CN110516850A - Gas storage operation model optimization method based on historical learning mode - Google Patents

Abstract

The invention discloses a gas storage operation model optimization method based on a historical learning mode, which comprises the steps of firstly learning the gas supply condition of a gas area during the annual gas consumption peak period, and establishing a peak regulation operation model; establishing a relation between a peak-to-valley ratio and a peak-shaving operation model; obtaining a plurality of peak shaving operation models under the specified working gas quantity; and optimizing a plurality of peak-shaving operation models under the specified working gas quantity, generating one well number corresponding to each working gas quantity according to different peak-to-valley ratios, and obtaining the minimum reasonable well number under the corresponding working gas quantity, wherein the corresponding operation model is the optimal operation model. According to the method, the injection and production well number of the running model of the low-permeability gas reservoir is optimized by learning the historical running rule of the gas area, so that the gas reservoir can run economically and efficiently in a peak regulation mode.

Description

A Method for Optimizing the Operation Model of Gas Storage Based on History Learning Mode

technical field

The invention belongs to the technical field of gas field development, and in particular relates to a method for optimizing an operating model of a gas storage based on a history learning mode.

Background technique

Since 2010, PetroChina Changqing Oilfield Company started the exploration stage of underground gas storage, and the operation history of underground gas storage put into large-scale operation in China is only 4 years. Due to the short construction history of China's underground gas storage, little experience accumulation, low permeability of lithologic gas reservoirs, strong heterogeneity, and complex geological conditions, the range of well control in a single operation cycle of peak shaving in winter is limited. The output declines rapidly, and the operation mode cannot meet the operation needs of the underground gas storage, which will bring three kinds of harm:

1. During the gas production period of the underground gas storage, the working gas volume index of the underground gas storage cannot be reached due to insufficient number of production wells and insufficient cumulative gas production;

2. If there are too many production wells during the gas production period of the underground gas storage, it will increase the difficulty of operation and management of the gas storage, reduce the benefits, and fail to meet the efficient operation under optimal economic conditions;

3. If there are too many or too few production wells during the gas production period of the underground gas storage, it will lead to the inability to coordinate the operation of the well construction project and the ground support, which will increase the difficulty of subsequent adjustments. Based on the changing law of single well, it is imminent to optimize the number of injection-production wells to meet the operation needs of underground gas storage.

The role of underground gas storage in regulating peaks and replenishing gas in the natural gas market determines that within the capacity of the gas storage, its gas production and supply rules must be the same as the actual gas supply capacity of the gas area. Therefore, the scheme design concept and scheme index selection of underground gas storage must be based on the working gas capacity of the gas storage, on the premise of adapting to the demand law of the gas market, and with the goal of meeting the gas demand scale of the market as much as possible .

According to the winter peak shaving demand of Changqing Oilfield Company gas field and the actual operation effect of underground gas storage, learn the historical peak shaving mode, create a set of reasonable operation model, and optimize the design technology with a reasonable number of gas production wells as the core. It is of great significance to understand the design and operation model of rock-type gas storage and improve the level of scheme design.

Contents of the invention

The technical problem to be solved by the present invention is to provide a gas storage operation model optimization method based on the history learning mode in view of the deficiencies in the above-mentioned prior art, to learn the historical operation rules of the gas area, and to inject the operation model of the low-permeability gas storage The number of production wells is optimized to meet the economical and efficient operation of the gas storage under the peak-shaving mode.

The present invention adopts following technical scheme:

A method for optimizing an operating model of a gas storage based on a historical learning mode, comprising the following steps:

S1. Study the gas supply situation during the peak period of gas consumption in the gas area every year, and establish a peak-shaving operation model;

S2. Establish the relationship between the peak-to-valley ratio and the peak-shaving operation model;

S3. Obtain multiple peak-shaving operation models under the specified working gas volume;

S4. Optimize multiple peak-shaving operation models under the specified working gas volume. According to different peak-to-valley ratios, a number of wells is generated corresponding to each working gas volume, and the minimum reasonable number of wells under the corresponding working gas volume is obtained. The corresponding operation model is the most According to the optimal operation model, the minimum reasonable number of wells corresponding to the peak-to-valley ratio is obtained according to the optimal operation model, which corresponds to the peak-shaving gas volume in winter under the corresponding peak-to-valley ratio of the gas storage.

Specifically, in step S1, the gas supply curve is generated by learning the gas supply situation during the peak period of gas consumption each year in the gas area, and the normalized daily gas supply volume of the warehouse is regressed, so as to obtain the regression formula and establish the operation model during the peak period.

Further, the binomial equation of the daily gas production of the gas storage corresponding to the working gas volume is:

Q _i ＝-0.1071t ² +16.713t+Q ₀

Wherein, Q _i is the peak shaving output of the gas storage, Q ₀ =0, t=1, 2, 3...120.

Furthermore, the working gas volume G of the gas storage is:

G=Q ₁ +Q ₂ +Q ₃ . . . +Q ₁₂₀ .

Specifically, in step S2, the peak-to-valley ratio of the curve is obtained according to the regression _curve to obtain the relationship between the corresponding peak-to-valley ratio and the operating model. The ratio M=Q _max /Q _min of the average daily production Q _min .

Specifically, in step S3, the initial production of the gas storage is calculated and determined according to the different peak-to-valley ratio models established, and the corresponding peak-to-valley ratio peak-shaving operation model conforming to the working gas volume is obtained, and multiple peak-shaving operation models are obtained.

Specifically, in step S4, the reasonable number of wells N corresponding to each day is:

N=Q _i /q _i

Among them, Q _i is the peak shaving output of the gas storage, and q _i is the gas production capacity of a single well.

Compared with the prior art, the present invention has at least the following beneficial effects:

The invention is a method for optimizing the operation model of the gas storage based on the history learning mode. On the basis of learning the historical operation rules of the gas area, the operation mode optimization of the different peak shaving intensities of the underground gas storage is carried out, and the method based on the history learning mode is established. The optimization method of the gas storage operation model. It can be used to rank the favorable areas of the model for reservoir construction and optimize the already formed schemes. It has been applied to the evaluation, construction and planning research of the lithologic gas storage business in Changqing Oilfield. The model has been applied to the Shaanxi XX gas storage Through optimization, drilling one less horizontal well in Shaanxi XX gas storage can save 68 million yuan in drilling costs, which is of practical value for the development and evaluation of gas storage.

Further, establishing a gas storage operation model for a gas field that meets the requirements (such as Changqing Gas Field) and learning the historical operation rules of the gas area is more in line with the reality of the gas field. The optimized peak operation plan can be implemented faster and more smoothly, ensuring the storage The working gas volume index of the gas storage reaches the standard. This is the most critical point.

Furthermore, the relationship between the peak-to-valley ratio and the peak-shaving operation model was established in China based on the experience value of the Dagang gas storage. The relationship between the valley ratio and the peak shaving operation model lays the foundation for subsequent optimization.

Furthermore, the purpose of obtaining multiple peak-shaving operation models under the specified working gas volume is to determine so many operating models that meet the different working gas volumes of each gas storage in the Changqing gas field. There can be many kinds of models, but which one is more suitable requires further optimization below.

Further, it can be determined which of the corresponding operation models is the optimal operation model, which is more economical and efficient.

To sum up, the present invention optimizes the number of injection-production wells in the operation model of the low-permeability gas storage by learning the historical operation rules of the gas field, so that it can meet the economical and efficient operation of the gas storage in the peak-shaving mode.

The technical solutions of the present invention will be described in further detail below with reference to the accompanying drawings and embodiments.

Description of drawings

Figure 1 is a peak shaving gas production curve in 2018 in a certain gas area;

Figure 2 is a peak shaving gas production curve in 2014 in a certain gas area;

Figure 3 is the peak-shaving operation model corresponding to the peak-to-valley ratio;

Fig. 4 is the relationship curve between the peak-to-valley ratio of production and the number of wells under a certain working gas volume in a certain gas area.

Detailed ways

The present invention is a method for optimizing the operation model of the gas storage based on the history learning mode, which learns the annual gas supply capacity curve of the gas area to establish the relationship between the gas production operation model of the underground gas storage and the peak-to-valley ratio, and returns each model of the gas output area According to the binomial equation, according to different peak-to-valley ratios, a reasonable number of wells will be generated corresponding to each working gas volume, and the minimum reasonable number of wells under the corresponding working gas volume will be obtained. The corresponding operation model is the optimal operation model. The specific steps as follows:

S1. Obtain the peak shaving operation model;

Study the gas supply situation during the peak period of gas consumption in the gas area every year, generate a gas supply curve, and regress the normalized daily gas supply volume of the warehouse, so as to obtain the regression formula and establish an operation model during the peak period.

S2, establishing the relationship between the peak-to-valley ratio and the peak-shaving operation model;

According to the regression curve, the peak-to-valley ratio of the curve is obtained, so as to obtain the relationship between the corresponding peak-to-valley ratio and the operating model.

S3. Obtain multiple peak-shaving operation models under the specified working gas volume;

Under a certain working gas volume, calculate and determine the initial production of the gas storage according to the different peak-to-valley ratio models established, so as to obtain the corresponding peak-to-valley ratio peak-shaving operation model under this working gas volume, and obtain multiple peak-shaving operation models.

S4. Optimizing multiple peak-shaving operation models under a specified working gas volume.

The number of wells required per day (daily gas production in the reservoir area/daily gas production of a single well) is calculated for each operating peak-shaving model, and the maximum number of daily wells calculated by each model is the reasonable number of wells for this model. In this way, each model will get the most reasonable number of wells. Compared with each model, the model with the smallest number of gas production wells is the reasonable operating model under this working gas volume. According to the optimal operating model, the minimum reasonable operating model corresponding to the peak-to-valley ratio The number of wells corresponds to the peak-shaving gas volume in winter that satisfies the corresponding peak-to-valley ratio of the gas storage, as shown in Figure 4.

In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments. The components of the embodiments of the invention generally described and illustrated in the drawings herein may be arranged and designed in a variety of different configurations. Accordingly, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

The close correlation between underground gas storage and gas market determines the imbalance of gas storage production. During the gas supply period, it is necessary to determine the change of the gas production volume of the gas storage based on the actual gas supply capacity of the gas area to meet the market demand. Otherwise, the operation models of the gas storage are diverse and cannot be implemented, and it is necessary to determine the most suitable for the characteristics of the gas area. Run the model.

According to the law of peak gas consumption in the winter market, usually within 120 days of winter from mid-November to mid-March of the next year, the gas consumption market has experienced a low-high-low gas consumption change process, and the underground gas storage group and gas storage A low-high-low gas production change process occurred correspondingly in each gas storage, and the gas production peak-shaving curve of the gas storage had an inverted "parabolic" distribution. The gas production volume of the gas storage reaches its peak value during the Spring Festival, and reaches its low value at the beginning and end of the gas production period, and the rest of the time is the mid-to-low peak period. The ratio of the daily gas production during the peak period to the low period is called the peak-to-valley ratio.

By using the characteristics of gas consumption and learning the annual gas supply capacity curve of the low permeability gas area, the relationship between the gas production operation model of the underground gas storage and the peak-to-valley ratio can be established, and the binomial equation of each model in the gas output area can be regressed, as shown in Figure 1. and Figure 2,

(1) According to the binomial equation (Equation 1), put t=1,2,3...120 into Equation 1 to determine the peak-to-valley ratio (Qmax/Qmin) and the corresponding operating model Q at this time ₀ = 0, see Figure 3, forming a list of peak-to-valley ratios, as shown in Table 1.

Table 1 The operation model corresponding to the peak-to-valley ratio coefficient during the peak-shaving period in a certain gas area

(2) Substitute Equation 1 into Equation 3 to obtain Q ₀ , and obtain the binomial equation of the daily gas production of the gas storage corresponding to the working gas volume, and further obtain the daily Qi, thereby obtaining the corresponding number of wells N per day, and the total peak-shaving period The maximum number of wells is the reasonable number of wells.

(3) Corresponding to each working gas volume, different peak-to-valley ratios will produce a reasonable number of wells, as shown in Table 2, so that the minimum reasonable number of wells can be obtained, and the corresponding operation model is the optimal operation model;

Table 2 Reasonable number of wells with working gas volume corresponding to peak-to-valley ratio

Q _i ＝-0.1071t ² +16.713t+Q ₀ (1)

G＝Q ₁ +Q ₂ +Q ₃ ...+Q ₁₂₀ (3)

N=Q _i /q _i (4)

M＝ _Qmax / _Qmin

Among them, t=1, 2, 3...120, the key indicators of the peak-shaving gas production operation of the gas storage are as follows:

1. Working gas volume G of gas storage:

The total amount of gas produced during the gas production period (10,000 m3), the specific value is determined by the gas storage plan. Due to the different scales of different gas storages, the working gas volume values of the gas storages are different.

2. Gas storage period t:

The number of days for peak shaving gas production is d, and the length of the abscissa in the figure, the specific value is determined according to the gas storage plan. In this place, t=120d is taken according to the gas supply law in the northern region.

3. Gas storage peak shaving output Q _i :

The daily gas production (ten thousand square meters) of a certain day during the gas production period, the ordinate value corresponding to a certain day in the figure.

4. The reasonable number of gas production wells N of the gas storage:

The number of gas production wells required to meet the daily gas production during the peak gas production period and the daily gas production during the low peak gas production period at the same time.

5. Gas production capacity q _i of a single well:

The daily gas production of a single well on a certain day during the gas production period (Wanfang)

6. Peak-to-valley ratio M:

The ratio of the average daily gas production during the peak gas production period of the gas storage to the average daily gas production during the low peak gas production period is called the peak-to-valley ratio, ie Qmax/Qmin.

Due to the short construction history of lithologic underground gas storage and little experience accumulation, the peak-shaving operation model of the gas storage and the number of gas production wells are the key indicators for its design. For this parameter, there is no mature technical method matching the actual situation. As a result, the actual operating gas volume of the underground gas storage and the number of injection-production wells cannot achieve an economical and effective match, which directly affects the efficient operation of the underground gas storage.

On the basis of learning the historical operation rules of gas fields, the operation mode optimization of different peak shaving intensities of underground gas storage is carried out, and the operation model optimization method of gas storage based on the historical learning mode is established. This method can be used to rank the favorable areas for model storage construction and optimize the already formed schemes. It has been applied to the evaluation, construction and planning research of the lithologic gas storage business in Changqing Oilfield. The model has been applied to Shaanxi XX With the optimization of the gas storage, every one less horizontal well drilled in the Shaanxi XX gas storage can save 68 million yuan in drilling costs, which is of practical value for the development and evaluation of the gas storage.

The above content is only to illustrate the technical ideas of the present invention, and cannot limit the protection scope of the present invention. Any changes made on the basis of the technical solutions according to the technical ideas proposed in the present invention shall fall within the scope of the claims of the present invention. within the scope of protection.

Claims (7)

1. the gas storage moving model optimization method under a kind of mode based on history learning, which comprises the following steps:

S1, study gas area use the gas supply situation of gas peak period every year, establish peaking operation model；

S2, peak-to-valley ratio and peaking operation relationship model are established；

S3, multiple peaking operation models under assigned work tolerance are sought；

S4, peaking operation models multiple under assigned work tolerance are optimized, according to different peak-to-valley ratios, each corresponding work Make tolerance and generate a well number, obtain the smallest Reasonable Well Number under corresponding Working gas capacity, corresponding moving model is optimized operation Model obtains the minimum operational well number under corresponding peak-to-valley ratio according to optimized operation model, and correspondence meets gas storage and corresponds to peak-to-valley ratio Lower winter peak regulation tolerance.

2. the gas storage moving model optimization method under the mode according to claim 1 based on history learning, feature exist In in step S1, study gas area uses the gas supply situation of gas peak period to generate gas supply curve every year, returns out normalized library day Air demand establishes the moving model of peak period to obtain regression formula.

3. the gas storage moving model optimization method under the mode according to claim 2 based on history learning, feature exist In the air reservoir of corresponding Working gas capacity produces tolerance binomial equation daily are as follows:

Q_i=-0.1071t²+16.713t+Q₀

Wherein, Q_iFor air reservoir peak regulation yield, Q₀=0, t=1,2,3......120.

4. the gas storage moving model optimization method under the mode according to claim 3 based on history learning, feature exist In air reservoir Working gas capacity G are as follows:

G=Q₁+Q₂+Q₃...+Q₁₂₀。

5. the gas storage moving model optimization method under the mode according to claim 1 based on history learning, feature exist In obtaining the relationship of corresponding peak-to-valley ratio and moving model, peak valley according to the peak-to-valley ratio that the curve of recurrence obtains curve in step S2 It is the air reservoir peak gas production phase averagely to produce tolerance Q daily than M_maxWith ebb gas production phase average daily production Q_minRatio M=Q_max/Q_min。

6. the gas storage moving model optimization method under the mode according to claim 1 based on history learning, feature exist In, in step S3, according to the different peak-to-valley ratio models of foundation calculate determine air reservoir initial production, obtain meeting under this Working gas capacity Correspondence peak-to-valley ratio peaking operation model, obtain multiple peaking operation models.

7. the gas storage moving model optimization method under the mode according to claim 1 based on history learning, feature exist In, in step S4, daily corresponding Reasonable Well Number N are as follows:

N=Q_i/q_i

Wherein, Q_iFor air reservoir peak regulation yield, q_iFor individual well gas deliverability.