Natural gas yield zoning planning method based on GIS (Geographic Information System) technology

Abstract

The invention discloses a natural gas yield zoning planning method based on a GIS (Geographic Information System) technology, and belongs to the technical field of gas reservoir yield area planning. The natural gas yield zoning planning method comprises the following steps: step A, analyzing gas reservoir zoning peak yield; step B, partition yield calculation under multi-factor control; and C, researching the yield planning model under the constraint of the three-dimensional Gaussian mixture model. In order to carry out yield planning and risk analysis on different natural gas areas, it is necessary to carry out natural gas yield partition planning research based on the GIS technology, and the yield peak value distribution situation of all the areas and the influence situation of risk factors are analyzed. Therefore, fuzzy analysis and a multivariate Gaussian mixture model are applied, the peak yield range and the implementation probability of each production area are calculated, a yield peak distribution model diagram is generated, finally, a three-dimensional digital elevation model is established based on the GIS technology, a three-dimensional yield distribution diagram under geographic coordinates is obtained, and guidance is provided for planning and development work of a gas field.

Description

Technical Field

[0001] The present invention belongs to the technical field of gas reservoir production regional planning, and relates to a GIS digital elevation model, production growth prediction, a multivariate Gaussian mixture model, and multi-factor fuzzy analysis, and specifically relates to a natural gas production zoning planning method based on GIS technology. Background Art

[0002] In recent years, my country'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 continued to increase. Natural gas peak production prediction and analysis is an important part of gas field development planning and one of the core tasks of gas field development planning. It plays a pivotal macro-guidance role in the realization of gas field development planning indicators and the completion of production indicators. Gas reservoirs in different regions are affected to different degrees by various factors, 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 volume, recovery rate, and proven rate of each region are determined, and the technically recoverable reserves are calculated to determine the range of the final recoverable reserves. 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 zone is calculated according to the membership matrix, and the planned production realization probability matrix of each gas zone is determined. The generated production is evenly arranged in the geographic coordinate system according to the Gaussian distribution, so as to determine the final recoverable reserves URR and peak production Qm at different geographic coordinate positions in each block. Finally, a three-dimensional production planning model is established based on GIS technology and multivariate Gaussian mixture model.

[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 under geographic coordinates to provide guidance for the planning and development of gas fields. Through literature and patent research, it is very clear that the existing technologies of this type have the following shortcomings: In terms of scientific and technological literature, at present, the research on natural gas production zoning planning at home and abroad is still in its infancy. By consulting relevant domestic and foreign literature and investigating the existing production peak prediction and risk quantification analysis methods, it is found that the planned production in the industry at this stage has not been carried out according to different influencing factors. Conduct targeted research on gas reservoirs in different regions. In terms of existing patents, no patents related to methods for natural gas production zoning planning have been retrieved.

[0004] In general, current natural gas production planning methods often have problems such as difficulty in prediction and lack of planning for the prediction range, which are important technical issues that technical personnel in this field urgently need to solve. Summary of the invention

[0005] The purpose of the present invention is to solve the above-mentioned shortcomings of the prior art. The purpose of the present invention is to provide a natural gas production zoning planning method based on GIS technology. Fuzzy analysis and multivariate Gaussian mixture model are used to calculate the peak production range and realization probability of each production area, and a production peak distribution model diagram is generated. Finally, a three-dimensional digital elevation model is established based on GIS technology to obtain a three-dimensional production distribution map under geographic coordinates, which provides guidance for the planning and development of gas fields.

[0006] The objective of the present invention is achieved through the following technical solutions:

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

[0008] Step A: gas reservoir zone peak production analysis;

[0009] Step B: Calculation of zone yield under multi-factor control;

[0010] Step C: Study on production planning model under the constraints of three-dimensional Gaussian mixture model.

[0011] Preferably, the step A includes the following steps: step A1: re-evaluate the resource volume of each area, calculate the technically recoverable reserves based on the resource volume, recovery rate and proven rate; select the optimal and worst states of the recoverable reserves to determine the range of the final recoverable reserves; step A2: improve the Hubbert model, refine the model parameters to achieve accurate analysis and prediction of production, and calculate the highest and lowest possible peak production values ​​in combination with the production status of each block, so as to determine the range of the peak production.

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

[0013] TRR=S p ×P r ×E R Formula (1);

[0014] Where: TRR is technically recoverable reserves, s p is the resource volume, P r is the detection rate, E R is the recovery rate.

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

[0016] URR=TRR×K c Formula (2);

[0017] Among them: URR is the ultimate recoverable reserves, K c It is a technical and economic index.

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

[0019] Q m =a×b×URR / (1+c) 2 Formula (12);

[0020] Where: Q m is the peak production; a and b are the relationship expression coefficients between cumulative production and production, in order to realize the transformation of the relationship between cumulative production and production; URR is the ultimate recoverable reserves; c is the model parameter.

[0021] Preferably, by taking the maximum impact value and the minimum impact value of each factor respectively, the optimal state and the worst state of peak production can be obtained, and the peak production realization range of each region can be obtained.

[0022] Preferably, the step B includes the following steps: step B1: according to the URR utilization degree, investment level, exploration technology level, development level, theoretical breakthrough, policy, unconventional gas subsidy, and gas price influencing factors, an indicator set U of the weight of the influencing factors is given, and a membership function of multi-factor fuzzy analysis is constructed. According to the natural gas development level of the gas reservoir, a value selection scheme is formulated for each gas zone according to its influencing factor system, thereby constructing a membership matrix R; step B2: according to the actual project, the initial weight A of each factor is given, and the comprehensive influence degree B of the constraint conditions on each gas zone is obtained through membership matrix operation, thereby calculating the actual planned peak production range Q of each gas zone, and determining the planned production realization probability matrix of each gas zone; step B3: selecting different realization probability situations, and evenly arranging the calculated URR and peak production in the geographic coordinate system according to Gaussian distribution, thereby forming a zoned production under multi-factor control.

[0023] Preferably, in step B2, the comprehensive impact degree B is calculated by formula B=A·R, and then the comprehensive impact degree B is calculated by formula Q=B·Q m The actual planned peak production range is calculated, and finally the peak production under different probabilities are combined into a matrix, that is, the planned production realization probability matrix.

[0024] Preferably, the step C comprises the following steps: step C1: analyzing the geographic location information of the target object through geographic space analysis, performing module analysis using principles such as logical operations and mathematical statistics analysis, treating the gas reservoir resource data set in the research area as a mixed distribution of multiple Gaussian distributions, and then performing zoning prediction, and placing the production bar graph that conforms to the Gaussian mixed distribution into the gas zone model map according to the longitude and latitude coordinates; step C2: establishing a three-dimensional digital elevation model of the production area based on GIS technology, and placing the production planning Figure 3After vectorization, multiple layers are fused into a three-dimensional planning geographic vector map, and combined with the production planning Gaussian mixture distribution model bar chart to generate a three-dimensional oblique image map of the planned production of each gas zone.

[0025] Preferably, in step C1, a weight table of zoning influencing factors is established based on the original data of the tight gas zone, the rasterized factors and the current status of gas reservoir utilization are rasterized, and after converting the data into ASCII data, Python software is used for correlation analysis, and a histogram of URR and natural gas peak production is established through a Gaussian mixture model.

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

[0027] 1. The present invention provides a method for natural gas production zoning planning based on GIS technology. In order to carry out production planning and risk analysis for different natural gas regions, it is necessary to carry out research on natural gas production zoning planning based on GIS technology, and analyze the distribution of production peaks in each block and the impact of risk factors. Therefore, the present invention uses fuzzy analysis and multivariate Gaussian mixture models to calculate the peak production range and realization probability of each production area, and generates a production peak distribution model diagram. Finally, a three-dimensional digital elevation model is established based on GIS technology to obtain a three-dimensional production distribution map under geographic coordinates, which provides guidance for the planning and development of gas fields.

[0028] Second, the method for natural gas production zoning planning based on GIS technology provided by the present invention improves the Hubbert model, refines the model parameters, and improves the accuracy of production prediction. The maximum and minimum values ​​of each variable in the production calculation formula are optimized, and the optimal and worst states of peak production can be obtained, thereby obtaining the peak production realization range of each region.

[0029] 3. The method of natural gas production zoning planning based on GIS technology provided by the present invention analyzes the factors affecting production, constructs the membership function of multi-factor fuzzy analysis, and calculates the comprehensive influence of constraint conditions on each gas zone in combination with the weight of factors in actual engineering, thereby obtaining the actual planned peak production range and realization probability matrix of each gas zone. The influence of factors on production is reflected in the data, providing guidance for the formulation of production planning schemes. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0031] Figure 2 This is the recoverable reserves data map of the core production area in central Sichuan;

[0032] Figure 3 This is the final recoverable reserves data map of the core production area in central Sichuan;

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

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

[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 realization probability of 50%.

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

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

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

[0040] Fig.11 This is a diagram showing the process of establishing a three-dimensional DEM model for tight gas production areas in the Sichuan Basin;

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

[0042] Fig.13 It is a three-dimensional oblique image of the planned output of the production area in Northwest Sichuan;

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

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

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

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

[0047] The present invention is further described in detail below in conjunction with 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 volume of each area, calculate the technical recoverable reserves according to the recoverable reserves formula, and select the optimal and worst states of recoverable reserves to determine the final recoverable reserves range based on the recovery rate and proven rate.

[0052] First, the resources and proved reserves of each area are evaluated based on the favorable production conditions and working area of ​​the gas field, and then the technically recoverable reserves are calculated using formula (1), as shown below.

[0053] TRR=S p ×P r ×E R Formula (1)

[0054] TRR is technically recoverable reserves, S p is the resource volume, P r is the detection rate, E R is the recovery rate.

[0055] From formula (1), it can be seen that when the resource volume is certain, the technically recoverable reserves depend on the proven rate and the recovery rate. Therefore, it is necessary to determine the range of the recovery rate and the proven rate based on historical exploration data, and then substitute them into the recoverable reserves calculation formula to determine the range of recoverable technical reserves in each area. After obtaining the range of the recovery rate and the proven rate, the maximum and minimum impact values ​​of each factor are taken respectively, and the optimal and worst states of the technically recoverable reserves can be obtained.

[0056] Then, the ultimate recoverable reserves (URR) is determined based on the range of technically recoverable reserves. The ultimate recoverable reserves are controlled by technical and economic factors and are therefore closely related to technically recoverable reserves. Formula (2) is used to calculate the ultimate recoverable reserves (URR), as shown below.

[0057] URR=TRR×K c Formula (2)

[0058] Among them, K c It is a technical and economic index (similar to the conversion rate of technically recoverable reserves to available reserves in a natural gas development plan).

[0059] Similarly, according to the determined technical and economic index (conversion rate) range, the maximum impact value and the minimum impact value are taken respectively, and the optimal state and the worst state of the final recoverable reserves can be obtained.

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

[0061] Step A2: Improve the Hubbert model and refine the model parameters to achieve accurate analysis and prediction of production. Combined with the production status of each block, calculate the highest and lowest possible peak production values ​​to determine the range of peak production.

[0062] The prediction model used to predict natural gas production is the Hubbert model. The Hubbert model is a commonly used life cycle model for predicting natural gas production peak. The model assumes that after an oil and gas field is put into development, the production starts from 0 and increases with the extension of development time, and after reaching a peak value, the production decreases with the extension of development time. When the development time approaches infinity, the area between the production curve and time is equal to the ultimate recoverable reserves of the oil and gas field. The formula mainly completes the derivation of the model through the quadratic equation of production P and cumulative production Q, as shown in formula (3).

[0063]

[0064] Among them, a and b are the coefficients expressing the relationship between cumulative output and output, so as to realize the transformation of the relationship between cumulative output and output.

[0065] When the cumulative production is close to the ultimate recoverable resource URR, the production is close to 0, as shown in formula (4).

[0066] P=aURR+bURR 2 =0 Formula (4)

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

[0068]

[0069] Substituting formula (5) into formula (3) yields:

[0070]

[0071] In the formula, Q represents the cumulative output. The left side of formula (6) integrates Q in the interval (Q0, URR), and the right side integrates t in the interval (t0, t m ) is integrated to obtain the cumulative production prediction model, as shown in formula (7).

[0072]

[0073] In formula (7), let We can get:

[0074]

[0075] Differentiating formula (8) yields the production prediction model, namely:

[0076]

[0077] Where P represents the annual output. By taking the second-order derivative of equation (9) and setting it equal to zero, we can obtain two time inflection point formulas, as shown in equations (10) and (11).

[0078]

[0079]

[0080] Among them, a and c are model parameters; t0 is the time when resource mining starts; t g1 is the inflection point on the left side of the curve; t g2 The inflection point on the right side of the curve. The inflection point of the forecast curve represents the turning point of the production rate. The inflection point on the left represents the point when the output transitions from rapid growth to slow growth, and the inflection point on the right represents the point when the output transitions from rapid decline to slow decline.

[0081] When t = t m When the output growth reaches its peak, the cumulative output Q has the largest change rate, that is, dQ / dt is the largest. At this time:

[0082] Q m =a×b×URR / (1+c) 2 Formula (12)

[0083] The final formula (12) is the peak output Q m The calculation formula for .

[0084] Similarly, by taking the maximum and minimum impact values ​​of each factor respectively, we can obtain the optimal and worst states of peak production and the peak production realization range of each region.

[0085] Step B: Calculation of zone yield under multi-factor control

[0086] Step B1: According to the influencing factors such as URR utilization degree, investment level, exploration technology level, development level, theoretical breakthrough, policy, unconventional gas subsidy, gas price, etc., the index set U of the weight of the influencing factors is given, and the membership function of multi-factor fuzzy analysis is constructed. According to the development level of natural gas in the gas reservoir, the value selection scheme is formulated for each gas zone according to its influencing factors, so as to construct the membership matrix R.

[0087] First, each influencing factor is numbered, and an indicator set of the weights of the influencing factors is given according to the eight influencing factors, namely, URR utilization degree, investment level, exploration technology level, development level, theoretical breakthrough, policy, unconventional gas subsidy, and gas price:

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

[0089] Then, for each region, the values ​​are taken according to the eight influencing factors, and the value schemes of five gas zones are temporarily listed. In actual work, the value range of each factor needs to be determined according to the actual indicators on site. After the value is taken, the gas zone type V and the influencing factor U are used as rows and columns respectively to construct the membership matrix R.

[0090] Step B2: According to the actual project, the initial weight A of each factor is given, and the comprehensive influence degree B of the constraint conditions on each gas zone is obtained through membership matrix operation, 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.

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

[0092] 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, so as to form the zoned yield under multi-factor control.

[0093] First, select different scenarios. For example, when the probability of realization is higher: P75 (the probability of realization is 75%), URR = 1030 × 10 8 m 3 ; Scenario with lower probability of realization: When P50 (probability of realization is 50%), URR = 1045 × 10 8 m 3 Then, they were evenly arranged in the geographic coordinate system according to the Gaussian distribution, and 29 coordinate points were actually selected to construct a three-dimensional URR and yield distribution map, with longitude and latitude as the x and y coordinate axes, and URR and yield as the z coordinate axis.

[0094] Step C: Study on production planning model under the constraints of three-dimensional Gaussian mixture model

[0095] Step C1: The geographical location information of the target object is studied through geographic spatial analysis, and module analysis is performed using principles such as logical operations and mathematical statistics. The gas reservoir resource dataset in the study area is treated as a mixed distribution of multiple Gaussian distributions, and then zoning prediction is performed. The production bar chart that conforms to the Gaussian mixed distribution falls into the gas zone model map according to the longitude and latitude coordinates.

[0096] Firstly, according to the original data of tight gas areas, a weight table of zoning influencing factors was established. The rasterized factors and the current status of gas reservoir utilization were rasterized and converted into ASCII data. Then, Python software was used for correlation analysis. The Gaussian mixture model was used to establish a bar chart of URR and natural gas peak production.

[0097] The Gaussian mixture model is a distribution of a mixture of multiple Gaussian distributions, that is, the weighted sum of multiple Gaussian distributions. The model can divide the unknown data set into several subsets with Gaussian distributions by adjusting the mean and covariance of a sufficient number of Gaussian probability density functions and the linear combination coefficients, using the expectation maximization algorithm. The gas reservoir resource data set in the study area is treated as a mixed distribution of multiple Gaussian distributions, and then a zoning prediction is performed. Finally, the production bar chart that conforms to the Gaussian mixture distribution falls into the model map of the tight gas field in the Sichuan Basin according to the longitude and latitude coordinates.

[0098] Step C2: Establish a three-dimensional digital elevation model of the production area based on GIS technology and plan the production volume. Figure 3 After vectorization, multiple layers are fused into a three-dimensional planning geographic vector map, and combined with the production planning Gaussian mixture distribution model bar chart to generate a three-dimensional oblique image map of the planned production of each gas zone.

[0099] Elevation data requires digital elevation models (DEMs) in multiple scenarios such as terrain analysis, slope and aspect analysis, and contour line analysis. The process of establishing a three-dimensional DEM model for tight gas production areas in the Sichuan Basin is as follows: first, a planning map of the tight gas area is generated, and then it is three-dimensionally vectorized. After obtaining the vector map, multiple layers of DEM data are added, and the geographic elevation map is introduced. Multiple three-dimensional layers are fused to obtain a three-dimensional planning geographic vector map. Finally, the production planning distribution model of the gas area and the gas area planning Gaussian mixture distribution model column chart are combined to generate a three-dimensional oblique image map of the planned production of each gas area.

[0100] Example 2

[0101] like Figure 1 As shown, a natural gas production zoning planning method based on GIS technology includes the following steps:

[0102] Step A: Peak production analysis of tight gas reservoirs

[0103] Step A1: Re-evaluate the resource volume of each area, calculate the technical recoverable reserves according to the recoverable reserves formula, and select the optimal and worst states of recoverable reserves to determine the final recoverable reserves range based on the recovery rate and proven rate.

[0104] First, the resource potential of five favorable exploration and development areas, namely the core production area in central Sichuan, the accelerated evaluation areas in southwestern and northwestern Sichuan, and the successor evaluation areas in eastern and southern Sichuan, was evaluated, as shown in the following table. After the re-evaluation of resources, the resources in the main tight gas production areas reached 6.9 trillion cubic meters, and the estimated proven reserves reached 1.12 trillion cubic meters.

[0105]

[0106]

[0107] Then, according to the resource volume of the five gas zones, the range of their technically recoverable reserves is calculated respectively. Taking the core production area in central Sichuan as an example: the estimated resource volume of the core production area in central Sichuan is 3800 billion cubic meters. According to historical exploration data, the range of the recovery rate and proven rate is determined to be 30% to 50% and 10% to 20%, and then substituted into formula (1) TRR = S p ×P r ×E R The calculations were carried out and the technically recoverable reserves of the area were obtained as shown in the following table. The final range was determined to be 114-380 billion cubic meters.

[0108]

[0109] After obtaining the range of recovery rate and proven rate, taking the maximum and minimum impact values ​​of each factor respectively, the optimal and worst states of technically recoverable reserves can be obtained. For the core production area in central Sichuan, the range of recovery rate is 30% to 50%, and the range of proven rate is 10% to 20%. Therefore, the optimal state is 38000×50%×20%=380 billion cubic meters, and the worst state is 38000×30%×10%=114 billion cubic meters. Using this idea, the range of technically recoverable reserves can also be obtained. After calculation, the technically recoverable reserves of the five gas zones are shown in the following table.

[0110]

[0111] Therefore, we can obtain the technically recoverable reserves range of each block of tight gas in the Sichuan Basin as follows:

[0112] The core production area in central Sichuan: The technically recoverable reserves in this area are determined to range from 114 to 380 billion cubic meters.

[0113] Northwest Sichuan Accelerated Evaluation Area I: The technically recoverable reserves in this area are determined to range from 30 billion to 100 billion cubic meters.

[0114] Southwest Sichuan Accelerated Evaluation Area II: The technically recoverable reserves in this area are determined to range from 21 billion to 70 billion cubic meters.

[0115] East Sichuan Successor Evaluation Area I: The technically recoverable reserves in this area are determined to range from 24 to 80 billion cubic meters.

[0116] Southern Sichuan Successor Evaluation Area II: The technically recoverable reserves in this area are determined to range from 18 billion to 60 billion cubic meters.

[0117] Then, using formula (2) URR = TRR × K c To determine the range of the ultimate recoverable reserves (URR) of each block, the conversion rate (K c ) ranges from 50% to 70%.

[0118] Similarly, taking the optimal and worst states of recovery factor, proven rate and conversion rate, the final recoverable reserves of the five gas zones are calculated through formulas as shown in the following table.

[0119]

[0120] Therefore, we can get the final recoverable reserves range of each block of tight gas in the Sichuan Basin as follows:

[0121] The core production area in central Sichuan: The final recoverable reserves in this area are determined to range from 42.75 to 226.1 billion cubic meters.

[0122] Northwest Sichuan Accelerated Evaluation Area I: The ultimate recoverable reserves in this area are determined to range from 11.25 to 59.5 billion cubic meters.

[0123] Southwest Sichuan Accelerated Evaluation Area II: The ultimate recoverable reserves in this area are determined to range from 7.875 to 41.65 billion cubic meters.

[0124] East Sichuan Successor Evaluation Area I: The ultimate recoverable reserves in this area are determined to range from 9.0 to 47.6 billion cubic meters.

[0125] Southern Sichuan Successor Evaluation Area II: The ultimate recoverable reserves in this area are determined to range from 6.75 to 35.7 billion cubic meters.

[0126] Based on the calculated data, a three-dimensional map of the reserves data of each gas zone can be drawn. The recoverable reserves data map and the final recoverable reserves data map of the core production area in central Sichuan are shown in the figure below. Figure 2 and Figure 3 shown.

[0127] Step A2: Improve the Hubbert model and refine the model parameters to achieve accurate analysis and prediction of production. Combined with the production status of each block, calculate the highest and lowest possible peak production values ​​to determine the range of peak production.

[0128] According to the URR range determined above, the improved Hubbert model is adopted, that is, formula (12) Q m =a×b×URR / (1+c) 2 to perform the calculation.

[0129] Similarly, by taking the maximum and minimum impact values ​​of each factor respectively, we can obtain the optimal and worst states of peak production and the peak production realization range of each region. The calculation results are shown in the following table.

[0130]

[0131] Therefore, we can conclude that the total peak production of tight gas in the Sichuan Basin can be achieved in the range of 4.74 to 56.68 billion cubic meters.

[0132] Step B: Calculation of zone yield under multi-factor control

[0133] Step B1: According to the influencing factors such as URR utilization degree, investment level, exploration technology level, development level, theoretical breakthrough, policy, unconventional gas subsidy, gas price, etc., the index set U of the weight of the influencing factors is given, and the membership function of multi-factor fuzzy analysis is constructed. Then, according to the natural gas development level of the gas reservoir, the value selection scheme is formulated for each gas zone according to its influencing factors, so as to construct the membership matrix R.

[0134] First, according to the eight influencing factors of URR utilization, investment level, exploration technology level, development level, theoretical breakthrough, policy, unconventional gas subsidy, and gas price, the index set of influencing factor weights is given: U = [u1, u2, u3, u4, u5, u6, u7, u8]. The specific membership function constructed is as follows:

[0135] (1) Development level: URR conversion coefficient. For example, the URR peak value of the core production area is 226.1 billion cubic meters. Temporarily take 60% of it, which is about 130 billion cubic meters. Use the resource utilization function as the membership function: u1(x) = x / 1300.

[0136] (2) Investment level: The investment constraint is 5 billion, so u2(x) = x / 50.

[0137] (3) Exploration technology level: The gap with the international advanced level is within 20 years, u3(x) = 1-x / 20.

[0138] (4) Development supporting level: the matching degree between the mid- and downstream ground supporting level and the production scale. u4(x) = x / 100.

[0139] (5) Theoretical breakthroughs: The number of internationally leading achievements, based on 2020 as the base year, is determined by the growth percentage u5(x)=x / 100.

[0140] (6) Policy: Using the logistic equation, a=0.25 is used to maintain the current policy, b=0.75 is used to reach the international advanced level, and above 0.75 is used to exceed the international level. The rest are between ba. u6(x)={2((xa) / (ba))^2} or 1-2((xb) / (ba))^2.

[0141] (7) Unconventional gas subsidy: 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.

[0142] (8) Gas price: Based on 2020, it is determined according to the growth percentage, so uh(x) = x / 100.

[0143] Then, according to the current level of natural gas development in tight gas reservoirs in the Sichuan Basin, value selection schemes are formulated for the five gas zones according to their influencing factors, as shown in the following table.

[0144]

[0145] After the values ​​are taken, the gas zone 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.

[0146] Gas Zone I Gas Zone II Gas Zone III Gas Zone IV Gas Zone V u1 0.99839 0.96618 0.91787 0.80515 0.64412 u2 1 0.95 0.9 0.8 0.7 u3 0.5 0.95 0.75 0.6 0.75 u4 0.8 0.5 0.8 0.7 0.5 u5 0.6 0.4 0.5 0.3 0.2 u6 0.68 0.5 0.82 0.5 0.92 u7 0.8 0.76 0.7 0.6 1 u8 0.2 0.15 0.4 0.2 0.5

[0147] Step B2: According to the actual project, the initial weight A of each factor is given, and the comprehensive influence degree B of the constraint conditions on each gas zone is obtained through membership matrix operation, 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.

[0148] After obtaining the membership matrix R, the initial weights of each factor are given according to the actual project: A = [0.22, 0.2, 0.1, 0.1, 0.2, 0.1, 0.05, 0.03]. The comprehensive influence 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]. Then the actual planned peak production range of each gas zone is calculated using the formula:

[0149]

[0150] Finally, the peak outputs under different probabilities are combined into a matrix, that is, the planned output realization probability matrix. The specific data are shown in the following table.

[0151]

[0152] Among them, P10 represents the realization probability of 10%. Only five realization probabilities are selected in the table, and the peak production Q is calculated according to the URR corresponding to different gas zones. m .

[0153] 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, so as to form the zoned yield under multi-factor control.

[0154] Taking the core production area in central Sichuan as an example, the URR of P50 and P75 were taken as the center, and the URR and Q of different geographical coordinates in each block were randomly changed within a range of 10%. m .

[0155] (1) When P75 (realization probability is 75%), URR = 1030 × 10 8 m 3 The data of URR and production with geographic coordinates under different distributions of tight gas are evenly arranged in the geographic coordinate system according to Gaussian distribution. Actually, 29 coordinate points are selected, and the specific data are shown in the following table.

[0156]

[0157] The URR and yield distribution with a probability of 75% are as follows Figure 4 and Figure 5 shown.

[0158] (2) When P50 (probability of realization is 50%), URR = 1045 × 10 8 m 3 The data of URR and production with geographic coordinates under different distributions of tight gas are evenly arranged in the geographic coordinate system according to Gaussian distribution. Actually, 29 coordinate points are selected, and the specific data are shown in the following table.

[0159]

[0160] The URR and yield distribution with a probability of 50% are as follows Figure 6 and Figure 7 shown.

[0161] Step C: Study on production planning model under the constraints of three-dimensional Gaussian mixture model

[0162] :Through geographic spatial analysis, the geographical location information of the target object is studied, and the module analysis is carried out using the principles of logical operations, mathematical statistics analysis, etc. Then, based on the original data of the tight gas zone, a weight table of zoning influencing factors is established, and the rasterized factors and the current status of gas reservoir utilization are rasterized. After converting the data into ASCII data, Python software is used for correlation analysis, and a URR and natural gas peak production bar chart is established through a Gaussian mixture model. Finally, the bar chart is arranged at a reasonable geographical location according to the latitude and longitude coordinates determined by the three-dimensional Gaussian mixture calculation model.

[0163] GIS technology and 3D Gaussian mixture model zoning planning concept Figure 8-Figure 10 As shown in the figure, the three-dimensional Gaussian mixture theory model and the URR geographical distribution map or natural gas peak production bar chart of each region are arranged on the GIS digital elevation model. The GIS digital elevation model needs to integrate GIS technology with the production zoning planning model and establish a three-dimensional digital elevation model (DEM) of the production area.

[0164] The process of establishing the three-dimensional DEM model of the tight gas production area in the Sichuan Basin is as follows: first, a planning map of the tight gas area is generated, and then it is three-dimensionally vectorized. After obtaining the vector map, multi-layer DEM data is added, and the geographic elevation map is introduced. Multiple three-dimensional layers are merged to obtain a three-dimensional planning geographic vector map. Finally, the production planning distribution model of the gas area and the gas area planning Gaussian mixture distribution model column chart are combined to generate a three-dimensional oblique image map of the planned production of each gas area.

[0165] The process of establishing the 3D DEM model of the tight gas production area in the Sichuan Basin is as follows: Fig.11 As shown in the figure, the three-dimensional oblique image of the planned production of each gas zone is as follows Figure 12-Figure 17 shown.

[0166] Finally, it should be noted that the above embodiments are only used to illustrate rather than limit the technical solutions of the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the present invention can still be modified or replaced by equivalents. Any modification or partial replacement that does not depart from the spirit and scope of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A natural gas production zoning planning method based on GIS technology, characterized in that: The following steps are involved: Step A: gas reservoir zone peak production analysis; Step B: Calculation of zone yield under multi-factor control; Step C: Study on production planning model under the constraints of three-dimensional Gaussian mixture model.

2. The method for natural gas production zoning planning based on GIS technology according to claim 1 is characterized by: The step A comprises the following steps: Step A1: Re-evaluate the resources in each area, calculate the technically recoverable reserves based on the resources, recovery rate and proven rate; select the best and worst states of recoverable reserves to determine the final recoverable reserves range; Step A2: Improve the Hubbert model and refine the model parameters to achieve accurate analysis and prediction of production. Combined with the production status of each block, calculate the highest and lowest possible peak production values ​​to determine the range of peak production.

3. The method for natural gas production zoning planning based on GIS technology according to claim 2 is characterized by: In step A1, the technically recoverable reserves are calculated using the formula (1): TRR=S p ×P r ×E R Formula (1): Where: TRR is technically recoverable reserves, S p is the resource volume, P r is the detection rate, E R is the recovery rate.

4. The method for natural gas production zoning planning based on GIS technology according to claim 3 is characterized by: The final recoverable reserves are calculated using the formula (2): URR=TRR×K c Formula (2): Among them: URR is the ultimate recoverable reserves, K c It is a technical and economic index.

5. The method for natural gas production zoning planning based on GIS technology according to claim 4 is characterized by: In step A2, the peak yield is calculated using formula (12): Q m =α×b×URR / (1+c) 2 Formula (12); Where: Q m is the peak production; a and b are the relationship expression coefficients between cumulative production and production, in order to realize the transformation of the relationship between cumulative production and production; URR is the ultimate recoverable reserves; c is the model parameter.

6. The method for natural gas production zoning planning based on GIS technology according to claim 5 is characterized by: By taking the maximum and minimum impact values ​​of each factor respectively, we can obtain the optimal and worst states of peak production and the peak production realization range of each region.

7. The method for natural gas production zoning planning based on GIS technology according to claim 1 is characterized by: The step B comprises the following steps: step B1: according to the URR utilization degree, investment level, exploration technology level, development level, theoretical breakthrough, policy, unconventional gas subsidy, and gas price influencing factors, an index set U of the weight of the influencing factors is given, and a membership function of multi-factor fuzzy analysis is constructed. According to the natural gas development level of the gas reservoir, a value selection scheme is formulated for each gas zone according to its influencing factor system, thereby constructing a membership matrix R; step B2: according to the actual project, the initial weight A of each factor is given, and the comprehensive influence degree B of the constraint conditions on each gas zone is obtained through membership matrix operation, thereby calculating the actual planned peak production range Q of each gas zone, and determining the planned production realization probability matrix of each gas zone; step B3: selecting different realization probability situations, and evenly arranging the calculated URR and peak production in the geographic coordinate system according to Gaussian distribution, thereby forming a zoned production under multi-factor control.

8. The method for natural gas production zoning planning based on GIS technology according to claim 7 is characterized by: In step B2, the comprehensive impact degree B is calculated by formula B=A·R, and then the comprehensive impact degree B is calculated by formula Q=B·Q m The actual planned peak production range is calculated, and finally the peak production under different probabilities are combined into a matrix, that is, the planned production realization probability matrix.

9. The method for natural gas production zoning planning based on GIS technology according to claim 1 is characterized by: The step C comprises the following steps: step C1: analyzing the geographic location information of the target object through geographic space analysis, performing module analysis by using principles such as logical operations and mathematical statistics analysis, treating the gas reservoir resource data set of the research area as a mixed distribution of multiple Gaussian distributions, and then performing zoning prediction, and placing the production bar graph that conforms to the Gaussian mixed distribution into the gas zone model map according to the longitude and latitude coordinates; step C2: establishing a three-dimensional digital elevation model of the production area based on GIS technology, 3D vectorizing the production planning map, fusing multiple layers into a three-dimensional planning geographic vector map, combining the production planning Gaussian mixed distribution model bar graph, and generating a three-dimensional oblique image map of the planned production of each gas zone.

10. A method for natural gas production zoning planning based on GIS technology according to claim 9, characterized in that: In step C1, a weight table of zoning influencing factors is established based on the original data of the tight gas zone, the rasterized factors and the current status of gas reservoir utilization are rasterized, and after converting to ASCII data, Python software is used for correlation analysis, and a URR and natural gas peak production bar chart is established through a Gaussian mixture model.

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