CN111706323A - Water flooded layer fine interpretation and evaluation method based on GWO-LSSVM algorithm - Google Patents
Water flooded layer fine interpretation and evaluation method based on GWO-LSSVM algorithm Download PDFInfo
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Abstract
The invention provides a water flooded layer fine interpretation and evaluation method based on an integrated GWO-LSSVM algorithm, and relates to the technical field of petroleum engineering. According to the method, data collection and optimization can be performed by analyzing weights of different flooding intensity evaluation indexes according to the theoretical basis of logging curve evaluation and GWO-LSSVM algorithm, and then the data are input into a prediction module to calculate an output flooding intensity result. The flooding level result is optimized by at least three kernel function parameters. The method can solve the limitation caused by one parameter of the traditional evaluation method of the water flooded layer, has less test steps, low difficulty and high precision in the evaluation method, and can effectively provide reference data for actual engineering or experiments.
Description
Technical Field
The invention belongs to the technical field of petroleum engineering, and relates to a water flooded layer fine interpretation and evaluation method of an integrated GWO-LSSVM algorithm.
Background
Due to the long-term development of oil fields, oil fields enter a high water cut, high production stage. However, a great deal of crude oil is still stored underground, and the contradiction of oil field development becomes increasingly prominent to produce under the condition of high and medium water content. Particularly, aiming at a low-permeability reservoir under a fracture condition, under the condition that the water content is gradually increased, the current important problem is how to improve the recovery ratio and increase the final yield of an oil reservoir. Therefore, the oil reservoir should be further deeply known, the development form should be analyzed, the production problem should be solved, the current situation of the human situation development of the logging and testing data should be fully utilized, the distribution rule of the residual oil should be clarified, the potential excavation and adjustment work in the middle and later stages of the oil field development should be done economically and effectively, and the overall development level and the final benefit of the oil field should be improved. The accurate evaluation of the saturation of the water flooded layer and the residual oil is important for the implementation of the adjustment of the excavation and oil extraction schemes in the middle and later stages of oil field development. The existing evaluation methods of the water flooded layer are various, such as open hole logging, production logging and the like. Are limited to a certain logging data and have the following problems:
1. the evaluation performed through the logging data is easily influenced by human subjective factors, and the accuracy is easily deviated;
2. regardless of which logging data is used for evaluation, the misselection of interpretation parameters causes deviation of evaluation results;
3. the evaluation mode of the logging information is time-consuming in use and the oil field application is complicated.
In 1977, the R.P.Murphy considers that the method of measuring one water injection and measuring one water saturation can be calculated, and the calculation accuracy of the method for analyzing the actual effect is higher;
in 1996, Sundming developed the thickened oil water logging evaluation, introduced the desalination coefficient equation, had higher coincidence rate, and was the first thickened oil water logging evaluation method in China;
in 1998, logging data, core analysis data, oil testing data and production data are combined when Songzaiqi and Tanzhong are thousand and the like, and a high water-cut period water flooded layer evaluation method is provided by using a gray system mathematical model;
in 2000, Fred Aminzadeh reduces the number of input parameters of the artificial neural network model by a dimensionality reduction method, combines new input data, obviously improves the classification precision of the artificial neural network model, and can be further used for calculating reservoir parameters;
in 2002, the original oilfield utilizes a carbon-oxygen ratio energy spectrum to perform logging evaluation on a water flooded layer, the carbon-oxygen ratio logging can overcome the complex change of the salinity of stratum water, the oil saturation of a reservoir layer is measured in a casing, and the logging evaluation on the water flooded layer based on the logging method has good application effect, but is high in cost and relatively limited in applicability;
in 2005, the Cijianfa and the Truezu pointed out the defects existing in the logging evaluation of the water flooded layer and the development direction and the suggestion of the future logging evaluation of the water flooded layer;
in 2008, Zhonggui grid analyzes the actual influence of the anisotropy of an oil layer on the flooding process based on a conceptual model and numerical simulation, provides the maximum flooding degree in the exploitation process of the heterogeneous oil layer, and analyzes the change mode of the water content of a reservoir when the heterogeneous oil layer changes along with the flooding degree;
in 2010, Shizhanwei, Zhai Ming, applied to Changming, analyzed the pore-permeation characteristics of the oil reservoir when flooded to obtain the change rule of the resistivity curve and the natural potential curve when the oil reservoir of the oil field grape blossoms was flooded to calculate and establish the logging evaluation method of the water flooded layer of the block through reservoir parameters.
Disclosure of Invention
The invention aims to provide a method for finely explaining and evaluating a flooded layer to realize comprehensive evaluation of logging of the flooded layer. The method has the advantages that the GWO-LSSVM intelligent algorithm can be combined with the well logging method, different parameters are preferably selected as evaluation indexes, the evaluation speed is improved, and meanwhile the accuracy of well logging evaluation of a water flooded layer can be greatly guaranteed. The method avoids the error problem caused by the traditional single-factor evaluation method, and can provide effective reference data for actual engineering and experimental analysis.
In order to realize the purpose of the invention, the invention adopts the scheme that:
acquiring sample values of flooding intensity evaluation indexes of different well groups in the same area, wherein the flooding intensity evaluation indexes comprise influence factors such as resistivity, argillaceous content, available water saturation, water absorption profile and liquid production profile;
carrying out data training on sample values of flooding strength evaluation indexes of different well groups to obtain a weight of the flooding strength evaluation indexes;
the method comprises the steps of initializing gray wolf algorithm parameters, analyzing corresponding characteristics of flooding strength evaluation indexes before and after flooding, obtaining a physical property change rule of the evaluation indexes, and analyzing a flooding layer according to change conditions of logging curve change, single-well oil testing, production data and the like to calculate a weight of the appropriate flooding strength evaluation indexes;
performing least square support vector machine prediction according to the optimal flooding intensity evaluation index and the optimal parameter calculated by the Grey wolf algorithm, wherein the result obtained by prediction is the flooding level
The invention has the following beneficial effects:
the fine interpretation and evaluation method for the oil field water flooded layer comprises comprehensive identification of well logging of the water flooded layer and optimization of evaluation indexes of water flooded strength. Specifically, gray wolf algorithm parameter optimization is carried out according to sample values of flooding strength evaluation indexes of different well groups in the same area, so that the optimal weight affecting the flooding strength evaluation indexes is obtained. And finally predicting the result by a least square support vector machine. Compared with the flooding intensity value obtained by traditional logging evaluation, the flooding intensity value of the flooding layer determined by the method has higher accuracy, and because the factors such as other oil test and production data, water absorption profile and liquid production profile except logging data are comprehensively considered, the error problem caused by a single factor evaluation method is avoided. Meanwhile, the method is carried out by adopting an intelligent algorithm, and the timeliness is greatly improved compared with that of the traditional logging curve method.
Drawings
FIG. 1 is a schematic structural diagram of GWO-LSSVM of the present invention
FIG. 2 is a graph of the relationship between two zone reservoir permeability and well log
FIG. 3 is a graph of two-zone reservoir porosity versus well log
FIG. 4 is a two-zone water flooded layer resistivity and water content intersection chart
FIG. 5 is a two-zone water flooded layer resistivity and 4 m resistance intersection chart
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
1. Reservoir lithology, physical property, electrical property and oil-gas containing property of different well groups in the same area are analyzed to obtain a flooding strength index sample value, and meanwhile, a data set of the method is established;
2. based on the data of the analysis of the physical properties of the different well groups in the two regions of the study area, we fortunately obtained the data of 4 core wells, 202 points. The attributes in the raw data include starting depth, ending depth, deep lateral resistivity, latent lateral resistivity, wash zone resistivity, sonic moveout, compensation density, and the like. There are 4 flooding levels, namely, strong flooding, medium flooding, weak flooding and no flooding, and the flooding levels correspond to 4 classes. Using 160 points as learning samples, and using the points as test samples to analyze the relation between the reservoir physical properties and the well logging curve, as shown in fig. 2 and 3
3. Preprocessing logging data:
(1) normalization process
In the long-term oil field exploration and development process, it is difficult to ensure that all logging curves are measured by the same instrument, the same standard scale and the same operation mode, so that errors necessarily exist among different logging data. Therefore, in addition to the necessary environmental impact corrections to the log data, the log must also be normalized. We used the normalization method:
wherein x (i) is the ith log, x (i)minIs the minimum of the ith log, x (i)maxIs the maximum of the ith curve.
(2) Selection of kernel functions and parameters
The radial basis function, the polynomial function and the sigmoid function are respectively adopted as the kernel function of the support vector machine, and the parameters of each function are adjusted to achieve the best effect
4. Initializing GWO parameters, inputting flooding intensity evaluation indexes before and after flooding in a research area into GWO genetic algorithm, analyzing the change conditions of the evaluation indexes before and after flooding, and simultaneously calculating the optimal fitness value by respectively taking a logging curve value, single-well oil testing, production data and the like as wolf group parameters;
5. judging the shutdown criterion through the change condition of the flooding strength evaluation index and the optimal fitness value of the wolf pack parameter, outputting the optimal flooding strength evaluation index value if the shutdown criterion is reached, and updating the wolf pack parameter for GWO optimization and recalculation if the shutdown criterion is not reached;
6. and inputting the optimal flooding intensity evaluation index value into a least square support vector machine for calculation and prediction. The predicted final result is the flooding level. The obtained flooding level intersection map is shown in fig. 4 and 5.
Claims (6)
1. A water flooded layer fine interpretation evaluation method of an integrated GWO-LSSVM algorithm is characterized by comprising the following steps: acquiring sample values of flooding intensity evaluation indexes of different well groups in the same area, wherein the flooding intensity evaluation indexes comprise influence factors such as resistivity, argillaceous content, available water saturation, water absorption profile and liquid production profile; carrying out data training on sample values of flooding strength evaluation indexes of different well groups to obtain a weight of the flooding strength evaluation indexes; the method comprises the steps of initializing gray wolf algorithm parameters, analyzing corresponding characteristics of flooding strength evaluation indexes before and after flooding, obtaining a physical property change rule of the evaluation indexes, and analyzing a flooding layer according to change conditions of logging curve change, single-well oil testing, production data and the like to calculate a weight of the appropriate flooding strength evaluation indexes; and (3) performing least square support vector machine prediction according to the optimal flooding intensity evaluation index and (optimal parameter) calculated by the Grey wolf algorithm, wherein the result obtained by prediction is the flooding level.
2. The method of claim 1 for evaluating the waterflooding level of a comprehensive GWO-LSSVM algorithm, wherein: and when acquiring the water logging strength index sample values of reservoirs of different well groups in the same area, the samples are in the same reservoir.
3. The method of claim 1 for evaluating the waterflooding level of a comprehensive GWO-LSSVM algorithm, wherein: all data of lithology, physical property, electrical property and oil-gas containing property are required to meet the requirements of the industry standard when being analyzed.
4. The method of claim 1 for evaluating the waterflooding level of a comprehensive GWO-LSSVM algorithm, wherein: the GWO-LSSVM algorithm was run using MATLAB programming software.
5. The method of claim 1 for evaluating the waterflooding level of a comprehensive GWO-LSSVM algorithm, wherein: the kernel function should be selected using three or more functions as the kernel function.
6. The method of claim 1 for evaluating the waterflooding level of a comprehensive GWO-LSSVM algorithm, wherein:
step 1: reservoir lithology, physical property, electrical property and oil-gas containing property of different well groups in the same area are analyzed to obtain a flooding strength index sample value, and meanwhile, a data set of the method is established;
step 2: based on the analysis data of the related physical properties of different well groups in two areas of a research area, we fortunately obtain data of 4 core wells, 202 points, wherein the attributes in the original data comprise a starting depth, an ending depth, a deep lateral resistivity, a latent lateral resistivity, a flushing zone resistivity, an acoustic wave time difference, a compensation density and the like, 4 water flooding levels are strong water flooding, medium water flooding, weak water flooding and non-water flooding, the water flooding levels correspond to 4 types, 160 points are taken as learning samples, and the points are taken as test samples to analyze the relation between the physical properties of the reservoir and a logging curve, as shown in attached figures 2 and 3;
and step 3: preprocessing logging data:
(1) normalization process
In the long-term oil field exploration and development process, it is difficult to ensure that all logging curves are measured by the same instrument, the same standard scale and the same operation mode, so that errors necessarily exist among different logging data, and therefore, the logging curves need to be standardized except for carrying out necessary environmental influence correction on the logging data, and a normalization method is adopted:
wherein x (i) is the ith log, x (i)minIs the minimum of the ith log, x (i)maxIs the maximum of the ith curve;
(2) selection of kernel functions and parameters
Respectively adopting a radial basis function, a polynomial function and a sigmoid function as kernel functions of a support vector machine, and adjusting parameters of each function to achieve the best effect;
and 4, step 4: initializing GWO parameters, inputting flooding intensity evaluation indexes before and after flooding in a research area into GWO genetic algorithm, analyzing the change conditions of the evaluation indexes before and after flooding, and simultaneously calculating the optimal fitness value by respectively taking a logging curve value, single-well oil testing, production data and the like as wolf group parameters;
and 5: judging the shutdown criterion through the change condition of the flooding strength evaluation index and the optimal fitness value of the wolf pack parameter, outputting the optimal flooding strength evaluation index value if the shutdown criterion is reached, and updating the wolf pack parameter for GWO optimization and recalculation if the shutdown criterion is not reached;
step 6: and inputting the optimal flooding intensity evaluation index value into a least square support vector machine for calculation and prediction, wherein the predicted final result is a flooding level, and the obtained flooding level intersection graph is shown in fig. 4 and 5.
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