for comparison, the error sizes among different types of features can be compared more clearly, the features with the maximum deviation degree can be positioned by utilizing a screening function, and meanwhile, the sub-models with errors can be positioned more accurately by utilizing the features.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
FIG. 1 is a step diagram of a modeling research method of an oilfield fine geological model according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
Example 1:
as shown in fig. 1, a modeling research method for an oil field fine geological model is characterized by comprising the following steps: s1, acquiring oil field modeling basic data; s2, based on the oil field modeling basic data, four sub models are established, which are respectively: constructing a lattice model, a sedimentary microfacies model, a reservoir attribute model and a fracture model, wherein the fracture model comprises a plurality of fracture sub-models which are classified and scaled for modeling; s3 equivalently merging the structural grid model, the sedimentary microfacies model, the reservoir attribute model and the fracture model into a high-precision three-dimensional geological model; s4, carrying out proofreading and correction on the three-dimensional geological model; s5 stores the three-dimensional geological model in a cloud storage area.
As mentioned above, the invention provides a fine geological modeling research method applied to oil fields, and the high-precision three-dimensional geological model provided by the method comprises sub-models such as a structural lattice model, a sedimentary microfacies model, a reservoir property model and a fracture model. And implementing the distribution of the bottom grid and the fracture system by developing the fine research of the structural characteristics to establish the structural grid model. The sedimentary microfacies model is built by performing feature recognition on sedimentary facies and predicting the distribution of the sedimentary facies. Before the reservoir attribute model is established, reservoir spreading characteristic research, reservoir physical property and pore microscopic characteristic research and reservoir heterogeneity characteristic research need to be carried out. In order to reflect the influence of the fractures on the physical properties of the reservoir, the local mass model also comprises a fracture model. The fracture model is constructed based on data such as rock core data, imaging logging data, seismic data, dynamic monitoring data and the like. The cracks can be divided into tectonic cracks and diagenetic cracks according to geological causes, and the tectonic cracks are mainly controlled by tectonic action and have certain scales and shapes; the diagenetic cracks are formed in the process of deposition or diagenetic, have limited distribution range and are mainly horizontal cracks. Therefore, classification is required when modeling the fracture. The size of the fracture is different, and different sizes of fractures have causal connection, for example, large-size fractures have causal constraint on small-size fractures. Therefore, the modeling and characterization of the fracture needs to be performed in a classified scale by establishing a plurality of fracture sub-models. And correcting the three-dimensional geological model in the fourth step of the modeling method, so that the established three-dimensional geological model can more accurately and precisely reflect the real attribute condition of the oil and gas reservoir geology.
Example 2:
this example is further defined on the basis of example 1:
the oil field modeling basic data comprises: seismic information, core data, well logging interpretation results and geophysical interpretation results. In this technical feature, the modeling-base data is not limited to the listed data materials. Seismic information, core data and well logging interpretation results can be used to describe the development of a single fracture. The geophysical interpretation effort can be used to develop a study of dephasing and reservoir spread.
Because the accurate structure grid model is the correct root of the reservoir model, the subsequent work performed under the correct structure model is meaningful. It is therefore provided that the construction grid model comprises a layer model and a fault model. The layer model displays the micro-structure style of each small layer, and simultaneously can reflect the release relation and the overlapping style of each small layer.
The method for correcting the three-dimensional geological model comprises the following steps:
s401, retrieving historical data of characteristics i of the exploited oil wells and the areas of the oil field simulated by the three-dimensional geological model
Wherein i represents the feature type, i takes values from 1 to m, m is the total number of the evaluated feature types, e represents that the data type belongs to historical data, and t represents the time coordinate of the numerical value; selecting historical data at the initial time of the feature i, i.e. when t is 0
Inputting the data into a three-dimensional geological model as input data to be simulated to obtain simulated data of the characteristic i
Wherein i represents the feature type, i takes values from 1 to m, m is the total number of the evaluated feature types, a represents that the data type belongs to analog data, and t represents the time coordinate of the numerical value;
s402, calculating historical data of the characteristic i
Analog data with feature i
Mean value of error δ betweeni，
Wherein n is the total number of the time coordinates;
s403, determining reference error precision delta, error average value delta of different characteristics i with m in totaliTo give authority lambdaiAnd the sum is taken to the total error delta,
if delta is larger than delta, the three-dimensional model is unqualified, the next correction operation is carried out, if delta is smaller than or equal to delta, the three-dimensional model is qualified, and S5 operation of storing the three-dimensional geological model to a cloud storage area is carried out;
s404, calculating a second-order error f between the historical data of the characteristic i and the simulation data of the characteristic ii，
Wherein n is the total number of the data number;
s405 second order error f for different characteristics iiTo give authority lambdaiEstablishing a screening function MaxF (i), MaxF (i) max [ lambda ]1f1；λ2f2；......λmfm]Screening out the characteristic type i with the maximum second-order error value after weighting;
s406, aiming at the feature type, positioning the sub-models related to the feature generation, 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 smaller than the reference error precision Δ.
As noted above, because the wells and zones that have been produced already have abundant 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 historical data of a particular feature type i at a particular time t
As a standard value for correction and comparison, the letter e is used to distinguish between the history data and the subsequent simulation data. Selecting historical data at the initial time of the feature i, i.e. when t is 0
The data is input into a three-dimensional geological model as input data to be simulated, and simulated data of the characteristic changing along with time can be obtained, namely
Where a is an indication that the data type is analog data. In step S401, the history data and the simulation data having the characteristics and the times corresponding to each other are selected to perform the mutual comparison correction operation subsequently.
Step S402 of determining the history data of the calculation feature i
Analog data with feature i
Mean value of error δ betweeni(ii) a Step S403 weights and sums the error average values of different features i, and compares the weighted and summed error average values with the determined reference error accuracy Δ. Step by stepSteps S402 and S403 together constitute a comparison and determination step. In the operation of the existing three-dimensional geological model precision test, a probability distribution consistency test means of a single type of feature is generally adopted, and the efficiency has a further improved space. Meanwhile, in the steps of the scheme, the overall error obtained by weighting the multiple characteristic errors is calculated and judged, and the accuracy of the integral three-dimensional model comprising the multiple sub-models can be reflected by utilizing the overall error for judgment. Since the importance of different features is different, in the scheme, the error average values of different features are weighted before the error average values are summedi。
And when the error is found not to meet the precision requirement, carrying out the next correction step. Step S404 is to calculate a second-order error between the historical data of the feature i and the simulation data of the feature i
Compared to the error average value δ in step S402iHigher order errors can reflect more the magnitude of the difference in error between different features i. S405 second order error f for different characteristics iiTo give authority lambdaiEstablishing a screening function MaxF (i) ═ max [ lambda ]1f1；λ2f2；......λmfm]And screening out the characteristic type i with the maximum second-order error value after weighting. This step enables fast localization of the maximum signature i of the simulated data error. And then, positioning a sub-model according to the characteristic i, and modifying the sub-model in a targeted manner to obtain a new three-dimensional geological model. S407 repeats steps S401 to 406 until the total error δ is smaller than the reference error precision Δ. In the repeating and re-comparing and correcting process of step S407, since the three-dimensional geological model has been modified, it is imperative that the deviation value of the feature i that generates the largest error in the above steps is reduced, at this time, if the total error still does not satisfy the error precision, in the subsequent screening function screening process, another and new feature that generates the largest relative error value may be screened out, and the feature is corrected again, and so on, it is satisfied that all the features are adjusted, so that the precise and re-comparing and correcting process of the three-dimensional geological model is performedThe degree meets the standard precision requirement to the maximum extent.
The characteristics i comprise porosity, permeability, oil field oil production and single well water content.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (5)
1. A modeling research method for an oil field fine geological model is characterized by comprising the following steps:
s1, acquiring oil field modeling basic data;
s2, based on the oil field modeling basic data, four sub models are established, which are respectively: constructing a lattice model, a sedimentary microfacies model, a reservoir attribute model and a fracture model, wherein the fracture model comprises a plurality of fracture sub-models which are classified and scaled for modeling;
s3 equivalently merging the structural grid model, the sedimentary microfacies model, the reservoir attribute model and the fracture model into a high-precision three-dimensional geological model;
s4, carrying out proofreading and correction on the three-dimensional geological model;
s5 stores the three-dimensional geological model in a cloud storage area.
2. The method of claim 1, wherein the oilfield modeling basic data comprises: seismic information, core data, well logging interpretation results and geophysical interpretation results.
3. The method of claim 1, wherein the construction grid model comprises a bedding model and a fault model.
4. The modeling research method of the oilfield fine geological model according to claim 2, wherein the method for correcting the three-dimensional geological model comprises the following steps:
s401, retrieving historical data of characteristics i of the exploited oil wells and the areas of the oil field simulated by the three-dimensional geological model
Wherein i represents the feature type, i takes values from 1 to m, m is the total number of the evaluated feature types, e represents that the data type belongs to historical data, and t represents the time coordinate of the numerical value;
selecting historical data at the initial time of the feature i, i.e. when t is 0
Inputting the data into a three-dimensional geological model as input data for simulation to obtain simulation data of the characteristic i,
wherein i represents the feature type, i takes values from 1 to m, m is the total number of the evaluated feature types, a represents that the data type belongs to analog data, and t represents the time coordinate of the numerical value;
s402, calculating historical data of the characteristic i
Analog data with feature i
Mean value of error δ betweeni，
Wherein n is the total number of the time coordinates;
s403, determining reference error precision delta, error average value delta of different characteristics i with m in totaliTo give authority lambdaiAnd the sum is taken to the total error delta,
if delta is larger than delta, the three-dimensional model is unqualified, the next correction operation is carried out, if delta is smaller than or equal to delta, the three-dimensional model is qualified, and S5 operation of storing the three-dimensional geological model to a cloud storage area is carried out;
s404, calculating a second-order error f between the historical data of the characteristic i and the simulation data of the characteristic ii，
Wherein n is the total number of the data number;
s405 second order error f for different characteristics iiTo give authority lambdaiEstablishing a screening function MaxF (i),
MaxF(i)＝max[λ1f1；λ2f2......λmfm]，
screening out the characteristic type i with the maximum second-order error value after weighting;
s406, aiming at the feature type, positioning the sub-models related to the feature generation, 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 smaller than the reference error precision Δ.
5. The modeling research method of the oilfield fine geological model according to claim 4, wherein the characteristics i comprise porosity, permeability, oilfield oil production, and single well water content.
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