CN108509694B - Proppant paving form prediction method based on BP neural network - Google Patents
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Abstract
A proppant laying form prediction method based on a BP neural network comprises the following steps: selecting sand ratio, proppant particle size, proppant density and construction discharge capacity as main factors influencing the laying form of the proppant, and selecting balance height, balance time and sand bank front edge distance as main parameters for describing the laying form of the proppant; simulating the laying form of the propping agent in the crack under different parameters by using a crack simulation experiment device; collecting and collating experimental data and dividing the experimental data into training samples and inspection samples; setting parameters of a BP neural network, and establishing a nonlinear mapping relation from sand ratio, proppant particle size, proppant density, construction discharge capacity to balance height, balance time and sand bank leading edge distance; training and checking the neural network; predicting the new sample proppant placement morphology; the method realizes the prediction of the laying form of the propping agent by utilizing the strong learning capability of the BP neural network, has simple and convenient operation and is convenient for the field application of the oil field.
Description
Technical Field
The invention relates to the field of petroleum engineering, in particular to a proppant laying form prediction method based on a BP neural network, which is used for predicting the laying form of a proppant in a fracture in a fracturing process and provides a basis for selection of a proppant and construction parameters in an oil field.
Technical Field
Due to the fact that the conventional oil and gas resources cannot meet the social requirements in recent years, development of unconventional reservoirs is increasingly deepened. The hydraulic fracturing technology has become a very effective means for developing unconventional reservoir oil and gas resources, however, in order to form sand-filled fractures with high conductivity in a reservoir, a proppant must be effectively filled in the fractures, so that the research on the migration rule and the laying form of the proppant in the fractures is particularly important. The research on the laying form of the proppant in the fracture mainly focuses on simulating the settling and migration process of the proppant by utilizing a fracture simulation experiment device; however, the proppant is not only influenced by the type of the proppant but also influenced by construction parameters and other factors in the migration process, so that a mathematical expression conforming to the actual settling and migration rule of the proppant on site is difficult to establish, and the prediction of the laying form of the proppant in the fracture is greatly limited.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a proppant paving form prediction method based on a BP neural network, which can predict the proppant paving form and perform weight analysis by using a trained neural network so as to obtain the weight of main factors influencing the proppant paving form, provide scientific and practical weight basis for further establishing a proppant evaluation model, and avoid experimental investment and complex mathematical operation.
In order to overcome the defects of the prior art, the technical scheme of the invention is as follows:
a proppant laying form prediction method based on a BP neural network comprises the following steps:
the method comprises the following steps: selecting sand ratio, proppant particle size, proppant density and construction discharge capacity as main parameters influencing the laying form of the proppant, and selecting balance height, balance time and sand bank front edge distance as main parameters describing the laying form of the proppant;
step two: simulating the laying form of the propping agent in the fracture with different parameters by using a fracturing fluid loss parallel plate fracture simulation device, collecting and collating experimental data, and dividing the experimental result into two groups, wherein one group is used as a training sample and the other group is used as an inspection sample;
step three: selecting main parameters influencing the laying form of the proppant as an input layer of a BP (back propagation) neural network, setting parameters of the neural network, and inputting a training sample into the neural network for training so as to establish a nonlinear mapping relation from the input layer to the output layer, wherein the parameters describing the laying form of the proppant are the output layer of the BP neural network;
step four: after the BP neural network is trained, inputting a test sample into the trained neural network to obtain a prediction result, calling MATLAB program corrcoef to compare and judge between a numerical matrix of the prediction result and a true value matrix of the test sample, if the similarity is more than 0.9, the model is valid, and if the similarity does not meet the requirement, the neural network parameters and the number of hidden nodes are reset until the precision requirement is met;
step five: and inputting the values of the sand ratio of the new sample, the particle size of the proppant, the density of the proppant and the construction discharge capacity into a neural network model to predict the laying form of the proppant.
The concrete method of the third step is as follows:
adopting three layers of BP neural networks, wherein an input layer comprises four neuron nodes of sand ratio, proppant particle size, proppant density and construction discharge capacity; the output layer comprises three neuron nodes of balance height, balance time and sand bank leading edge distance, and the initial value of the number of hidden nodes is set to be any integer within 3 to 12, so that the nonlinear mapping relation from the input layer to the output layer is established.
The expressed neural network adopts no less than 20 groups of experimental data, a training function is selected as a self-adaptive Ir gradient descent method with a learning rate of no more than 0.05 and an error of no more than one percent, the iteration step length is 5000, and the network is trained.
The invention has the beneficial effects that: the method selects sand ratio, proppant particle size, proppant density and construction discharge capacity as main parameters influencing the laying form of the proppant, selects balance height, balance time and sand bank front edge distance as main parameters describing the laying form of the proppant, utilizes a parallel plate fracture simulation experiment device to simulate the laying form of the proppant under different parameters, arranges experimental data into training samples and detection samples, establishes a BP neural network model and sets network parameters, trains and adjusts the network so as to establish an optimal nonlinear mapping relation from the sand ratio, the proppant particle size, the proppant density, the construction discharge capacity to the balance height, the balance time and the sand bank front edge distance.
Drawings
FIG. 1 is a diagram of the BP neural network structure of the present invention.
Detailed Description
The technical solution of the present invention is further specifically described below by way of examples, but the present invention is not limited to the examples described below.
The method comprises the following steps: the sand ratio, the particle size of the proppant, the density of the proppant and the construction discharge capacity are selected as main parameters influencing the laying form of the proppant, and the balance height, the balance time and the front edge distance of the sand bank are selected as main parameters describing the laying form of the proppant.
Before the experiment begins, the slickwater fracturing fluid with the viscosity of 1 mPa.s is selected as experimental parameters, the sand ratio comprises six of 0.02, 0.03, 0.04, 0.05, 0.06 and 0.08, the particle size of the propping agent comprises six of 0.21mm, 0.32mm, 0.45mm, 0.64mm and four, and the density of the propping agent comprises 1450kg/m3、1540kg/m3、1890kg/m3、2600kg/m3、2770kg/m3、2880kg/m3、3020kg/m3Seven kinds of the construction displacement comprises 4m3/h、5m3/h、6m3/h、7m3/h、8m3Five,/h, different combinations of these parameters were used for the design experiments.
Step two: the parallel plate fracture simulation device for fracturing fluid loss is used for simulating the laying form of the propping agent in the fracture under different parameters, collecting and arranging experimental data, and dividing experimental results into two groups, wherein one group is used as a training sample, and the other group is used as an inspection sample.
The device is connected with an experimental pipeline well and used for checking the tightness of a pipeline valve and the device under the name of 'a parallel plate fracture simulation device for fracturing fluid loss' with the patent number of 201420450355.6; adding fracturing fluid into the liquid storage tank, and starting a stirring pump to start stirring; according to the sand ratio, the particle size of the proppant and the density of the proppant determined by the design of the experimental scheme, weighing a certain amount of the proppant, slowly adding the proppant into a liquid storage tank, and continuously stirring to mix the proppant with the fracturing fluid; and (2) opening a data acquisition system, starting a screw pump, gradually increasing the discharge capacity of the pump to the designed discharge capacity, ensuring that the flow rate of the sand-carrying liquid is stable, stopping the pump after the proppant accumulation height reaches the balance height, recording three parameter values of the balance height, the balance time and the front edge distance of the sand bank to obtain the experimental data shown in the table 1, taking the first 20 groups of the experimental data as training samples, and taking the last 5 groups of the experimental data as inspection samples.
Step three: selecting main parameters influencing the laying form of the proppant as an input layer of a BP (back propagation) neural network, setting parameters of the neural network, and inputting a training sample into the neural network for training so as to establish a nonlinear mapping relation from the input layer to the output layer, wherein the parameters describing the laying form of the proppant are the output layer of the BP neural network;
establishing a BP neural network, selecting sand ratio, proppant particle size, proppant density and discharge capacity as input layer neurons of the neural network, selecting balance height, balance time and sand bank leading edge distance as output layer neurons of the neural network, setting a transfer function from an output layer to a hidden layer as an S-type tangent function, setting a training function as an adaptive lr gradient descent method, thereby establishing a nonlinear mapping relation from the input layer to the output layer, wherein the nonlinear mapping relation is detailed as shown in FIG. 1,
step four: after the BP neural network is trained, inputting a test sample into the trained neural network to obtain a prediction result, calling an MATLAB program to carry out normalization processing on input layer data and output layer data of the training sample, calling an MATLAB neural network tool kit program package to establish a network, setting the initial value of the number of hidden nodes to be any integer value within 3 to 12, setting the learning rate to be 0.05, setting the error to be 0.01 and setting the iteration step length to be 5000; and calling MATLAB program corrcoef to compare and judge the numerical matrix of the prediction result with the true value matrix of the test sample, if the similarity is more than 0.9, the model is effective, and if the similarity does not meet the requirement, the neural network parameters and the number of hidden layer nodes are reset until the precision requirement is met.
And training the established neural network by using the training sample data subjected to normalization processing until the error iteration is terminated.
In order to test and improve the precision of the neural network, an MATLAB program is utilized to carry out normalization processing on input layer data of a test sample and input the data into the neural network, the obtained result is subjected to reverse normalization processing to obtain a predicted value, the similarity between a predicted value matrix and a truth value matrix of the test sample is compared, the number of hidden nodes is adjusted, the highest similarity is found when the number of hidden nodes is 7, the neural network is optimal, and the specific numerical value comparison is shown in a table 2.
Step five: inputting the values of the sand ratio of the new sample, the particle size of the proppant, the density of the proppant and the construction discharge capacity into a neural network model to predict the laying form of the proppant; the laying form of the new sample proppant under different discharge capacities can be predicted by utilizing the established BP neural network, a simulation experiment is not needed in the process, the investment of manpower and financial resources is greatly saved, and the method is simple, effective and convenient to popularize.
Table 1 proppant placement simulation experiment result data table
TABLE 2 BP neural network truth and predicted value comparison table
The above description is only a partial description of the preferred embodiments of the present invention, and any person skilled in the art may modify the above technical solutions. Therefore, any simple modifications or equivalent substitutions made according to the technical solution of the present invention belong to the scope of the claims of the present invention.
Claims (3)
1. A proppant laying form prediction method based on a BP neural network is characterized by comprising the following steps:
the method comprises the following steps: selecting sand ratio, proppant particle size, proppant density and construction discharge capacity as parameters influencing the laying form of the proppant, and selecting balance height, balance time and sand bank front edge distance as parameters describing the laying form of the proppant;
step two: simulating the laying form of the propping agent in the fracture with different parameters by using a fracturing fluid loss parallel plate fracture simulation device, collecting and collating experimental data, and dividing the experimental result into two groups, wherein one group is used as a training sample and the other group is used as an inspection sample;
step three: selecting parameters influencing the laying form of the propping agent as an input layer of a BP (back propagation) neural network, setting parameters of the neural network, describing the laying form of the propping agent as an output layer of the BP neural network, inputting a training sample into the neural network for training, and establishing a nonlinear mapping relation from the input layer to the output layer;
step four: after the BP neural network is trained, inputting a test sample into the trained neural network to obtain a prediction result, calling MATLAB program corrcoef to compare and judge between a numerical matrix of the prediction result and a true value matrix of the test sample, if the similarity is more than 0.9, the model is valid, and if the similarity does not meet the requirement, the neural network parameters and the number of hidden nodes are reset until the precision requirement is met;
step five: and inputting the values of the sand ratio of the new sample, the particle size of the proppant, the density of the proppant and the construction discharge capacity into a neural network model to predict the laying form of the proppant.
2. The method for predicting the proppant placement form based on the BP neural network as set forth in claim 1, wherein the concrete method of the third step is as follows:
adopting three layers of BP neural networks, wherein an input layer comprises four neuron nodes of sand ratio, proppant particle size, proppant density and construction discharge capacity; the output layer comprises three neuron nodes of a balance height, a balance time and a sand bank front edge distance, and the initial value of the number of hidden nodes is set to be any integer within 3 to 12, so that a nonlinear mapping relation from the input layer to the output layer is established.
3. The method for predicting the proppant placement form based on the BP neural network as set forth in claim 1 or 2, wherein the neural network is trained by selecting the training function as an adaptive Ir gradient descent method with an iteration step size of 5000 and using not less than 20 sets of experimental data and a learning rate of not more than 0.05 and an error of not more than one percent.
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| CN109063403B (en) * | 2018-10-22 | 2022-09-13 | 西安石油大学 | Optimal design method for slickwater fracturing |
| CN110965977B (en) * | 2019-11-20 | 2021-01-08 | 中国石油大学(北京) | Fracturing construction analysis method |
| CN111622730B (en) * | 2020-05-29 | 2022-04-01 | 中国石油大学(华东) | Fracturing sand adding design method based on large-scale parallel plate proppant migration and placement model experiment |
| CN112761609B (en) * | 2021-02-19 | 2022-02-01 | 西南石油大学 | Optimization method for efficient laying of propping agent in hydraulic fracturing operation |
| CN113095398B (en) * | 2021-04-08 | 2022-07-12 | 西南石油大学 | Fracturing data cleaning method of BP neural network based on genetic algorithm optimization |
| CN115199238B (en) * | 2022-09-15 | 2022-11-25 | 四川省贝特石油技术有限公司 | Method and system for controlling feeding of superfine temporary plugging agent for gas reservoir exploitation |
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