A natural gas production zoning planning method based on GIS technology

Abstract

The application discloses a natural gas production zoning planning method based on GIS technology, belongs to the technical field of gas reservoir production area planning, and comprises the following steps: step A, gas reservoir zoning peak production analysis; step B, zoning production calculation under the control of multiple factors; and step C, production planning model research under the constraint of a three-dimensional Gaussian mixed model. In order to perform production planning and risk analysis on different regions of natural gas, it is necessary to carry out the natural gas production zoning planning research based on the GIS technology, analyze the peak production distribution of each block and the influence of risk factors. Therefore, the peak production range and realization probability of each production area are calculated by using fuzzy analysis and a multivariate Gaussian mixed model, a peak production distribution model diagram is generated, a three-dimensional digital elevation model is established based on the GIS technology, a three-dimensional production distribution diagram under geographical coordinates is obtained, and guidance is provided for the planning and development work of a gas field.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of gas reservoir production regional planning, and relates to GIS digital elevation model, production growth prediction, multivariate Gaussian mixture model and multi-factor fuzzy analysis, in particular to a natural gas production regional planning method based on GIS technology. BACKGROUND

[0002] In recent years, China's natural gas industry has developed rapidly, and natural gas consumption has continued to grow rapidly, and its importance in the national energy system has been increasing. Natural gas peak production prediction and analysis is an important part of gas field development planning and one of the core work of gas field development planning, which plays a crucial role in the realization of gas field development planning indicators and the completion of production indicators. Gas reservoirs in different regions are affected by various factors to different degrees, so it is necessary to conduct targeted research on the peak production of gas reservoirs in different regions. First, according to historical exploration data, the resource quantity, recovery rate, and proven rate of each region are determined, and the technically recoverable reserves are calculated to determine the range of ultimate recoverable reserves, and the improved Hubbert model is used to predict the range of peak production. Then, combined with various influencing factors, the membership function of multi-factor fuzzy analysis is constructed, the comprehensive influence degree of each gas region is calculated according to the membership matrix, the planning production realization probability matrix of each gas region is determined, and the generated production is uniformly arranged in the geographic coordinate system according to the Gaussian distribution, so as to determine the ultimate recoverable reserves URR and peak production Qm of different geographic coordinate positions in each block. Finally, based on GIS technology and multivariate Gaussian mixture model, a three-dimensional production planning model is established.

[0003] The goal is to study the peak production range under the control of multiple factors in different regions and establish a three-dimensional production distribution map in geographic coordinates to provide guidance for the planning and development of gas fields. Through literature and patent research, it can be clearly known that the existing such technology has the following shortcomings: in terms of scientific and technological literature, at present, the research on natural gas production regional planning at home and abroad is still in its infancy. Through the review of relevant literature at home and abroad, it is found that the existing peak production prediction and risk quantification analysis methods do not conduct targeted research on gas reservoirs in different regions according to different influencing factors. In the existing patents, no patent related to natural gas production regional planning method has been searched.

[0004] In summary, the current natural gas production planning method often has problems such as difficult prediction, no planning of prediction range, and other important technical problems that need to be solved by technicians in this field. SUMMARY

[0005] The purpose of the present application is to solve the above-mentioned deficiencies existing in the prior art, and the purpose of the present application is to provide a natural gas production zoning planning method based on GIS technology, which uses fuzzy analysis and multivariate Gaussian mixture model to calculate the peak production range and realization probability of each production area, and generates a production peak distribution model diagram, and finally establishes a three-dimensional digital elevation model based on GIS technology to obtain a three-dimensional production distribution diagram under geographic coordinates, which provides guidance for the planning and development work of gas fields.

[0006] The purpose of the present application is to solve the above-mentioned deficiencies existing in the prior art, and the purpose of the present application is to provide a natural gas production zoning planning method based on GIS technology, which uses fuzzy analysis and multivariate Gaussian mixture model to calculate the peak production range and realization probability of each production area, and generates a production peak distribution model diagram, and finally establishes a three-dimensional digital elevation model based on GIS technology to obtain a three-dimensional production distribution diagram under geographic coordinates, which provides guidance for the planning and development work of gas fields.

[0007] A natural gas production zoning planning method based on GIS technology, comprising the following steps:

[0008] Step A, gas reservoir zoning peak production analysis;

[0009] Step B, zoning production calculation under the control of multiple factors;

[0010] Step C, production planning model research under the constraint of three-dimensional Gaussian mixture model.

[0011] Preferably, in step A, the following steps are included: Step A1: re-evaluate the resource amount of each area, calculate the technically recoverable reserves according to the resource amount, recovery rate and proven rate; select the optimal and worst state of the recoverable reserves to determine the range of the final recoverable reserves; Step A2: improve the Hubbert model, refine the model parameters to realize accurate analysis and prediction of production, and calculate the highest value and the lowest value of the peak production possible according to the production state of each block, so as to determine the range of the peak production.

[0012] Preferably, in step A1, the technically recoverable reserves are calculated by formula (1):

[0013] Formula (1);

[0014] Wherein: is the technically recoverable reserves, is the resource amount, is the proven rate, is the recovery rate.

[0015] Preferably, the final recoverable reserves are calculated by formula (2):

[0016] Formula (2);

[0017] Wherein: URR is the final recoverable reserves, is the technical and economic index.

[0018] Preferably, in the step A2, the peak production is calculated by formula (12):

[0019] Formula (12);

[0020] Wherein: is the peak production; and is the relationship between the cumulative production and the production, so as to realize the conversion of the relationship between the cumulative production and the production; URR is the final recoverable reserves; is a model parameter.

[0021] Preferably, the maximum and minimum influence values of each factor are taken respectively, so as to obtain the optimal state and the worst state of the peak production, and the peak production implementation range of each region is obtained.

[0022] Preferably, in the step B, the following steps are included: step B1: according to the URR exploitation degree, investment level, exploration technology level, development level, theoretical breakthrough, policy, unconventional gas subsidy, and gas price influencing factors, an index set U of influencing factor weight is given, a membership function of multi-factor fuzzy analysis is constructed, and according to the gas reservoir natural gas development level, a value scheme is formulated for each gas region according to its influencing factors, so as to construct a membership matrix R; step B2: according to the actual project, the initial weight A of each factor is given, the comprehensive influence degree B of the constraint condition on each gas region is obtained through the membership matrix operation, so as to calculate the actual planning peak production range Q of each gas region, and determine the planning production implementation probability matrix of each gas region; step B3: different implementation probability situations are selected, the calculated URR and peak production are uniformly arranged in the geographical coordinate system according to the Gaussian distribution, so as to form the partitioned production under the control of multiple factors.

[0023] Preferably, in the step B2, the formula B=A·R is used to calculate the comprehensive influence degree B, then the formula Q=B·Q m is used to calculate the actual planning peak production range, and finally the peak production under different probabilities is combined into a matrix, that is, the planning production implementation probability matrix.

[0024] Preferably, in the step C, the following steps are included: step C1: the geographical location information of the target object is researched through geographical space analysis, the principle of logical operation and mathematical statistical analysis is used for module analysis, the gas reservoir resource data set of the research area is taken as a mixed distribution of multiple Gaussian distributions, and then the partitioned prediction is carried out, the yield column chart conforming to the Gaussian mixed distribution is dropped into the gas region model chart according to the longitude and latitude coordinates; step C2: based on the GIS technology, a three-dimensional digital elevation model of the production region is established, and the yield planning Figure ThreeAfter vectorization, multiple layers are combined into a three-dimensional planning geographic vector map, and a three-dimensional inclined image map of the planning yield of each gas zone is generated by combining the yield planning Gaussian mixture distribution model column chart.

[0025] Preferably, in step C1, according to the original data of the dense gas zone, a partition influence factor weight table is established, the factors and the gas reservoir utilization status are rasterized, after being converted into ASCII data, correlation analysis is carried out by using Python software, and a URR and natural gas peak yield column chart is established by using a Gaussian mixture model.

[0026] The beneficial effects of the technical solution are as follows:

[0027] Firstly, the present application provides a natural gas yield zoning planning method based on GIS technology, in order to plan the yield and analyze the risk of different regions of natural gas, it is necessary to carry out natural gas yield zoning planning research based on GIS technology, analyze the yield peak distribution of each block and the influence of risk factors. Therefore, the present application uses fuzzy analysis and a multivariate Gaussian mixture model to calculate the peak yield range and realization probability of each production area, and generates a yield peak distribution model chart, and finally establishes a three-dimensional digital elevation model based on GIS technology to obtain a three-dimensional yield distribution chart under geographic coordinates, thereby providing guidance for the planning and development of gas fields.

[0028] Secondly, the natural gas yield zoning planning method based on GIS technology improves the Hubbert model and refines the model parameters, thereby improving the accuracy of yield prediction. The maximum and minimum values of each variable in the yield calculation formula are optimized, the optimal state and the worst state of the peak yield can be obtained, and the peak yield realization range of each region can be obtained.

[0029] Thirdly, the natural gas yield zoning planning method based on GIS technology analyzes the yield influencing factors, constructs a membership function of multi-factor fuzzy analysis, and calculates the comprehensive influence degree of the constraint conditions on each gas zone by combining the weight of the factors in the actual engineering, thereby obtaining the actual planning peak yield range and realization probability matrix of each gas zone. The influence of factors on yield is reflected in the data, thereby providing guidance for the formulation of yield planning scheme. DETAILED DESCRIPTION

[0030] Figure 1 is a flow chart of the prediction method of the present application.

[0031] Figure 2 is a recoverable reserve data chart of the core built-up production area in the central Sichuan Basin;

[0032] Figure 3 is a final recoverable reserve data chart of the core built-up production area in the central Sichuan Basin;

[0033] Figure 4 It is the URR distribution diagram with a realization probability of 75%;

[0034] Figure 5 It is the output distribution diagram with a 75% probability of realization;

[0035] Figure 6 It is the URR distribution diagram with a realization probability of 50%.

[0036] Figure 7 It is the output distribution diagram with a 50% probability of realization.

[0037] Figure 8 It is a three-dimensional Gaussian mixture theory model diagram;

[0038] Figure 9 This is the geographical distribution map of URR in the eastern Sichuan assessment area;

[0039] Figure 10 It is a GIS digital elevation model map;

[0040] Figure 11 This is a diagram of the process of establishing a 3D DEM model for the tight gas production area in the Sichuan Basin;

[0041] Figure 12 It is a three-dimensional oblique image of the planned output of the production area in central Sichuan;

[0042] Figure 13 It is a three-dimensional oblique image of the planned output of the northwest Sichuan production area;

[0043] Figure 14 It is a three-dimensional oblique image of the planned output of the eastern Sichuan production area;

[0044] Figure 15 It is a three-dimensional oblique image of the planned output of the southern Sichuan production area;

[0045] Figure 16 It is a three-dimensional oblique image of the planned output of the southwestern Sichuan production area;

[0046] Figure 17 This is a comparison chart of the production of five regions. DETAILED DESCRIPTION

[0047] The present invention will be further described in detail below with reference to the examples, but the embodiments of the present invention are not limited thereto.

[0048] Example 1

[0049] like Figure 1 As shown, a natural gas production zoning planning method based on GIS technology includes the following steps:

[0050] Step A: Gas reservoir zone peak production analysis

[0051] Step A1: Re-evaluate the resource quantity of each region, calculate the technically recoverable reserves according to the recoverable reserves formula, and select the optimal and worst conditions of recoverable reserves according to the recovery rate and proven rate to determine the range of final recoverable reserves.

[0052] First, the resource quantity and proven reserves of each region are evaluated according to the favorable conditions of gas production and working area, and then the technically recoverable reserves are calculated using formula (1) as follows.

[0053] Formula (1)

[0054] Wherein, is the technically recoverable reserves, is the resource quantity, is the proven rate, is the recovery rate.

[0055] As can be seen from formula (1), when the resource quantity is constant, the technically recoverable reserves depend on the proven rate and recovery rate, so the range of recovery rate and proven rate is determined according to historical exploration data, and then substituted into the recoverable reserves calculation formula to determine the technically recoverable reserves range of each region. After obtaining the range of recovery rate and proven rate, the maximum and minimum values of each factor are taken to obtain the optimal and worst conditions of technically recoverable reserves.

[0056] Then, the final recoverable reserves URR are determined according to the range of technically recoverable reserves. The final recoverable reserves are controlled by technical and economic factors, so they are closely related to technically recoverable reserves. The final recoverable reserves URR are calculated using formula (2) as follows.

[0057] Formula (2)

[0058] Wherein, is the technical and economic index (analogous to the conversion rate of technically recoverable reserves to recoverable reserves in natural gas development plan).

[0059] Similarly, according to the range of the determined technical and economic index (conversion rate), the maximum and minimum values are taken to obtain the optimal and worst conditions of the final recoverable reserves.

[0060] Through the above calculation, the final value range of URR is obtained, which is used as the data basis for subsequent calculation of peak production.

[0061] Step A2: Improve the Hubbert model, refine the model parameters to realize accurate analysis and prediction of production, and calculate the highest and lowest values of peak production that may occur according to the production status of each block, so as to determine the range of peak production.

[0062] The prediction model used for predicting the natural gas production is Hubbert model. Hubbert model is a commonly used life cycle model for predicting the peak of natural gas production. The model believes that after the oil and gas field is put into development, the production rises with the extension of development time from 0, and then decreases with the extension of development time after reaching the peak. When the development time tends to infinity, the area of the production curve and time is equal to the ultimate recoverable reserves of the oil and gas field. The formula is mainly completed by a quadratic equation of production P and cumulative production Q, as shown in formula (3).

[0063] Formula (3)

[0064] Wherein, And is the relationship expression coefficient between cumulative production and production, so as to realize the transformation of the relationship between cumulative production and production.

[0065] When the cumulative production approaches the ultimate recoverable reserves URR, the production approaches 0, as shown in formula (4).

[0066] Formula (4)

[0067] By transforming formula (4), the calculation expression of parameter b can be obtained as follows:

[0068] Formula (5)

[0069] Substituting formula (5) into formula (3) can obtain:

[0070] Formula (6)

[0071] In the formula, Indicates the cumulative production. The left end of formula (6) is integrated with respect to t in the interval ( , URR), and the right end is integrated with respect to t in the interval ( , ), to obtain the cumulative production prediction model, as shown in formula (7).

[0072] Formula (7)

[0073] In formula (7), let , and the following can be obtained:

[0074] Formula (8)

[0075] Differentiating formula (8) obtains the production prediction model, that is:

[0076] Equation (9)

[0077] Where P represents annual production, taking the second derivative of Equation (9) and setting it equal to zero, two time inflection point equations can be obtained, as shown in Equations (10) and (11).

[0078] Equation (10)

[0079] Equation (11)

[0080] Where, and are model parameters; is the time when resource exploitation begins; is the inflection point on the left side of the curve; is the inflection point on the right side of the curve. The predicted curve inflection point represents a turning point in production rate, with the left side inflection point representing the time when production transitions from rapid growth to slow growth, and the right side inflection point representing the time when production transitions from rapid decline to slow decline.

[0081] When , the production growth reaches a peak. At this time, the cumulative production has the maximum rate of change, i.e. is the maximum. At this time:

[0082] Equation (12)

[0083] The final Equation (12) is the calculation formula for the peak production .

[0084] Similarly, by taking the maximum and minimum impact values of each factor, the optimal and worst states of peak production can be obtained, and the peak production implementation range of each region can be obtained.

[0085] Step B: Partition production calculation under multi-factor control

[0086] Step B1: According to the influence factors of URR mobilization degree, investment level, exploration technology level, development level, theoretical breakthrough, policy, unconventional gas subsidy, and gas price, an index set U with given influence factor weight is given, a membership function of multi-factor fuzzy analysis is constructed, and according to the gas reservoir natural gas development level, a value scheme is formulated for each gas region according to its influence factors, thereby constructing a membership matrix R.

[0087] First, each influence factor is numbered. According to the URR mobilization degree, investment level, exploration technology level, development level, theoretical breakthrough, policy, unconventional gas subsidy, and gas price, an index set U with given influence factor weight is given:

[0088] U=[u1, u2, u3, u4, u5, u6, u7, u8]

[0089] Then according to each area, respectively according to 8 influence factors, temporarily cited five gas zone value scheme, actual work needs to be determined according to the actual index of each factor value range. After the value is completed, respectively, the gas zone type V and the influence factor U are taken as the row and column, and the membership matrix R is constructed.

[0090] Step B2: according to the actual engineering, the initial weight A of each factor is given, the comprehensive influence degree B of the constraint condition to each gas zone is obtained by membership matrix operation, and the actual planning peak production range Q of each gas zone is calculated, and the planning production realization probability matrix of each gas zone is determined.

[0091] After obtaining the membership matrix R, according to the influence factor weight matrix A obtained by the actual work, the comprehensive influence degree B is calculated according to the formula B=A·R, and then the formula Q=B·Q m The actual planning peak production range is calculated, and finally the peak production under different probability is combined into a matrix, that is, the planning production realization probability matrix.

[0092] Step B3: select different realization probability cases, and arrange the calculated URR and peak production according to Gaussian distribution in the geographical coordinate system, so as to form the partition production under the control of multiple factors.

[0093] First, select different scenarios, for example, the scenario with high realization probability: P75 (realization probability is 75%) URR=1030×10 8 m 3 ; The scenario with low realization probability: P50 (realization probability is 50%) URR=1045×10 8 m 3 . Then arrange them in the geographical coordinate system according to Gaussian distribution, actually select 29 coordinate points, construct three-dimensional URR and production distribution graph, and the longitude and latitude are x and y coordinate axes, and URR and production are z coordinate axes.

[0094] Step C: research on production planning model under three-dimensional Gaussian mixture model constraint

[0095] Step C1: research the geographical location information of the target object through geographical space analysis, use the principles of logical operation and mathematical statistics analysis for module analysis, take the gas reservoir resource data set of the research area as a mixed distribution of multiple Gaussian distributions, and then carry out partition prediction. The production column chart conforming to the Gaussian mixed distribution is dropped into the gas zone model graph according to the longitude and latitude coordinates.

[0096] Firstly, according to the original data of the tight gas area, a partition influence factor weight table is established, and the factors and the utilization status of the gas reservoir are rasterized. After converting the data into ASCII, correlation analysis is carried out by using Python software, and a URR and natural gas peak production column chart is established by using a Gaussian mixture model.

[0097] The Gaussian mixture model is a distribution of multiple Gaussian distributions, that is, a weighted sum of multiple Gaussian distributions. By adjusting the mean and covariance of a sufficient number of Gaussian probability density functions and the linear combination coefficient, the unknown data set can be divided into several subsets with Gaussian distribution by using the expectation maximization algorithm. The gas reservoir resource data set of the study area is regarded as a distribution mixed by multiple Gaussian distributions, and then the partition prediction is carried out. Finally, the production column chart conforming to the Gaussian mixture distribution is dropped into the Sichuan Basin tight gas area model chart according to the latitude and longitude coordinates.

[0098] Step C2: Based on the GIS technology, a three-dimensional digital elevation model of the production area is established, and the production planning Figure Three After vectorization, multiple layers are combined into a three-dimensional planning geographic vector map, and the three-dimensional tilt image map of the production planning of each gas area is generated by combining the production planning Gaussian mixture distribution model column chart.

[0099] Elevation data is needed in many scenarios such as terrain analysis, slope analysis, contour analysis, etc. The three-dimensional DEM model of the Sichuan Basin tight gas production area is established as follows: first, generate the planning map of the tight gas area, then vectorize it in three dimensions, add multiple DEM data to the vector map, introduce a geographic elevation map, fuse multiple three-dimensional layers, obtain a three-dimensional planning geographic vector map, and finally combine the production planning distribution model of the gas area with the gas area planning Gaussian mixture distribution model column chart to generate a three-dimensional tilt image map of the production planning of each gas area.

[0100] Example 2

[0101] A natural gas production partition planning method based on GIS technology, comprising the following steps:

[0102] Step A: Analysis of peak production of tight gas reservoir partition

[0103] Step A1: Re-evaluate the resource quantity of each area, calculate the technically recoverable reserves according to the recoverable reserves formula, and determine the range of final recoverable reserves according to the optimal and worst conditions of recoverable reserves according to the recovery rate and proven rate.

[0104] First, the core of the Sichuan Basin, Sichuan Southwest, Sichuan Northwest to speed up the evaluation area, Sichuan East, Sichuan South to replace the evaluation area of the five exploration and development of favorable areas for resource potential evaluation, as shown in the table below. After re-evaluation of resources, the main area of the tight gas resource is 6.9 trillion cubic meters, and the estimated proven reserves are 1.12 trillion cubic meters.

[0105]

[0106] Then according to the resource of five gas area, respectively, the range of technically recoverable reserves is calculated. For example, the core of the Sichuan Basin: the estimated resource of the core of the Sichuan Basin is 38000 billion cubic meters, according to the historical exploration data, the recovery rate and the proven rate are determined to be 30%~50% and 10%~20%, then the formula (1) When the calculation is carried out, the technically recoverable reserves of the area are as shown in the table below, and the final range is 1140-3800 billion cubic meters.

[0107]

[0108] After getting the range of recovery rate and proven rate, the maximum and minimum values of each factor are taken respectively, and the optimal state and the worst state of technically recoverable reserves can be obtained. For the core of the Sichuan Basin, the range of recovery rate is 30%~50%, and the range of proven rate is 10%~20%, so the optimal state is 38000×50%×20%=3800 billion cubic meters, and the worst state is 38000×30%×10%=1140 billion cubic meters. Using this approach, the range of technically recoverable reserves can also be obtained, and after calculation, the technically recoverable reserves of the five gas areas are as shown in the table below.

[0109]

[0110] Therefore, we can get the range of technically recoverable reserves of each block of Sichuan Basin tight gas as follows:

[0111] The core of the Sichuan Basin: the range of technically recoverable reserves of the area is determined to be 1140~3800 billion cubic meters.

[0112] Sichuan Northwest to speed up the evaluation area I: the range of technically recoverable reserves of the area is determined to be 300~1000 billion cubic meters.

[0113] Sichuan Southwest to speed up the evaluation area II: the range of technically recoverable reserves of the area is determined to be 210~700 billion cubic meters.

[0114] Sichuan East to replace the evaluation area I: the range of technically recoverable reserves of the area is determined to be 240~800 billion cubic meters.

[0115] Sichuan South to replace the evaluation area II: the range of technically recoverable reserves of the area is determined to be 180~600 billion cubic meters.

[0116] Then, the range of the ultimate recoverable reserves URR of each block is determined by formula (2) The conversion rate of the five blocks ranges from 50% to 70%.

[0117] Similarly, the optimal state and the worst state of the recovery rate, the proven rate, and the conversion rate are taken, and the final recoverable reserves of the five gas regions are calculated by formula as shown in the following table.

[0118]

[0119] Therefore, the range of the final recoverable reserves of each block of the tight gas in the Sichuan Basin is as follows:

[0120] Central Sichuan core production area: the range of the final recoverable reserves of the area is determined to be 427.5-2261 billion cubic meters.

[0121] Northwest Sichuan accelerated evaluation area I: the range of the final recoverable reserves of the area is determined to be 112.5-595 billion cubic meters.

[0122] Southwest Sichuan accelerated evaluation area II: the range of the final recoverable reserves of the area is determined to be 78.75-416.5 billion cubic meters.

[0123] East Sichuan replacement evaluation area I: the range of the final recoverable reserves of the area is determined to be 90-476 billion cubic meters.

[0124] South Sichuan replacement evaluation area II: the range of the final recoverable reserves of the area is determined to be 67.5-357 billion cubic meters.

[0125] According to the calculated data, a three-dimensional graph of the reserves data of each gas region can be drawn. The recoverable reserves data graph and the final recoverable reserves data graph of the central Sichuan core production area are shown in Figure 2 and Figure 3 .

[0126] Step A2: Improve the Hubbert model, refine the model parameters to realize accurate analysis and prediction of production, combine the production state of each block, calculate the highest value and the lowest value of the peak production, and determine the range of the peak production.

[0127] According to the URR range determined above, the improved Hubbert model, i.e. formula (12) is used for calculation.

[0128] Similarly, the maximum and minimum influence values of each factor are taken to obtain the optimal state and the worst state of the peak production, and the range of the peak production of each region is obtained, and the calculation results are shown in the following table.

[0129] ​

[0130] Therefore, we can get the total peak production of Sichuan Basin tight gas range for 47.4~566.8 million cubic meters.

[0131] Step B: Partition production calculation under multi-factor control

[0132] Step B1: According to the influence factors of URR utilization degree, investment level, exploration technology level, development level, theoretical breakthrough, policy, unconventional gas subsidy, gas price, the index set U of given influence factor weight is constructed, the membership function of multi-factor fuzzy analysis is constructed, and then according to the gas reservoir natural gas development level, the value scheme is formulated for each gas area according to its influence factors, so as to construct the membership matrix R.

[0133] First, according to the influence factors of URR utilization degree, investment level, exploration technology level, development level, theoretical breakthrough, policy, unconventional gas subsidy, gas price, the index set U of given influence factor weight is constructed: U=[u1, u2, u3, u4, u5, u6, u7, u8], and the constructed membership function is as follows:

[0134] (1) Development level: URR conversion coefficient. For example, according to the peak value of core build-up area URR is 2261 million cubic meters, take 60% of it about 1300 million cubic meters, and use the resource utilization function as the membership function: u1(x)=x / 1300.

[0135] (2) Investment level: investment constraint is 50 billion, so u2(x)=x / 50.

[0136] (3) Exploration technology level: within 20 years of the gap with international advanced level, u3(x)=1-x / 20.

[0137] (4) Development matching level: the matching degree of midstream and downstream surface matching level and production scale. u4(x)=x / 100.

[0138] (5) Theoretical breakthrough: the number of international leading research results, taking 2020 as the base, according to the growth percentage to determine u5(x)=x / 100.

[0139] (6) Policy: use logistic equation, keep the current policy a=0.25, take b=0.75 when reaching international advanced level, take 0.75 above when exceeding international level, and take between b-a when others. u6(x)={2((x-a) / (b-a))^2} or 1-2((x-b) / (b-a))^2.

[0140] (7) Subsidy for unconventional gas: Based on the production in the previous year, the subsidy is RMB 0.01 per cubic meter, so u7(x)=x1*0.01 / (x1=x0), where x1 is the production in the next year and x0 is the production in the previous year.

[0141] (8) Gas price: Based on 2020, it is determined by the percentage of growth, so uh(x)=x / 100.

[0142] Then, based on the current level of natural gas development in tight gas reservoirs in the Sichuan Basin, value selection schemes were formulated for the five gas zones according to their influencing factors, as shown in the following table.

[0143]

[0144] After the values ​​are taken, the gas region type V and influencing factor U are used as rows and columns respectively to construct the membership matrix R. The specific data are shown in the following table.

[0145]

[0146] Step B2: Based on the actual project, the initial weight A of each factor is given, and the comprehensive impact B of the constraint conditions on each gas zone is obtained through membership matrix calculation, so as to calculate the actual planned peak production range Q of each gas zone and determine the probability matrix of the planned production realization of each gas zone.

[0147] After obtaining the membership matrix R, the initial weights of each factor are given based on the actual project: A=[0.22, 0.2, 0.1, 0.1, 0.2, 0.1, 0.05, 0.03]. The comprehensive impact of the constraint conditions on each gas zone is obtained through membership matrix calculation: B=AR=[0.77, 0.72, 0.76, 0.61, 0.61]. The actual planned peak production range of each gas zone is then calculated using the formula:

[0148]

[0149] Finally, the peak output under different probabilities are combined into a matrix, that is, the planned output realization probability matrix. The specific data is shown in the following table.

[0150]

[0151] Among them, P10 represents the realization probability of 10%. Only five realization probabilities are selected in the table, and the peak production is calculated based on the URR corresponding to different gas zones. .

[0152] Step B3: Select different realization probability situations and evenly arrange the calculated URR and peak yield in the geographic coordinate system according to Gaussian distribution, thereby forming the zoned yield under multi-factor control.

[0153] Take the core production area in Sichuan as an example, the URR of P50 and P75 is taken as the center, and the range of variation is 10%, so as to determine the URR and .

[0154] (1) P75 (probability of 75%) URR = 1030 x 10 8 m 3 The URR and yield of different distributions of tight gas are generated with geographic coordinates, and are arranged uniformly in the geographic coordinate system according to Gaussian distribution. 29 coordinate points are actually selected, and the specific data are shown in the following table.

[0155]

[0156] The URR and yield distribution with a probability of 75% are shown in Figure 4 and Figure 5 .

[0157] (2) P50 (probability of 50%) URR = 1045 x 10 8 m 3 The URR and yield of different distributions of tight gas are generated with geographic coordinates, and are arranged uniformly in the geographic coordinate system according to Gaussian distribution. 29 coordinate points are actually selected, and the specific data are shown in the following table.

[0158]

[0159] The URR and yield distribution with a probability of 50% are shown in Figure 6 and Figure 7 .

[0160] Step C: Study on yield planning model under the constraint of three-dimensional Gaussian mixture model

[0161] : Through the geographical position information of the target object, the module analysis is carried out by using the principles of logical operation and mathematical statistical analysis, and then according to the original data of tight gas area, the partition influence factor weight table is established. The factors and gas reservoir utilization status are rasterized, and after being converted into ASCII data, the correlation analysis is carried out by using Python software. The URR and natural gas peak yield column chart are established by Gaussian mixture model, and finally the column chart is arranged in a reasonable geographic location according to the latitude and longitude coordinates determined by the three-dimensional Gaussian mixture calculation model.

[0162] The partition planning concept of GIS technology and three-dimensional Gaussian mixture model is shown in Figures 8-10As shown in the figure, the three-dimensional Gaussian mixture theory model and the URR geographical distribution map or the natural gas peak production column chart of each region are arranged on the GIS digital elevation model. The GIS digital elevation model needs to fuse the GIS technology and the production zoning planning model, and establish the three-dimensional digital elevation model (DEM) of the production area.

[0163] The three-dimensional DEM model of the tight gas production area in Sichuan Basin is established as follows: first, generate the planning map of the tight gas area, then vectorize it in three dimensions, add multi-layer DEM data to the vector map after obtaining the vector map, introduce the geographical elevation map, fuse multiple three-dimensional layers to obtain the three-dimensional planning geographical vector map, and finally combine the production planning distribution model of the gas area and the column chart of the gas area planning Gaussian mixture distribution model to generate the three-dimensional oblique image map of the planning production of each gas area.

[0164] The three-dimensional DEM model of the tight gas production area in Sichuan Basin is established as follows: first, generate the planning map of the tight gas area, then vectorize it in three dimensions, add multi-layer DEM data to the vector map after obtaining the vector map, introduce the geographical elevation map, fuse multiple three-dimensional layers to obtain the three-dimensional planning geographical vector map, and finally combine the production planning distribution model of the gas area and the column chart of the gas area planning Gaussian mixture distribution model to generate the three-dimensional oblique image map of the planning production of each gas area. Figure 11 Figures 12-17 The three-dimensional DEM model of the tight gas production area in Sichuan Basin is established as follows: first, generate the planning map of the tight gas area, then vectorize it in three dimensions, add multi-layer DEM data to the vector map after obtaining the vector map, introduce the geographical elevation map, fuse multiple three-dimensional layers to obtain the three-dimensional planning geographical vector map, and finally combine the production planning distribution model of the gas area and the column chart of the gas area planning Gaussian mixture distribution model to generate the three-dimensional oblique image map of the planning production of each gas area.

[0165] Finally, it should be noted that the above examples are only used to illustrate, but not to limit the technical solutions of the present application. Although the present application has been described in detail with reference to the above examples, those skilled in the art should understand that the present application can still be modified or equivalently replaced without departing from the spirit and scope of the present application. Any modification or partial replacement should be covered in the scope of the claims of the present application.​

Claims

1. A GIS technology-based natural gas production zoning planning method, characterized in that, It comprises the following steps: Step A, gas reservoir partition peak production analysis; Step B, partition production calculation under the control of multiple factors; Step C, production planning model research under the constraint of three-dimensional Gaussian mixed model; In the step A, it comprises the following steps: Step A1: re-evaluate the resource quantity of each region, calculate the technically recoverable reserves according to the resource quantity, recovery efficiency and proven rate, and select the optimal and worst state of the recoverable reserves to determine the range of the final recoverable reserves; Step A2: improve the Hubbert model, refine the model parameters to realize accurate analysis and prediction of production, combine the production status of each block, calculate the highest value and the lowest value of the peak production, and determine the range of the peak production; In the step B, it comprises the following steps: Step B1: according to the URR dynamic degree, investment level, exploration technology level, development level, theoretical breakthrough, policy, unconventional gas subsidy and gas price influencing factors, give the index set U of the influencing factor weight, construct the membership function of multi-factor fuzzy analysis, according to the gas reservoir development level, develop a value scheme for each gas area according to its influencing factors, and thus construct the membership matrix R; Step B2: according to the actual engineering, give the initial weight A of each factor, through the membership matrix operation, obtain the comprehensive influence degree B of the constraint condition on each gas area, and thus calculate the actual planning peak production range Q of each gas area, and determine the planning production realization probability matrix of each gas area; Step B3: select different realization probability conditions, arrange the calculated URR and peak production in the geographic coordinate system according to the Gaussian distribution, and thus form the partition production under the control of multiple factors; In step B2, the comprehensive influence degree B is calculated according to the formula B=A·R, and then the formula Q=B·Q is used to calculate the actual planning peak yield range m The actual planning peak yield range is calculated, and finally the peak yields under different probabilities are combined into a matrix, i.e., a planning yield realization probability matrix; In the step C, it comprises the following steps: Step C1: through the geographic spatial analysis of the geographic location information of the target object, using the principles of logical operation and mathematical statistical analysis for module analysis, taking the gas reservoir resource data set of the research area as a mixed distribution of multiple Gaussian distributions, and then performing partition prediction, the yield column chart conforming to the Gaussian mixed distribution is dropped into the gas area model chart according to the longitude and latitude coordinates; Step C2: based on GIS technology, establish a three-dimensional digital elevation model of the production area, after three-dimensional vectorization of the yield planning graph, combine multiple layers to form a three-dimensional planning geographic vector graph, combine the yield planning Gaussian mixed distribution model column chart to generate a three-dimensional oblique image graph of the planning yield of each gas area; In the step C1, according to the original data of the tight gas area, a partition influencing factor weight table is established, the factors and the utilization status of the gas reservoir are rasterized, after conversion to ASCII data, correlation analysis is performed by using Python software, and the URR and natural gas peak production column chart are established by using the Gaussian mixed model. 2.The GIS technology-based natural gas production zoning planning method according to claim 1, characterized in that: In the step A1, the technically recoverable reserves are calculated by using formula (1): Equation (1); wherein: is the technically recoverable reserves, is the resource quantity, is the proven rate, is the recovery rate. 3.The GIS technology-based natural gas production zoning planning method according to claim 2, characterized in that: The final recoverable reserves are calculated by using formula (2): Formula (2); Where: URR is ultimate recoverable reserves, is the technical and economic index.

4. The GIS technology-based natural gas production zoning planning method according to claim 3, characterized in that: In the step A2, the peak production is calculated by using formula (12): Formula (12); Wherein: is the peak production; and is the relationship expression coefficient between the cumulative production and the production to realize the conversion of the relationship between the cumulative production and the production; URR is the final recoverable reserves; is the model parameter.

5. The GIS technology-based natural gas production zoning planning method according to claim 4, characterized in that: By taking the maximum and minimum influence values of each factor respectively, the optimal state and the worst state of the peak production can be obtained, and the peak production realization range of each region can be obtained.

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